esa-sceva/satcom-chunk-collection huggingface dataset:
# Scenario-Based Configuration Refinement for High-Load Cellular Networks: An Operator View
## 2 Analysis of High Traffic Load Cells  
### Network Traffic Analysis Framework  
As shown in Figure 1, we can capture network traffic features by two approaches. One is to analyze traffic features by collecting signaling packets. This approach is a reactive method with less overhead. Another approach is to send network probes to proactively obtain network traffic features in real time. According to these two approaches, we can distinguish users in terms of basic business data, location, current status, and preference. For different users, we can identify different network performance metrics by analyzing collected field data, which is discussed in the following subsections. In addition, the web and the application service are offered to display and to configure the network parameters.  
Figure 1: The framework of network traffic feature analysis.
## 4 Conclusions  
In this work, in order to reduce the usage of computational resources while at the same achieve optimal performance, we first propose a framework to represent urban road traffic network congestion levels. This was used to utilize historical records of traffic congestion data with a large size to predict future short-term traffic congestion levels. We captured raw snapshots of congestion maps for an urban road traffic network in Guiyang, Guizhou province, China. These snapshots were preprocessed and transformed into a dataset consisting of matrices representing traffic congestion levels at different times. To evaluate the effectiveness and cost-efficiency of our proposed MAV pooling operation, we compared its prediction performance with that of three other existing methods within our proposed representation framework. We also propose a deep learning neural network STCN for traffic congestion prediction, using it with the back-testing method. The results as regards our aforementioned dataset show that MAV achieves optimal overall performance and can effectively and cost-efficiently represent congestion levels in an urban road traffic network for short-term traffic congestion forecasting.  
On the other hand, this study only focuses on the evaluation of the representation performance of traffic congestion levels using raw snapshots of a single urban area traffic network. In addition, MAV has a limited compression ratio because it depends on the grid-based partition of urban road traffic networks, restricting its scope in terms of applicable road networks. In future work, we will try to experiment with snapshots of congestion maps for different scales of road networks and look for other representation schemes to improve the compression ratio of urban road traffic networks, as to make it feasible to investigate congestion of traffic networks with larger scales.  
**Author Contributions:** Conceptualization, S.Z. and X.L.; methodology, S.Z., Y.Y. and X.L.; software, S.Z.; validation, S.Z., X.L. and Y.Y.; formal analysis, S.Z.; investigation, S.Z.; resources, S.L.; data curation, S.Z.; writing--original draft preparation, S.Z., Y.Y. and X.L.; writing--review and editing, S.Z. and Y.Y.; visualization, S.Z.; supervision, S.L.; project administration, S.L.; funding acquisition, S.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by The National Natural Science Foundation of China under Grant No. 91746116 and 51741101, and Science and Technology Project of Guizhou Province under Grant Nos. [2018]5788, Talents [2015]4011 and [2016]5013, Collaborative Innovation [2015]02.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Quality Assessment of 2.5D Prints Using 2D Image Quality Metrics
## 3 Methodology
### Full-Reference Image Quality Metrics  
We chose to work with FR IQMs because we have reference images and FR IQMs' universality is higher than that of NR IQMs [50].  
The SSIM metric is selected because it is a HVS-based FR metric that considers structural information from the image scene [51]. This metric can be used to assess lightness, contrast, sharpness, and artifact attributes [2]. Additionally, [PERSON] and [PERSON] [1] mentioned its potential to detect artifacts. It is used to calculate the perceived difference between the reference and distorted input images and it is independent of both average luminance and contrast [51]. It is appropriate for use with 2.5D prints because it is based on structure and most of the aspects that have been changed in the dataset influence structure.  
The Multi-Scale SSIM (MS-SSIM) is selected because it incorporates variations of image resolution and viewing conditions [52]. We used MS-SSIM at scale 2 (scale 1 is the same as SSIM) for both structure and contrast terms because at higher scales it assesses a portion of an image due to downsampling.  
The improved Color Image Difference (iCID) is selected because it considers the reference image's color, structure, and contrast [53]. The metric was used with 51 cm viewing distance and downsampling was deactivated. We used its lightness difference, lightness contrast, and lightness structure maps.  
We included the color difference metric-CIEDE2000 [54] to see how a pixel-based metric will behave on 2.5D prints' QA. It works with the CIELAB color space.  
