esa-sceva/satcom-chunk-collection huggingface dataset:
## Discussion  
### Dataset Dependence of the SIFc-GPP Relationship  
The linear SIFc-GPP relationship, forced to go through the original point developed from GPP and two contiguous SIFc datasets, is SIFc dataset-dependent (Figure 2). This SIFc dependency can be explained by the fact that GOSIF is generally higher in value than CSIF across biomes and years (Figures S3 and S4). The stronger SIFc-GPP relationship derived  
Figure 8: Scatter plots and linear regression of SIFc (both CSIF and GOSIF) and GPP for all biomes across six spatiotemporal scales (\(p\) < 0.001). The statistical measures for the linear regression listed in the top left corner correspond to different colors. All the linear regressions were forced to go through the origin. The solid lines represent the fitted major axis regression models for different SIFc–GPP combinations: CSIF–GPP (blue) and GOSIF–GPP (red). (**A**) Site-daily; (**B**) site-yearly; (**C**) site-multi-yearly; (**D**) biome-daily; (**E**) biome-yearly; (**F**) biome-multi-yearly.  
from GOSIF compared to that from CSIF was consistent with previous studies [31]. [PERSON] and [PERSON] [29] found that the R\({}^{2}\) value of the linear relationship between GPP derived from 40 EC flux towers and CSIF ranges from 0.01 to 0.93, with a median value of 0.64. Moreover, [PERSON] and [PERSON] [31] reported a higher linear relationship between GOSIF and flux GPP (R\({}^{2}\) = 0.73, \(p\) < 0.001) based on the GPP from 91 EC flux towers. However, GOSIF did not always perform better than CSIF in some years and some places (Figures 4 and 5), which might be influenced by the meteorological conditions input. On the other hand, as the differences existing between both SIFc datasets should include variability ranges, systematical bias between different SIFc datasets would lead to different offsets for the linear relationship. Hence, further efforts in improving the SIFc-GPP relationship must reconcile differences from different SIFc datasets and further understand their implications [29; 30; 31].
## 4 Experiments and Results  
### Dataset  
In this section, we evaluated the performance of our proposed defect detection system. We compared our proposed system with a defect detection system using real data. We applied the proposed system to the real-world fabric samples with various defects. The fabric samples were captured by VTC-2K10.5G-C19 [18] which is produced by Viewworks. We captured three types of fabric: dataset 1, 2, and 3 as shown in Figure 5. Each of the datasets is comprised of 64,000 images of \(256\times 256\) size, and we resized the image to \(128\times 128\) for training.  
To measure the performance of the system, we captured 6000 images of real defected fabric samples also. The defects in the fabric samples are produced during the manufacturing process, or artificially applied to the fabric using knives, awls, inks, etc.
## 4 Application
### Tolerance Analysis Model  
For the tolerance analysis of the mechanism, the _vector loop approach_ (see Section 3.1) is used. Applying Equation (1) to the shown 8-bar mechanism consisting of eight linkages \(g\) and seven kinematic joints \(n\), it becomes apparent that two vector loop equations are required for solving the vector loop model [11]. The summarised equations are listed in the following:  
\[L_{1}\cdot e^{i\cdot\theta_{1}}+c_{12}\cdot e^{i\cdot\gamma_{12}}+L_{2a}\cdot e ^{i\cdot\theta_{2}}+c_{23}\cdot e^{i\cdot\gamma_{23}}-L_{AB}\cdot e^{i\cdot \theta_{A}}-L_{3}\cdot e^{i\cdot\theta_{23}}=0, \tag{4}\]  
\[\begin{array}{l}L_{1}\cdot e^{i\cdot\theta_{1}}+c_{12}\cdot e^{i\cdot\gamma_ {12}}+e^{i\theta_{12}}(L_{2a}+L_{2b}\cdot e^{-i\cdot\theta_{2a2b}})+c_{23} \cdot e^{i\cdot\gamma_{23}}+\\ \qquad+c_{24}\cdot e^{i\cdot\gamma_{24}}+L_{4}\cdot e^{i\cdot\theta_{24}}+c_{ 45}\cdot e^{i\cdot\gamma_{45}}+L_{5}\cdot e^{i\cdot\theta_{5}}-L_{AC}\cdot e ^{i\cdot 0}=0.\end{array} \tag{5}\]  
For solving this set of non-linear equations, the Newton-Raphson method is applied and missing information concerning the clearance vector and the dimensional deviations of the linkages needs to be derived through MBS and predictive models (cf. procedure in Figure 5).  
In order to determine the missing angle \(\gamma_{ij}\) of the clearance vector \(\vec{c_{ij}}\) for solving the vector loop equation, a MBS (see Section 3.2) of the presented 3D-printed mechanism is built up, using the commercial software MSC ADAMS as it is suitable for virtually model systems in motion. Herein the so-called 'Impact-function' is implemented, which is similar  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Definition** & **Design Parameter** & **Nominal Value** \\ \hline Linkage 1 & \(L_{1}\) & 81 mm \\ Linkage 2a & \(L_{2a}\) & 116 mm \\ Linkage 2b & \(L_{2b}\) & 87 mm \\ Linkage 3 & \(L_{3}\) & 161 mm \\ Linkage 4 & \(L_{4}\) & 60 mm \\ Linkage 5 & \(L_{5}\) & 53 mm \\ Linkage AC & \(L_{AC}\) & 203.5 mm \\ Angle & \(\theta_{2a2b}\) & 215\({}^{\circ}\) \\ Radial clearance & \(c_{ij}\) & 0.4 mm \\ Planar clearance & \(c_{planar}\) & 0.2540 mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Nominal design parameters of the presented case study.  
