esa-sceva/satcom-chunk-collection huggingface dataset:
# Innovative Approaches to Semi-Transparent Perovskite Solar Cells
## 1 Introduction
The CRI value range lies between 0 and 100, revealing the ability of ST-PSCs to transmit light with the actual color of the observed light. Higher CRI values refer to a high color rendering capacity, whereas lower values refer to a lower rendering capacity. CCT accuracy stems from the color space standard developed in 1931 known as the CIE xy chromaticity diagram [17]. Depending on the combination between PCE and optical properties, ST-PSCs could be extended to various technologies, such as TSC and BIPV. Furthermore, their wide-ranging applications, including windows, curtain walls, canopies, balustrades and shading, have drawn a lot of interest [18; 19]. However, ST-PSCs still do not have high-efficiency characteristics due to various factors that cause tradeoffs between efficiency and light transmittance. For example, fabricating ST-PSCs requires a compromise in film thickness. This leads to drawbacks such as V\({}_{\text{OC}}\) and J\({}_{\text{SC}}\) reductions, in turn reducing the device's efficiency. In addition, the fabrication of thin perovskite films makes it difficult and intricate to scale-up productions; perovskite thin films can easily make pinholes, which can be a major factor in reducing efficiency and stability. Numerous techniques have been developed to concurrently increase solar cells' transparency and performance in order to circumvent these limitations, e.g., partially covering perovskites and creating island-shaped structures, fabricating ultra-thin perovskites for restraining light absorption, making discontinuous perovskites or utilizing wide band-gap perovskite (WBG) for enhancing transparency [20]. However, the trade-off between high transmittance and efficiency still makes the development of ST-PSCs difficult. These issues highlight the need to explore current development progress and derive potential requirements and strategies for ST-PSC development. Therefore, the present review focuses on various potential possibilities to develop ST-PSCs and covers five important areas, including components and applications. In detail, this review provides insights into various aspects of ST-PSCs in which advancements in perovskite materials, charge transport materials and different types of the most exploited electrodes used along with various types of transparent electrodes are discussed. Finally, applications focusing on BIPV and tandem applications are covered in the following sections: (i) perovskite photoactive layers for ST-PSCs, (ii) transparent electrodes for ST-PSCs, (iii) stability of ST-PSCs, (iv) device structure for ST-PSCs and (v) applications of ST-PSCs. Finally, the conclusion and perspectives provide further insight into improvementsin ST-PSC for future research. The structure and performance of the representative ST-PSCs and used component material are summarized in the tables for each section.
# Two-Sided Matching Decision Method of Electricity Sales Package Based on Disappointment Theory
## 2 Construction of Evaluation Index System for Electric Power Customers and Power-Selling Companies
### Electric Power Customers' Evaluation Index of Electricity Sales Package
#### 2.1.1 Clean Energy Ratio of the Package  
\"Carbon peaking\" refers to the phenomenon where the total amount of carbon dioxide emissions reaches a historical peak at a certain point in time, during which there will still be fluctuations in the total amount of carbon emissions, but the overall trend is flat, and then the total amount of carbon emissions will gradually and steadily decline. \"Carbon neutrality\" refers to the phenomenon of neutralizing all carbon dioxide emissions, or even achieving negative carbon emissions, through fixed carbon emissions, afforestation, and other methods on the basis of \"carbon peaking\". Carbon peaking and carbon neutrality will have a huge impact on all aspects of our lives. The development of clean energy is an important means to help China achieve its carbon peak and carbon neutrality goals [16].  
In 2003, the British energy white paper \"Our Energy Future: Creating a Low-Carbon Economy\" put forward the concept of a low-carbon economy for the first time. Since then, global energy has gradually entered a transitional stage toward a green and low-carbon energy structure. China has gradually promoted the development of clean energy into marketization, developing a clean energy structure system consisting of wind power, photovoltaic power generation, tidal power generation, hydropower, biomass power generation, and other clean energy sources [17].  
According to data from the National Bureau of Statistics, the main power generation mode in China is still traditional thermal power generation. However, the proportion of clean electricity consumption in total energy consumption has increased year by year, from 20.5% in 2017 to 25.5% in 2021 [18], as shown in Figure 1.  