The SCIELAB is selected because of its spatial filtering to mimic the HVS's spatial blurring [55]. We modified this metric so that it works with the CIEDE2000 formula. The viewing distance in SCIELAB was set to 51 cm. The white point was D65 in both CIEDE2000 and SCIELAB.  
For all metrics, the input images were color images of 904 \(\times\) 550 pixels. The color images were converted into grayscale following ITU recommendation BT.601-7 for SSIM and MS-SSIM. We used the default parameters for the selected IQMs.
# Advances in Bio-Hydrogen Production: A Critical Review of Pyrolysis Gas Reforming
## 3 Hydrogen Production by Catalytic Reforming of Pyrolysis Volatiles
### Slow Pyrolysis
#### 3.1.1 Pyrolysis Temperature  
Pyrolysis temperature can be considered a key operating condition in the two-stage catalytic reforming and pyrolysis process. Generally, increasing pyrolysis temperature has a beneficial effect on gas yield. The impacts of increasing wood sawdust pyrolysis temperature were investigated in a research study performed by [PERSON] et al. [67] to compare numerical modeling results with experimental results. The obtained experimental results showed that increasing the pyrolysis temperature from 300 \({}^{\circ}\)C to 600 \({}^{\circ}\)C and reforming pyrolysis volatiles at 800 \({}^{\circ}\)C increased gas distribution considerably, so the gas yield increased from 21.65 wt% to 58.04 wt%. Furthermore, the hydrogen yield increased from 4.24 mmol/g to 11.01 mmol/g in this temperature range [67]. A comparison of two studies using waste wood as a feedstock for the catalytic reforming of pyrolysis volatiles at 800 \({}^{\circ}\)C in the presence of a Ni-based catalyst showed that the hydrogen yield was enhanced from 10.5 mmol/g up to 27.52 mmol/g by increasing the pyrolysis temperature from 600 \({}^{\circ}\)C to 800 \({}^{\circ}\)C [61; 62]. Increasing pyrolysis temperature can beneficially affect gas production and hydrogen yield in the two-stage process. The effect of pyrolysis temperature on hydrogen yield in the catalytic reforming of woody biomass pyrolysis volatiles is reported in Figure 5.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Reaction** & **Description** \\ \hline Biomass \(\rightarrow\) H\({}_{2}\)O + H\({}_{2}\) + CO + CO\({}_{2}\) + C\({}_{x}\)H\({}_{y}\)O\({}_{x}\) + C\({}_{x}\)H\({}_{y}\) + char & Biomass pyrolysis \\ \hline C\({}_{x}\)H\({}_{y}\)O\({}_{x}\) \(\rightarrow\) H\({}_{2}\)O + H\({}_{2}\) + CO + CO\({}_{2}\) + CH\({}_{4}\) + C\({}_{n}\)H\({}_{m}\) & Catalytic tar cracking \\ \hline C\({}_{x}\)H\({}_{y}\)O\({}_{x}\) + H\({}_{2}\)O \(\rightarrow\) H\({}_{2}\) + CO & Tar steam reforming \\ \hline C\({}_{n}\)H\({}_{m}\) + H\({}_{2}\)O \(\rightarrow\) H\({}_{2}\) + CO & Hydrocarbon volatile steam reforming \\ \hline C\({}_{x}\)H\({}_{y}\)O\({}_{x}\) + CO\({}_{2}\) \(\rightarrow\) H\({}_{2}\) + CO & Tar dry reforming \\ \hline C\({}_{n}\)H\({}_{m}\) + CO\({}_{2}\) \(\rightarrow\) H\({}_{2}\) + CO & Hydrocarbon volatile dry reforming \\ \hline CO + H\({}_{2}\)O \(\rightarrow\) CO\({}_{2}\) + H\({}_{2}\) & Water gas shift \\ \hline C + H\({}_{2}\)O \(\rightarrow\) CO + H\({}_{2}\) & Char steam gasification \\ \hline C + CO\({}_{2}\) \(\rightarrow\) 2 CO & Char CO\({}_{2}\) gasification \\ \hline \hline \end{tabular}
\end{table}
Table 3: All possible reactions in the process of reforming pyrolysis volatiles [33].
