esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Relationships between XCO\({}_{2}\) and Surface CO\({}_{2}\) Mixing Ratio  
In Table 3, we used the surface CO\({}_{2}\) mixing ratio measured by EC flux towers as the input of the SIF-CO\({}_{2}\)-GPP models. However, the ultimate goal of the proposed model is to map distributions of GPP in large extents. Hence, it is critical to find an appropriate proxy to the surface CO\({}_{2}\) mixing ratio. XCO\({}_{2}\) is the main products of OCO-2. Both XCO\({}_{2}\) and SIF products are retrieved from spectral measurements of OCO-2. Therefore, XCO\({}_{2}\) and SIF products of OCO-2 share the same footprints with respect to both time and space dimensions. If there was a robust relationship between surface CO\({}_{2}\) mixing ratio and XCO\({}_{2}\), it would be a key foundation for mapping GPP globally using the proposed SIF-CO\({}_{2}\)-GPP model. Hence, additional experiments were conducted to investigate the relationship between the CO\({}_{2}\) mixing ratio observed in the EC flux tower (hereafter referred to as tower CO\({}_{2}\)) and the XCO\({}_{2}\) product of OCO-2. For this sake, we carried out an additional experiment to explore the relationship between surface CO\({}_{2}\) mixing ratio of EC flux towers, abbreviated as Tower CO\({}_{2}\) hereafter, and XCO\({}_{2}\) products of OCO-2. Figure 6 illustrates the linear correlation between XCO\({}_{2}\) of OCO-2 and Tower CO\({}_{2}\). x represents XCO\({}_{2}\) of OCO-2 while y stands for Tower CO\({}_{2}\), and N is the number of valid  
Figure 5: Validation of the SIF-GPP model and the SIF-CO\({}_{2}\)-GPP model for different EC flux towers. The black dots represent the GPP estimated by the SIF-CO\({}_{2}\)-GPP model, and the red circles represent the GPP estimated by the SIF-GPP model. The fit lines for GPP of the flux towers and GPP\({}_{\rm M}\) are indicated by black lines. Fitting lines for GPP for flux towers and GPP\({}_{\rm S}\) are indicated by red lines.  
samples. CA-TP4, CA-TPD and US-Syv had less than 8 samples, resulting in an unreliable relationship between XCO\({}_{2}\) and tower CO\({}_{2}\). Therefore, these sites were excluded from this experiment. First of all, R between XCO\({}_{2}\) and Tower CO\({}_{2}\) fluctuate between 0.54 and 0.87, showing there are strong linear correlation between XCO\({}_{2}\) and Tower CO\({}_{2}\). That feature would be very beneficial to replacing surface CO\({}_{2}\) mixing ratio by XCO\({}_{2}\) in the SIF-CO\({}_{2}\)-GPP model. Meanwhile, it is also illustrated that slopes of linear relationship between XCO\({}_{2}\) and tower CO\({}_{2}\) deviate significantly from 1. It is hence concluded that there is need for correlating XCO\({}_{2}\) and surface CO\({}_{2}\) mixing ratio by means of observations and atmospheric models. Another alternative solution is to directly establish the SIF-CO\({}_{2}\)-GPP model using XCO\({}_{2}\) instead of Tower CO\({}_{2}\) if there were adequate valid samples.
## 3 Problem Definition and Optimization Model
### Model Formulation  
#### 3.2.1 Basic Assumptions  
To facilitate the research of the PAVP and the establishment of the model, we assume the followings:  
1. The type of liquid cargo, carrying capacity, handling process requirements and other information loaded of the arriving vessel in a planning cycle are known. The port production operation plan must collect this information before the vessel arrives at the port.
2. Only the unloading path of liquid cargo is considered, and the cargo of pipeline is flowed in a single direction.
3. When the vessel reaches at the designated berth, before the cargo is unloaded, the oil sampling and analysis should be carried out in the cargo hold to determine the quality of the cargo and the fuel quantity should be calculated. Unloading operations shall not be carried out until the oil quantity calculation and oil sample analysis are completed. The unloading time is calculated from the initiation of the oil transfer arm's operation until it ceases. The early sampling time is included in the preparation and handling duration of the oil transfer arm.
4. A vessel may be carrying multiple types of cargo at the same time, and unloading one type of cargo counts as one operation. And each type of cargo on the same vessel is carried out only once per operation.
