esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions and Future Research Direction  
To deal with increasing shipments of containers, improving the efficiency of a container terminal is important. To achieve this, one effective way is to directly improve the operational efficiency of a CT. As most past studies were found to have solved the three seaside operational problems separately, which tends to result in poor overall system performance, solving the three seaside operational problems together is thus necessary. Therefore, in this research we have employed a graphical tool, termed an OOTPr/Tr net, to model and solve them at the same time.  
Due to the search in a reduced solution space, one advantage of our approach is that it can avoid the computationally intractable problems often encountered by exact approaches when they are used to deal with problems of practical size. Another advantage of our approach is that it can avoid the problems of being time-consuming and labor-intensive that are faced by \"what-if\" analyses in a traditional simulation approach. In our approach, after giving formal definitions to the three seaside operational problems, we transformed them into a three-level framework. This framework was then further modeled by a novel graphical tool, the OOTPr/Tr net. Finally, we implemented this OOTPr/Tr net as an evaluation tool using the Prolog programming language. Our experimental results showed the applicability of this tool. The contributions of this research are highlighted as follows.  
1. We have initiated a novel graphical tool, termed OOTPr/Tr net, which can be used for modeling and problem solving.
2. Using this novel graphical tool, we have successfully modeling the three seaside operational problems. This model was found to be quite flexible since resources such as ships, berths, and QCs are represented as tokens that can easily added into the model. In addition, we have implemented the derived OOTPr/Tr net as an evaluation tool.
3. The approach proposed in this study can be regarded as a simulation approach based on a reduced search space formed by the combinations of BAP and QCAP heuristic rules.  
As this research has only studied a few combinations of the various heuristic rules, future research can investigate the results obtained from other heuristic rules. In addition, one can treat the arrival times and processing times as uncertain factors to take uncertainty into consideration.  
Figure 3: The berthing planning for 9 calling ships.  
**Author Contributions:** [PERSON] guided the research direction and found the solutions; [PERSON] helped design the heuristic rules and experiments; [PERSON] summarized and analyzed the data; [PERSON] contributed to the literature review; [PERSON] helped revise and edit this paper. All authors have contributed to this research.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results
### Validation of Existing Models M1 and M2
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Watershed**} & **A** & _hyp_ & **Main** & **WW** & \(\mathbf{U}\) & \(\mathbf{P}\) & **FF** & \
\begin{tabular}{c} **M1** \\ **(SD-MEP)** \\ \end{tabular} &
\begin{tabular}{c} **M2** \\ **(SD-SEMEP)** \\ \end{tabular} \\ \cline{2-10}  & **(km2)** & **(m)** & **(km)** & **(\%)** & **(\%)** & **(mm)** & **(-)** & **(tons/km2-year)** & **(tons/km2-year)** \\ \hline W1 (after Geumho River) & 13,926.19 & 307.7 & 320.1 & 2.38 & 4.59 & 1142.24 & 0.44 & 53.93 & 42.67 \\ \hline W2 (before Hwang River) & 15,070.54 & 304.8 & 363.2 & 2.82 & 4.68 & 1198.33 & 0.40 & 53.38 & 44.54 \\ \hline W3 (after Hwang River) & 16,405.51 & 301.5 & 366.1 & 3.12 & 4.87 & 1216.16 & 0.36 & 53.74 & 46.45 \\ \hline W4 (before Nam River) & 16,973.5 & 295.7 & 389.8 & 3.32 & 5.12 & 1257.5 & 0.36 & 54.94 & 50.24 \\ \hline W5 (after Nam River) & 18,064.13 & 292.5 & 395.6 & 3.56 & 5.32 & 1283.65 & 0.28 & 55.61 & 50.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Validation result of models M1 and M2. Figure 7: Measured total suspended sediment load at **(a)** Geumho, **(b)** Hwang, and **(c)** Nam River. contradictory results from the previous studies is thought to be the spatial lumping problem caused by the fact that the sediment yield at the gauging points at which the measurements were acquired for validation was larger compared with that generated and transported by other main river streams and by the temporal lumping problem between validation and calibration data.
