esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis of the Basic Unit of GSMLI  
The generalized topology presented in this work was simulated and experimentally verified by considering one unit in symmetric and asymmetric mode. It has also been tested for TSV, and optimal capacitance was calculated in this section. The basic units act as a level generator that can produce two voltage levels from a solitary DC power supply. Figure 3 shows the conducting paths of the proposed topology for asymmetric configuration. Switches \(S_{2}\) and \(S_{4}\) and diodes \(D_{I}\) and \(D_{2}\) of the basic units are utilized for the charging purpose of capacitors \(C_{1}\) and \(C_{2}\), respectively, while switches \(S_{1}\) and \(S_{3}\) are used for discharging capacitors \(C_{1}\) and \(C_{2}\), respectively, in order to take the participation of the capacitor voltages into consideration in voltage level generation. The switches used in the modified H-Bridge are \(T_{1}\), \(T_{2}\), \(T_{3}\), \(T_{4}\), \(T_{5}\), and \(T_{6}\). Positive voltage levels are generated by utilizing switches \(T_{1}\), \(T_{4}\), and \(T_{6}\), whereas negative voltage levels are generated by utilizing switches \(T_{2}\), \(T_{3}\), and \(T_{5}\). Thus, the operation of switch pairs (\(T_{1}\), \(T_{2}\)), (\(T_{3}\), \(T_{4}\)), (\(T_{5}\), \(T_{6}\)), (\(T_{7}\), \(T_{8}\)), (\(S_{1}\), \(S_{2}\)), and (\(S_{3}\), \(S_{4}\)) are complementary. This topology is capable of generating \(0V\), \(\pm V_{1}\), \(\pm(V_{1}+V_{CI})\), \(\pm V_{2}\), \(\pm(V_{1}+V_{2})\), \(\pm(V_{1}+V_{CI}+V_{2})\), \(\pm(V_{2}+V_{C2})\), \(\pm(V_{1}+V_{2}+V_{C2})\), and \(\pm(V_{1}+V_{C1}+V_{2}+V_{C2})\) voltage levels. This topology is self-balancing for capacitor voltage because charging and discharging take place in an alternate manner and, if there is any voltage drop in capacitors voltage due to discharging, then it is regained during charging. This topology can damp out the disparate voltage between the capacitor and power supply, due to which it acts as a practically effective power circuit.  
Figure 3: \(Cont.\)
# A Review of Lithium-Sulfur Batteries Based on Metal-Organic Frameworks: Progress and Prospects
## 3 Alternative Strategies for Metal-Organic Frameworks to Improve the Performance of LSBs  
The incorporation of a catalyst material into the sulfur cathode can effectively curb sulfur expansion and hinder the dissolution of soluble polysulfides. However, this incorporation inevitably results in a decrement in the cathode's active material loading. The  
Figure 9: (**a**) Schematic preparation of In\({}_{2}\)O\({}_{3}\)@C, In-In\({}_{2}\)O\({}_{3}\)@C, and In@C composites. (**b**) Tafel curves of In\({}_{2}\)O\({}_{3}\)@C, In-In\({}_{2}\)O\({}_{3}\)@C, and In@C symmetric cells (reprinted/adapted with permission from [65]; copyright 2024, _Cenamics International_). (**c**) Schematic of the preparation process of NiCo-PCNF. (**d**) SEM morphology of NiCo-PCNF. (**e**) Cycling performances of S/NiCo PCNF, S/Ni-PCNF, S/Ni-PCNF, S/PCNF, and S/PCNF at 0.5 C. (**d**) SEM morphology image of NiCo-PCNF. (**e**) Cycling performances of S/NiCoMOF (reprinted/adapted with permission from [67]; copyright 2024, _Journal of Electroanalytical Chemistry_).  
utilization of MOFs transcends the confines of the cathode in LSBs, extending to their application within interlayers, separators, and electrolytes as well. In contrast to conventional lithium batteries and other lithium-metal batteries systems, the impact of the intermediate layer in LSBs on performance is significantly more pronounced due to the presence of soluble polysulfides. Given the high porosity, chemical absorption capabilities, and catalytic properties of MOFs, the MOF-modified interlayer can effectively hinder the diffusion of polysulfides towards the counter electrode, thereby mitigating the polysulfide shuttle effect. Theoretically, MOFs with ordered porous channels possess the capacity to accommodate a diverse range of liquid species and achieve the desired molecular/ion sieving/transport effects. Consequently, diaphragms modified with MOFs can facilitate ordered ion transport, promoting uniform lithium deposition [73]. Furthermore, the MOF-modified electrolyte inhibits polysulfide shuttling through potent electrostatic repulsion while providing abundant pathways for rapid lithium-ion transport and safeguarding the lithium anode. These attributes collectively contribute to enhancing the cycling stability and capacity retention of the battery.