# Experimental Investigation into the Influence of Plasma Technology on Seed Surface Wettability
## 3 Results
The maximum impact (reduction to 65\({}^{\circ}\)) was achieved for the variant treated at 800 W for 60 s (\(p=0.000\)), whereby the contact angle was about 40% lower than for the untreated one. Figure 4: Effect of APD treatment on water contact angle on the surface of the wheat seeds (the same letter signs the variants that are not significantly different; vertical lines denote standard deviation bars). Figure 5: Effect of LPMD treatment on water contact angle on the surface of the wheat seeds (the same letter signs the variants that are not significantly different; vertical lines denote standard deviation bars). It can be seen in Figure 6 that the untreated sunflower seeds were not so hydrophobic compared to other reported types. Their apparent water contact angle is lower and only achieved 72\({}^{\circ}\). However, it is also clear that the APD treatment of the sunflower seeds had only a negative effect on their wettability. Compared to the untreated sample, the apparent contact angle values were significantly higher for all sets of process parameters, except for the variant T10F50, where the increase was the lowest and statistically nonsignificant (\(p\) = 0.338). The most harmful effect was observed for the 60 s treatment at the airflow of 30 SCFH, where the contact angle increased by 38% (approximately 99\({}^{\circ}\), \(p\) = 0.000). The contradictory results of the LPMD treatment of sunflower seeds are presented in Figure 7. However, it is possible to find some similar trends for both microwave powers. The apparent contact angle of the treated variants was higher than that of the untreated one for treatment durations of up to 30 s and lower for the 60 s treatment. The most prolonged  
Figure 6: Effect of APD treatment on water contact angle on the surface of the sunflower seeds (the same letter signs the variants that are not significantly different; vertical lines denote standard deviation bars). Figure 7: Effect of LPMD treatment on water contact angle on the surface of the sunflower seeds (the same letter signs the variants that are not significantly different; vertical lines denote standard deviation bars). treatment significantly reduced the contact angle compared to a shorter duration. However, concerning the untreated sample, there was a statistically significant difference only at 500 W (\(p\) = 0.000), while it was nonsignificant for the power of 800 W (\(p\) = 0.246). The best effect (decrease to approximately 56\({}^{\circ}\)) was observed for the T60P500 variant, and its contact angle achieved 78% of the untreated variant value.
## 3 Proposed System Model
### Offloading Problem Formulation and Analysis  
Our proposed IoT system scenario is mission-critical delay-sensitive signaling for which the time constraint is 60 ms based on 3 rd Generation Partnership Project (3 GPP) QCI Release 17 3 GPP TS 23.203 V17.1.0 (2021-06) list [44].  
We let \(x=\{x_{i}|i\in I\}\) denote the offloading-decision matrix. To ensure that the task is only offloaded to the MEC server or locally executed, the following constraints must be satisfied: \(x_{i}=0\) or \(x_{i}=1\), where if \(x_{i}=0\), the task will be locally executed, and if \(x_{i}=1\), the task will be executed at the MEC server. Additionally, we denote \(N_{off}=\{n\in N,m\in M|x_{i}=1\}\) as the set of user devices that offload their tasks to the MEC server. Next, we denote the computational-resource matrix for the MEC as the set of user devices that offload their tasks to the MEC server. Finally, we denote the computational-resource matrix for the MEC as \(V=\{V_{m}^{r}|m\in M,n\in N\}\), where \(V_{m}^{r}>0\) is the resource allocation of the edge server to computational task \(i\) offloaded from user device \(m\in M\). Normally, the amount of computing resources at the edge server is limited; therefore, the following constraint must be satisfied: \(\sum_{ntch_{off}}V_{m}^{r}\leq V_{m}^{max},\forall n\epsilon N\), where \(V_{m}^{max}\) denotes the maximum computing capability of edge servers. Moreover, \(V_{m}^{r}=0\) if \(n\) does not belong to \(N_{off}\), which means that the edge server does not adjust any resources to the mobile node.
# Convolutional Sparse Modular Fusion Algorithm for Non-Rigid Registration of Visible-Infrared Images
###### Abstract  
Existing image fusion algorithms involve extensive models and high computational demands when processing source images that require non-rigid registration, which may not align with the practical needs of engineering applications. To tackle this challenge, this study proposes a comprehensive framework for convolutional sparse fusion in the context of non-rigid registration of visible-infrared images. Our approach begins with an attention-based convolutional sparse encoder to extract cross-modal feature encodings from source images. To enhance feature extraction, we introduce a feature-guided loss and an information entropy loss to guide the extraction of homogeneous and isolated features, resulting in a feature decomposition network. Next, we create a registration module that estimates the registration parameters based on homogeneous feature pairs. Finally, we develop an image fusion module by applying homogeneous and isolated feature filtering to the feature groups, resulting in high-quality fused images with maximized information retention. Experimental results on multiple datasets indicate that, compared with similar studies, the proposed algorithm achieves an average improvement of 8.3% in image registration and 30.6% in fusion performance in mutual information. In addition, in downstream target recognition tasks, the fusion images generated by the proposed algorithm show a maximum improvement of 20.1% in average relative accuracy compared with the original images. Importantly, our algorithm maintains a relatively lightweight computational and parameter load.  
