esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Chemical Composition of Bio- and Petroleum-Based Modifiers on Asphalt Binder Rheology
## 1 Introduction
[PERSON] et al. [22] developed a rheological parameter based on dynamic shear rheometer (DSR) frequency sweep tests, that strongly correlates to low-temperature ductility; a simplification of this parameter is known as the Glover-Rowe (GR) parameter [23]. Literature provides \(\Delta\)T\({}_{\rm c}\) and GR parameters as good low-temperature cracking indices [19]. Therefore, frequency sweeps and bending beam rheometer (BBR) tests were considered in this study to obtain them and evaluate asphalt binder's expected cracking performance. Limitations of using Superpave's \(|\)G\({}^{*}\)\(|\)sin\(\delta\) to assess intermediate-temperature cracking susceptibility of asphalt binders are well reported [3; 24]. Black space diagrams may provide insights to the rheological properties that drive cracking susceptibility at intermediate temperature. Asphal cracking is further aggravated with aging. Field aging depends on geographical location and environmental factors and varies along the pavement depth. The current pressure aging vessel (PAV) aging for 20 h at 90, 100, or 110 \({}^{\circ}\)C and a pressure of 2.1 MPa is not sufficient to represent realistic long-term aging of binders [22; 25]. Meanwhile, researchers are investigating 40-h PAV as an alternate solution [26]. Aging up to 60-h PAV has also been used to evaluate AB long-term performance [27]. Laboratory aging conditions of 20-h, 40-h, and 60-h PAV were investigated in this study. Chemical composition of AB plays an important role in durability of asphalt pavements [28]. Modifiers' chemical composition can affect AB compatibility and susceptibility to oxidation, changing its rheological properties and long-term performance [3; 27; 29; 30]. Chemical characterization of ABs and MABs was conducted, in many studies, using elemental analysis, gel permeation chromatography (GPC), Fourier-transform infrared spectroscopy (FTIR), and thin-layer chromatography flame ionization detection (TLC-FID). Carbonyl and sulfoxide indices from FTIR have been widely used to track oxidation and evaluate the impacts of long-term aging on binder characteristics [31; 32; 33; 34; 35]. Molecular weight tends to increase with aging in binders and has been reported by several authors [30; 33; 36; 37; 38]. Asphalt binders are composed of maltenes and asphaltenes and their interaction with aging drives the mechanical properties of the binder [28; 30; 38; 39; 40; 41]. There is a need for understanding the impact of various modifiers on ABs and their resulting long-term field performance. Simple laboratory protocols with reasonable and consistent predictive capabilities of field performance are essential to optimize modifier selection and dosage. Therefore, the objective of this study was to evaluate modifiers' chemical characteristics and their impact on MAB's long-term cracking potential. The fundamental relationship between chemical composition of modifiers and its effect on MAB's rheological properties was investigated.
## 5 Supply Chain Operations with RFID
### RFID Testing  
Under an operations management perspective, it is worth noting how RFID technology may influence: (i) the processing facility, through silos tracing and real-time inventory updating; and (ii) the delivery warehouse, where the real-time issuing of documents and the inventory levels are updated. An experimental campaign of the RFID reading test was conducted. The data in this portion of the study were also used for a preliminary assessment and quantification of the KPIs. During this phase, different locations of tags were tested to enhance the reading rate and avoid the shield effect. This issue was faced by sticking the RFID tag at the top of each bag after it was closed as shown in the Figure 5.  
Figure 4: Shipping warehouse operations.  
Figure 5: Examples of tagged packages and pallets.  
Due to the passive tag stuck on the bags, the company defined new procedures describing ways to lock packages, attach the RFID tag, and to pick/load packages on the pallet.  
In the processing facility, the assessment was conducted on five fruit supplies through 40 trails in the central warehouse on 25 fresh bags and 15 cooked bags, on 60 trails in the delivery warehouse on 20 pallets of fresh and 40 of cooked fruits.  
The assessment resulted in an identification accuracy of 99.4% for bags and 98.7% for pallets.
