esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
However, the luminous intensity of the sample showed a decreasing trend (Figure 6b). It increased in the initial 10 h, and then went through slight decreasing. Finally, it decreased only by 9.7%, to 0.903 after 70 h. The results exhibited the excellent light stability of CsPbBr\({}_{3}\) nanocrystals, due to the encapsulation of IBOMA and the coating of APTES. To explore the stability in air, the CsPbBr\({}_{3}\)-IBOMA nanocrystals block sample was placed in the air without any protection and the emission spectrum was measured every 4 days under laboratory conditions (K = 298 K, 57% RH). The emission peak of the CsPbBr\({}_{3}\) sample kept at about 510 nm steadily without any shift (Figure 6c), indicating unchanged particle size. In addition, the fluorescence intensity maintained a high level of 89.5% at 30 days (Figure 6d), compared with the previous work [48]. To evaluate the water and thermal stability of CsPbBr\({}_{3}\) nanocrystals, we kept the CsPbBr\({}_{3}\)-IBOMA nanocrystals block sample immersed in water and heated to 348.15 K. The fluorescence intensity of the emission spectrum was tested at 15 h intervals. As shown in Figure 6e, the emission spectra shape and peak position of the sample nearly did not change, while the luminous intensity decreased gradually (Figure 6f). Within the initial 15 h, it went through a slight rise and then decreased slowly. Finally, after 135 h, the luminous intensity reached 75.6%. The result is comparable with previous reported CsPbBr\({}_{3}\) nanocrystals [29]. Figure 6: PL spectra and intensity attenuation of CsPbBr\({}_{3}\)-IBOMA block sample (**a**,**b**) at 5 h intervals under 395 nm UV LEDs; (**c**,**d**) in air at 4-day intervals; (**e**,**f**) in water at 15 h intervals. (All samples were excited under 395 nm light).
# Reservoir Performance Prediction in Steam Huff and Puff Injection Using Proxy Modelling
## 3 Results and Discussion
### Proxy Models for Predicting Reservoir Performance
The polynomial model is the equation of the \(q_{end}\) output as a function of all input parameters. The neural network model of \(q_{end}\) has an architecture of five hidden layers with an 8-8-6-4-4 neuron configuration. For the training data, the R-square for the polynomial regression model and the neural network model of \(q_{end}\) has an architecture of five hidden layers with an 8-8-6-4-4 neuron configuration. 8-8-6-4-4 neuron configuration. For training data, the R-square values for the polynomial regression model and the neural network model are 0.561 and 0.953, while the R-square for the testing data is 0.524 and 0.847. The MAE of the training and testing data for the polynomial regression model is 29.1 bbl/d and 31.1 bbl/d and, for the neural network model, 8.6 bbl/d and 11.8 bbl/d. The RMSE of the training and testing data for the polynomial regression model is 43.0 bbl/d and 45.9 bbl/d and, for the neural network model, 14.1 bbl/d and 21.7 bbl/d. The polynomial regression model performs poorly in comparison to the neural network model in this output model, as shown in Figures 9 and 10. Though the deviation is noticeable, overall, the data points for training and testing data have a good correlation to the best fit curve. The prediction for both the training and testing data is below the 20 bbl/d and started to drift further from the best fit curve. The data results show that around 80% of the data points are below 20 bbl/d, making the  
The other affecting parameters for each proxy model can be seen in the table, though their contributions are significantly lower compared to the first two parameters. For the \(N_{p}\) model, reservoir pressure and the bulk volume of the reservoir are the next contributors to the model. For the \(q_{0 max}\) model, these are reservoir pressure and permeability; and for the \(q_{0 end}\) model, the production period of the steam huff and puff cycle, reservoir pressure, and the drainage area of the well.
