esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Geological Background
### Distribution and Geological Characteristics of the JRNF  
To the north of the JRNF, the mountainous region belongs to the central part of the eastern Dagingshan segment of the Yinshan Mountains, primarily characterized by a  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Time** & **Magnitude (M)** & **Latitude (\({}^{\circ}\))** & **Longitude (\({}^{\circ}\))** & **Depth (km)** \\ \hline
2021–01–24 09:53:07 & 3.2 & 40.65 & 111.49 & 13 \\
2021–01–23 10:59:00 & 3.8 & 40.44 & 110.24 & 10 \\
2020–10–16 21:44:33 & 3.8 & 40.67 & 110.01 & 22 \\
2020–08–31 07:09:55 & 3 & 40.37 & 111.98 & 15 \\
2020–05–19 07:23:07 & 3.2 & 40.69 & 113.9 & 12 \\
2020–03–30 16:20:59 & 4 & 40.14 & 111.85 & 14 \\
2018–01–31 07:05:24 & 2.9 & 40 & 113.81 & 5 \\
2016–12–07 02:00:40 & 3.3 & 41.92 & 112.71 & 10 \\
2016–01–19 16:19:00 & 3 & 40.46 & 110.62 & 8.2 \\
2015–12–27 19:46:36 & 1.6 & 40.35 & 111.8 & 1.8 \\
2015–10–29 22:03:19 & 2.5 & 40.61 & 111.94 & 19.3 \\
2013–02–22 12:02:04 & 3.3 & 40 & 113.9 & 5 \\ \hline \end{tabular}
\end{table}
Table 1: Earthquake statistics in and around the study area in the past decade (data from CENC, [[https://news.ceic.ac.cn/](https://news.ceic.ac.cn/)]([https://news.ceic.ac.cn/](https://news.ceic.ac.cn/)) (accessed on 25 October 2022)).  
medium to low mountain terrain with a base of Neogene volcanic rocks. To the south lies the Jining Basin, where Quaternary deposits mainly consist of alluvial and colluvial gravel layers, gravelly soil, and residual slope colluvial gravel soil, with localized lacustrine deposits. The thickness of the Quaternary sediments generally does not exceed 1 m, though in valley and depression areas, it can reach up to 5-10 m. The lithologic composition varies significantly and is complex in origin. High-resolution satellite imagery reveals that the fault has developed multiple alluvial fans and piedmont alluvial plains along the northern front of the basin (Figure 2). To the west, it does not distinctly control the landforms. The JRNF is starting from the valley north of Hadatu Township, it extends eastward through Datucheng Village, Xiaotucheng Village, and Dashihao Village, becoming less distinct east of Chagan Sum. The fault trends NEE and is approximately 50 km in length (Figure 2). The fault's tectonic geomorphological features are prominent, cutting through the mountain ridges on the NW side and effectively defining the northern boundary of the Jining Basin. It serves as a significant geomorphological boundary between high mountain and basin terrains, forming notable linear fault valleys, fault scarps, and fault triangular facets.
# Evaluation of 5G Positioning Based on Uplink SRS and Downlink PRS Under LOS and NLOS Environments
## 2 Methodology
### SRS and PRS Signal Configuration  
This research analyzes the influence of PRS and SRS signal configurations on positioning accuracy by identifying and tuning key parameters for efficient signal transmission. Accurate configuration of these parameters is essential to ensure reliable communication and high-precision localization. The research establishes a robust framework for evaluating 5G positioning performance by systematically modifying signal characteristics. The parameters are selected with great care to ensure the maintenance of signal integrity and enhancement of synchronization across the network. This, in turn, serves to strengthen the foundation for accurate localization and improved network efficiency. This advancement is instrumental in facilitating the development of high-accuracy 5G positioning systems.  
The selected configuration parameters support the evaluation of UL-SRS and DL-PRS signals for 5G positioning. This comparative framework provides critical insight into the trade-offs between uplink and downlink-based positioning methods, as well as their robustness in challenging environments. Within the 5G NR standard, these signals are precisely structured to facilitate accurate time and frequency synchronization, which is essential for reliable UE localization. The critical role of key configuration parameters in TOA measurements is essential to the effectiveness of positioning algorithms. These elements, which include subcarrier spacing, symbol duration, cyclic prefix, resource block allocation, transmission periodicity, comb size, and signal-to-noise ratio, play a crucial role in determining the accuracy of the 5G measurements. This configuration strategy  
Figure 5: Visualization of the 5G resource grid.  
enables the research to deliver a comprehensive performance evaluation, thus offering applicable insights for the deployment and optimization of 5G positioning systems. Table 1 outlines the PRS and SRS configuration parameters along with their respective values and descriptions.
