esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Activation Pooling for Blind Image Quality Assessment
## 3 Experiments
### Implementation Details  
For fair comparison, we extract the same features as in [19] for each high-contrast patch. These features include the MSCN, MSCN products, gradients, log-Gabor filter responses and color-based features. As for a high-contrast patch, these five kinds of features are directly concatenated to obtain the feature vector of the patch, as shown in Figure 1c. The images are normalized into \(504\times 504\). The patch size and the percentage of the selected high-contrast patches for each image are empirically set to \(84\times 84\) and \(75\%\), respectively. The corresponding activation-response region sizes of the CSM in the 4 th and the 7 th convolutional layers are \(42\times 42\) and \(21\times 21\), respectively. To verify the effectiveness, the proposed methods are compared with the representative inventive mechanisms, including three opinion-aware methods (BRISQUE [14], BLIINDS2 [26] and CORNIA [27]) and two opinion-unaware methods (NIQE [17] and IL-NIQE [19]). We also compare with HPSP+DAP, which indicates that we utilize the pristine images for selecting high-contrast patches as in [19], and the proposed DAP for learning pooling weights. The proposed method in this paper is denoted as HPSC+DAP, which means that we employ the CSM in 4 th convolutional layer for high-contrast patch selection and the DAP for pooling weight learning.  
In a CNN, the shallow convolutional layers usually contain structural and textural local features, while the deep convolutional layers usually contain high-level semantic information. In the opinion-unaware BIQA task, to discover the useful structural and textural information, we select the  
Figure 4: Some samples from the CSIQ database.  
Figure 5: Some samples from the LIVE database.  
CSM in the shallow convolutional layers for high-contrast patch selection and pooling weight learning. We evaluate the performance of the proposed method when using CSM in different convolutional layers. For high-contrast patch selection, the index of convolutional layers varies from 3 to 8 with a step of 1, as shown in the rows of Table 2. For pooling weight learning, the index of convolutional layers also varies from 3 to 8 with a step of 1, as shown in the columns of Table 2. From Table 2, the experimental results indicate that the proposed method achieves the highest result when it utilizes the CSM in the 4 th convolutional layer for selecting high-contrast patches and the CSM in the 7 th convolutional layer for learning pooling weights.  
The number of the selected high-contrast activation-response regions \(K\) is an important parameter, and it affects the final results of BIQA. Hence, we investigate the influence of \(K\). The range of \(K\) is from 1600 to 3200 with a step of 400, and the results on the CSIQ database are shown in Figure 6. The best performance is obtained when \(K\) is equal to 2400.
## 3 Finite Element Model of STACER  
### Design of the STACER Model  
The STACER is a constant force spring reel, which is geometrically understood to be formed by a thin wall strip rotating at a certain pitch and helix angle, as shown in Figure 1. Each of STACER layers presents characteristics with equal natural curvature. The diameters are equal everywhere without external constraints and self-constraints. The STACER shows a constant force property that maintains a constant mechanical output during deployment and gathering processes. The thin walls are spirally covered layer by layer and increase the diameter. It shows an equidistant spiral when projected from the small diameter end to the large end. According to this geometrical feature, a modeling method is proposed for STACER.  
The strip model in Figure 3a is rotated around the Z axis by \(\theta_{0}\) and meshed, as shown in Figure 3b. Then, the strip is curled over and the top view after curling (projection of the helix in the xz plane), as shown in Figure 3c. The relationship between the helix radius and the rotation angle is shown in Equation (14).  
\[\begin{array}{l}L=\int_{0}^{\theta}{(R_{0}+t\frac{\theta}{2\pi})d\theta}\\ R_{1}=R_{0}+t\frac{\theta}{2\pi}\end{array}, \tag{14}\]  
In the equation, \(L\) is the arc length; \(R_{0}\) is the minimum radius position; \(t\) is the strip thickness; \(\theta\) is the rotation angle; \(R_{1}\) is the radius of corresponding position with \(\theta\).  