## 3 Results and Discussion
### Characterization of Starch/MCC Films
#### 3.2.1. Appearances and Thickness  
Starch films incorporated with MCC at various contents (0%, 1%, 3%, and 5% \(w/w\) of starch) and glycerol as a plasticizer were successfully prepared, as shown in Figure 3a-d. Glycerol enhances film flexibility and prevents brittleness by improving their capacity to deform without fracturing [33]. The films are strong enough to be handled without deformation and are non-brittle, making them suitable for further investigation of physicochemical and mechanical properties.  
Transparency is an essential characteristic of packaging materials, as it allows consumers to visually inspect the product before purchasing. The study found that all prepared films were uniformly light-transparent, meaning that the logo and alphabet could be seen clearly through the films, as shown in Figure 3. This indicates that incorporating MCC into starch films within the range of 1% to 5% \(w/w\) of starch did not adversely affect the transparency of the resultant films. This suggests that the films have good transparency, which can enhance consumer experience and product visibility.  
Typically, the thickness of commercial medical packaging films ranges from 25 to 750 \(\upmu\)m, depending on their application [34]. This study focused on developing starch/MCC films for use in wound care packaging. The wound care packaging is mostly used in thickness ranges of 50-200 \(\upmu\)m [35]. In our study, all prepared films showed thicknesses of 127  
Figure 3. Appearances of films (**a**) Starch, (**b**) Starch/1%MCC, (**c**) Starch/3%MCC, and (**d**) Starch/5%MCC.  
144 (Table 3), indicating they were suitable for wound care packaging. The thickness of starch/MCC films slightly increased with increasing MCC contents in the films. The observed increase in thickness of starch/MCC films with rising MCC contents is in line with previous findings, such as those reported by [PERSON] et al. (2023) [PERSON] et al. (2023). They found that the addition of 1% (_w/w_) MCC extracted from banana (Musa Saba) midrib residues increased the thickness of wheat starch films from 165 to 181 \(\upmu\)m. As the concentration of MCC increases in the films, the overall solid content also increases. This leads to a higher volume of material being deposited, resulting in thicker films. Furthermore, the thickness can be adjusted for other medical packaging applications by controlling the solution volume for film casting. This flexibility allows for customization of the film thickness to meet the requirements of different packaging needs within the medical industry.
## 2 Materials and Methods  
### Literature Search Strategy and Study Selection  
The PubMed, Scopus and Google Scholar electronic databases were used between March and August 2021 to find articles that employed an automated method for assessing OCTA images, regardless of the specific application (i.e., DR, dermatology, etc.). The keywords that were used for the electronic database search within the title and/or abstract were as follows: \"optical coherence tomography angiography\", \"OCTA\", \"quantification\", \"quantifying\", \"segmentation\", \"automatic\", \"classification\". In particular, the specific query that was used to search was (\"optical coherence tomography angiography\" OR \"OCTA\") AND (\"quantification\" OR \"quantifying\" OR \"segmentation\" OR \"automatic\" OR \"classification\"). The database search was limited to initial studies that were published after January 2016. Once the electronic database search was concluded, the reference lists of the identified articles were further analyzed in order to select any additional relevant studies.  
Once the initial electronic database search was completed, the articles were screened by reading the titles, the abstracts, and briefly analyzing the Methods section to establish their suitability for inclusion in this review. Specifically, articles were excluded if they  
Figure 2: **(A)** Simple block diagram of an OCT system and the signal processing unit to obtain OCTA A-scan signals. **(B)** Example of en face ophthalmology OCTA image. Image available in the ROSE dataset [13]. **(C)** Example of dermatology OCTA volume, color coded by depth.  
(i) were not written in English, (ii) were too similar to other studies, (iii) were not available in full text, (iv) did not enroll a sufficient number of subjects (<5 subjects) or only provided preclinical phantom or animal studies, (v) did not provide enough detail regarding the quantification/classification algorithm or if only a commercial software was employed or if only manual segmentations were employed, (vi) required multi-modal images for the correct implementation of the algorithm (e.g., OCTA image analysis based on fundus image), and (vii) were focused mainly on the characterization of quantitative features for a specific clinical disease and not on the quantitative feature extraction or classification. Furthermore, articles were excluded if they were out-of-topic with respect to the aims of the present review, such as methods or algorithms for the sole purpose of artefact removal for OCTA images. Hence, we excluded studies that focused only on OCTA image preprocessing, and studies that have an OCTA application but use mainly structural OCT data for the method implementation (e.g., retina layer segmentation) [14; 15].
