esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Heuristic Scheduling Method
### Insertion-Based Local Adjustment Strategy  
To explain this strategy, an example as shown in Figure 2 is provided. With regard to the original scheduling in Figure 2a, when the processing time of an operation increases, it may delay the start time of its subsequent operations within the same job or those that are processed on the same machine, resulting in the right-shift phenomenon as shown in Figure 2b. This can lead to two issues: (1) the emergence of idle intervals in areas where none existed in the original schedule, and (2) the expansion of existing idle intervals. To address these issues, an insertion-based local adjustment strategy is proposed, and the specific steps are described as follows:  
Figure 1: Flowchart of the proposed heuristic scheduling method in the DDFISP-VPT.  
Step 1: Construct the binary tree for the operation with an increased processing time as shown in Figure 3. Each node represents an operation.  
Step 2: Calculate the completion time after the right-shift for each node in the binary tree. As to the tree shown in Figure 3, the two values in the brackets which are labeled on each node represent the completion time of the corresponding operation before and after the right-shift, respectively.  
Step 3: Traverse the binary tree layer by layer to handle each operation. Let \(I^{lk}=[I^{lk}_{s},I^{lk}_{e}]\) be the valid idle interval on machine \(k\) in factory \(l\) beginning from \(I^{lk}_{s}\) and ending at \(I^{lk}_{e}\). For the current operation \(O_{ij}\), evaluate all available idle intervals on its candidate machines. Let \(S^{*}_{ij}\) be the start time of the operation following \(O_{ij}\) within the same job and \(C^{*}_{ij}\) be the completion time of the operation that precedes \(O_{ij}\) within the same job. If an idle interval satisfies Equation (22), the operation is inserted into the corresponding machine; otherwise, no adjustment is made.  
\[\max(C^{*}_{ij},I^{lk}_{s},t_{l})+p^{lk}_{ij}\leq\min(S^{*}_{ij},I^{lk}_{e}). \tag{22}\]  
To demonstrate the effectiveness of the insertion-based local adjustment strategy, Figure 2 provides an example in which the processing time of operation \(O_{12}\) increases from 4 to 5, causing its completion time to be delayed from 9 to 10. A binary tree for the subsequent operations of \(O_{12}\) is then constructed, as illustrated in Figure 3. Figure 2b shows that all operations in the binary tree are affected, not only increasing the completion  
Figure 3: Binary tree of subsequent operations.  
Figure 2: Illustration of the local adjustment strategy.  
time but also potentially creating or enlarging idle intervals. To address this challenge, the operations in the binary tree are locally adjusted by attempting to insert them into available idle intervals on other machines, as shown in Figure 2c. The newly obtained schedule in Figure 2d shows that the overall makespan remains unchanged after the adjustment, which effectively demonstrates that a reasonable local adjustment strategy can respond to variable processing times and maintain product planning to some extent.
## 3 Profile Fitting and Geometric Modeling
### Measurement System Camera Calibration
The results are shown in Figure 10. Under the premise that the number of corner points is greater than 10, the calibration algorithm is more stable, and the accuracy is higher, with a relative error of 0.013%.
# A Survey of Machine Learning Methods for Time Series Prediction
## 4 Deep Learning Architectures
### Attention-Based Architectures  
Attention-Based Architectures, particularly Transformer Models [88], are the least explored deep learning approach among the surveyed literature in time series applications. Unlike RNNs, which use recurrent layers to process data sequentially, Transformers use a self-attention mechanism to identify ways that distant data features in a series are related to each other. They operate by processing entire sequences in parallel, making them much faster to train than RNNs. The application of Transformers has mainly been used for Natural Language Processing (NLP) models and less so for time series applications. Among the surveyed literature, only five papers [28,33,70,74,77] explore attention-based models in this domain. Early results suggest that Transformers can outperform traditional RNN-based models in tasks requiring long-term dependency modeling, though their suitability for specific time series tasks warrants further investigation.