## 4 Conclusions  
Industry 4.0 has emerged as a primary focus in the manufacturing sector in response to the rise of digitalization and smart manufacturing. In the era of Industry 4.0, a smart factory represents a core pillar in digital transformation. It paves the way for a smooth transition from classical (standard) to intelligent production with the help of cutting-edge innovations in intelligent robotics, cyber-physical systems, smart sensors, and instrumentation, the internet of things, cloud computing, Big Data, and artificial intelligence.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & **Time (ms)** \\ \hline Average Time from KRC4 to LabVIEW API & 8.744 \\ Average Read Time from PLC Data Block & 2.537 \\ Average Producer Loop Iteration Time & 104.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Time required for a data block transmission within the local interfacing network.  
Figure 12: Data visualization in IBM Watson IoT.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & **Time (ms)** \\ \hline Average connection time & 264.3 \\ Average publish time (QoS0: at most once) & 0.9215 \\ Average publish time (QoS1: at least once) & 108.4 \\ Average publish time (QoS2: exactly once) & 217.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Time required for an MQTT-IoT message transmission.  
In this paper, a hierarchical design for a smart factory that takes into account the most recent findings in the field is proposed. This design, which consists of all of the advanced technologies associated with all smart factory layers, has been implemented and verified in a test-bed platform, which has shown the effectiveness of the adoption of the smart factory in manufacturing processes.  
With this method, it can be seen how the adoption of the smart factory paradigm with its high-tech components has led to a more efficient manufacturing process, better data exchange, and a greater degree of autonomy. The findings revealed that the framework promotes communication and cooperation among system modules, resulting in a faster production process (decreased response time) and high productivity. One of the limitations of the proposed method is that the model's application scope is limited to a single production theme. The smart factory aims to produce products that are more adaptable and customizable through reconfigurable manufacturing. This subject, along with those mentioned in Section 5, will be further investigated in future works.
# A Two-Step Framework to Recognize Emotion Using the Combinations of Adjacent Frequency Bands of EEG
## 3 Methods  
### Combinations of All the Adjacent Frequency Bands  
The different frequency bands in EEG signals can reflect the specific brain consciousness of humans [33; 34], and the performances of the EEG-based emotion recognition methods are closely related to the choice of the selected frequency bands [35]. Taking face recognition task as an example, [PERSON] et al. [30] explained that faces can show obvious features and structures in different parts, and different parts provide complementary information to each other; reasoning by analogy, they considered that the combinations of all the adjacent frequency bands have different emotional characteristics and complement each other.  
In view of the fact that different frequency bands contain identification and complementary information, we assume that the combinations of all the adjacent frequency bands have different emotional characteristics and complement each other. Based on this assumption, we arrange the five frequency bands of EEG signals in order, and to the adjacent frequency bands, there are 15 combinations in total with the combined scale ranging from one to five. For example, when we consider the situation of scale two, there are four combinations, such as delta with theta, theta with alpha, alpha with beta, and beta with gamma frequency bands.
## 7 Practical Implications  
The medical field, long known for its pioneering advancements, is now poised for another transformative leap: the rise of medical digital twins (MDTs). These digital replicas of individual patients, although in early development, promise to revolutionize healthcare by providing a customized view into the intricacies of human health and illnesses. MDTs are capable of forecasting disease progression in specific patients, enabling early diagnosis and intervention before severe symptoms arise. This personalized predictive power could significantly alter the management of various diseases, ranging from chronic conditions like diabetes to more complex ailments such as cancer.  
