esa-sceva/satcom-chunk-collection huggingface dataset:
# FRP Bridges in the Flanders Region: Experiences from the C-Bridge Project
## 5 In Situ Testing
### Dynamic Vibration Tests  
As demonstrated in [44], due to the low self-weight compared to a concrete bridge and the limited stiffness compared to a steel bridge, human-induced vertical vibrations are a typical design criterion for GFRP footbridges. It is therefore important to accurately assess the vibration response. The dynamic behaviour, including the first natural bending frequency and the structural damping ratio, can be determined in situ by means of vibration tests, which can be performed with a large amount of highly accurate tri-axial acceleration sensors or by using the smartphone's internal acceleration sensors. Both testing methods have been used during the C-Bridge project.  
* A previously reported [27] vibration study used tri-axial acceleration sensors (GeoSIG GMS-18-63) arranged in a dedicated measurement grid with three reference sensors(which remain in all setups) and twelve roving sensors (which are relocated for the different setups) on the Canada footbridges. In this study, an evaluation was made of the comfort level of one of both composite bridges based on the measured vertical accelerations using the comfort limits defined in the design guides. The results from these tests are very accurate, but the measurements are costly and time-consuming.
* Another vibration study used the internal acceleration sensors of a smartphone and the VibSensor application [45] to assess the dynamic behaviour on ten composite bridges in Flanders. The elaboration and results of this study can be found in [44]. Herein, experimental data for the dynamic properties (i.e., first fundamental flexural frequency, damping ratio, comfort class) of ten web-core sandwich panel FRP composite footbridges are presented. The paper shows that data based on smartphone accelerometers enables easy, quick, affordable, and abundant measurements while at the same time yielding reliable experimental values. Given the relatively short spans, the heel and excitation test methods were used rather than the ambient vibration method. The tests indicated damping ratios of one to three percent, which are strongly dependent on the number of people on the bridge during the measurement. Additionally, comfort analysis tests indicated that the current design guidelines for pedestrian comfort analysis [46; 47; 48] are overconservative and do not reflect the effect of pedestrian-induced damping, which is especially apparent for this bridge type given its very low modal mass and relatively low damping ratio.
# Enhanced Knowledge Graph Embedding by Jointly Learning Soft Rules and Facts
## 5 Experiment
### Results  
#### 5.3.1 Link Prediction Results  
Table 3 shows the link prediction results of SoLE, SoLE-NNE, and the baselines on the test sets of FB15K (or FB15K-sparse) and DB100K. From the results, we can see that SoLE-NNE outperformed all the baselines in the MRR and HITS@1 metrics on both datasets. Without the non-negativity constraints on entities (NNE), SoLE still achieved the best performance in HITS@1. Specifically, compared to the model ComplEx on which SoLE was based, SoLE achieved an improvement of 11.6% in MRR and 18.4% in HITS@1 on FB15K, as well as an improvement of 5.9% in MRR and 15.9% in HITS@1 on DB100K. Since more rules were extracted from FB15K and more groundings were generated, the improvements on FB15K were more significant than those on DB100K. Besides, compared to the state-of-the-art baselines, which also incorporated soft rules, our method surpassed not only ComplEx\({}^{R}\), RUGE, and ComplEx-NNE+AER after applying the NNE constraint to SoLE and pLogicNet, but also IterE on the sparse test sets of FB15K. As we can see from the results marked by \"<<\" in Table 3, SoLE achieved an improvement of 6.4% in MRR and 9.6% in HITS@1 on FB15K compared to IterE. The results  
demonstrated that the devised joint algorithm for soft rules and the introduction of forward chaining indeed improved KG embeddings, and our method was superior to the baseline methods.  
Moreover, Table 4 shows the link prediction results achieved by SoLE and SoLE_OTI on FB15K (the results on DB100K are the same). From Tables 3 and 4, we can observe that compared to the method RUGE, which generated groundings via one time inference and constrained the derived triples by soft rules, SoLE_OTI significantly outperformed RUGE in MRR and HITS@1, which indicated that our joint training algorithm was superior to RUGE.