# Phytosterols in Seaweeds: An Overview on Biosynthesis to Biomedical Applications
## 2 Biosynthesis of Phytosterols  
The sterol biosynthesis pathway is an important pathway of living organisms, exhibited by certain bacteria and all eukaryotes [10; 11]. Recent discoveries have increased the complexity of this pathway, urging researchers to decipher it further in order to deepen perceptions of its activity. Phytosterol biosynthesis is a branch of sterol synthesis found in almost all plant species and it can be distinguished from the sterol biosynthesis pathway of all eukaryotic kingdoms, such as animals and fungi. Although seaweeds are known to be potential producers of phytosterols, not all species of seaweeds have evolved to do so. Only limited species, such as brown seaweed, have the ability to produce phytosterols such as fucosterol and saringosterol. These sterols have therapeutic implications, such as neurostimulatory effects, and thus have piqued the interest of the clinical community [12; 13]. The paucity of investigation and the diversity of macroalgal species have obscured the identification of pathways responsible for phytosterol synthesis. Nevertheless, [PERSON] et al. [14] postulated that seaweeds may use the traditional pathways of plants for isoprene unit synthesis. As in plant species, the phytosterol biosynthesis in macroalgae can be stratified into three major segments: (i) the biosynthesis of isoprene, (ii) the condensation of isoprene into triterpenoids and their epoxidation and (iii) the biosynthesis of phytosterols [15]. In these sequential reactions, products of glycolysis are converted into hydrocarbons such as isoprene to triterpenoids and final end products of phytosterols [16].
## 2 Study Area and Data Sets
### Data Sets  
#### 2.2.1 SAR Images  
The Sentinel-1 satellite is a two-satellite constellation developed by the European Commission (EC) and the European Space Agency (ESA) for the Copernicus Global Observing Program. It can be used for the all-weather, all-time, and high-resolution monitoring of global coastal zones, land, and airlines. With the development of remote sensing (RS), the new generation of SAR Sentinel-1A/B satellites were successfully launched in 2014 and 2016, respectively. The default imaging mode of the constellation is interference in wide-band mode, which can acquire SAR data with a width of 250 km. Thus, Sentinel-1 provides a large quantity, wide coverage, short period, short baseline, and free C-band SAR data, which could greatly support the development and application of InSAR technology [40].  
The SAR dataset used in this study was composed of 44 images, which were acquired by three C-band (\(\sim\)5.7-cm wave length) satellites sensors, covering the total time period from October 2014 to December 2018, which were used to investigate the ground deformation of the Erhai region for the most recent four years. In order to ensure the continuity of the time series, ascending and descending flight direction SAR images of Sentinel-1 were obtained, including Sentinel-1A and Sentinel-1B (launched on 3 April 2014 and 25 April 2016, respectively). The Sentinel-1 descending images were acquired between 26 October 2014 and 10 November 2018, and the ascending images were acquired between 8 March 2018 and 14 December 2018 (Table 1). The Digital Elevation Model (DEM) used in differential interferometry are Shuttle Radar Topography Mission (SRTM) data with a resolution of 30 m [41]. Those SAR images were used to calculate the ground deformation information period from 2015 to 2018, and the result of two kinds of flight direction images were used to cross-validate.  
Figure 1: Study Area. Note that the lake in the middle of the picture is the Erhai region; the blue lines represented rivers connected with Erhai; the black triangle represents the observed water level.
# A Novel Strategy for VNF Placement in Edge Computing Environments
## 7 Experimental Results
### Packet Delay  
The rationale behind such an experiment is to verify whether the proposed placement algorithm influences the total packet processing time throughout the VNFs of the SFCs in comparison with the other approaches. The goal is to verify whether the adoption of our  
Figure 10: Allocated memory resources.  
Figure 9: Allocated CPU resources.  
such a novel aspect can optimize the use of well-positioned nodes by concentrating VNFs from several requests in the same node.  