In order to adapt to the accelerating adjustment of the global energy structure and the unchangeable trend of clean energy power generation, electricity sales companies need to increase the proportion of clean energy power generation in the package when formulating electricity sales plans and promote the development of \"green electricity\". At the same time, this will also become a crucial factor to electric power customers when choosing electricity packages. Now, we define the proportion of clean energy power supply to the total power supply in the electricity sales package as QJ. If \(0<QJ\leq 15\), then the proportion of clean energy is relatively small; if \(15<QJ\leq 25\), then the proportion of clean energy is moderate; if \(QJ>25\), then the proportion of clean energy is relatively high, and electricity users will also have a higher satisfaction with it [19].
## 3 Cardiovascular Disease
### Atherosclerotic Arterial Disease
#### 3.2.2 ECM in Atherosclerosis  
ECM changes are observed in various CVD conditions with MMPs playing a key role in remodelling of the ECM. In atherosclerosis, excessive activation of MMPs contributes to disease progression and plaque destabilisation. In atherosclerotic vessels MMPs act as inflammatory mediators linking inflammation with vascular remodelling. In disease vessels MMPs function as inflammatory cytokines and their expression and activity increases. However, MMPs are only present in low concentration and activity in healthy vessels. An important cell type in atherosclerosis is vascular smooth muscle cells (VSMCs). VSMCs are present in a quiescent/contractile state in healthy arteries and switch to a proliferative/synthetic state in atherosclerosis [54] rendering atherosclerotic plaques rich in collagen [54]. VSMC proliferation is dependent on type I collagen and results in plaque progression [54]. Adhesion of the ECM to cells is largely regulated by integrins, a large family of heterodimeric cell adhesion molecules that anchor cells to ECM and neighbouring cells. In this regard, integrin \(\beta\)1 is the most predominant integrin expressed on the surface of VSMCs and all collagen-binding integrins share the common \(\beta\)1 subunit [55; 56]. Important extracellular matrix components and imaging targets in atherosclerosis include elastin, collagen, fibrin and metalloproteinases.
## 5 Predefined Path-Based Techniques
### Dual Virtual Paths (DVP)
#### 5.2.3 Advantages and Disadvantages  
Like ODP, DVP significantly reduces redundant unicast traffic compared with the standard HSR protocol by forwarding the unicast traffic through two predefined paths instead of duplicating and flooding in overall network. Instead of building and maintaining the link metric table for building dual paths as in the ODP approach, the DVP approach establishes dual virtual paths for each connection pair of terminal nodes by sending and receiving control messages.  
However, since the DVP approach establishes dual paths for each connection pair of terminal nodes, the number of connection pairs is very large. The connection pairs required to build dual paths is equal to \((n\times(n-1))/2\) for a network with \(n\) terminal nodes. This results in high control overhead for discovering and building dual paths in HSR networks.
## 3 Methods
### Optimization Techniques  
This work uses two techniques, Gaussian blur and region of interest (ROI) [60, 61], to optimize and enhance image processing accuracy. Both techniques play a crucial and significant role in improving system performance, helping to increase the speed and precision of conveyor belt misalignment detection.  
Gaussian blur is employed as a filter to reduce the noise in the image, which is applied before Canny edge detection to ensure that the edges detected by Canny are more exact and durable. The mathematical formula for Gaussian blur [60] is as follows:  
\[G(x,y)=\frac{1}{2\pi\sigma^{2}}\exp\left(-\frac{x^{2}+y^{2}}{2\sigma^{2}} \right).\]  
Figure 5: Flowchart of the conveyor belt misalignment detection and correction algorithm using Raspberry Pi and image processing. Purple arrows indicate return to the beginning of the process under specific conditions, while grey arrows represent normal forward progress toward the end of the process.  
In this formula, \(G(x,y)\) represents the value of the filtered pixel, and \(\sigma\) is the standard deviation, which determines the level of smoothing. A higher value of \(\sigma\) results in a more blurred image.  