# Online Street View-Based Approach for Sky View Factor Estimation: A Case Study of Nanjing, China
## 3 Results
### The Uncertainty of the SVF  
The concept of spatial uncertainty is introduced to describe the factors influencing the visible range of the sky in SVI, including trees that change with the seasons, traffic signs, and wires. This allows for a more accurate estimation of the SVF. The impact of spatial uncertainty on BSV scarcely alters the sky range, and SVF values obtained through both approaches appear comparable when the sky openness is moderately high. However, when spatial uncertainty exerts significant influence, it leads to considerable divergence in the estimated SVF (see Figure 13). This intervention forms the basis of our forthcoming research.  
Furthermore, despite the higher semantic segmentation accuracy provided by deep neural networks, there is uncertainty introduced in the estimation of SVF, encompassing both data uncertainty and model uncertainty. Data uncertainty may arise from disparities between the source and target domains of SVI, representing an inherent attribute of data distribution. Model uncertainty is introduced by deep neural networks. [PERSON] et al. integrated a Bayesian neural network incorporating MC dropout and Concrete dropout strategies into DeepLabv3+ to qualitatively and quantitatively assess uncertainty (see Figure 14) [46]. In future work, we will delve deeper into studying the impact of data uncertainty and model uncertainty from deep neural networks on SVF estimation in SVI. It aims to analyze the sensitivity of SVF values to variations in uncertainty.
## 3 System Architecture and Channel Models  
In this section, the system architecture of Bayesian learning-based channel estimation is presented first. Then, the noise-resistant channel measurement model and the clustered-sparse channel distribution model are constructed.
# Research on Visual Positioning of a Roadheader and Construction of an Environment Map
## 3 Pose Estimation and Optimization of the Roadheader
### Pose Optimization
#### 3.4.2 Pose Graph Optimization  
According to the pose graph optimization model shown in Figure 6, let the inter-frame transformation matrix obtained in the pose solution stage or closed-loop detection be \(\mathbf{T}_{ij}\), and the pose estimation error be \(e_{ij}\); then, \(e_{ij}\) can be expressed as [33]:  
\[e_{ij}=\ln\Bigl{(}\mathbf{T}_{ij}^{-1}\mathbf{T}_{i}^{-1}\mathbf{T}_{j}\Bigr{)}^{\vee} \tag{19}\]  
where the sign \(\vee\) means to transform the matrix into a vector.  
To optimize the global pose graph, the least squares expression of the objective function, \(G(\mathbf{T})\), based on the error term is constructed, that is [34]:  
\[G(\mathbf{T})=\operatorname*{argmin}_{T}\sum_{ij}e_{ij}^{T}\Sigma_{ij}^{-1}e_{ij} \tag{20}\]  
Figure 6: Optimized model of the pose graph.  
where \(\mathbf{\Sigma}_{\bar{y}}\) is the covariance matrix of the pose measurement, which is used to represent the uncertainty of the measurement.  
Equation (20) is nonlinear and is solved using an iterative method in this paper. Let \(\Delta x\) be the iterative increment, \(x^{*}\) be the initial value of the pose estimation, and then the Taylor expansion form of the error, \(e_{\bar{y}}\), at \(x^{*}\) can be expressed as:  
\[e_{\bar{y}}(x^{*}+\Delta x)=e_{\bar{y}}+\mathbf{J}_{\bar{y}}\Delta x \tag{21}\]  
where \(\mathbf{J}_{\bar{y}}\) is the Jacobian matrix of \(e_{\bar{y}}\) at \(x^{*}\).  
Combined with Equations (20) and (21), Equation (22) can be obtained after approximate processing, that is:  
\[\left\{\begin{array}{l}\quad\mathbf{H}\Delta x=-\mathbf{b}\\ \quad\mathbf{H}=\sum\
olimits_{\bar{y}\bar{\imath}}^{T}\Sigma_{\bar{y}\bar{\imath} }^{-1}\mathbf{I}_{\bar{y}}\\ \quad\mathbf{b}=\sum\
olimits_{\bar{y}}e_{\bar{y}}^{T}\Sigma_{\bar{y}\bar{\imath} }^{-1}\mathbf{I}_{\bar{y}}\end{array}\right. \tag{22}\]  
By introducing Lagrange multiplier \(\rho\) on the basis of Equation (22), the increment equation based on the Levenberg-Marquad method is shown in Equation (23):  
\[(\mathbf{H}+\rho\mathbf{I})\Delta x=-\mathbf{b} \tag{23}\]  
where \(\mathbf{I}\) is the identity matrix.  