Our proposal also targets different application QoS requirements while decreasing costs for the infrastructure provider. To this end, the algorithm is aware of latency, computational resource capacity, and energy consumption. Therefore, it selects edge nodes to place VNF requests by verifying those characteristics in the candidate edge nodes. In contrast with most VNF placement solutions, we consider four communication delays in our delay model, providing accurate decisions regarding latency. The energy consumption of edge nodes is essential for guaranteeing QoS due to their power constraints. This consumption includes, for example, data generation, transmission, aggregation, storage, and processing tasks.  
As future work, we envision (i) leveraging our model to support the continuous arrivals of user SFC requests over time; (ii) enhancing the proposal with the feature of being aware of user mobility and with actions that might be taken as a result of such user movements; and (iii) to create a mechanism to dynamically change the limit of SFCs that can share the same VNF instance based on the current workload.  
Writing--original draft, methodology, software, validation, and formal analysis, A.L.E.B., E.L.C.M., M.I.P.J., H.B. and D.P.d.M.; Writing--review and editing F.C.D. and P.F.P.; Supervision, and project administration, A.C.B.d.O., F.C.D., P.F.P. and D.C.M.-S. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by DELL EMC, and by the Brazilian funding agencies Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES), CAPES-Print, Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq--grant number 306747/2018-9), Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP--grant number 2015/24144-7), and Fundacao de Amparo a Pesquisa do Estado do Rio de Janeiro (FAPERJ). [PERSON], [PERSON], and [PERSON] are CNPq Fellows.  
Not Applicable, the study does not report any data.  
The authors declare no conflict of interest.
# Ku-Band Mixers Based on Random-Oriented Carbon Nanotube Films
## 2 Materials and Methods
### Preparation of the Randomly Oriented Carbon Nanotube Films
#### 2.1.3 Characterization of the CNT Films  
CNTs with semiconducting purities greater than 99.99% were deposited onto a SiO\({}_{2}\)/Si substrate to create randomly oriented CNT films with a relatively high level of uniformity. Figure 1a displays SEM images of these films after undergoing an Y\({}_{2}\)O\({}_{3}\)-based cleaning process and thermal annealing treatment.
## 2 Methodology
### Collision Check Using k-d Tree Search  
To navigate in unknown environments, one requirement is real-time sensing of surroundings by remote sensors such as 3D scanners, light detection and ranging (LiDAR) sensors, time-of-flight cameras, and depth sensors. Such sensors measure many points on the external surfaces of objects around them, and the set of those data points is called a point cloud. In other words, since the sensor observes the relative distances between the outer surfaces of the objects and the sensor, the point cloud includes all distance vectors with respect to the center of the sensor, expressed in the sensor reference frame. In fact, depending on computational avail ability, downsampling the point cloud is sometimes beneficial. In addition, when navigating in dynamic environments, prior mapping is meaningless. Although graph-search algorithms like A* that require map information beforehand need an additional technique or process to deal with dynamic obstacles [37], the proposed approach does not need any additional mapping process, and the updated point cloud, whenever new information becomes available, is useful to represent real-time obstacles.  
When the MPs-based motion planning in Section 2.1 are ready for collision checking, an autonomous UAV first extracts sample points from the planned path within the observable area (e.g., sensing range and limited field-of-view (FOV)), shown in Figure 2. Indeed, the maximum distance between two consecutive samples does not exceed the resolution of the point cloud.  
Next, the k-d tree search algorithm [34] aims to find the nearest obstacle point to each extracted sample-path point. If all closest obstacle points exist outside a certain margin (e.g., collision risk sphere) of the corresponding sample points, then the planned path is assumed to be safe, so the vehicle continues to follow the trajectory. In other words, if the nearest obstacle point exists within the collision risk sphere centered at the corresponding point on the path, re-planning to avoid that path point, called \(\mathbf{p}_{\text{col}}\), is performed at the next step in Section 2.3. As a corner case, if \(\mathbf{p}_{\text{col}}\) is the current location (e.g., when an obstacle moves towards the UAV), then the UAV stops the mission and lands autonomously.