# BeiDou Short-Message Satellite Resource Allocation Algorithm Based on Deep Reinforcement Learning
## 3 Algorithm Design  
Based on the work of predecessors in multi-objective optimization, we propose DRL-SRA to solve the problem of short message satellite resource allocation. The area division strategy and the short message resource allocation algorithm based on the DDRG framework are used to solve two challenges: (1) Data preprocessing of SMSCS; and (2) DRL solves resource allocation in a continuous state.
# Impact of Satellite Attitude on Altimetry Calibration with Microwave Transponders
## 3 Satellite Attitude and Transponder Calibration
### Determination of the Satellite Attitude
^{\mathrm{T}}, \tag{4}\]  
with the components \(\mathbf{R}_{1}\), \(\mathbf{R}_{2}\) and \(\mathbf{R}_{3}\) defined as  
\[\mathbf{R}_{1}=\frac{\mathbf{r}}{|\mathbf{r}|},\quad\mathbf{R}_{3}=\frac{ \mathbf{r}\times\mathbf{v}}{|\mathbf{r}\times\mathbf{v}|},\quad\mathbf{R}_{2 }=\mathbf{R}_{3}\times\mathbf{R}_{1}, \tag{5}\]  
and the rotation matrix \(\mathbf{R}_{\mathrm{ORB\to RFY}}\) given by  
\[\mathbf{R}_{\mathrm{ORB\to RFY}}= \begin{bmatrix}0&1&0\\ 0&0&-1\\ -1&0&0\end{bmatrix} . \tag{6}\]  
After the calculation of \(\mathbf{R}_{\mathrm{RFY\to SAT}}\) from Equation (2) through (6), the evaluation of roll, pitch and yaw angles, denoted as \(\theta_{r}\), \(\theta_{p}\) and \(\theta_{y}\), respectively, is performed using the elements of \(\mathbf{R}_{\mathrm{RFY\to SAT}}\), as follows:  
\[\theta_{r}=-\arctan\left(\frac{\mathbf{R}(3,2)}{\mathbf{R}(3,3)}\right),\quad \theta_{p}=\arcsin\left(\mathbf{R}(3,1)\right),\quad\theta_{y}=-\arctan\left( \frac{\mathbf{R}(2,1)}{\mathbf{R}(1,1)}\right), \tag{7}\]  
with \(\mathbf{R}(i,j)\) denoting the \(i\)-th row and \(j\)-th column element of the rotation matrix \(\mathbf{R}\). All rotation angles are considered passive (i.e., the reference systems are actually rotated and not the contextual vectors) and positive in the counter-clockwise direction.
# Publish/Subscribe-Middleware-Based Intelligent Transportation Systems: Applications and Challenges
## 1 Introduction
This review aims to explore and synthesize the existing knowledge surrounding the application of publish/subscribe middleware in transportation, offering a solid understanding of their varied implications and contributions. This review seeks to analyze papers focused on middleware in transportation, exploring its applications, contributions, employed models and techniques, and associated challenges. Table 1 shows the research questions this research aims to answer. The table has four columns: the first column contains the research questions (RQs) ID, RQ1, RQ2, RQ3, RQ4, and RQ5. Thus, heading RQ# means Research Question Number, where # is a serial number identifying each research question. The second column explains the corresponding RQs. The third column shows the section that answers the RQs, and the fourth presents the objectives of the respective research questions. The rest of the review is organized as follows. Section 2 discusses the methodology for the review. Section 3 discusses middleware background, the basics of publish/subscribe middleware, and how it is applied to ITS. Section 4 reviews the applications of publish/subscribe middleware in ITS. Section 5 gives a comparative analysis of reviewed middleware applications studied in Section 4. Section 6 presents the challenges of applying to publish/subscribe middleware in ITS. Finally, Section 7 concludes the study.
# Joint QoS and Congestion Control Based on Traffic Prediction in SDN
## 7 Simulation Results
### Packet Loss  
Before the evaluation of the proposed schemes, a simple topology is considered to show the impacts of flow matrix prediction on the QRA algorithms. Figure 4 illustrates a small topology with four switches in order to clarify prediction impact on total packet loss. As it can be seen, there are three flows with sizes of 160, 170 and 20 MB/s, respectively. For the sake of simplicity, we assume that there is no change in the size of flows 2 and 3, e.g., consider flows 2 and 3 are constant rate video streaming.  
Figure 3: Real data center topology.  
On the other hand, flow 1 is a Hadoop connection and increases the rate of the flow during the time. Consider there is an estimation algorithm that forecasts \(b\) MB/s increment in the size of the first flow.  