# ODGNet: Robotic Grasp Detection Network Based on Omni-Dimensional Dynamic Convolution
## 2 Related Work
### Omni-Dimensional Dynamic Convolution  
With the proposal of LeNet [25], convolutional neural networks have become the basis of various network architectures and have driven the development of deep learning. The current convolutional neural network architecture is widely used in the fields of computer vision and natural language processing, specifically including target detection, image segmentation, grasp detection, speech recognition, and other related application scenarios. In modern convolutional neural network architectures, each convolutional layer has only one static convolutional kernel applied to all the input features. To achieve a better network model performance, it is common practice to stack more convolutional layers according to a specific structure or to use an attention mechanism. However, this approach still treats the convolutional kernel parameters as fixed values. In contrast, dynamic convolution applies the attention mechanism to n convolutional kernels in order to use different linear combinations of n kernels for different input features [11].  
The idea of dynamic convolution was first introduced by [PERSON] et al. [26] in Conditionally Parameterized Convolutions (CondConv); they proposed the computation of the parameters of the convolution kernel from the attention function of the input features. Later, [PERSON] et al. [27] proposed dynamic convolution (DyConv), in which they improved the attention mechanism and facilitated the learning of the attention model by constraining the attention output based on CondConv. The above dynamic convolution methods, although they can significantly improve the performance of the model, only endow the dynamic properties of the convolution kernel in the dimension of the number of convolution kernels, ignoring the spatial size, the number of input channels, and the number of output channels of each convolution kernel in the space of the convolution kernels. Therefore, [PERSON] al. [11] proposed omni-dimensional dynamic convolution, which utilizes a novel multi-dimensional attention mechanism and a parallel strategy to learn the attention parameters of the convolution kernel in each convolutional layer along the four dimensions of the kernel space. Thanks to its multidimensional attention mechanism, even ODConv with only one convolutional kernel exhibits a better performance than CondConv and DyConv networks with multiple convolutional kernels.
# Prioritization of Drugs for Fungal Keratitis Eye Infections: An In-Silico Analysis
## 3 Materials and Methods
### Molecular Docking  
The 3D structure of the selected anti-fungal agents, including Natamycin (CID_5281099), Ketoconazole (CID_456201), Voriconazole (CID_71616), Itraconazole (CID_55283), Micronazole (CID_4189), Fluconazole (CID_3365), Econazole (CID_3198), and Clotrimazole (CID_2812), were retrieved in the SDF formatfrom the Pubchem database [43]. The obtained compounds in the SDF format were virtually screened to reveal their binding efficiencies through docking in the predicted binding pockets of the modeled RNAP structures of fungi by using FlexX [44], with docking parameters such as triangle matching base placements, zero full score and No score contributions, and thresholds for full score and no score contributions of 30 and 70, respectively, clash handling values of 2.9 A\({}^{03}\) and 0.6 for protein ligand clashes, with a maximum allowed overlap volume and intra-ligand clash factors, while considering the hydrogen in internal clash tests, and 200 as the default docking value for the maximum number of solutions per iteration and per fragmentation [45].
## 2 System Model  
### New Design for a Cooperative MIMO-NOMA Scheme  
The present paper examines a new cooperative MIMO-NOMA model for emerging 5G wireless networks and networks beyond. Figure 1 depicts the system model containing a BS, coupled relay \(\mathcal{R}=\{R_{1},R_{2}\}\), and multiple devices \(D_{n}=\{D_{1},\ldots,D_{N}\}\). To benefit a massive MIMO-NOMA network, the study equips a large number of antennas \(A_{0}\), \(A_{1}\), and \(A_{2}\) at the network nodes and BS, coupled relays, and devices, respectively. In addition, we assume that the BS has full knowledge of the CSI [26; 33]. As with the system models in the major studies [28; 29; 30; 31], the massive MIMO underlay cooperative NOMA model shown in Figure 1 contains two time slots to complete a transmission block. It is important to illustrate the difference to previous major studies. In [16; 17; 18; 19], the authors verified that relays are a good solution to assist in combating channel fading. In other major studies, the authors deployed a SISO relay to aid a multi-destinations [29] or a multiple SISO relay to aid a single destination [31]. The authors also proposed relay selection strategies to select the best nearest relay [17] and max-instantaneous data rate [18] (Equation (16)) to assist the destinations. The present study deploys coupled relays to assist massive MIMO-NOMA networks, however, only the relay with better power capacity among the coupled relays is selected for cooperating devices in an odd/even transmission block., i.e., while one relay is selected to forward the signal to devices, another relay has to maintain EH from the BS.  