The utility of MDTs goes beyond mere prediction. They have the potential to fine-tune treatment schedules by assessing an individual's response to current therapies. MDTs could suggest tailored treatment plans and dosages, enhancing effectiveness and reducing adverse effects. Furthermore, MDTs can simulate the impact of potential new treatments within a virtual patient environment, fostering personalized drug development and lowering the risks linked to clinical trials. The analysis of the extensive data generated by MDTs may lead to the discovery of new disease-specific biomarkers or enable real-time tracking of disease progression. Additionally, MDTs can provide insights into the interactions between drugs and biological pathways, offering a deeper understanding of the action mechanisms of existing and prospective treatments.  
MDTs can take various forms. Some are data-driven, leveraging extensive patient datasets to statistically predict disease paths. Others are based on mechanistic computational models that replicate the complex functions of the human body at a cellular level. Future MDTs might merge these approaches, offering a more complete and accurate depiction of individual health.  
However, developing and implementing MDTs face several challenges, such as ensuring data privacy and security, addressing ethical issues concerning patient data ownership, and integrating MDTs into current healthcare systems.  
Despite these obstacles, the benefits of MDTs are unmistakable. By leveraging these virtual models, we can enter a new phase of personalized medicine, where healthcare is customized for everyone's specific needs, leading to better health outcomes and a brighter, healthier future for everyone.
## 4 Experiment and Results
### Model Generalization Ability Analysis

Table 3: Quantitative results of test datasets from CVC-ClinicDB and Kvasir. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{8}{c}{**CVC-ColonDB**} & \multicolumn{8}{c}{**CVC-300**} \\  & **Mean Dice** & **Mean IoU** & \(F_{\beta}^{\omega}\) & \(S_{\mathbf{\mathrm{a}}}\) & \(E_{\mathbf{\varphi}}^{max}\) & **MAE** & **Mean Dice** & **Mean IoU** & \(F_{\beta}^{\omega}\) & \(S_{\mathbf{\mathrm{a}}}\) & \(E_{\mathbf{\varphi}}^{max}\) & **MAE** \\ \hline U-Net & 0.398 & 0.335 & 0.366 & 0.684 & 0.740 & 0.036 \\ U-Net++ & 0.401 & 0.344 & 0.390 & 0.683 & 0.776 & 0.035 \\ ACSNet & 0.578 & 0.509 & 0.530 & 0.754 & 0.764 & 0.059 \\ SAM & 0.551 & 0.507 & 0.544 & 0.751 & 0.687 & 0.030 \\ EU-Net & 0.687 & 0.609 & 0.636 & 0.793. & 0.841 & 0.067 \\ PraNet & 0.628 & 0.567 & 0.600 & 0.794 & 0.841 & 0.031 \\ SANet & 0.750 & 0.654 & 0.685 & 0.849 & 0.897 & 0.015 \\ Ours & **0.825** & **0.744** & **0.781** & **0.911** & **0.936** & **0.009** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Quantitative results of test datasets from ETIS-LaribPolypDB. However, the CVC-300 dataset closely resembles the surrounding normal tissues in terms of texture, color, and other characteristics, making it challenging to differentiate. Nonetheless, our proposed model BMFA-Net excelled at extracting features through multiple scales and at multiple levels, providing superior classification performance compared with a single classification network. The use of parallel multi-branching structures gives the network a wider range of receptive fields so that it is able to capture a wider range of contextual information and more detailed information. The boundary constraint reverse attention module can effectively constrain the polyp boundary to enhance the segmentation results. All these data suggest that the BMFA-Net model can be easily extended to multi-color data with different domain distributions.
## 4 Sensitivity Analysis
The _LCOE\({}_{1}\)_ values are presented in Figure 8. As shown, _LCOE\({}_{1}\)_ values depend on the _OM_ costs, considering that the _CI_ are covered by government incentives, in most cases. The bigger the variation in _OM_ costs, the smaller the _LCOE\({}_{1}\)_ values. At -30% variation it can go below 2 c\(\epsilon\)/kWh for the 7 kW systems for almost all PV technologies, except for HJT technology. Meanwhile, the highest _LCOE\({}_{1}\)_ values are obtained at a +30% variation by the 3 kW systems, with values around 4 c\(\epsilon\)/kWh. The _LCOE\({}_{2}\)_ values are also dependable on the _OM_ costs, as presented in Figure 9. Less affected by the changing of these costs are 7 kW systems, which present lower variations of _LCOE\({}_{2}\)_ values, ranging from around 5.5 c\(\epsilon\)/kWh at a \(-\)30%, to 6.8-7 c\(\epsilon\)/kWh at a +30%. A stiff increase in _LCOE\({}_{2}\)_ values is achieved by 3 kW systems, being more affected by the  
Figure 8: _LCOE\({}_{1}\)_ values obtained based on variation in _OM_ costs. Figure 7: Results obtained for _LCOE\({}_{2}\)_ based on variation in _CI_ cost. increase in _OM_ costs. The \(LCOE_{2}\) values are around 8-9 c\(\ell\)/kWh for a +30% variation. These values are lower than the ones obtained by changing the _CI_ cost, concluding that the _CI_ cost influences \(LCOE_{2}\) values more than the _OM_ costs.