The impact of this increment (\(b\)) on the total packet loss is depicted in Figure 5. As can be seen, the total packet loss is susceptible to the amount of flow increments. Conventional approaches incur lots of packet loss due to lack of information about the future behavior of flows. As an example, in order to minimize the maximum link utilization, conventional approaches routes flows 1 and 3 via the path \(1\to 2\to 4\); similarly, the path \(1\to 3\to 4\) will be selected for flow 2. Therefore, although the maximum link utilization is 95% for current traffic, the packet loss ratio goes up by increasing the first flow size. This happens because the conventional approaches' routes flow based on their current state without considering their future patterns and do not change the network configuration during the different time slots. On the other hand, our scheme (which is a prediction based approach) selects \(1\to 2\to 4\) and \(1\to 3\to 4\) for flows [1] and \(\{2,3\}\), respectively.  
It can be shown that, although the maximum link utilization for current flow matrix is 100%, the total packet loss decreases dramatically. In this case, the total packet loss is decreased significantly, in comparison with conventional approaches. It should be mentioned that the network capacity for flows from switch 1 to 4 is 400 MB/s, which means that the packet loss is unavoidable when the total flows size goes far from this threshold, i.e., the packet loss is unavoidable when the size of flow 1 goes up from 200 MB/s, which is illustrated in Figure 5.  
Figure 4: A sample four nodes topology.  
Figure 5: Prediction impact on packet loss. The impact of the proposed algorithm is more clear when the size of the traffic flows increases during the time (transmission control protocol (TCP) connections have similar patterns).
## 3 Current Non-Destructive Testing Practices for Port Cranes and Their Drawbacks
### Drawbacks of Current NDT Practices  
* Subjectivity and Human Factors--VI is highly subjective and depends on the inspector's experience and skills. Human factors can lead to missed detections or misjudgements. [PERSON] et al. [49] pointed out that the subjectivity of VI results in significant variability in defect detection outcomes, influenced by the inspector's experience and skills.
* Limited Detection Capabilities--Some NDT methods can only detect surface or near-surface defects but cannot identify internal defects. For example, MPT and ECT are only effective for specific material types and defect locations.
* High Requirements for Component Preparation--Certain conventional UT and RT often require the tested component to be cleaned, paints removed, and surface prepared, thereby increasing inspection time and labor costs. [PERSON] et al. [68] indicated that the preparation of components for UT and RT is time-consuming and labor-intensive, impacting inspection efficiency [69].
* Safety Risks--RT involves radiation hazards, necessitating strict safety measures to protect inspectors and nearby personnel. The operation of the NDT equipment may also pose safety risks. [PERSON] et al. [70] emphasized the radiation hazards associated with RT and the need for stringent safety protocols during testing [71].
* Inconvenience and Low Efficiency--Traditional NDT methods often require inspectors to access hard-to-reach or elevated areas of the crane, increasing inspection difficulty and risk. Typically performed during scheduled shutdowns, these methods interrupt operations and reduce port efficiency [72].
* Lack of Real-Time Monitoring--Most current NDT methods are periodic inspections that cannot provide real-time information on the structural health of port cranes. Defects that develop between inspections may go undetected, potentially leading to sudden failures [73].  
The limitations of current NDT practices underscore the need for more effective and efficient inspection methods. The integration of vibration and AE sensors with the IoT offers a promising solution. By enabling real-time continuous monitoring without frequent shutdowns, this approach can significantly enhance the safety, reliability, and operational efficiency of port cranes [74].
## 5 Real-Time Experimental Validation
### Case Three: Experimental Results of Proposed FDSC Capability during Communication Time Delay  
In this scenario, we conducted tests to evaluate the effects of time delays on our proposed control approach. In this configuration, we initially connect a resistive load of \(R_{L_{1}}=10\)\(\Omega\) and activate both the droop and proposed FDSC. Then, another resistive load, \(R_{L_{2}}=15\)\(\Omega\), is connected to the microgrid at \(t=8\) s and disconnected at \(t=16\) s. Utilizing Topology \(Y\) in our analysis in this case study, we explore different time delays between the secondary controls of the DERs; specifically, we set \(k_{34}=150\) ms, \(k_{23}=250\) ms, and \(k_{12}=100\) ms. The experimental results are shown in Figures 29 and 30. The observation reveals that the performance of the proposed FDSC in regulating the voltage and ensuring a consensus current sharing, with the different time delays within the microgrid, is satisfactory. Once again, these experimental results validate the efficacy of our proposed FDSC technique, and it is safe for implementation.