5. The flow velocity of liquid cargo in the pipeline is constant.
6. The inner diameter of all pipelines is constant, i.e., the cross-sectional area is uniform.
7. The time required for the valve to open or close is not considered, and the time for liquid cargo to wait for the valve to open is not considered. Only the number of valve operations is calculated. Valves are assumed to be initially fully closed.
8. Long-distance pipeline docking and transshipment are not considered; only the internal transportation from the berth to the oil storage tank within the terminal is included. The vessel's onboard pump is assumed to provide sufficient pressure for unloading from the vessel to the tank.
9. Operations such as cargo oil sweeping, checking bilge oil levels, and crude oil washing are considered part of the pipeline oil circuit emptying time.  
Figure 2: Schematic diagram of valves and pipelines.
# Performance Comparison of Deep Learning Models for Damage Identification of Aging Bridges
## 2 Related Works
### Loss Values of Deep Learning Models  
Attempts to reduce the loss values of deep learning models have been conducted in various fields. First, one study has been conducted on normalization techniques, which control the complexity of the model and enhance its generalization performance [21]. Both batch normalization and layer normalization can improve the stability and learning speed of neural networks, contributing to a reduction in loss values. In addition, data augmentation--a technique that artificially transforms training data to increase diversity--is used. By applying transformations such as rotation, translation, and scaling, the training dataset is expanded to improve the model's generalization performance. Regularization alleviates overfitting by imposing penalties on model complexity [22]. Methods such as L1 and L2 regularization and dropout are used to constrain the network's weights or reduce overfitting by randomly deactivating some units. It is also crucial to set the initial weights to facilitate efficient learning and exploration of optimal solutions, aiding in loss value reduction. Optimizer algorithms, used when updating the model's parameters, leverage gradient-descent methods to minimize loss values. Adam and RMSprop are widely used optimizers because of their efficiency in updating parameters. The architecture of the neural network itself also affects the reduction of loss values. Innovative designs, such as CNNs, residual connections, and attention mechanisms, have generated considerable attention. In the present study, we aim to compare the performance of three models with different neural network architectures by comparing their loss values.
## 6 Results and Discussion
### Saint Gall  
The Saint Gall dataset has no punctuation or accentuation marks. In addition, the set is composed only of the Latin language, which has few characters, and the volume of data is much smaller than the previous one. In this context, our proposed model with Luong 1024 performed up to 94% of corrected sentences, followed by the models with Bahdanau, Luong 512, with 86% and 76%, respectively. The Transformer model had the worst result among neural networks, reaching only 18%. Similarity (\(N=2\)) and SymSpell (\(N=2\)) both reached 20% correction, while Norvig (\(N=3\)) with 18%. It is important to note that Kaldi (\(N=11\)), even the last with about 8% sentence correction, ranks first among statistical methods when evaluating the character error rate. Figure 18 details the results of each technique.  
Among the evaluated datasets, Saint Gall has the longest tokens. In a way, this scenario of long tokens and no punctuation or accentuation marks facilitate the model's learning. On the other hand, statistical methods presented difficulties in discerning wrong tokens (formed by small ones), causing a false correction. Figure 19 exemplifies a sentence correction between the techniques.  
When trained on the All in One, the Bluche optical model had a small improvement in recognition, while Puigcerver and Flor worsened a little, that is, spelling correction results worsened, as the RIMES dataset. Thus, the model proposed with Luong 1024 reached 50% corrected sentences, followed by SymSpell (\(N=2\)) and Similarity (\(N=2\)) with 15% and 16%, respectively. The model with [PERSON] 512 and Norvig (\(N=3\)) had equivalent results, both with 12% correction. Finally, the model with Bahdanau reaching 7%, Kaldi with 3% and the Transformer with only 2% of corrected sentences.  
Figure 18: Error rates in the Saint Gall dataset (lower is better), (**a**) Character Error Rate (CER); (**b**) Word Error Rate (WER); (**c**) Sequence Error Rate (SER).