m +
Footnote †: journal: Applied sciences  
ultimodal image fusion; convolutional sparse coding; image registration; feature extraction
# A Coupled Variational System for Image Decomposition along with Edges Detection
## 4 Experimental Results and Discussion
### Image Decomposition Results Discussion
Figure 5: The decomposition results using different algorithms. From left to right: (**a**) Cartoon and texture [5]. (**b**) Cartoon and texture obtained by [26]. (**c**) Cartoon and texture obtained by [15]. (**d**) Cartoon, texture and the pseudo edge obtained by [40]. (**e**) Cartoon, texture and the pseudo edge \(|\omega|\) obtained by the proposed method. Figure 6 displays the decomposition results for Table image, which has a high presence of texture combined with cartoon parts. The parameters in [8; 12] are chosen \(\beta=0.5,\ \mu_{1}=\mu_{2}=0.1,\ \mu_{3}=0.01,\ \gamma=10\) and \(\tau=0.002,\ \mu=0.001,\ \beta_{1}=1,\ \beta_{2}=0.1,\ \beta_{3}=0.001,\) respectively. The decomposition results in [26] can be run from the author's homepage, the scale is chosen 3 to reach the best result in their homepage. The parameters are \(\tau=2,\ \lambda=0.1,\ \theta=0.5,\ \mu=0.05,\ \beta=0.02\) for the proposed method. Visually, the cartoon images in Figure 6a,b are more blurry; [26; 40] smooth the non-textured part, such as the legs and the arm; the texture image in Figure 6c is relative good; for the pseudo edge images in Figure 6d,e, the new method obtain the better results which can be observed from the red rectangular area. In a word, the new model performs comparable results with the other methods. Figure 6: _Cont._  
Figures 7 and 8 are the experimental results for [PERSON] and [PERSON] images, respectively. The parameters are \(\tau=1,\ \lambda=0.2,\ \theta=0.5,\ \mu=0.1,\ \beta=0.01\) and \(\tau=1,\lambda=0.2,\ \theta=0.5,\ \mu=0.1\), \(\beta=0.04\) for the proposed method, respectively. The parameters are chosen to get the better results in [11]. The parameters are \(\lambda=0.2,\ K=5,\ \Delta t=0.1\) and \(\lambda=0.2,\ K=3,\ \Delta t=0.1\) in [39], respectively. The parameters are in [40]\(\lambda=0.2,\ \mu=0.2,\ \theta=5,\ \Delta t=0.1\) and \(\lambda=0.2,\ \mu=0.2,\ \theta=1,\ \Delta t=0.1\), respectively. Because [11; 39] mainly studied image restoration, the residue \(f-u\) denote the texture. Form Figures 7 and 8, we can see that the cartoon images and the pseudo edges by three methods are similar visually and the texture images obtained by [11; 39; 40] contain obvious cartoon information, i.e., cartoon and texture are not completely separated. In comparison, the new method achieves better results. Figure 8: _Cont_. Figure 7: Tom decomposition experiment. From left to right: (**a**) cartoon, texture and the pseudo-edge with [39]; (**b**) cartoon, texture and the pseudo edge with [40]; (**c**) cartoon, texture and the pseudo-edge with [11]; (**d**) cartoon, texture and the pseudo edge \(|\omega|\) obtained by the proposed method.
## 5 Challenges and Future Development of the MIoT
### Challenges for the A2S Integrated MIoT  
The development of the A2S integrated MIoT has facilitated the deep integration of aviation and maritime systems, but at the same time, it faces a series of formidable challenges. These challenges include the limited endurance of aerial base stations, a restricted communication range, and data processing issues. To address these challenges, a comprehensive set of technologies and strategies can be employed.  