## 2 Simulation Setup and Estimation
### Simulated Measurement Errors  
The simulated measurement errors for the L-band are collected in Table 3 and for the optical links in Table 4. A more detailed description of the simulation of the measurement techniques and the measurement errors is given in [15; 17]. For the L-band, we only simulated white noise, while for the optical measurement types, we also accounted for other noise types. Furthermore, we used different troposphere models in the simulation and the further analysis. In the case of the L-band, we simulated Phase Center Variations (PCV) as an elevation-dependent error. Tables 3 and 4 show that we separated the interchange bias into a phase bias contribution and an optical bias contribution. Hence, for our simulations we assumed that these biases remain from the calibration with the optical links.
## 1 Introduction  
Mixing of fuel and air is one of the most important aspects affecting the combustion performance in thermal power plants. Insufficient contact between fuel and air results in emissions of carbon monoxide, unburnt hydrocarbons and char particles in the flue gases. For combustion of solid fuels, good mixing is not easy to achieve due to the multi-phase nature of the process. Therefore, combustion facilities operate with an excess of air compared to what is needed for stoichiometric combustion. This is undesirable for several reasons:  
* The excess air increases gas flows which increases the physical size of boiler, which in turn increases its capital costs and operational costs.
* The excess air increases gas velocity and the wear on heat transferring surfaces.
* The larger than necessary gas flow increases the heat loss associated with hot flue gas exiting the stack.
* It also increases the power consumption of support equipment such as fans.  
In practice, excess air results in decreased thermal efficiency, increased fuel consumption, increased boiler footprint, increased environmental footprint and increased cost of generating heat and power.  
Thus, it is highly desirable to reduce the amount of excess air required in thermal power plants, as long as emissions of unburnt do not increase above acceptable levels.  
Fluidized bed (FB) boilers are attractive for combustion of solid fuels. In such boilers, the fuel is mixed with and burnt in a fluidized bed of solid particulate matter. For combustion of biomass and waste, the bed material usually is silica sand. The bed material is important: (i) to propagate heat transfer throughout the boiler; (ii) to equalize temperature in space and time; (iii) for the hydrodynamic performance of the boiler; and (iv) for absorption of certain elements present in fuel ash. For biomass combustion, these abilities are not necessarily connected to the chemical nature of the bed material. This suggests that switching from silica sand to a different bed material could be feasible. In this study, the use of chemically active bed materials capable of facilitating combustion and reducing the need for excess air is explored.
## 2 Materials and Methods  
The epitaxial wafers used in the experiment were InGaN based blue micro-LEDs with a peak wavelength of 457 nm, composed of a 20 nm buffer layer, a 3.5 \(\upmu\)m undoped GaN layer, a 1.8 \(\upmu\)m n-GaN layer, 9 pairs of InGaN/GaN multiple quantum wells (MQWs), a 50 nm p-AlGaN layer and a 165 nm p-GaN layer. For bare LED devices preparation, a 120 nm indium tin oxide (ITO) current spreading layer was deposited on the p-GaN. Subsequently, ITO was annealed at 550 \({}^{\circ}\)C for 3 min by rapid thermal annealing in an ambient of 200 sccm N\({}_{2}\) and 35 sccm O\({}_{2}\). Then, the mesa regions and the designed sidewall structures, which were etched approximately 1.3 \(\upmu\)m, were defined by using the Cl\({}_{2}\)/BCl\({}_{3}\) induced coupled plasma (ICP) etching. In the preparation process of the mesa, a photoresist layer acted as a mask layer was first deposited on the top surface of the ITO and p-GaN. The sidewalls with flat patterns, concave circular patterns, and concave-convex circular composite structure patterns were then formed by using lithography technology. Subsequently, the patterned sidewalls were transferred to the LED wafer by use of ICP etching techniques. Thereafter, a 1-\(\upmu\)m-thick silicon dioxide (SiO\({}_{2}\)) passivation layer was deposited on the surface of ITO and mesa sidewall by plasma enhanced chemical vapor (PECVD). Finally, Cr/Al/Ti/Au (50/800/200/200 nm) multi-layer metals served as the p- and n-electrodes were sequentially deposited on the top surface of ITO layer and n-GaN layer by electron beam evaporation, respectively.  