###### Abstract  
The article proposes a new switchable coupled relay model for massive MIMO-NOMA networks. The model equips a much greater number of antennas on the coupled relays to dramatically improve capacity and Energy Efficiency (EE). Each relay in a coupled relay is selected and delivered into a single transmission block to serve multiple devices. This paper also plots a new diagram of two transmission blocks which illustrates energy harvesting and signal processing. To optimize the system performance of a massive MIMO-NOMA network, i.e., Outage Probability (OP) and system throughput, this paper deploys a Transmit Antenna Selection (TAS) protocol to select the best received signals from the pre-coding channel matrices. In addition, to achieve better EE, Simultaneously Wireless Information Power Transmit (SWIPT) is implemented. Specifically, this paper derives the novel theoretical analysis in closed-form expressions, i.e., OP, system throughput and EE from a massive MIMO-NOMA network aided by switchable coupled relays. The theoretical results obtained from the closed-form expressions show that a massive MIMO-NOMA network achieves better OP and greater capacity and expends less energy than the MIMO technique. Finally, independent Monte Carlo simulations verified the theoretical results.  
on-orthogonal multiple access; multiple devices; multiple antennas; massive MIMO; transmit antenna selection; energy harvesting 2021  
21101110
## 5 Conclusions  
The proposed hand achieves a significant improvement over other previous prosthetic hands because it can grasp much more stably and naturally, as shown in Figure 22. The proposed hand added more functions such as the arch and hyperextension, which results in integration in terms of both the motion performance and appearance. There are three servomotors embedded in the adult human sized palm, but the whole hand weighs only 132.5 g. Figure 23 summarizes the achievable types of motion and weights of the prosthetic and three previous hands. Clearly, the proposed hand is the lightest one, but can perform the most types of motion. It weighs 132.5 g, accounting for less than 40% of the weight limitation and it performs 13 types of motion, accounting for more than 80% based on the on the Cutkosky taxonomy. Thus, the proposed hand successfully combines dexterity and light weight, thereby addressing the trade-off issue between the two.  
Nevertheless, several problems remain to be addressed. Although the stable function can provide a certain trajectory for the finger joints, reliability problems may still occur at times owing to tendon loosening. Furthermore, the proposed hand cannot provide high grasping power, as it employs small  
Figure 23: Comparison between the achievable types of motion and entire weight: x-coordinates express the number of achievable types of motion based on the Cutkosky taxonomy; y- coordinates express the entire weight of prosthetic hand.  
motors, owing to the weight and size limitations. Therefore, the directions for future work include addressing the above-mentioned problem, as well as realizing all 16 grasp motions.  
The following are available online at [[http://www.mdpi.com/2076-3417/9/20/4203/s1](http://www.mdpi.com/2076-3417/9/20/4203/s1)]([http://www.mdpi.com/2076-3417/9/20/4203/s1](http://www.mdpi.com/2076-3417/9/20/4203/s1)), Video S1: Pick-and-place experiment with the USB memory stick.  
Conceptualization and methodology X.J.; investigation and writing-original draft preparation X.Y.; writing-review and editing, G.L. and Y.J.; project administration, H.Y.  
This work was partially supported by the National Key Project of Research and Development Plan of China Inter-Governmental Cooperative Science and Technology Project (2016 YFE0128000), Shenzhen Governmental Basic Research Grants (#JCYJ20160331185848286), Guangdong Province Natural Science Fund (#2016A030313179), JSPS KAKENHI Grant Numbers JP18H03761 and JP19K12877, and a project commissioned by the New Energy and Industrial Technology Development Organization (NEDO).  
The authors declared that they have no conflict of interest to this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.
## 2 System Structure and Problem Formulation
### System Structure  
In this paper, we consider a relay-assisted FSO system in DF mode, i.e., a cooperative DF-FSO system, as shown in Figure 2. A cooperative DF-FSO system contains one source node, one destination node and \(N\) relay nodes. In order to achieve higher received power, the narrow divergence angle technique is always utilized in FSO systems. In this sequel, it common that only one relay node can receive the signals from the source node in each time slot (TS), which is selected by switching the direction of the transmit beam. According to Ref. [22], optical switches can be implemented with either spatial light modulators (SLM) or optical micro-electromechanical systems (MEMS). Correspondingly, the selected relay node decodes and forwards the received signal to the destination node, which forms the R-D link.  