## 1 Introduction
### Objectives and Scope  
This study proposes an intelligent fault rate prediction framework for rail transit platform screen door systems. Firstly, a data enhancement method based on TimeGAN is adopted. Automatic coding components, including embedding and recovery functions, and adversarial components, including generators and discriminators, are jointly trained to learn sequence features, generated sequences, and cross-time iterations simultaneously. This method can solve the problem of sparse data in the original rail transit platform screen door fault dataset and generate augmented data that conform to the temporal characteristics of the original dataset. Secondly, a prediction model based on CNN-LSTM is used to extract effective data features from low-dimensional data with insufficient feature depth through a CNN, and shared convolution kernel parameters are used to reduce the model parameters. At the same time, the sparrow search algorithm (SSA) is used to optimize the key parameters of LSTM to solve the problem in which the scale of the neural network model is difficult to determine so as to improve the accuracy of the prediction of long-range dependent time series. Finally, the prediction framework mentioned above is applied to the fault rate prediction of platform screen door systems on four lines of the Shanghai rail transit system.  
The rest of this article is organized as follows: Related work is described in Section 2. In Section 3, the proposed method for fault rate prediction in a rail transit PSD system is introduced. In Section 4, data from four lines of the Shanghai rail transit system are used in an engineering application to verify the effectiveness and accuracy. Finally, the conclusion and prospects are summarized in Section 5.
# Base Isolation Compared to Capacity Design for Long Corner Periods and Pulse-Type Seismic Records
## 3 Results
### Structural Performance during Design Earthquake
#### 3.1.2 Base-Isolated Building  
For the baseisolated structure, the results of the non-linear time-history analyses are shown in Table 5. For the results obtained using the three spectrum-compatible accelerometers, the mean values are presented.  
Using base isolation results in:  
* a 45% drop in the maximum response acceleration at the top of the building;
* the isolation system accommodates up to 90% of the earthquake-induced horizontal movement. The structure above the isolation level moves like a rigid box. No yielding of structural elements is registered and no damage is foreseen;
* the base shear force is reduced by 65-80%.  
The vertical earthquake component causes axial force variations of 4% in the range of high ground acceleration amplitudes. No axial tension forces were registered at the isolation layer.
## 4 VLPC Network Based on SICD-OPRC Scheme
### Simulation Results
Compared to the OZCZ scheme, the OPRC scheme demonstrates relatively better performance, reducing the average BER and PE to 2.3 \(\times\) 10\({}^{-2}\) and 19.9 cm, with approximately 17% of test points complying with the FEC standard. Most strikingly, the SICD-OPRC scheme excels under asynchronous conditions. This scheme not only reduces average BER and PE to 4.3 \(\times\) 10\({}^{-4}\) and 2.7 cm but also achieves BER compliance with the FEC standard in approximately 96% of test points. Additionally, the PE does not exceed 5.6 cm for 90% of test points, nearly matching the performance of synchronous transmission systems. This result demonstrates the SICD-OPRC scheme's effective suppression of MAI in asynchronous environments, precise separation of VLPC signals from different LEDs, and ensures high-performance VLC and VLP services even in the presence of MAI. Figure 12: For the asynchronous VLPC system, the color-mapped surface plots of BER estimated at all test points when using **(a)** SICD-OPRC; **(b)** OPRC; and **(c)** OZCZ, with the pink plane representing the FEC threshold of 3.8 \(\times\) 10\({}^{-3}\), and the color-mapped contour plots of PE estimated at all test points when using: **(d)** SICD-OPRC; **(e)** OPRC; and **(f)** OZCZ.