Also, region of interest (ROI) [62] defines the portion of the image that requires processing. This project restricts this region to a specific part of the conveyor belt where misalignment detection is required. By accomplishing this, only the relevant area is selected for processing, reducing the computational load and considerably raising processing speed and decreasing latency. At the same time, the system's precision is maintained as only the essential parts of the image are analyzed, demonstrating the practical benefits of this technique. Figure 6 identifies the region of interest (ROI) before applying Gaussian blur, and Figure 7 shows the region of interest (ROI) after applying Gaussian blur.  
In image processing, particularly in industrial environments, noise and undesirable disturbances, such as dust and other factors in the image, can cause unnecessary lines to be wrongly recognized as the target lines. In this project, where the purpose is to detect white lines on the conveyor belt, some disturbances in the image, such as sudden changes in the light or environmental noise, might cause lines to be detected incorrectly, which, in fact, either do not exist or do not contribute to accurate detection.  
As shown in Figure 6, however, at this stage, disruptions may cause lines to be detected that are not the intended white lines. This can reduce detection accuracy and lead to diagnostic errors as the system may struggle to distinguish between actual lines  
Figure 6: Region of interest before Gaussian blur.  
Figure 7: Region of interest after Gaussian blur.  
and unnecessary noise. In the following image, as shown in Figure 7, Gaussian blur was applied first to smooth the image and remove noise. This method eliminated the slight noise from the image, going only to the authentic and essential lines for detection. As a result, the exactness of the white line detection was improved.
# DRL-Based Dynamic Destroy Approaches for Agile-Satellite Mission Planning
## 1 Introduction
The results show that the proposed methods have fewer iteration times to convergence and a higher task-scheduled rate than other traditional heuristic algorithms in large-scale scenarios. The DRL-LNS method outperforms other algorithms in Area instances, while the DRL method performs better in World scenarios. The remaining sections of this study are structured as follows: In Section 2, the problem description is introduced. In Section 3, the modeling process of the D3 RL model is presented in depth. The effectiveness of the suggested applications are verified through comparison experiments in Section 4, and the conclusions are drawn in Section 5.
## 4 Results and Discussion
### Flowability  
The flowability was identified as the primary metric for evaluating the performance of the grouting material, with the hydration reaction rate serving as a key factor influencing flowability. Figure 4 demonstrates the impact of various factors on slurry flowability. It was established that, as the dosage of zeolite powder increased, the flowability of the slurry gradually decreased, reaching a minimum value of 28.43 cm. The increase in the specific surface area of the compound grouting material, resulting from the substantial replacement of GGBFS by zeolite powder, led to a decrease in the average water content per unit surface area. This, in turn, led to excessively dry or viscous characteristics in the prepared slurry, thereby reducing its flowability. Moreover, an increase in slurry flowability was observed with a 17.86% rise in the CL dosage. In the field of construction materials, CL is used as a water-reducing agent. Under constant water-binder ratio conditions, the dosage of CL is increased, resulting in elevated levels of free water molecules within the slurry.  
Figure 4: Effects of different factors on flowability: (**a**) zeolite powder-GGBFS; (**b**) CL dosage; (**c**) NaOH dosage; (**d**) water-binder ratio.  
As the NaOH dosage increased, the slurry flowability decreased more gradually, from 31.5 cm to 30 cm. Accordingly, the impact of the NaOH dosage on flowability was found to be negligible, and the resulting flowability values met the established minimum standard criteria. When the water-binder ratio was 0.6, the flowability of the slurry showed a substantial enhancement, exhibiting a 117.5% increase in comparison to the water-binder ratio of 0.5. The test pattern was similar to that in the results reported by [PERSON] et al. [31]. The greater number of water molecules in the slurry led to their attraction to each other, forming a smooth interface. As a result, this enhanced the flowability of the slurry. According to the Chinese technical specification GB/T 50448-2015 [32], the flowability of the slurry should be not less than 260 mm. Therefore, Groups 15 and 16 did not meet this standard.
## 5 High Capacity 2 \(\times\) 2 OPS Node with Contention Resolution and Fiber Transmission  
This section describes the most up-to-date OPS experiment with a record capacity of 12.8 Tb/s/port. Compared to Section 4, we used an improved transmitter setup and the optical packet signal was transmitted over 100 km of dispersion compensated SMF.