Note that the present study is designed to serve multiple devices simultaneously. The coupled relays must first have the device CSI and report this information to the BS. Based on the device CSI, the BS allocates PS factors, whereas the coupled relays feed back their own CSIs to the BS and other devices. Based on the energy capacity information, the BS selects the best powered relay to forward the signal. The devices then wait to receive the signal from the strongest powered relay. In the case of insufficient CSI, the BS may select the poorest powered relay to forward the signal, leading to a reduced lifespan of the relay. This may interrupt signal propagation because the devices are waiting for a non-cooperative relay.
# A Review of Edge Computing Technology and Its Applications in Power Systems
## 4 Review of Edge Computing in Power Systems
### Energy Storage  
An increasing number of wide-range power outages have become more frequent in recent years due to drastic climate change. Nowadays, extreme weather happens more frequently, and these conditions challenge the stability and recovery capability of the power system. The impact of power outages is more significant than before. To enhance the reliability of the power system, [PERSON] et al. [114] presented an advanced mobile energy storage system (MESS) to enhance the resilience of the power system. MESS can be considered a primary technology to offer a clean alternative to diesel or gas generators and provide emergency outage management. Typically, the MESS is small in size and cannot supply all the critical loads during the outage recovery time. Meanwhile, there are two more challenges: reconfiguring the customer connections and dedicating the backup energy storage system; the increasing size and number of devices are considered critical loads. As the technology of MESS evolves, the system size and the grid scale are growing rapidly. The article introduces several MESS use cases with planned or unplanned power outages. It also shows the business benefits the MESS can bring. The results indicate that MESS is an ideal power supply during recovery time. However, MESS is dependent on geography and some other distribution system characteristics. Utilities should use MESS based on their conditions and requirements.
# Smooth Bend Structures Using Hybrid Compensation for Common-Mode Noise Reduction
## 5 Conclusions  
Differential transmission lines are widely used in high-speed PCBs, due to their excellent noise immunity. However, unbalanced bends are always present due to their limited element placement or layout area. Therefore, we analyzed the smooth bend structure and examined whether or not it successfully reduced the unbalancing effect.  
As a result of this, the L-C-L compensation type is recommended. In our analysis, the suppression of Scd21 in the simulation from DC to 6 GHz was below \(-17.3\) dB, and the measurement from DC to 6 GHz was below \(-17.9\) dB. It also maintained differential signals. The Sdd21 of the L-C-L compensation type remained above \(-2.18\) dB, and the measurement result from DC to 6 GHz exceeded \(-2.15\) dB. In the time-domain analysis, the L-C-L compensation type reduced common-mode noise by 48.9% in simulation and by 60.5% in measurement. Furthermore, in the eye diagram measurement, the eye height and eye width improved compared with the 150-degree bend structure.  
For validation, the proposed design was fabricated on PCBs, and the measured results were highly consistent with the simulated results.  
It is worth mentioning that PCB fabrication shows us that inductive compensation design is more challenging to implement than capacitive compensation design, due to the trace dimension W2 and spacing S2. However, tolerances are always present in PCB fabrication. In other words, the inductive compensation design is more sensitive. That is why the inductive compensation design needs more time for implementation in PCB fabrication. The compensation method could be implemented at a more high-speed transmission for future development, such as serial advanced technology attachments (SATA) III/6 Gbit/s, the universal serial bus (USB) 3.2/10 Gbit/s, and the peripheral component interconnect express (PCIe or PCI-e) 4.0/16 GT/s.  