After multiple iterations of Equation (23), when the termination condition of the iteration is satisfied, the optimized pose estimation can be obtained. Subsequently, a closed-loop control and deviation correction can be carried out for the traveling of the roadheader according to the optimized body pose, and trajectory planning of the cutting arm can be performed according to robotics to ensure the forming quality of the roadway section.
## 1 Introduction
However, the multi-path routing strategy generally requires the computing capacity of the node, and it will increase the signaling overhead, which needs further study. Recently, deep learning has been garnering research attention in the network routing area [13]. The deep-learning-based intelligent routing strategies are discussed in [14; 15; 16]. It has been proved in these papers that deep learning methods can be used to design the routing strategy in WSNs. A Deep Reinforcement Learning (DRL)-based Quality-of-Services (QoS) routing strategy was proposed in [17] to handle the secure routing problem in a software-defined network. A deep-learning-based link prediction model was proposed in [18]. A resilient routing algorithm was presented to ensure the integrality of transmitted data and shorten the length of the transmission path selectively. A value matrix-based deep learning approach was proposed in [19]. In [19], the deep Convolutional Neural Network (CNN) and the Deep Belief Network (DBN) were used together to deal with the large-scale network problem. Through the above research, we find that the traffic between nodes has some regularity, which can help to improve the performance of the routing strategy. In this paper, the traffic flow prediction strategy based on the Recurrent Neural Network (RNN) combined with the Deep Deterministic Policy Gradient (DDPG) method is proposed to predict the network traffic distribution. Furthermore, the node state of the WSNs is analyzed, which considers various situations such as free, busy, and congested. While the multi-hop node state is given, a Double-Deep Q Network (DDQN) is trained to achieve the optimal next hop. Therefore, a Multi-Hop State-Aware routing strategy based on Traffic Flow Forecasting (MHSA-TFF) is proposed. In the MHSA-TFF, the routing strategy is divided into two parts, the traffic flow prediction part and the next-hop selection part. Simulation results show that the MHSA-TFF can improve transmission delay, average routing length, and energy efficiency. The remainder of this paper is organized as follows. In Section 2, the system model of WSNs is presented. The topology of the network and the multi-hop node state-aware model are described. In Section 3, the traffic flow prediction part based on the RNN combined with the DDPG method is presented. In addition, the optimal next hop decision routing strategy based on the Double Deep Q Network is discussed in detail. In Section 4, the simulation results are discussed, including the packet delivery ratio and the end-to-end delay under the topological state changes. Finally, Section 5 draws the conclusion.
## 2 Antenna Synthesis and Modeling
### Inter-Element Coupling  
To assess the mutual coupling between array elements, full-wave electromagnetic simulations were performed in the time domain using the methodology described in [38], with each element excited via a coaxial port. The analysis was restricted to selected portions of the array--specifically, the central core for the \(S_{0}\) elements (Figure 6a) and a configuration including three adjacent sectors plus the central one (Figure 6b). This simplification is based on the assumption that coupling with non-adjacent sectors is negligible compared to that with immediate neighbors. As a result, the simulation domain is significantly reduced, yielding savings in both computational time and memory usage. The objective of this analysis is to extract an accurate electromagnetic model of the radiating element--capturing both impedance matching and radiation characteristics--in the presence of neighboring elements and edge effects. This model is subsequently used to feed the commercial CAD tool employed in the system-level performance evaluation presented in the next section.  
Figure 5: Radiating element design and performance. (**a**) Top view of the element showing the stacked structure. (**b**) Feeding network of the element. (**c**) Mean reflection-coefficient and standard deviation envelopes. (**d**) Gain pattern at center frequency (29.25 GHz).  
Figure 7 presents a selection of the results for mutual coupling: Figure 7a shows mutual coupling in \(S_{0}\) when port 1 is fed; Figure 7b shows mutual coupling when port 15 is fed; this analysis encompasses both setups in Figure 6. Even if only a very limited set of curves is reported for the sake of brevity, these are representative of the global behavior and mutual coupling never exceeds \(-14\,\mathrm{dB}\) over the whole bandwidth.  