# Dynamic Behaviour of an Automotive Dual Clutch Transmission during Gear Shift Maneuvers
## 3 The Torsional Model for the Driveline Analysis  
The driveline torsional model adopted in the present analysis refers to the physical scheme reported in Figure 1. The model considers the torsional stiffness and damping of the shafts connecting the gear wheels. The gear meshing has been modelled following a theory, adopted for this kind of impacting contacts, considering the squeeze effect of the oil lubricant between the gear teeth. In this way the gear rattle, arising in all the unloaded gears of the gear box, can be highlighted and a deep investigation can be conducted [10]. The phases of clutch operations, as for the gear synchronisation manoeuvres, have been modelled adopting linear functions; in particular, friction forces in the clutches have been modelled adopting the Stribeck hypothesis. The clutch and synchroniser plays a sensitive role for the dynamic behaviour of the transmission and therefore it is important to have an adequate level of theoretical approximation for these components [13; 14; 15]. In the following the motion equations for the transmission gear box described in Figure 1 are reported. The transmission gear box has six gear pairs and every gear wheel, comprising the flywheel and the last wheel \(W_{D}\), is counted in the model. Considering the engine torque \(T_{e}\) and the reduced resisting torque \(T_{r}\), the motion equations of the flywheel and \(W_{D}\) gear are expressed as:  
\[\textbf{Flywheel:}-I_{I}\ddot{\theta}_{f}+T_{e}-T_{cl1}-T_{cl2}=0 \tag{1}\]  
\[\textbf{Gear wheel}\ W_{D}\textbf{:}-I_{D}\ddot{\theta}_{D}-F_{cD}r_{D}-T_{r}=0 \tag{2}\]  
The motion equations for the odd and the even gears, respectively, have been so developed; the equations' parameters are specified in Table 1.  
**Odd branch (1)**  
\[\textbf{Clutch 1:}-I_{cl1}\ddot{\theta}_{cl1}-\sigma_{5p}(\dot{\theta}_{ cl1}-\dot{\theta}_{5p})-K_{5p}(\theta_{cl1}-\theta_{5p})+T_{cl1}=0 \tag{3}\]  
\[\textbf{Gear 1p:}-I_{cl1p}\ddot{\theta}_{1p}-\sigma_{13}(\dot{\theta}_{1p}- \dot{\theta}_{3p})-K_{13}(\theta_{1p}-\theta_{3p})-F_{c1}r_{1p}=0 \tag{4}\]  
\[\textbf{Gear 1s:}-I_{1s}\ddot{\theta}_{1s}+F_{cl}r_{1s}+F_{sinc\_dd_{i}}=0 \tag{5}\]  
\[\textbf{Gear 3p:}-I_{3p}\ddot{\theta}_{3p}-\sigma_{13}(\dot{ \theta}_{3p}-\dot{\theta}_{1p})-K_{13}(\theta_{3p}-\theta_{1p})-\\ F_{c3}r_{3p}-\sigma_{35}(\dot{\theta}_{3p}-\dot{\theta}_{5p})-K_{3 5}(\theta_{3p}-\theta_{5p})=0 \tag{6}\]  
\[\textbf{Gear 3s:}-I_{3s}\ddot{\theta}_{3s}+F_{c3}r_{3s}+F_{sinc\_dd_{i}}=0 \tag{7}\]  
Figure 1: Schematic of a Dual Clutch Transmission system for automotive driveline.