In an arbitrary \(k\)-th TS, it is supposed that the \(N_{k}\)-th relay node is selected. Then, the channel capacity of a cooperative DF-FSO system can be expressed by Equation (1), where the intensity modulation/direct detection (IM/DD) is employed:  
\[\mathbb{C}_{k}^{\prime}=\min\biggl{\{}\log_{2}\biggl{(}1+\left(\eta P \mathbf{h}_{k}^{N_{k}}\right)^{2}/\sigma_{r}^{2}\biggr{)},\log_{2}\biggl{(}1+ \left(\eta P\mathbf{g}_{k}^{N_{k}}\right)^{2}/\sigma_{d}^{2}\biggr{)}\biggr{\}} \tag{1}\]  
where \(\eta\) denotes the photodetector responsivity, \(\sigma_{r}^{2}\) and \(\sigma_{d}^{2}\) represent the variance of the equivalent noise in S-R link and R-D link, respectively, \(P\) is the transmitting power in the source node and the selected relay node, \(h_{k}^{N_{k}}\) and \(g_{k}^{N_{k}}\) stand for the channel gains from the source node to the \(N_{k}\)-th relay node and from the \(N_{k}\)-th relay node to the destination node, respectively. Then, vector \(\mathbf{h}_{k}\) is defined as the sets of CSI in all S-R links in the \(k\)-th TS, i.e., \(\mathbf{h}_{k}=\left[h_{k}^{1},h_{k}^{2},\ldots,h_{k}^{N}\right]\). Similarly, vector \(\mathbf{g}_{k}\) is defined as \(\mathbf{g}_{k}=\left[g_{k}^{1},g_{k}^{2},\ldots,g_{k}^{N}\right]\), representing the sets of CSI of R-D links in the \(k\)-th TS. According to Ref. [5], the channel gain contains both the turbulent fading and the pointing errors, whose PDF (probability density function) is described with Equation (2):  
\[f_{h}(h)=\frac{\alpha\beta\rho^{2}}{A_{0}\Gamma(\alpha)\Gamma(\beta)}\mathbf{G }_{1,3}^{3,0}\biggl{(}\frac{\alpha\beta h}{A_{0}}\bigg{|}\begin{array}{c} \rho^{2}\\ \rho^{2}-1,\alpha-1,\beta-1\end{array}\biggr{)} \tag{2}\]  
where \(\mathbf{G}_{p,q}^{m,n}\left(\bullet\big{|}\mathbf{a}_{p}=a_{1},a_{2},\cdots,a _{p};\mathbf{b}_{q}=b_{1},b_{2},\cdots,b_{q}\right)\) stands for the Meijer' G function, \(\alpha\) and \(\beta\) represent the effective numbers of large-scale or small-scale turbulent eddies respectively, which describe the Gamma-Gamma distribution, \(\rho\) stands for the ratio between the equivalent beam radius and the standard deviation of the pointing errors, \(A_{0}\) is the maximum fraction of the collected power in the receiving lens.
# Effects of a Curved Heel Shape in a Running Shoe on Biomechanical Variables and Comfort
###### Abstract  
(1) Background: The purpose of this study was to investigate the effects of a rounded heel shoe (RHS) and rounded lateral heel shoe (RLHS) on impact and lower extremity stability as well as their relationships with comfort during running. (2) Methods: Twenty healthy male adults participated in the study. The data were collected using eight infrared cameras while participants were running at a speed of 2.7 m/s in three shoe conditions on an instrumented treadmill. (3) Results: The peak vertical ground reaction force (PVGRF) was statistically smaller for the RHS and RLHS compared with the normal shoes (NS) (\(p<0.05\)). The range of motion of inversion-everism at the ankle joint was statistically smaller for the RLHS compared with the NS and RHS (\(p<0.05\)). Increased dorsiflexion of the ankle joint at heel contact was negatively related to the comfort of a running shoe, and increased dorsi-plantarflexion ROM was positively related to comfort. (4) Conclusions: Based on these results, a curved heel shape of a running shoe may provide a positive influence on the biomechanical function and the comfort of running shoes. Future study, including measurements of lower extremity muscle activations and long-term comfort, would be beneficial to help validate current findings and develop further applications.  
rounded heel shoe; impact variables; joint angles; comfort; running Article
## 3 Results  
### Primary Outcome Measures  
Table 1 shows the results of the experiments. The top three rows of this table show the average performances of the 5 models for each pretraining method when evaluated individually, where the numbers in brackets show the standard deviations. The trained GastroNet models outperform the ImageNet and no-pretraining models on all metrics by approximately 5 and 10 percentage points, respectively. The standard deviations of the experimental results are notably smaller with GastroNet pretraining compared to ImageNet pretraining, indicating that domain-specific pretraining converges more reliably to suitable values when compared to ImageNet pretraining.  