The result of this analysis provides a coupling matrix which will be exploited in the system-level simulator to account for the effects in matching and radiation characteristics of the single elements, non-isolated, when evaluating the performance of the array, also including alterations of the radiation pattern and SLL degradation.
# Optimizing Activation Function for Parameter Reduction in CNNs on CIFAR-10 and CINIC-10
## 4 Training and Results  
### Hardware and Software of the Platform  
The neural network training was conducted on a single NVIDIA GeForce RTX 3080 (12 GB VRAM) GPU (Most Computers, Sofia, Bulgaria), which provided significant computational power for handling deep learning workloads efficiently. We trained the system using PyTorch version 2.4, taking advantage of its optimized support for CUDA acceleration, automatic mixed-precision training, and efficient tensor operations.  
CUDA Cores are parallel processors inside NVIDIA GPUs that handle multiple tasks simultaneously. They function similarly to CPU cores but are optimized for high-throughput computations, making them ideal for deep learning, gaming, and computing. In the case of the NVIDIA RTX 3080 (12 GB), there are 8960 CUDA Cores, meaning it can perform thousands of matrix multiplications and tensor operations at once--key processes in training deep neural networks.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Block Type** & **Number of Parameters** \\ \hline ConvBlock1 & 1221 \\ ConvBlock2 & 12,837 \\ ConvBlock3 & 51,269 \\ FFNN1 & 131,202 \\ FFNN2 & 1290 \\
**Total:** & **197,819** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of parameters in our proposed architecture’s network blocks.  
Figure 5: (**a**) Structure of a classic perceptron, (**b**) visualization of a classic FFNN.  
With 8960 CUDA Cores, the RTX 3080 provides excellent performance for both model training and inference. However, newer GPUs, such as the RTX 4090 and enterprise-grade options like the NVIDIA H100, offer significantly improved performance, higher memory capacity, and better efficiency for large-scale deep learning workloads.
## 4 Discussion  
Data comparisons indicate that the results obtained by the proposed web application are like the results obtained with OsiriX DICOM Viewer. The difference between the two tools is attributed to the fact that OsiriX software could use a low-pass filtering technique that is different from the one used in our proposal to reduce variations in the images. Pacsbin (Orion Medical Technologies, Baltimore, MD, pacbin.com, accessed on 7 October 2022) is a picture archiving and communication system (PACS) that allows for viewing DICOM images; with support for the most common image manipulation tools, its main contribution is to bring a fully featured PACS environment to the web for teaching cases and research. Recent articles [45; 46; 47] mentioned the importance of Pacsbin in distance learning in the training of future radiologists, which is possible because it was developed as a web-based system. Although Pacsbin is an application with multiple image analysis tools, it does not allow for the measurement of calcium in coronary arteries like the web application proposed in this article.  
[PERSON] [48] provided a comparative table of the 10 DICOM viewers used in their study. The evaluated software can read and display DICOM images, and all are free-access or open-source; some paid software was included if a free trial version was available. Of the 10 listed programs, only two could be used from a web browser (Postdicom and Papaya Viewer), and two allow for calculating the CS (Horos Viewer, 3D slicer), but they do not meet both characteristics. In other web applications related to CS [49; 50], statistical studies of patients with CAC were carried out, and calculators that estimate the percentage  
Figure 11: Example of home page with two scans loaded.  
derived from the age, sex, ethnicity and CS of the patient were proposed, so the application suggested in this article complements this type of tools used by physicians.
### Real-World Tests of Remote Operation  
The remote operation of AVs is currently being tested in a variety of real-world laboratories. These labs usually are collaborations between research-focused and industrial partners which examine the feasibility of novel technologies in a real-world setting, aiming at maximizing ecological validity. Thanks to their incorporation into naturalistic settings within the intended context of use, real-world labs offer invaluable insights into the in situ application of devices or systems that have previously only been investigated in higher controlled oftentimes experimental, yet less realistic environments. Thus, situations and phenomena are more likely to occur that may not have been observed in a more controlled setting. Since they demonstrate the interplay of the technology with users and other actors in a less standardized environment, they yield scenarios with a larger external validity, i.e., transferring them to other (real-world) contexts may be facilitated.  