## 4 Discussion
This study assessed the patients in the interictal phase, i.e., out of a migraine headache attack, and our results indicate that in migraine patients, fear of movement-related pain may not be associated with a constant neck and head movement avoidance that could lead to lower cervical ROM. These characteristics may be restricted to ictal phases (i.e., during the attacks), since aggravation of pain by cervical movement and avoidance of head movement during a headache attack have been indicated as sensitive and specific manifestations of migraine [52]. This may also occur because fear related to movement in patients with migraine is not only restricted to head and neck motion [53]. Therefore, the lack of correlation between TSK and cervical ROM in our sample of female patients with migraine could be a consequence of the content of TSK items, which do not particularly consider head and neck movement. Furthermore, correlation analysis is better characterized when it includes a great variability of observations. As the interquartile range of TSK indicates a narrow amplitude of this variable, it may not have been enough to demonstrate a linear correlation. Although the TSK was not developed or validated in a sample of migraineurs, it is the only scale that measures fear of movement, and since kinesiophobia is a behavior caused by a negative belief, it is not conditioned to the individual's clinical condition. Nevertheless, we consider the use of TSK as one of the limitations of this study. The development of a migraine-specific scale should be considered in future studies. Another limitation is the study design, as a cross-sectional study, no causality between variables can be assumed. Finally, considering the sample, the results reported here are sex-specific, so they may not be the same when assessing male patients with migraine. However, this study was the first to assess the correlation between cervical ROM, psychosocial factors, and clinical presentations of migraine. The results confirm that cervical ROM is not linearly related to the clinical aspects of migraine or the presence of neck pain, or to fear of movement-related pain as measured by the TSK; however, it seems to be weakly related to the depressive symptoms. Based on these findings, we suggest that physiotherapists investigate the presence and severity of depressive symptoms since it may be associated with reduced mobility and consequently impair rehabilitation progress. Further, depressive symptoms suggest the necessity of psychological approaches. Futures research can contribute to a better understanding of other associations between psychosocial conditions and musculoskeletal impairments (i.e., muscle function) in patients with migraine.
## 3 Methodology of Reconstructing the 3D Foot Shape
### Feature Extraction  
The next step will be to extract metadata from the images, sensor and device information, and the focal length obtained from the resulting dataset. This is necessary to calibrate the camera in space. Thus, in the process of image creation, the image pixel coordinate system, the camera coordinate system, and the world coordinate system are applied. The process of extracting informative features involves detecting distinctive groups of pixels, which are invariant to changes in camera viewpoints during shooting from different angles. For this purpose, the algorithms use the scale-invariant transformation of features in SIFT and DSP-SIFT. The essence of these algorithms is the extraction of a set of binary descriptors from the image. Binary descriptors describe the area around a key point as a binary string obtained by pairwise comparison of the brightness of pixels in a given area. These algorithms can be divided into four main steps:  
Figure 2: This scene demonstrates the angle that must be used when shooting a person’s foot in a circular manner.  
* Definition of extremums of the scale space;
* Precise localization of key points;
* Defining the orientation of the camera in space;
* Calculation of the local image descriptor.  
These algorithms are complemented by the AKAZE method [36], which in turn uses the fast explicit diffusion method to form a nonlinear scale space. Thus, the idea of this method is to create a series of intermediate images at different scales (multiscale space) by applying different kinds of filtering to the original image. To detect and extract features in such a space, the determinant of the Hessian matrix is calculated for each of the images in the set. The calculated Hessian determinants for N-matrices, 3 \(\times\) 3 for each of the N-images, form informative features, which, as a result, form descriptors.  
The obtained determinants are normalized by the scale. All descriptors computed by the algorithms are robust in handling various types of image transformations (changes in viewpoint, noise, blur, changes in contrast); all extracted features remain sufficiently distinguishable for further processing and comparison.
## 3 Results
### Kinematic and Dynamic Parameters  
The kinematic (range of knee and ankle flexion angle, CoM-CoP angle (MPA)) and dynamic (knee and ankle flexion moments during the landing phase (KFM and AFM) parameters, and the anterior/posterior and medial/lateral GRF (AP GRF and ML GRF)) results are shown in Figure 2.  
Compared to the double-leg jump, the single-leg jump showed a significant decrease in the range of knee flexion angle (KFA) (\(p\) = 0.0098), while no significant differences were found in the range of ankle flexion angle between the two jumps (Figure 3a). Significant differences were observed in the peak knee flexion moment (PKFM) and the valley of ankle flexion moment (VAFM) during the landing phase between the two jumps (PKFM: \(p\) = 0.0195; VAFM: \(p\) = 0.0039) (Figure 3b). The results showed that peak AP GRF for the single-leg jump was significantly larger than that for the double-leg jump (\(p\) = 0.0039), while there was no significant difference in peak ML GRF for these two jumps (Figure 3c). Additionally, the CoM-CoP angle during the landing phase of the single-leg jump was significantly smaller than that of the double-leg jump (\(p\) = 0.0273) (Figure 3d).