## 2 New Production Scheduling Methodology  
Production scheduling can benefit from optimization. Each optimization problem requires the definition of criteria, constraints, and parameters. In the case of scheduling, the parameters determined are the time distribution of technological operations and the selection of production resources for the implementation of these operations. Resources are understood as employees and tools used in a given task. Optimization constraints ensure that there are no conflicts in accessing the resources of individual technological operations and secure the continuity of a product flow. In the event of such a conflict or a problem with the transfer of products, the production schedule will not be feasible. The time limit for the execution of an order may also be included in the constraints. Less restrictive criteria, most often minimized during optimization, refer primarily to the cost and execution time of the orders' set. The term \"set of orders\" means production orders that the plant has received from outside for execution. After the scheduling process, they become jobs consisting of technological operations. After defining these three components of the optimization problem, we can use a large number of available optimization algorithms. Based on very different ideas, algorithms continuously developed are aimed at finding the global optimum. However, due to the limitation in the allowed time to generate the schedule, the solution obtained is often suboptimal. Therefore, the applied algorithmic tools are always a compromise.  
However, the aim of the presented research was not to develop another optimization algorithm. As it will be explained later in the paper, the authors tried to completely isolate the research results from the influence of the optimization algorithm used. As shown in the Introduction, currently used scheduling methodologies do not take into account the individual influence of workers and tools used in a given production operation when selecting production resources. This can be completed not by another modification of the optimization algorithm, but by an appropriate definition of the optimization criteria and constraints. Later in this section, the modifications of these components of the optimization process, implementing this strategy, will be discussed.  
In summary, current scheduling methodologies do not take into account a comprehensive analysis of the influence of assigned production resources for the execution of technological operations on the quality of the production schedule, understood as the time and cost of its implementation. In view of the above state of affairs, the authors of this publication propose a new production scheduling methodology, which, using the analysis of data from a production company, takes into account the influence of selected production resources on the duration and cost of individual production operations.
## 4 Experiment and Results
### Generalization Experiments of SIT-Conversion  
To demonstrate the effectiveness and robustness of SIT-conversion across various scales of Transformer variants that might be used in other fields, three typical CNN-Transformer hybrid architecture models, EdgeNeXt [66], Next-ViT [67], and UniFormer [68], were selected to validate the proposed method on CIFAR10 [69], CIFAR100 [70], and ImageNet-1K [71] datasets. EdgeNeXt and Next-ViT represent relatively shallow Transformer models, whereas UniFormer represents a deeper Transformer model.  
We longitudinally compared the performance of SNNs generated by SIT-conversion from different Transformers on CIFAR10, CIFAR100, and ImageNet-1K datasets, discussing the universality of the proposed method, and quantitatively discussing the energy efficiency of the proposed method through calculations. Finally, we compared the effects of SIT-conversion on Transformer models of different sizes with state-of-the-art conversion techniques to validate the superiority of the proposed method.
# Design of a Computed Tomography Automation Architecture
## 5 Industrial Applications
### Automotive Industry  
Automated CT inspection is also used in the automobile industry. [PERSON] et al. [35] worked on rapid robotic X-ray CT inspection in automobile production. A \"U\"-shaped X-ray CT unit was used to inspect the cars, controlled by an automated rotating industrial robot arm. The robot approached predefined positions on an Audi in this study. Thiseliminated the need for sample preparation, which was useful since the sample being imaged was of large mass and size. The sample stayed in place, and the robot moved the CT unit to the proper locations all around the sample.  
[PERSON] et al. [36] described the RoboCT unit, which was used in BMW's prototype plant. This system was comprised of four cooperating industrial robots, which held the X-ray imaging components, designed to image fully assembled cars. The four robots were Kuka Quantec extra KR90 R3100 HA on two linear axes. The four robots carried two Comet VarioFocus X-ray sources XRS-225 VF, a Varex XRD3025 detector, a Perkin Elmer XRD 1621, and a Gocator optical 3D sensor. The geometry in the prototype plant fit the largest BMW models with a 6-m length. RoboCT assisted with the inspection of joints in the car body, such as rivets, screws, or adhesive bonding. The technology's first implementation in the production environment was introduced in this study.