The German Aerospace Center (DLR) is involved in real-world labs that use both RD and RA for remote operation. Figure 1 displays examples of vehicles that are remotely operated within these projects. Regarding RD, the modular electric AV concept \"U-Shift\" that caters to different urban mobility demands, including transportation of people and goods, encompasses Remote Drivers in its system architecture [15]. Pertaining to RA, DLR is engaged in urban mobility projects that provide last-mile shuttle services from major hubs of transportation, e.g., train stations, to the final destination. In the \"Hamburg Electric Autonomous Transportation\", or \"HEAT\", project, a self-driving minibus ran along a fixed route through the Harbor District of the city incorporated in the public transport provider \"Hochbahn\"'s network [16]. \"The Real-World Lab Hamburg (\"Reallabor Hamburg\") provided on-demand service from a suburban railway hub to nearby neighborhoods that could be booked via a cellphone application [17]. The Berlin-based \"KIST\" project aims at demonstrating an AI-based system for connected mobility and at examining the interaction between human operators in the control center with passengers of remote-controlled AVs [18], utilizing an RA approach.
# Understanding How Image Quality Affects Transformer Neural Networks
### Related Works  
In the realm of computer vision, the ability of machines to interpret and understand visual information is paramount. However, this capability is significantly challenged by the presence of image noise, a ubiquitous phenomenon that arises from various sources. Image noise refers to random variations in pixel values that deviate from the true representation of the scene [30]. These variations can manifest as graininess, distortion, or irregular patterns in an image [31]. The sources of image noise are diverse, ranging from electronic sensor limitations in imaging devices to external factors like poor lighting conditions [32]. Regardless of the origin, the impact of image noise on computer vision algorithms is multifaceted and can impede the accuracy and reliability of visual information processing [1,33].  
One of the primary challenges posed by image noise is its adverse effect on image analysis tasks. Computer vision algorithms often rely on precise pixel values to identify patterns, edges, and textures within an image. Image noise introduces spurious variations that can mislead these algorithms, leading to inaccurate feature extraction and compromising the overall quality of image analysis. As a consequence, tasks such as segmentation [34], image enhancement [35], and feature detection [36] become more challenging in the presence of noise. For instance, face recognition in low-quality images is a hot research topic in the literature [37]. [PERSON] and [PERSON] [38] devised a face super-resolution method to reconstruct a higher resolution image for face recognition. In contrast, [PERSON] et al. [39] introduced the coupled mapping procedure, which first projects the images with different resolutions onto a lower dimensional space. Further, locality-preserving [40] was introduced in the optimization criterion to force identical labels as close as possible to each other in the new feature space.
# Evaluation of Clumping Effects on the Estimation of Global Terrestrial Evapotranspiration
## 4 Simulation Cases and Model Validation  
For the purpose of investigating leaf clumping effects on the estimated global terrestrial ET, three cases of model simulations were conducted. For Case I, both of the true LAI and CI retrieved from remote sensing data were used. This Case was believed to be unbiased. For Case II, the true LAI was used but clumping was ignored (CI = 1). This case would cause the overestimation of sunlit LAI and the underestimation of shaded LAI compared to Case I. For Case III, the effective LAI (L\({}_{\text{e}}\)) calculated as the product of remote sensing true LAI (GLOBMAP LAI) and remote sensing CAS-CI was used and clumping was ignored. In this case, the sunlit LAI is not biased but the shaded LAI is underestimated compared to Case I. The BEPS simulated soil moisture (SM) from Case I was used to drive the other case simulations for removing the effects of simulated SM on simulated ET among different cases.  
The BEPS model simulated ET has been validated extensively with the eddy-covariance (EC) measured ET for C3 and C4 crops in the Western Lake Erie Basin, the USA [54], boreal and temperate forest ecosystems in North America [11; 14; 55], forest, grassland and cropland in China [56; 57; 58]. In these studies, the BEPS model simulated half-hourly ET was compared with the EC measured ET and the R\({}^{2}\) is in the range of 0.43 to 0.86 and the RMSE ranging from 0.02 to 0.08 mm hr\({}^{-1}\). The Penman-Monteith equation used to estimate ET and the two-leaf model used to upscale ET from leaf-level to canopy scale have been proved to perform better than their alternatives [11; 59]. Thus, the global terrestrial ET simulated by the BEPS model in this study should be reasonable as long as the uncertainties from the input dataset could be well-constrained.