In Figure 3b, the coordinates of each node on a flat strip after meshing can be transformed by Equation (14) to obtain new coordinates for the spring reel, as shown in Figure 3d.  
In order to change the cumbersome problems of repeated modeling for the STACERs with different sizes, a parametric modeling method is proposed for STACERs with different diameters of the same series. The strip node of STACER can be directly replaced by the model description file.
# Dynamic QoS Management for a Flexible 5G/6G Network Core: A Step toward a Higher Programability
## 2 Background  
The future 5G/6G networks will be hybrid systems, complex architectures encompassing wireless and optical communications. Most research focuses on implementing new technologies in access networks and edge computing to realize the ultra-high peak rate, ultra-mass access, and ultra-high reliability in a network. However, only introducing these new technologies is not enough to solve all the challenges within the current architecture. Improving the network's core is necessary to achieve a robust, flexible, cost-effective, and intelligent network [11]. SDN and NFV technologies are viable enablers for providing such architecture and operations. The flexibility is achieved by separating network functions from hardware and implementing them into the software. This separation leads to high-level network programmability, enabling the necessary conditions for efficient implementation and shortening the time to market for new services [12]. The result is a rapid growth in the number of users, new types of communication, the emergence of more complex services, and the need to enable Over-The-Top (OTT) players to connect their services and applications to 5G/6G infrastructure [13].  
We examine how to improve the QoS performances of an SDN network when the number of supported services increases and network resource utilization changes. The SDN controller has a crucial role in satisfying the requirements of different services much more flexibly and providing precise control of each flow. However, some functionalities that should provide a more flexible configuration in the SDN network are still missing. For example, the existing OpenFlow switches allow only the Match-Action packet processing method. This method uses a fixed set of fields and the limited specification of the OpenFlow protocol, with a limited set of packet processing actions [14]. Our proposal aims to fulfill specific dynamic QoS requirements through dynamic queue management.
# Applications of Multi-Agent Deep Reinforcement Learning: Models and Algorithms
## 6 Application of Multi-Agent Deep Reinforcement Learning: State of the Art
### MADRL with Actors and Critics Applied to Distributed Entities in a Multi-Agent Environment
#### 6.5.1 [PERSON]'s MADRL Approach with Actors and Critics  
In [79], [PERSON]'s et al. embedded MADRL in distributed agents, which are the traffic lights (A.2). The MADRL approach enables a distributed agent to exchange information (i.e., policy) with neighboring agents. A multi-agent environment is collaborative (X.2.2) in nature. Using the proposed approach, distributed agents: (a) share knowledge with each other (X.1.1) to increase stability (O.1); and (b) scale down information from far-away distributed agents to increase learning speed (O.2) and scalability (O.5). The proposed approach addresses the challenges of high dynamicity (C.2) due to the dynamic condition of the road traffic pattern, and high dimension (C.3) with a large number of state-action pairs characterizing the large number of vehicles at an intersection.  
At an intersection, the traffic light \(i\) represents state \(s_{t}^{i}\) with a two-tuple information, including: (a) the cumulative delay of the first vehicle in each incoming lane; and (b) the queue length of vehicles along each lane. The action \(a_{t}^{i}\) represents a selected traffic phase. The reward \(r_{t+1}^{i}(s_{t+1}^{i})\) represents the queue length of vehicles in each lane. Each agent \(i\) manages the congestion level by exchanging knowledge (i.e., the last known policy \(\pi_{t-1}^{j}\)) with neighboring agent \(j\in J\), and use actors and critics to find the optimal policy. The latest sampled policy \(\pi_{t-1}^{j}\) of neighboring agent \(j\) and the current state \(s_{t}^{i}\) are included as the inputs of agent \(i\)'s DNN. The proposed scheme has been shown to reduce the congestion level (P.6).  