## 4 Results
### Investment Cost of the Proposed Energy Saving Scenarios  
In this section, we present the investment costs related to each of the proposed energy saving scenarios. Table 4 lists the approximated costs taking into account the suggested costs from the Ecuadorian construction sector [60; 61]. As can be observed, the costs related to implementation of high performance windows are the highest and also, it presented the highest payback (more than 100 years). On the other hand, the installation of daylighting controls provided the lowest costs and a payback of more than 2 years. For this, daylighting solutions could be considered as the most cost-effective strategy to provide energy savings in this building according to the present analysis. Also, changing the cooling setpoint of the building should not be ignored since this strategy does not present an investment cost. Other strategies as WWR-20% and the use of static shadings presented paybacks of less than 50 years. The implementation of this strategies should be carefully analyzed, especially due to the cost of the energy in this building, which is subsidized. Therefore, this decision would probably lie on the availability of external investors.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Energy Saving Scenario** & **Investment Cost** & **Unit** & **Total** & **Payback** \\ \hline Daylighting controls & & & & \\ \hline Dimming controller & From \$100.00/unit & 20 & From \$3200.00 & 2.34 years \\ Photosensor & From \$60.00/unit & 20 & From \$3200.00 & 2.34 years \\ \hline Window to wall ratio reduction & & & & \\ \hline WWR-20\% & From \$4.25/m\({}^{2}\) & 310 m\({}^{2}\) & & \\ Windows desmounting & From \$10.00/m\({}^{2}\) & 181 m\({}^{2}\) & From \$7126.50 & 18.7 years \\ Cement filling & From \$31.00/m\({}^{2}\) & 129 m\({}^{2}\) & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Investment costs of the proposed energy saving scenarios (authors elaboration based on costs established in [60; 61]). All costs are presented in USD$.  
Figure 12: Estimated annual; (a) energy consumption; and (b) savings obtained from combined scenarios (authors elaboration).
# Collecting Sensed Data with Opportunistic Networks: The Case of Contact Information Overhead
## 2 Problem Background
### Location-Based Solutions and Their Shortcomings  
Location-based forwarding approaches do not require nodes to exchange knowledge upon encounter, hence, contact information overhead is not incurred. They exploit the concept of \"home\" [29], which is based on the observation that nodes tend to stay around a particular location for longer periods of time. Some approaches (e.g., HERO [29], A-HERO [48] and HERO++ [49]) partition the geographical area of the network into smaller regions and identify the home-region of nodes. Justified by the phenomenon of spatial locality [50; 51; 52], the chances of delivering a message are improved by forwarding it to nodes whose home-regions are increasingly closer to that of the destination. LOC [53] also considers the angular direction and distance of nodes to the approximate location of the destination.  
Unfortunately, location-based forwarding approaches present drawbacks in terms of delivery guarantees. First, there is the issue of determining the best forwarder among nodes presenting identical characteristics, e.g., in HERO, nodes having the same home region as the destination, and in LOC, nodes located at similar distances to the destination. Simply put, the chances of locating the destination reduce with increasing node population. A-HERO addresses this by flooding the message among home nodes. In high node population, however, this leads to undesirable overheads and persistent buffer overflows that may eventually reduce throughput.  
Second, nodes that have good encounter history or social relationship with the destination are not often identified through location-based forwarding utilities alone [54]. In fact, nodes that have good data transfer opportunities may not come from the same home-region--take office colleagues or classmates, for instance--and similarity in node movement patterns does not always guarantee an encounter between them--take users that have never met but living in neighboring streets, for instance. Therefore, while location-based approaches may be able to carry messages spatially closer to their respective destinations, additional mechanisms are required to further improve throughput. This brings about our second contribution, in which we propose a forwarding algorithm that can be incorporated into existing encounter-based routing protocols to reduce contact information overhead without reducing throughput.