###### Abstract  
The paper reports the forecasting model for multiple time-domain photovoltaic power plants, developed in response to the necessity of bad weather days' accurate and robust power generation forecasting. We provide a brief description of the piloted short-term forecasting system and place under close scrutiny the main sources of photovoltaic power plants' generation forecasting errors. The effectiveness of the empirical approach versus unsupervised learning was investigated in application to source data filtration in order to improve the power generation forecasting accuracy for unstable weather conditions. The k-nearest neighbors' methodology was justified to be optimal for initial data filtration, based on the clusterization results, associated with peculiar weather and seasonal conditions. The photovoltaic power plants' forecasting accuracy improvement was further investigated for a one hour-ahead time-domain. It was proved that operational forecasting could be implemented based on the results of short-term day-ahead forecast mismatches predictions, which form the basis for multiple time-domain integrated forecasting tools. After a comparison of multiple time series forecasting approaches, operational forecasting was realized based on the second-order autoregression function and applied to short-term forecasting errors with the resulting accuracy of 87%. In the concluding part of the article the authors from the points of view of computational efficiency and scalability proposed the hardware system composition.  
photovoltaic power plant; short-term forecasting; data processing; data filtration; k-nearest neighbors; regression; autoregression Article
# NESO-Based Path Following Control for
## 2 Preliminaries and Problem Formulation
### Problem Formulation
#### 2.2.1 Nonlinear Hovercraft Dynamics with Uncertainties and SLC
\tag{8}\]  
\(\mathbf{M}=diag[m,m,I_{z}]\in\Re^{3}\), \(\boldsymbol{\tau}=\left[F_{xP},0,M_{2R}\right]^{T}\in\Re^{3}\), \(\boldsymbol{\eta}=\left[x,y,\psi\right]^{T}\in\Re^{3}\), \(\boldsymbol{\upsilon}=\left[u,v,r\right]^{T}\in\Re^{3}\), and the extended state vector \(\mathbf{d}=\left[d_{u},d_{v},d_{r}\right]^{T}\in\Re^{3}\), then the motion equations of underactuated hovercrafts can be expressed as  
\[\left\{ \begin{array}{l}\dot{\boldsymbol{\eta}}=\mathbf{R}(\psi)\boldsymbol{ \upsilon}\\ \dot{\boldsymbol{\upsilon}}=\mathbf{d}+\mathbf{M}^{-1}\boldsymbol{\tau}\end{array} \right. \tag{9}\]  
Figure 2: (**a**) The nonlinear time-varying turning safety limit of an underactuated hovercraft; (**b**) the diagram of the designed safe control strategy for the path following of underactuated hovercrafts. where the rotate matrix \(\mathbf{R}(\psi)\in\mathbf{\Re}^{3\times 3}\) is defined as  
\[\mathbf{R}(\psi)=\left[ \begin{array}{ccc}\cos\psi&-\sin\psi&0\\ \sin\psi&\cos\psi&0\\ 0&0&1\end{array} \right] \tag{10}\]  
**Remark 3**. : _The hydrodynamic force, the aerodynamic force, as well as the air momentum force in the kinetic model (6), are the time-varying uncertainties arising from the external environmental disturbances arising from wind, waves, and currents. The internal model uncertainties caused by unmodeled hydrodynamic terms are also unknown in practice. Therefore, the total uncertainties vector \(\mathbf{d}\), which should be approximated by the positions and heading measurement from GPS, is totally unknown._
## 2 Materials  
PA is a state in the northeastern United States with an area of 44,742 square miles and a population of 12.8 million [14]. By 2019, the proportion of the elderly (\(\geq\)65) population in PA had reached up to 19.0%, much higher than that of the whole U.S. (15.9%). Traffic crash data of PA from 2010 to 2019 were retrieved from the PA Department of Transportation. The raw crash data consist of multiple files, including crash, person, vehicle, roadway, etc., which could be joined by the unique crash record number (CRN). Firstly, SVHFOCs were identified by checking the number of involved vehicles and the collision type. In total, there were 225,762 SVHFOCs. Although they only accounted for 21.9% of total crashes, they led to 29.6% of total fatalities, indicating their great threat. Then, if the driver is aged between 65 and 98, it is thought to be an elderly SVHFOC; if the driver is aged between 25 and 64, it is a young one. Finally, 24,240 elderly SVHFOCs and 201,522 young SVHFOCs were identified. Table 1 shows the summary statistics of those crashes. To check whether elderly drivers have different traffic safety features from young drivers, their SVHFOCs were compared from multiple perspectives.  
\begin{table}
\begin{tabular}{l l l l} \hline
**Variable** & **Definition** & **Elderly Drivers (\(\geq\)65)** & **Young Drivers (25–64)** \\ \hline Severity & PDO & 49.3\% & 59.0\% \\  & Injury & 48.4\% & 39.9\% \\  & Fatal & 2.3\% & 1.1\% \\ \hline Object type & Tree & 22.1\% & 20.9\% \\  & Pole & 21.2\% & 20.6\% \\  & Embankment & 14.0\% & 15.6\% \\  & Others & 42.7\% & 42.9\% \\ \hline Gender & Male & 60.3\% & 64.7\% \\  & Female & 39.6\% & 35.2\% \\ \hline Unbelted & Unbelted in a crash & 9.7\% & 13.8\% \\ \hline \end{tabular}
\end{table}
Table 1: Summary statistics of the SVHFOCs in PA from 2010 to 2019.  