Conceptualization, D.-B.L.; methodology, M.-H.W.; software, Y.-L.H.; validation, [PERSON], Y.-L, and M.-H.W.; formal analysis, M.-H.W.; investigation, M.-H.W.; resources, D.-B.L.; data curation, Y.-L.H.; writing--original draft preparation, M.-H.W.; writing--review and editing, M.-H.W.; visualization, M.-H.W.; supervision, D.-B.L.; project administration, D.-B.L.; funding acquisition, D.-B.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by [Ministry of Science and Technology (Taiwan)] grant number [MOST 110-2221-E-011-052]. The authors declare no conflict of interest.
## 4 Simulation Case Study
### Key Performance Indicators  
The research goal was to study the behavior of the different models in different demand scenarios and configurations of a production system. The results were calculated for all model simulations to evaluate the response according to the following key performance indicators:  
* Cumulated market demand (# thousand products);
* Cumulated real demand (# thousand products);
* Cumulated production (# thousand products): the cumulative sum of all units produced over the 500 simulated-production weeks;
* \(\mathcal{O}\) Utilization rate of the production plant (%);
* \(\mathcal{O}\) Availability of the production plant (%);
* \(\mathcal{O}\) Performance at the final production step (# thousand products/week);
* \(\mathcal{O}\) Quality at the final production step with one-way and no loops (%);
* \(\mathcal{O}\) Labor productivity (products/employee \(\times\) week);
* Cumulated stocks (# million products);
* \(\mathcal{O}\) Production lead time (# weeks);
* Cumulated service level (%);
* Cumulated sales (USD million);
* Cumulated operational costs (USD million);
## 2 Literature Review  
One way to overcome the continuously increasing capacity in the wireless communication systems is to control the channel itself to develop an intelligent radio environment besides other existing solutions (diversity, high-frequency waves, etc.). RIS is a programmable arrangement that controls the propagation of electromagnetic waves by varying its surface's electric and magnetic characteristics. Furthermore, RIS can sense the radio environment by installing intelligent surfaces within the wireless system environment, which entirely or partially controls the features of the radio channels. Hence, RIS-assisted systems can improve the reliability and energy efficiency of wireless systems [1]. Lately, RIS has drawn much consideration as an up-and-coming technology that can suit future wireless systems demands [18], i.e., 6G and beyond. Hence, RIS has promoted wireless applications such as RIS-aided wireless power transfer [19], RIS-aided mobile edge computing [20], RIS-aided physical layer security [21; 22], RIS-aided UAV communications [23; 24], and mobility and handover management for RIS-aided wireless communications in high-speed trains [25].  
There are limited related research works investigating the impact of RIS deployment in mmWave networks. A general tractable model for the coverage performance of the RIS-assisted mmWave networks focused on RIS and base station (BS) densities using stochastic geometry was proposed in [26]. A privacy-preserving design paradigm combining federated learning (FL) with RIS in the mmWave communication system was proposed in [27]. A deep learning algorithm was proposed in [28] to set up a relation between CSI and RIS configurations for better optimal communication rate performance. An efficient cascaded channel estimation model for an RIS-assisted mmWave MIMO system, with the wideband effect on the transmission model, was considered in [29]. A hybrid precoding (HP) design for the RIS-aided multiuser (MU) mmWave communication systems was investigated in [30]. Artificial intelligence (AI)-empowered mmWave communications, especially using RIS, were studied in [31]. To the best of our knowledge, all the current research works on RIS-assisted mmWave assume perfect CSI information. Based on it, the PS vectors of BS and RIS are adjusted to maximize the achievable spectral efficiency at the RX. Without this CSI information, these PS vectors cannot be optimized due to the assumption of continuous PS. However, perfect CSI is a strong assumption violating the RIS hypothesis of being utterly passive without any channel estimation functionality.
# Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges
## 3 Datasets, Labelling, and Augmentation
### Extrinsic Calibration  
Multi-sensor extrinsic calibration requires calibration targets to be observed simultaneously by different modalities. The trihedral corner reflector is widely used for radar calibration because of its high RCS. Multiple reflectors are usually placed outdoors to avoid multi-path propagation. The difficulty lies in making the calibration target visible to both radar and other sensors. [PERSON] et al. [63] built a calibration facility by placing one Luneburg lens and seven trihedral corner reflectors with known inter-distances. To make the reflectors visually detectable, they painted each surface with different colours. [PERSON] et al. [64] designed a compact calibration target that can be simultaneously detected by the camera, radar, and LiDAR. As shown in Figure 5a, they placed a triangle-shaped chequerboard pattern in front of a trihedral corner reflector. The chequerboard is made of styrofoam and is transparent over a large radio frequency range, so that the millimetre wave can penetrate it and detect the corner reflector behind it. In Figure 5b, [PERSON] et al. [65] designed a styrofoam board with four circular holes and placed a corner reflector at the back. These circular holes are more easily detected by the sparse LiDAR beam since they have no horizontal lines.  
The extrinsic calibration of a 4D radar and other sensors can be easily performed by modifying the classical LiDAR to camera calibration methods [66; 67]. However, the calibration of conventional radar is a very difficult task, since it returns a 2D point cloud with no elevation resolution. This leads to the problem of vertical misalignment [64], which is defined as the angular deviation between the radar plane and ground plane. [PERSON] et al. [68] moved a corner reflector up and down to the cross-radar plane multiple times. Then, the plane was determined by connecting the peaks with the highest intensity in the sequence. [PERSON] et al. [64] proposed a two-step optimisation method to mitigate the uncertainty caused by the missing elevation angle. They modelled radar detections as arcs by extending their elevation angle. Similarly, they also converted 3D detections from other sensors to arcs by neglecting the elevation angle. In the first step, they optimised the reprojection error, which is the Euclidean distance of these projected arcs on the ground plane. In the second step, the parameters related to the elevation measurement are refined according to the RCS error. A second-order RCS model was built by fitting RCS measurements with elevation angles. Then, the L2 distance between the expected and measured RCS is minimised. Experiments showed their method enables smaller vertical misalignment than [PERSON]'s method. In order to improve the efficiency, some target-less online calibration approaches [69; 70] are proposed to leverage target trajectories for extrinsic estimation.
## 2 Background and Literature Review  
### Mfi  
In recent years, various machine learning methods have been applied to the field of Modulation Format Identification, including Random Forests [17], Support Vector Machines [18; 19], and several Artificial Neural Networks (ANNs) [20; 21; 22; 23]. However, these machine learning algorithms exhibit limitations in feature processing capabilities and often underperform when handling complex tasks. With the increasing demand for high speed transmission in communication systems, traditional MFI techniques have become inadequate to meet current requirements [24].  
Consequently, deep learning (DL) methods have emerged as powerful alternatives. These methods have significantly improved the performance metrics of MFI in practical applications. In the literature [4; 5; 6; 7; 8], researchers have employed features such as one-dimensional amplitude histograms, constellations, eye diagrams, asynchronous amplitude histograms, and amplitude histograms in combination with deep learning networks to achieve high accuracy in commonly used modulation formats like MPSK and MQAM. The study in [9] proposed an Elastic Convolutional Neural Network (ElsNet), which enhances model robustness by optimizing internal parameters and dynamically adjusting the connections between network neurons. In Ref. [10], the authors introduced a few-shot learning algorithm by incorporating auxiliary tasks into Model-Agnostic Meta-Learning (MAML), allowing the gradient of meta-tasks to descend more quickly in the optimal target direction, thereby accelerating the model. Furthermore, Refs. [11; 12] proposed multi-task models that can simultaneously handle tasks such as MFI and OSNR, improving efficiency while using additional tasks to constrain the accuracy of MFI, thus enhancing the efficiency and effectiveness of MFI systems.  
Despite significant improvements in accuracy, robustness, speed, and efficiency, research on interpretability remains relatively limited.