In order to study the effect of the sidewall structures on the micro-LED performance, six types of micro-LEDs with different sidewall structures were fabricated. The schematic diagrams and corresponding sidewall designs of the devices are shown in Figure 1. Device A, which had flat sidewall, was included for comparison. Device B had concave circular sidewall with a radius of 2 \(\upmu\)m. Micro-LEDs had concave-convex circular composite structure sidewall with a concave- and convex-circle radius of 2, 2.5, 3, 3.5 \(\upmu\)m were denoted as Devices C, D, E, and F, respectively. All investigative devices in this paper were prepared by using the same uniform LED wafer to limit process variations. The radius of the active region of all devices was 50 \(\upmu\)m. The six kinds of micro-LEDs with different sidewall structures were studied by the scanning electron microscope (SEM). The current density-voltage (J-V) and current density-light output power (J-LOP) characteristics of micro-LEDs were measured by use of a semiconductor parameter analysis system with an integrating sphere (QUATEK PG2101, Pegasus Instrument Inc., Taipei, Taiwan).
# The Role of Elasticity in the Vortex Formation in Polymeric Flow around a Sharp Bend
## 1 Introduction
They found that the drag, friction coefficient, increases or reduces depending on the average bulk velocity of the solution. As the solution concentration increases a larger bulk velocity is required to observe a reduction in the drag. Although the drag reduction's effects of the surfactants in the solution are lower in bend flow than in a straight pipe, the authors attribute the drag reduction to the suppression of the centrifugal effect and reducing the secondary flow due to the viscoelastic properties of the solutions (normal stress effect). Their analysis was performed at a high Reynolds number for both a Newtonian fluid and a highly viscoelastic fluid. They observed and predicted smaller velocity gradients near the wall for viscoelastic fluid flow than that for Newtonian fluid flow. Even in purely Newtonian fluid flow, understanding the hydrodynamic behavior in a bend channel or a pipe elbow remains relevant today. [PERSON] et al. [1] investigated the flow dynamics for a Newtonian fluid in a bent channel via two-dimensional direct numerical simulations. They investigated the flow structure along the channel as a function of both the bend angle and the Reynolds number. Their numerical work suggests a scaling relation between the shape of the separation bubble, a downstream vortex after the bend corner, and the flow conductance. Their simulations were carried at high Reynolds number but only for a Newtonian fluid. Nevertheless, they present an integrated phase diagram for the flow dynamics, where depending on bend angle and Reynolds number either the flow is uniform with no recirculation vortex forming, a vortex forms downstream of then bend corner, or vortices shed intermittently from the bend corner. In this paper we address the role of elasticity in the formation of vortices. With our analysis we are bridging the flow behavior of a polymeric fluid and that of a Newtonian fluid in a 90\({}^{\circ}\) bend narrow channel. In agreement with previous works [5; 6], we predict the formation of elastic and inertial vortices for polymer solutions with specific rheological properties and flow conditions. Ourprimary interest is exploring the possibility of controlling the alteration of the flow, either suppressing or promoting a vortex or separation bubble, by modifying the underlying properties of the polymer solution. The remainder of this paper is organized as follows. In Section 2, we introduce our hydrodynamic and polymer models. In Section 3, we describe in detail our numerical procedure including simulation parameters, and the computational domain including a few details about the meshing. In Section 4, we present the numerical results, specifically comparing Newtonian and polymer flow and discussing the role of elasticity and Reynolds number on vortex formation. We also construct two flow phase diagrams. In Section 5, we summarize our results and briefly comment on the future perspectives of the present work.