Crash severity is the primary concern for traffic safety analysis. Here, the proportions of injury crashes and fatal crashes are 50.7% and 2.3% for elderly SVHFOCs, but only 41.0% and 1.1% for young ones. That is, elderly drivers seem much more likely to be injured in SVHFOCs. Especially, the fatality rate of elderly SVHFOCs is more than 2 times that of young ones. This might be mainly attributed to their fragile bodies. Regarding the collision object, the most common fixed objects are trees, poles, and embankments. Among them, trees are the most dangerous (22.1%), followed by poles (21.2%) and embankments (14.0%). The casualty rate (61.6%) of vehicles driven by elderly drivers hitting trees is the highest, significantly higher than that of young drivers (49.9%). Meanwhile, the accident fatality rates caused by collisions with embankments (58.2%) and poles (57.1%) are significantly higher than those of young drivers (47.5% and 44.4%). In addition, car accidents are highly likely to have serious consequences, with the mortality rate of elderly drivers almost twice that of young drivers. It can be seen that SVHFOCs pose a huge threat to elderly drivers.
# An Improved Detail-Enhancement CycleGAN Using AdaLIN for Facial Style Transfer
## 3 The Proposed Method
### Loss Function
#### 3.3.2 Image Reconstruction Loss  
In this paper, to enhance the accuracy of CycleGAN, the image reconstruction loss is incorporated into the generator's loss function alongside the generative adversarial loss. This loss function component measures the disparity between the generator input image and its reconstructed counterpart. To quantify this similarity, the Structural Similarity Index Method (SSIM) is introduced. The structural similarity between the input image \(x\) and its reconstructed image \(\hat{x}\) is defined as shown in Equation (6):  
\[SSIM(\ x,\hat{x})=\frac{(2\mu_{X}\mu_{X}+c_{1})(2\sigma_{X}\sigma_{\hat{x}}+c_ {2})}{(\mu_{X}^{2}+\mu_{X}^{2}+c_{1})(\sigma_{X}^{2}+\sigma_{\hat{x}}^{2}+c_{ 2})} \tag{6}\]  
Figure 6: The network structure and parameters of the discriminator.  
where \(\mu_{x}\) and \(\mu_{\hat{x}}\) denote the mean values of \(x\) and \(\hat{x}\), respectively, \(\sigma_{x}\) and \(\sigma_{\hat{x}}\) denote the variances of \(x\) and \(\hat{x}\), respectively, \(\sigma_{x}\sigma_{\hat{x}}\) denote the covariance of \(\sigma_{x}\) and \(\sigma_{\hat{x}}\). where  
\[\hat{x}=F(G(x)) \tag{7}\]  
To address artifact issues, additional constraints are introduced into the loss function. The objective function of the stylization module incorporates a Laplacian regular term. This term in the loss function aims to retain the clarity and fidelity of details and textures in the generated image, preserving the nuanced features of the original content image. It serves to smooth out and maintain these details, contributing to high-quality image results. The Laplacian regular term is illustrated in Equation (8):  
\[Tr\Big{(}X\mathcal{L}X^{T}\Big{)}=\sum_{i,j=1}^{N_{p}}W_{i,j}\big{\|}x_{i}-x_{ j}\big{\|}_{2}^{2}=\sum_{Y_{i}\sim Y_{j}}\big{\|}x_{i}-x_{j}\big{\|}_{F}^{2}, \tag{8}\]  
where \(X\) and \(X^{T}\) denote the matrix and the transpose matrix, respectively, \(W_{i,j}\) denote the symmetric weight matrix, and \(x_{i}\) and \(x_{j}\) denote the elements in the matrix \(X\).