# Supervised Dynamic Correlated Topic Model for Classifying Categorical Time Series
## 2 Supervised Dynamic Correlated Topic Model
### Approximate Inference
For \(t=1,2,\ldots T\),  
**Observation Equation:**: \[\hat{\delta}_{d,t}\mid\delta_{d,t}\sim N(\delta_{d,t},\hat{\sigma}^{2}\mathbf{I}).\] (5)
**State Equation:**: \[\delta_{d,t}\mid\delta_{d,t-1}\sim N(\mathbf{\Phi}\delta_{d,t  
where  
\[\mathbf{K}_{d,t}=[\mathbf{\Phi}\mathbf{V}_{d,t-1}\mathbf{\Phi}^{\prime}+\mathbf{\Sigma}](\mathbf{\Phi}\mathbf{V }_{d,t-1}\mathbf{\Phi}^{\prime}+\mathbf{\Sigma}+\hat{\sigma}^{2}\mathbf{I})^{-1},\]  
is the Kalman gain matrix and the initial conditions are specified by \(\mathbf{m}_{d,0}\) and \(\mathbf{V}_{d,0}\). Similarly, the Gaussian variational backward smoothing distribution \(p(\delta_{d,t-1}\mid\delta_{d,1:T})\) using standard Kalman smoothing calculations is characterized as follows. For \(t=T\), \(T-1,\ldots,1\),  
\[\delta_{d,t-1}\mid\delta_{d,1:T} \sim N_{f}(\tilde{\mathbf{m}}_{d,t-1},\tilde{\mathbf{V}}_{d,t-1}),\] \[\tilde{\mathbf{m}}_{d,t-1} = (\mathbf{I}-\mathbf{I}_{d,t-1}\mathbf{\Phi})\mathbf{m}_{d,t-1}+\mathbf{I}_{d,t-1}\mathbf{m }_{d,t},\] \[p(\delta_{d},\mathbf{Z}_{d},\mathcal{D}_{d},\mathbf{y}_{d}\mid\mathbf{\Phi}, \mathbf{\Sigma},\mathbf{\beta},\mathbf{\eta}) = \prod_{t=1}^{T}\left(p(\mathbf{w}_{d,t}|\mathbf{Z}_{d,t},\mathbf{\beta})\times p (\mathbf{Z}_{d,t}|\delta_{d,t})\times\right.\]0  
where  
\[p(\delta_{d},\mathbf{Z}_{d},\mathcal{D}_{d},\mathbf{y}_{d}\mid\mathbf{\Phi}, \mathbf{\Sigma},\mathbf{\beta},\mathbf{\eta}) = \prod_{t=1}^{T}\left(p(\mathbf{w}_{d,t}|\mathbf{Z}_{d,t},\mathbf{\beta})\times p (\mathbf{Z}_{d,t}|\delta_{d,t})\times\right.\]1  
and the initial conditions are specified by \(\mathbf{m}_{d,T}=\tilde{\mathbf{m}}_{d,T}\) and \(\mathbf{V}_{d,T}=\tilde{\mathbf{V}}_{d,T}\). Using the Kalman filter equations, the approximate variational posterior for the \(d\)th document is given by  
\[p(\delta_{d},\mathbf{Z}_{d},\mathcal{D}_{d},\mathbf{y}_{d}\mid\mathbf{\Phi}, \mathbf{\Sigma},\mathbf{\beta},\mathbf{\eta}) = \prod_{t=1}^{T}\left(p(\mathbf{w}_{d,t}|\mathbf{Z}_{d,t},\mathbf{\beta})\times p (\mathbf{Z}_{d,t}|\delta_{d,t})\times\right.\]2  
where \(\mathbf{Z}_{d,t}\mid\gamma_{d,t}\) is \(Mult(1,\gamma_{d,t})\) and \(\delta_{d,t}\mid\delta_{d,1:T},\hat{\sigma}^{2}\sim N(\tilde{\mathbf{m}}_{d,t}, \tilde{\mathbf{V}}_{d,t})\) for \(t=1,2,\ldots,T\) and \(d=1,2,\ldots,M\). Constructing the Lower Bound  
Given the variational distribution defined in Equation (4), our goal is to estimate the variational parameters \(\gamma_{d,t}\), \(\delta_{d,t}\), and \(\hat{\sigma}^{2}\) for \(t=1,2,\ldots,T\) and \(d=1,2,\ldots,M\) in order to minimize the KL divergence between the variational distribution \(q(. )\) defined in Equation (4) and the true posterior \(p(. )\) defined in Equation (3). This optimization problem of minimizing the KL divergence is equivalent to maximizing the evidence lower bound (ELBO) [24] defined below.