# Geo-DMP: A DTN-Based Mobile Prototype for Geospatial Data Retrieval
## 4 Experimental Validation and Evaluation
### Partial Intermediate Cached Contents  
This experiment is a combination of the two situations above: partial contents are cached at Node 3, while the remainders could only be found at Node 4. Referring to Figure 18, we describe the interactions between nodes as follows:  
* About 200 s: Firstly, Node 2 takes all query bundles from Node 1.
* About 400 s: Node 2 and Node 3 come together at point C. Node 3 matches some queries from Node 2 with its local cached contents and gives the responses back to Node 2, then it carries the unfulfilled queries towards the RSU. Node 2 goes back to point B with those response bundles.
* About 600 s: Node 2 brings the first batch of responses back to Node 1. Almost at the same time, Node 3 gets the rest of the requested contents from Node 4.
* About 800 s: Node 2 and Node 3 meet at point C again. At this time the rest of the responses are unloaded from Node 3 to Node 2.
* About 1000 s: Finally, the second batch of responses are delivered to Node 1.  
Figure 16: The timeline of interactions—the case of no intermediate caches.  
Figure 17: The timeline of interactions—the case of full intermediate caches.  
This experiment verifies that a number of queries issued at one time can be satisfied by different content sources, and responses are supported to be returned to the requester separately.
# New Optimal Design of Multimode Shunt-Damping Circuits for Enhanced Vibration Control
## 2 Methodology
### Design Optimization
#### 2.4.3 Optimization Problem  
In the proposed method, the resistance values \(R_{i}\), the capacitances \(C_{i}\) and the inductance values of the shunt-damping sub-branch \(\widetilde{L}_{i}\) are considered as design variables. The inductance values \(\widetilde{L}_{i}\) are given by Equation (31), while the inductance of the simplified shunt circuit in Figure 3b is given by \(L_{i}=\widetilde{L}_{i}+\widetilde{L}_{i}\). Thus, the optimization problem of choosing the shunt circuit parameters for minimizing the performance index of Equation (53) is formulated as follows:  
Model 1 (Proposed Method):  
\[\begin{array}{c}\text{Find the optimal set of design variable (optimal design vector)}\ \mathbf{x}=\left\{R_{i},\ C,\ \widetilde{L}_{i}\right\}^{T}\text{ to}\\ \\ \text{minimize}\ f\left(R_{i},C_{i},\ \widetilde{L}_{i}\right)\\ \text{ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \  
Find design variables \(R_{i},\ C_{i}\) to  
\[\begin{array}{c}\text{minimize }f(R_{i},C_{i})\\ st\ R_{L}\leq R_{i}\leq R_{U}\ i=1,2,\ldots,n\\ C_{L}\leq C_{i}\leq C_{U}\ i=1,2,\ldots,n\end{array} \tag{56}\]  
where \(C_{L}\) and \(C_{U}\) are, respectively, the lower and upper bounds of \(C_{i}\).
# Applying Machine Learning to Preselective Weighing of Moving Vehicles
## 4 Vehicle Weight Estimation Model Using Machine Learning Algorithm
### Development of a Model for Vehicle Weight Estimation Using the XGBoost Algorithm
SVMs, on the other hand, could be useful for smaller datasets, but are not efficient enough for problems scaled to a large number of records. XGBoost has proven to be the best compromise between speed, scalability, and accuracy, making it the optimal choice for the preselective weighing of vehicles in motion. These results provide an encouraging and promising perspective on the use of the XGBoost algorithm in a machine learning model for estimating the weight of moving vehicles, based solely on the features presented in Table 1.  