A similar approach has been applied in the literature [87; 88]. In [87], [PERSON] et al., enable distributed agents with MADRL to coexist and share knowledge with each other in a competitive (X.2.1) and dynamic environment in order to ensure stability (O.1). MADRL addresses the challenges of high dynamicity (C.2) and ultra-densification (C.1) in wireless networks. The distributed agents sense spectrum, and use actors and critics to predict the unoccupied frequency bands by licensed users. The proposed approach has been shown to increase QoS--specifically throughput (P.3). In [88], the actor and critic networks of distributed agents predict idle frequency bands without knowledge exchange.
# SCMC: An Efficient Scheme for Minimizing Energy in WSNs Using a Set Cover Approach
## 2 Related Works
### Target Coverage
The challenges faced in implementing the DiLCO protocol lie in selecting the most appropriate leader in every single sub-location and the most suitable collection of active sensors to ascertain a high standard in coverage. In [21], the aim was to find the maximum number of sensor coverage sets. The authors used two centralized algorithms and one localized algorithm. The two centralized algorithms deal with how the sensors are added to make a connected cover. The localized algorithm is used for communication between the local nodes. All three algorithms were only run once during the initialization phase. They concluded that keeping one coverage set active in a round-robin fashion would extensively reduce the energy consumption.
###### Abstract  
Research on real-time spaceborne synthetic aperture radar (SAR) imaging has emerged as satellite computation capability has increased and applications of SAR imaging products have expanded. The orbit determination data of a spaceborne SAR platform are essential for the SAR imaging procedure. In real-time SAR imaging, onboard orbit determination data cannot achieve a level of accuracy that is equivalent to the orbit ephemeris in ground-based SAR processing, which requires a long processing time using common ground-based SAR imaging procedures. It is important to study the influence of errors in onboard real-time orbit determination data on SAR image quality. Instead of the widely used numerical simulation method, an analytical approximation model of the quadratic phase error (QPE) introduced by orbit determination errors is proposed. The proposed model can provide approximation results at two granularities: approximations with a satellite's true anomaly as the independent variable and approximations for all positions in the satellite's entire orbit. The proposed analytical approximation model reduces simulation complexity, extent of calculations, and the processing time. In addition, the model reveals the core of the process by which errors are transferred to QPE calculations. A detailed comparison between the proposed method and a numerical simulation method proves the correctness and reliability of the analytical approximation model. With the help of this analytical approximation model, the technical parameter iteration procedure during the early-stage development of an onboard real-time SAR imaging mission will likely be accelerated.  
quadratic phase error; SAR; approximation; spaceborne real-time SAR imaging; orbit determination error 2019 11 1663 10.3390/rs11141663
## 3 Results and Discussion
### Fracture Resistance Mechanism of Fly Ash and Slag  
The mineral admixture improved the fracture performance of the concrete under the conditions of a large temperature difference and saline soil (SO42\({}^{-}\) and Cl-) attack. According to the macroscopic mechanical properties and microscopic characteristics of the concrete containing mineral admixtures under the harsh environment, the mechanism of fracture resistance of the fly ash and slag as the mineral admixture in the concrete could be analyzed. On the one hand, when the temperature changes are large every day, the temperature stress in concrete changes due to the temperature change, resulting in internal cracks. The cement in concrete is replaced in equal amounts by the mineral admixture, which reduces the amount of cement inside the concrete and the heat of hydration due to cement hydration. This can reduce the temperature stress of concrete in the temperature difference cycle when experiencing high temperatures, which can reduce the cracking of concrete and the internal defects of concrete, to improve the performance of concrete fracture resistance. On the other hand, fly ash and slag are fine enough to fill the pores between the cement particles, which makes the concrete denser inside and reduces the initial internal defects. Moreover, the secondary hydration products of the mineral admixture can further fill the internal pores of the concrete and continue to improve the bond strength between aggregates. In addition, compared with ordinary concrete, the impermeability of concrete mixed with mineral admixture is improved, and the permeability of SO42\({}^{-}\) and Cl- under a harsh environment is reduced. At the same time, the secondary hydration of the mineral admixture consumes the Ca(OH)2 produced by the initial hydration of cement, to avoid the increase in internal pores and cracks in concrete caused by ion erosion, and to a certain extent, it plays the role of fracture resistance.  