The bottom three rows display the results when the 5 models per pretraining method are combined into an ensemble, leading to a single prediction per image, rather than 5 individual predictions. The numbers between the brackets indicate the number of correctly classified videos as a fraction of the total. As expected, ensembling improves the results for each pretraining method. However, Imagenet pretraining benefits more from the ensembling approach (+7% accuracy) compared to GastroNet pretraining (+5% accuracy) or no pretraining at all (+4% accuracy). However, the GastroNet ensemble still consistently outperforms the ImageNet ensemble by approximately 2% or even more. The Wilcoxon signed-rank test [23] was used to compare the diagnostic accuracy of the three pretraining methods based on the video-level predictions. A statistically significant difference (\(p<0.01\)) was found between the model that was pretrained with GastroNet and the other two models that used conventional pretraining. This shows the merit of endoscopy-driven pretraining over pretraining with natural images for CAD applications in endoscopy. This finding is in line with results from prior work, where the same conclusion was found for primary detection using white light endoscopy [10].  
Figure 3a shows the ROC curves for the three ensembles. In this figure, the green line indicates the ROC of the proposed ensemble with GastroNet pretraining, while the blue and yellow lines depict the ROCs of the ensembles pretrained with ImageNet and without pretraining, respectively. The AUC scores of both pretrained models (GastroNet \(AUC=0.91\) and ImageNet \(AUC=0.90\)) clearly outperform the model without pretraining (\(AUC=0.82\)). Although the difference is not as large, the GastroNet ensemble still slightly outperforms the ImageNet ensemble. The system performance is mostly interesting for high specificity (false positive rate between 0.1 and 0.3), where the green curve is notably better.
# Design and Evaluation of Novel Submerged Floating Tunnel Models Based on Dynamic Similarity
## 4 Cauchy Similarity Test
### Numerical Model  
According to the scale model of the SFT used in the Cauchy similarity test, two numerical models of the tunnel body were established based on the finite element software ANSYS. One is a high-fidelity numerical model, as shown in Figure 5, which meticulously mirrors the configuration of the scale model, incorporating both the PE pipe and the internal counterweight steel rings. The contact between the pipe and steel rings was set to be bound. The material properties of the PE pipe were determined from the material property test, while the material properties of the steel rings were the same as those of standard structural steel. All components in the numerical model were solid elements, which were discretized by SOLID185 tetrahedral elements. The maximum size of the mesh was 5 mm with an average quality of 0.83, which meets the requirements for static and dynamic analysis. The other was a simplified numerical model, which is conceptualized as a hollow cylinder  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **YWD-20** & **YX1211** & **941B** \\ \hline Sensitivity & 0.1 mV/mm & 0.5 mV/gal & 3 mV/gal \\ Range & \(\pm\)25 mm & 100 m/s\({}^{2}\) & 20 m/s\({}^{2}\) \\ Resolution & 0.01 mm & \(5\times 10^{-3}\) m/s\({}^{2}\) & \(5\times 10^{-6}\) m/s\({}^{2}\) \\ Weight & 130 g & 90 g & 870 g \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specifications of sensor parameters.  
Figure 4: Configuration of the experimental setup for Cauchy similarity testing. (a) Photograph of experimental devices; (b) schematic layout of the sensor distribution.  
featuring a virtual material with a high density and low elastic modulus. The two numerical models have the same geometric dimensions. By comparing the experimental results with the numerical simulation results, it can be judged whether the new SFT scale model meets the design requirements.  
In the Cauchy similarity test, although both ends of the model are fixed by the annular pipe carrier, slight rotation may still occur. Adjusting the rotational stiffness of the end constraint in the high-fidelity numerical model brings the numerical simulation results closer to the experimental results, thereby defining the boundary conditions of the numerical model. These boundary conditions are then applied to the simplified numerical model. By comprehensively comparing the experimental results and the numerical model results, it is feasible to determine whether the performance of this new type of SFT scale model meets the design objectives. Furthermore, these two numerical models are not only used for the similarity test of the scaled model; because they can accurately reflect the elastic deformation of the tunnel tube, and when combined with the fluid numerical model, they can simulate the fluid-structure interaction, thereby enhancing the reliability and accuracy of the numerical simulation of the hydrodynamic response of the submerged tunnel.