The above information on using the XGBoost algorithm to estimate vehicle weights in weigh-in-motion (WIM) systems can help identify practical recommendations for integrating the model into real-world WIM systems and strategies for its implementation. The technical infrastructure of the WIM system should be equipped with high-quality sensors (e.g., piezoelectric) and cameras to record vehicle data, such as speed, number of axles, and license plate images. A stable broadband connection (e.g., fiber optic) is also necessary to transmit data to a central computing unit. Installed on-premises servers or cloud computing support and train the XGBoost model on the data being collected. The XGBoost model should be trained on local data, optimizing parameters such as tree depth and training speed for best performance. Model implementation can be carried out using a dedicated API or software modules that take data from the WIM and pass it to the model to estimate the weight of passing vehicles. In the case of high traffic volumes, it is worth implementing distributed solutions, such as edge computing systems, which analyze data locally, relieving the load on central servers. As part of verifying and calibrating the proposed solution, it is proposed to regularly monitor model quality indicators such as R\({}^{2}\) and MAPE to assess their effectiveness. As part of testing and scaling the system, tests in real conditions at different locations are proposed to assess the scalability of the proposed solution. The results from Grodziec and Osowiec indicate the possibility of adaptation to other locations. After positive tests, the system can be implemented in other places, especially where the data are sufficiently representative. The data should be stored in accordance with regulations on personal data protection. Archiving allows for later analysis and training of improved models. Implementing the above steps can enable the use of the XGBoost model in WIM systems, improving vehicle preselection efficiency and reducing road infrastructure costs.
###### Abstract  
A novel electrochemical sensor, based on a Rh\({}_{2}\)O\({}_{3}\)-reduced graphene oxide (rGO) composite modified carbon electrode, has been developed for detecting bisphenol A (BPA) in beverages. The prepared Rh\({}_{2}\)O\({}_{3}\)/rGO and its precursor materials were characterized by scanning electron microscope (SEM) and X-ray diffraction (XRD). Under optimum conditions, the sensor presented good electrochemical performance for analyzing BPA, with a linear range of 0.6-40 uM, detection limit of 0.12 uM, good reproducibility, and excellent stability. The good performance can be attributed to the combination of the good catalytic properties of Rh\({}_{2}\)O\({}_{3}\) and good conductivity of rGO. The sensor is directly used for detecting BPA in the residual solutions of four beverages after simple filtration, with satisfactory recoveries of 93-99%.  
bisphenol A; rhodium oxide; reduced graphene oxide; electrochemical sensor +
Footnote †: journal: Applied Physics Letters B
## 1 Introduction  
Underwater acoustic communication networks (UACNs) play a critical role in marine environment monitoring, maritime rescue, and naval surveillance [1, 2]. However, UACNs present significant challenges due to their inherent limitations, including narrow bandwidths, prolonged transmission delays, and severe multipath effects [3, 4, 5]. These vulnerabilities make UACNs susceptible to interference, attacks, and eavesdropping on communication channels to steal sensitive information or disrupt communications by dynamically adjusting jamming power. Such attacks can deplete the energy of underwater sensor nodes and potentially trigger denial-of-service (DoS) scenarios.  
Traditional anti-jamming techniques, such as frequency hopping and spread spectrum, are impractical for UACNs due to the dynamic topology of underwater networks. Consequently, power control is critical for UACNs. However, conventional power control methods struggle to adapt to the rapid variability and complexity of underwater acoustic channels as they rely on convex optimization [6, 7]. Recently, reinforcement learning (RL) algorithms have been used to address interference using power control and relay selection technologies. In underwater relay cooperative communication networks, relay nodes are used to enhance transmission quality by forwarding signals. When channel conditions and interference models are unknown, RL algorithms optimize relay strategies to improve anti-jamming performance. For example, an RL-based anti-jamming transmission method [8] reduces the bit error rate by optimizing transmission power. Similarly, Q-learning in multi-relay cooperative networks is used to enhance communication efficiency [9].  
Based on such results, the anti-jamming problem in UACNs was solved in this study by using a novel transmitter anti-jamming method that integrated RL with relay-assisted communication. Specifically, a transfer reinforcement learning (TRL) algorithm based on a hybrid strategy was designed to optimize transmitter actions. By leveraging prior anti-jamming data from similar scenarios, the developed approach initializes Q-values and policy distributions to mitigate the inefficiencies of traditional RL methods, which often start with zero-initialized Q-value matrices. This initialization accelerates the convergence of optimal policies, enhancing the overall anti-jamming performance.