In the process of synergistic modification by fly ash and slag in concrete, the fly ash has higher activity than slag and can undertake more secondary hydration reactions. In addition, the secondary hydration products can fill the internal pores of the concrete to a greater extent and can resist the infiltration of corrosive ions. Therefore, the concrete with more fly ash content has lower Cl- content in the results of this study. Fly ash mainly protects the mechanical properties of concrete from the negative effects of slag. By observing the compressive strength, flexural strength, and fracture energy of different groups of concrete, it can be seen that the concrete with more fly ash content has higher mechanical properties, which can guarantee the strength of mineral admixture concrete. The slag can improve the elastic modulus of concrete and the anti-deformation ability of concrete. Therefore, mixing concrete with fly ash and slag can provide the concrete with better fracture resistance through the modification advantages of fly ash and slag.
# Systematic Analysis of the Hydrogen Value Chain from Production to Utilization
## 2 Hydrogen Production Technologies
### Comparison of Hydrogen Production Technologies
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Technology** & **Feedstock** & **Energy Source** & **Advanages** & **Disadvantages** & **Hydrogen Classification** & **Efficiency (vs)** &
\begin{tabular}{c} **LCOH (USDAgIlg)** \\ \end{tabular} & **TRL** & **REF** \\ \hline \multirow{4}{*}{Gasification} & \multirow{4}{*}{Biomass} & & - & Net-zero & - & Biomass unvalability & & & & \\  & & & - & emission process & - & Complex logistics & & & & \\  & & & - & Capside of producing & transportation for & & & & & \\  & & & a great amount & & decentralized sites & & & & & \\  & & & of hydrogen & - & Complex HS U\({}_{\odot}\) & Green & 35-50 & 1.77-27 & 7 & [23,24,32,33,41,57, 60,61] & \\  & & & - & Synergies with wake & separation process & & & & & \\  & & & management & - & High investment cost & & & & \\  & & & - & Chey feedback & - & Fluctuating H\({}_{\gamma}\) yields & & & & \\ \hline \multirow{4}{*}{Gasification + CCS} & \multirow{4}{*}{Fossil fuels} & \multirow{4}{*}{Thermal} & - & Lower efficiency & & & & & \\  & & & - & Can be used as a & - & Lower efficiency & & & & \\  & & & bridge technology & & & brain gasification & & & \\  & & & & from gyro to & & & & & \\  & & & & green phylogeny & & & & & \\ \hline \multirow{4}{*}{Pyrolysis} & \multirow{4}{*}{Fossil fuels} & \multirow{4}{*}{Thermal} & - & Lower emissions than any other process with fossil leaf feedback & - & Despite consuming loss & & & & \\  & & & - & Similar efficiency to & energy than SMM & & & & \\  & & & - & Synaptic process is still an & & & & & \\  & & & - & Consumes less energy & energy insurance process & & & & \\  & & & - & when compared with the most common thermogen & - & SMM at a maturity level & & & \\  & & & - & that enables large-scale & & & & \\  & & & - & Single & & & & & \\  & & & - & Systematic principle & & & & & \\ \hline \multirow{4}{*}{Biomass} & \multirow{4}{*}{Thermal} & \multirow{4}{*}{Thermal} & - & - & There are some challenges & & & & \\  & & & - & in scaling up the & & & & \\  & & & - & & technology (g. a, & & \\  & & & - & continuous biomass & & & & \\  & & & - & feeding system needs to be & & & & \\  & & & - & Fluctuating H\({}_{\gamma}\) yields & & & & \\  & & & - & Tar formation & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Cont. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Technology** & **Feedstock** & **Energy Source** & **Advantages** & **Disadvantages** & **Hydrogen Classification** & **Efficiency (F)** & **LCOH (USDkg/kg)** & **TRL** & **REF** \\ \hline Thermochemical & Water & Thermal (solar) & - Very low environmental impact & - & Not yet cost-effective & & & & & \\  &