* First, the limited endurance of ABSs is a critical issue restraining the development of the A2S integrated MIoT. To tackle this challenge, more advanced energy management technologies can be introduced. Solar charging and fuel cell technologies are two potential solutions. Solar charging systems can collect solar energy during the day and store it for use during the night or when cloud cover obstructs sunlight. Fuel cells, on the other hand, can provide more sustainable energy, extending the endurance of ABSs for complex and prolonged missions.
* Second, the limited communication range is another challenge that needs to be addressed. To expand the communication range, satellite communication and relaying systems can be utilized. Satellite communication can achieve broader coverage, allowing IoT devices to maintain connectivity in vast ocean areas far from land. Relaying systems can construct a multi-tiered communication network, transmitting signals through relay stations to extend the communication range. This hierarchical communication network structure enhances the reliability and stability of communication.
* Finally, considering the complexity of the maritime environment, optimizing the approach to data processing is essential. Edge computing technology can perform data processing near IoT devices, reducing the dependence on central data centers and enhancing the real-time response of data. This distributed data processing approach effectively alleviates the burden on central servers, improving the overall processing efficiency of the system. Moreover, employing advanced data compression and transmission algorithms can reduce the burden of data transfer and lower the bandwidth requirements for communication.  
In conclusion, addressing the challenges faced by the A2S integrated MIoT requires multifaceted technological innovation and strategic responses. By introducing advanced energy management technologies, expanding the communication range, and optimizing data processing methods, we can effectively enhance the endurance, communication range, and data processing efficiency of ABSs, promoting the sustainable development of the A2S integrated MIoT.
# A Review of Optical Neural Networks
## 1 Introduction
Optical interconnects can allow hybrid optoelectronic deep neural networks, where low-energy, highly parallel integration techniques can be used as part of an analog optical processor. Despite half a century of development, optical computing, which shows great potential, has not yet become a mature general-purpose practical technology [14, 15]. However, deep learning is well-suited to the implementation of all-optical or hybrid optoelectronic systems, especially for visual computing applications [16]. Here, we review the development of optical neural networks and compare various research proposals. We focus on fiber-based optical neural networks. We then highlight some new research directions and challenges.
# Optimum Slot and Pole Design for Vibration Reduction in Permanent Magnet Synchronous Motors
## 2 Materials and Methods
### Finite Element Calculations of the Natural Frequencies  
Several models were proposed to obtain the natural frequencies and vibration modes in electric motors. These machines are composed of many elements; however, to analyse the influence of the number of slots and poles, only the stator and windings are taken into account, since the shaft and rotor are considered negligible [33; 34].  
As the stator is comprised of a pack of several steel sheets, orthotropic equivalent material properties have to be applied to the solid part representing the laminated stator [21; 35; 36; 37; 38; 39]. The material properties are usually adjusted by an updating procedure in accordance with the experimental natural frequencies [21; 35; 36; 37; 38; 39; 40].  
Windings are even more complex to model. Most researchers define a solid part of the volume of material representing the windings [18; 21; 22; 37; 39; 40; 41] to account for the mass and stiffness that the windings impose. The material properties of that part are also estimated by an updating procedure in accordance with the experimental natural frequencies [37; 39; 40].  
A model of the assembly of the stator and windings was used (see Figure 1), where the stator and windings are two solid parts fixed to each other.  
The material properties applied to the model, adjusted by updating, [37, 40] are listed in Table 1.  
The FE calculations were carried out using ABAQUS software. Quadratic-order reduced integration elements were used to ensure that accurate results were obtained.
# Towards Hybrid Energy-Efficient Power Management in Wireless Sensor Networks
## 2 Survey of Energy Optimization Approaches in WSNs
### System-Level Techniques
#### 2.5.2 Dynamic Voltage Scaling (DVS) Techniques  
Authors in [31] describe a cooperative optimization technique that applies DVS and Dynamic Modulation Scaling (DMS) to minimize the power consumption. This technique uses a prediction mechanism to estimate the processor load and the radio communication device based on the log data for a good cooperation. However, it does not rely on the variability of the CPU load parameter which generates temporal irregularity inducing an erroneous result. Therefore, this solution proved to be ineffective to corroborate the performance criteria specific to WSNs.  
The paper in [32] aims to minimize the energy of the digital part of a node. To do this, the Quasi Delay Insensitive (QDI) asynchronous logic is used with the software/hardware partitioning of the application. This method implements certain functions of an energy-intensive application on software. The specification of a DVS coprocessor, which is another contribution of this work, allows the control of the processor speed according to a software set point.