# Design and Optimization of Vertical Axis Wind Turbines Using QBlade
## 3 Theoretical Background  
The fundamental equations that were used to build the mathematical model of the VAWT will be presented with details in this section. The relative velocity of the blade can be written as follows:  
\[\omega\ =\ V_{\infty}-R_{w} \tag{1}\]  
where \(V_{\infty}\) is undisturbed air velocity, \(R_{w}\) is the blade velocity at equator radius and \(\omega\) is speed of rotation for the blades.  
The significant velocities when using the DMS calculation approach are:  
1. \(V_{\infty}\): The inflow velocity of the undisturbed freestream flow.
2. \(V\): The upwind induced velocity that occurred owing to the extraction energy from blades at upstream rotor half.
3. \(V_{\ell}\): Equilibrium velocity that appeared in the plane between up--and downstream rotor half, which denotes the velocity of upstream rotor disk (wake) and the inflow velocity of the downstream rotor disk.
4. \(V\): The downwind induced velocity that occurred owing to the extraction energy of blades in the downstream rotor half.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameters** & **Specifications** \\ \hline Axis of rotation & Vertical \\ Turbine height & 0.562 m \\ Rotor radius & 0.22 m \\ Foil type & NACA 4418 \\ No. of blades & 3 \\ Swept area & 0.25 m\({}^{2}\) \\ Cut-in wind speed & 2 m/s \\ Cut out wind speed & 16 m/s \\ Wind speed range & 1–20 m/s \\ Rotational speed range RPM & 200–500 RPM \\ \hline \end{tabular}
\end{table}
Table 1: VAWT specifications.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**No.** & **Height (m)** & **Chord (m)** & **Radius (m)** & **Optimized Radius (m)** & **Foil** \\ \hline  
\hline \end{tabular}
\end{table}
Table 2: Vertical axis wind blade profile design for different sections of the blade.  
\(V^{\prime\prime}\): The wake velocity of the complete double disk.  
The first step of the calculations process is to determine these mentioned velocities, and then the parameters of extraction energy in the up-and downstream rotor half can be defined as follows:  
\[u\ =\ \frac{V}{V_{\infty}} \tag{2}\]  
\[u^{\prime}\ =\ \frac{V^{\prime}}{V_{e}} \tag{3}\]  
The global tip speed ratio is the relative speed of rotation of the (VAWT):  
\[\mathrm{TSR}\ =\ \frac{R_{w}}{V_{\infty}} \tag{4}\]  
Additionally, the simulation of the turbine enables the determination of the inflow wind profile. There are three potential options which are:  
1. Uniform velocity profile (like the simulation of a rotor);
2. Power law: \[V_{\infty}(z)\ =\ v_{ref}\left(\frac{z}{z_{ref}}\right)^{\alpha}\] (5) where \(V_{ref}\) is the simulated wind speed, \(Z_{ref}\) is the equator of the turbine and \(\alpha\) is the roughness exponent.
3. Logarithmic approach: \[V_{\infty}(z)\ =\ v_{ref}\left(\frac{\log\left(\frac{z}{z_{0}}\right)}{ \log\left(\frac{z_{ref}}{z_{0}}\right)}\right)^{\alpha}\] (6) where \(Z_{0}\) is depending on the surroundings of the wind turbine (e.g., \(Z_{0}\) = 0.2 mm for open water and \(Z_{0}\) = 2 m for highly built-up areas).
## 2 Experimental Study of HCC-Modified Asphalt
### Parameter Identification  
Hysteresis loops were plotted according to the data recorded by the instrument for each test condition. Then, the equal damping ratio and stiffness were calculated directly according to the hysteresis loops (see Figure 2). The equivalent damping ratio and stiffness can be expressed as follows [14]:  
\[\xi=\frac{\Delta W}{4\pi W} \tag{1}\]  
\[K_{\alpha\prime}=\frac{F_{\mathrm{max}}}{u_{0}} \tag{2}\]  
where \(\Delta W\) is the total absorbed energy, \(W\) is the elastic energy, \(F_{\mathrm{max}}\) is the maximum force, and \(u_{0}\) is the maximum displacement.  