# MAS-YOLO: A Lightweight Detection Algorithm for PCB Defect Detection Based on Improved YOLOv12
## 3 Experiment and Analysis
### Datasets
#### 3.1.2 Kaggle PCBA Defects Dataset  
The Kaggle PCBA Defects dataset contains approximately 2000 images. Notably, these images were captured by a high-definition industrial camera equipped with a 16-megapixel CMOS sensor from various angles (top, side, and oblique). Moreover, to accommodate different PCB sizes and prevent edge distortion, the camera is fitted with a distortion-free zoom industrial lens, featuring an adjustable focal length between 6 and 12 mm and a maximum aperture of f/1.6. In order to avoid mirror reflections and potential shadows on the board, and to minimize the impact of uneven lighting on subsequent processing steps, two frosted annular LED light sources equipped with special diffusion and extinction plates were introduced. This setup effectively mitigates the adverse effects of lighting, thereby closely mirroring actual industrial processes. The original images are 4608 \(\times\) 3456 pixels in resolution; however, due to GPU limitations, the initial dataset was partitioned into 3000 samples, each with a resolution of 1024 \(\times\) 1024 pixels. These samples were subsequently split into training, validation, and test sets following a 4:1:1 ratio. It is important to note that the training, validation, and test sets are maintained as distinct  
Figure 3: Six common PCB defect categories: Missing hole, Mouse bite, Open circuit, Short, Spur, and Spurious copper.  
and non-overlapping subsets to prevent data leakage and ensure the integrity of the evaluation process.
## 5 Conclusions  
We developed an evaluation framework integrating cultivated land functionality and degradation risk to assess cropland quality (CLQ) in Northeast China's black soil region (BSNC), highlighting sustainable land use. Our analysis showed that over 97.40% of the land excels in primary productivity (PP), with areas like LHP and CBMEL rated as Grades 1 and 2, yet biodiversity supply (BS) is notably weak, with over 98% of some areas in Grades 4 and 5. Degradation risks are low across 79% of the SNP region, but higher risks, such as those of erosion and nutrient depletion, persist in the Liaohe and Sanjiang Plains. Based on the specific degradation characteristics of the different regions mentioned above, it is essential to implement tailored strategies combining modern agricultural technologies and ecological conservation measures. These strategies include conservation tillage, soil improvement, and crop structure adjustment, aiming to achieve the sustainable utilization and efficient management of cultivated land. Additionally, it is crucial to strengthen the long-term monitoring of cultivated land degradation to provide scientific support for precise management and policy formulation.  
This study focuses on the black soil of Northeast China (BSNC) region, which may limit the generalizability of its findings to other black soil ecosystems with differing environmental or policy contexts. The selection of specific indicators might overlook additional factors influencing cropland quality (CLQ). Future research should investigate longitudinal trends in CLQ, expand the spatial scope, and incorporate additional indicators, such as microbial metrics, to advance sustainable cropland management.  
Conceptualization, H.T., Y.Z. and Y.D.; methodology, Q.L., Y.Z., Y.L. and J.N.; software, M.G., J.N. and Q.X.; investigation, H.T., J.N., Q.X., M.L. and Y.D.; resources, H.T. and Y.H.; data curation, H.T., Q.L. and Y.D.; writing--original draft preparation, H.T., Y.Z. and Q.L.; writing--review and editing, Y.Z., H.T. and M.G.; visualization, Y.Z., M.G., Y.L. and J.N.; supervision, H.T., Q.X., M.L. and Y.D.; project administration, H.T., Y.H., M.L. and Y.H.; funding acquisition, H.T., Y.L. and Y.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key R&D Program of China (Grant No. 2021 YFD1500201); the National Natural Science Foundation of China (Grant No. 69191019); and the Government Fund Project (Grant No. 20241411044). We would like to thank our editor and the anonymous reviewers for their valuable comments.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.