## 3 Fundamentals of RF Energy Harvesting
### Performance Metrics  
Evaluating the performance of RFEH systems relies on key metrics such as RF-to-DC conversion efficiency, sensitivity, and power density. RF-to-DC conversion efficiency measures the proportion of captured RF energy that is successfully converted into usable DC power. Optimizing impedance matching networks, rectifier circuits, and energy storage components is essential for improving this efficiency. Recent advancements have led to significant improvements in rectenna performance. As shown in Figure 8, a rectifier circuit achieved a peak efficiency of 88% at an input power of 14 dBm while maintaining over 60% efficiency within the input power range of 3 dBm to 16 dBm at 2.4 GHz. Similarly, at 5.8 GHz, it attained a maximum efficiency of 81% at 10 dBm input power, sustaining efficiency above 60% for input power levels between 7 dBm and 12 dBm [54]. Another study reported a rectenna system with a conversion efficiency of 66.7% at 2.4 GHz under an input power of 5 dBm [38]. Additionally, a dual-band rectenna design demonstrated peak conversion efficiencies of 72% at 1.8 GHz and 65% at 2.45 GHz [55]. These findings highlight ongoing advancements in rectenna design to enhance RF energy-harvesting capabilities.  
Sensitivity defines the minimum RF power level required for an energy-harvesting system to function effectively. Devices with higher sensitivity can operate in low-power RF environments, making them more versatile. For instance, a 2.4 GHz RFEH CMOS chip demonstrated a sensitivity of \(-\)19 dBm, achieving a peak efficiency of 51% and a power dynamic range of 24 dB, as reported in [56]. Power density refers to the amount of energy harvested per unit area of the receiving antenna, directly impacting the system's overall energy collection potential. Maximizing power density requires the optimization of antenna gain, the selection of suitable operating frequencies, and the strategic positioning of rectennas to maximize exposure to RF sources. Studies indicate that harvested power levels vary significantly based on signal strength and distance from RF sources, reinforcing the need for adaptive energy-harvesting strategies [8].
## 3 Physical Description of the On-Chip, Optical Wireless Link
### Optical Wireless Channel  
According to the chip layered representation, the electromagnetic waves springing out from the on-chip transmitting antenna are expected to bounce onto the interfaces between the layers, thus triggering multipath effects [8; 10; 14; 16; 18]. Assuming the antennas buried in a SiO\({}_{2}\) layer limited by two different, unbounded media on the lower and on the upper side [10; 18], multipath is fundamentally generated by the multiple reflections between the interfaces (Figure 5) and can be evaluated by means of the Ray Tracing (RT) approach discussed in [10]. It is worth pointing out that the RT modeling is particularly tailored to the investigation of the optical wireless channel, as geometrical optics is known to be an asymptotic theory, i.e., its accuracy increases at higher frequency.  
Figure 4: Gain functions in the \(\theta\) = 90\({}^{\circ}\) (xy) plane (**a**) and in the \(\phi\) = 0\({}^{\circ}\) (xz) plane (**b**) for the single Vivaldi antenna (black curve), the antenna array with N = 3 (blue dotted curve) N = 5 (green dashed curve) elements.  
Compared to other wireless networks, where the possible mobility of nodes and/or objects over quite large areas produces random-like fluctuations of the received signal intensity, the on-chip wireless channel is confined and static, as the chip layout is fixed and known beforehand [24]. Therefore, propagation modeling at chip-level is quite well fit for characterization through deterministic approaches like RT, which require the a priori knowledge of the physical environment.  
In order to run the RT simulation, both geometrical and electromagnetic properties of the link have to be set, the former referring to the SiO\({}_{2}\) layer thickness (t in Figure 5), to the antennas distance from the interfaces (t\({}_{\text{up}}\) and t\({}_{\text{down}}\)) and to the link distance (d\({}_{0}\)), the latter consisting of the materials refraction index (n\({}_{\text{down}}\), n\({}_{0}\) and n\({}_{\text{up}}\)). A 3D representation of the antenna radiation patterns is also necessary as input file. The assessment carried out in this work is limited to the Vivaldi antenna configurations briefly discussed in the previous section [9; 37], but any other radiation diagrams can be embedded into the RT tool, provided that they are encoded in the required format.  
Although propagation in Figure 5 is limited to the xz plane, a 3D description of the antenna patterns is nevertheless helpful, as the RT model can be then easily run regardless of the antenna orientation with respect to the propagation plane, e.g., for the assessment of the power received from an interfering transmitter. The geometrical/electromagnetic parameters considered herein for the RT simulations are summarized in Table 2.