Under sinusoidal excitation, the relationship between the damping force and excitation displacement can be written as follows [15; 16]:  
\[\left(\frac{F_{d}-K_{\mathrm{e}}u_{d}}{\eta K_{\mathrm{e}}u_{0}}\right)^{2}+ \left(\frac{u_{d}}{u_{0}}\right)^{2}=1 \tag{3}\]  
where \(F_{\mathrm{d}}\) and \(u_{\mathrm{d}}\) are the damping force and excitation displacement, respectively, \(u_{0}\) is the maximum displacement, \(\eta\) is the loss factor, and \(K_{\mathrm{e}}\) is the storage stiffness. It is clear that the shape of the hysteretic loops for viscoelastic dampers should be an ellipse. Equation (3) can be expanded to get the implicit second-order polynomial:  
\[a^{*}F_{\mathrm{d}}{}^{2}+b^{*}F_{\mathrm{d}}{}^{*}u_{\mathrm{d}}+c^{*}u_{ \mathrm{d}}{}^{2}-1=0 \tag{4}\]  
Figure 1: Test instrument: (**a**) cyclic simple shear instrument; (**b**) illustration of the instrument; (**c**) asphalt specimen; (**d**) illustration of the cyclic shear.  
\[a=\frac{1}{\eta^{2}K_{e}^{2}u_{0}^{2}} \tag{5}\] \[b=-\frac{2}{\eta^{2}K_{e}u_{0}^{2}}\] (6) \[c=\frac{1+\eta^{2}}{\eta^{2}u_{0}^{2}} \tag{7}\]  
where \(a\), \(b\) and \(c\) are unknown parameters.  
After generating the hysteretic loops from the experimental data, the unknown parameters can be identified by the direct least-square fitting method [17; 18], and then the damping parameters can be calculated:  
\[K_{e}=-\frac{b}{2a} \tag{8}\]  
\[\eta=\sqrt{\frac{4 ac-b^{2}}{b^{2}}} \tag{9}\]  
\[u_{0}=\sqrt{\frac{4 ac}{b^{2}\eta^{2}}} \tag{10}\]
# Efficient Online Log Parsing with Log Punctuations Signature
## 5 Discussion  
In this section, we compare LogPunk with tree-based methods and discuss the validity.  
**Comparison with tree-based methods.** As mentioned in Section 2, tree-based methods have two main problems: (1) needs to maintain a complex tree structure and updates the tree frequently as templates change. The updating process is time-consuming and complex; (2) does not guarantee the longest common subsequence (due to the limitation of the prefix tree itself). Unlike tree-based approaches, LogPunk works in a hash-like manner. Once a log signature is calculated, we can immediately locate the corresponding signature group. What we need to maintain is only a mapping relationship from the log signature to the signature group. This solution with a simple data structure brings more efficiency and accuracy improvements to LogPunk.  
Moreover, the invariance of log signature also brings potential benefits of parallel computing. Since most of the current log parsing methods are single-threaded, multi-threaded log parsers will greatly improve the efficiency of log parsing.  
**External validity.** LogPunk achieves a parsing accuracy of over 0.9 on more than half of the evaluated datasets. We cannot ensure that LogPunk can achieve the same high accuracy on other log datasets not tested in this work. However, it should also be noted that our evaluated log datasets come from systems in different domains. LogPunk is superior to other log parsers not only in accuracy, but also in efficiency. A future work is to verify the effectiveness of LogPunk on more types of log data and apply LogPunk to the practical log dataset from the production environment.  
**Internal validity.** The internal threat to validity mainly lies in the implementations of LogPunk and compared approaches. To reduce this threat, the implementation of LogPunk was completely based on the most primitive Python library and did not use any third-party library. We have also carefully examined the source code. Regarding compared approaches, we adopted their open-source implementations from the LogPai benchmark [15] directly.