Figure 6: 3D-printed, fully functional 8-bar non-assembly mechanism fabricated via FDM.  
to the continuous contact force model introduced by [PERSON] and [PERSON] [13]. This function is used for modelling the interaction between journal and bearing and thus the joint clearance of the regarded mechanism. After the substitute model of the mechanism was defined, the simulation of the motion behaviour was conducted for two revolutions, whereby the joint forces and the hence calculated angles \(\gamma_{ij}\) are subsequently exported for the second revolution since influences from the numerical run-in phase are thus avoided. The results are used for solving the vector loop equations including the clearance vectors \(\vec{c_{ij}}\) of the mechanism. In Figure 7, the forces of the kinematic joints \(J_{ij}\) for the 8-bar mechanism for the joint clearance \(c=0.4\) mm are illustrated.
## 1 Introduction
\"Maehyang\" (short-day) strawberry plants produced a significantly greater number of daughter plants under 280 umol m\({}^{-2}\) s\({}^{-1}\) photosynthetic photon flux density (PPFD) as compared with 140 umol m\({}^{-2}\) s\({}^{-1}\) and 220 umol m\({}^{-2}\) s\({}^{-1}\) (photoperiod 16 h) with a daily productivity of 0.27 daughter plants per stock plant [20]. However, these reports only focused on relatively low PPFD treatments (50 to 280 umol m\({}^{-2}\) s\({}^{-1}\)) and short-day strawberry cultivars. Typical plant responses to light intensity follow the logarithmic curve in which increases in photosynthesis are greater at the beginning but gains decrease over time until a saturation point is achieved [21]. At light saturation point, photosynthesis no longer increases with increases in light intensity since photosynthesis is Calvin cycle limited [21]. Different crops have different light saturation points. For strawberry plants, the saturation point for photosynthetic rate has been reported to be at PPFD of 400 to 600 umol m\({}^{-2}\) s\({}^{-1}\)[22; 23]. However, a maximum photosynthetic rate does not always lead to an increase of yield since photoassimilates can be translocated to parts of the plant that are not part of the harvest index, such as leaves, crown, roots, etc. The objective of the present study is to evaluate the effect of light intensity (250 to 450 umol m\({}^{-2}\) s\({}^{-1}\)) specifically for the production of daughter plants on day-neutral strawberry plants.
## 3 Methodology
### Small Multi-Model Guidance
#### 3.1.1 Multimodal Relation Data Augmentation  
Data augmentation is a necessity for improving data efficiency and addressing insufficient data volume. Due to the image-text coupling constraints in multimodality, previous works like ViLT [32] only used RandAugment [41] for image augmentation, and CLIP [25] just randomly resized cropping.  
These multimodal data augmentations are far from sufficient. Additionally, using traditional image augmentation techniques such as randomly adjusting cropping, flipping, and changing colors can cause mismatch between text and image information, leading to the inability of text to keep up with changes in image information. Therefore, we adopt a method similar to MixGen [42], augmenting data via image fusion and text concatenation without breaking image-text semantics. Image fusion through overlapping retains positional and color information of the original images, while text concatenation can seamlessly fuse textual information without changing semantics. For multimodal relational data, relation category fusion is performed by text concatenation as well, i.e., the relation information also doubles correspondingly for the generated image-text pairs with doubled information volume, as shown in Figure 3.  
For an image-text dataset with \(N\) pairs, images and texts are denoted as \(I\) and \(T\), respectively; then, for any two given image-text pairs \((I_{i},T_{i})\) and \((I_{j},T_{j})\) where \(i,j\in\{1,...,N\}\) and \(i\
eq j\), the formulation for generating a new sample \((I_{k},T_{k})\) is  
\[I_{k}=\lambda\cdot I_{i}+(1-\lambda)\cdot I_{j}, \tag{3}\]  
Figure 3: Our proposed Multimodal Relation Data Augmentation (MRDA) generates new image-text data by fusing images and concatenating texts and relation sequences for any image-text pair.  
\[T_{k} =concat(T_{i},T_{j}), \tag{4}\] \[R_{k} =concat(R_{i},R_{j}). \tag{5}\]
# Game Approach to HDR-TS-PV Hybrid Power System Dispatching
## 3 **Hdr-Ts-PV** Game Model
### HDR-TS-PV Cooperation Game Pattern  
If the three players in the hybrid power system intend to form coalitions, such as coordinated dispatching with a \(\mathit{PV}\) plant, the \(\mathit{HDR}\) power station can obtain greater benefits by providing auxiliary services and by the coordinated dispatching with a \(\mathit{TS}\) power station, and the \(\mathit{HDR}\) power station can use the \(\mathit{TOU}\) prices for arbitrage. The three players participate in cooperation under these conditions. Due to the limitations caused by the efficiency of the \(\mathit{ORC}\) generator, it is difficult for the \(\mathit{TS}\) power stations to profit from the \(\mathit{TOU}\) prices. Therefore, the \(\mathit{TS}\) power stations need to cooperate with the \(\mathit{HDR}\) power station or the \(\mathit{PV}\) plant to profit from waste geothermal or surplus \(\mathit{PV}\) power with zero marginal costs. Concurrently, because of the requirements of power grid assessment for the \(\mathit{PV}\) curtailment power and the abandonment load, the \(\mathit{PV}\) plant also cooperates in order to reduce the loss of the curtailed power and the penalty for abandoning the load.  
It is observed that through cooperation, the three players can obtain a greater payoff and allocate extra profits through energy settlement, which is only related to the cooperating players. Therefore, cooperation between \(\mathit{HDR}\), TS, and \(\mathit{PV}\) can be modeled by the characteristic function game (CFG). CFG uses the tuple \(G=\langle N,V\rangle\) to describe the cooperative game relationship, where \(N=\{1,2,\cdots,n\}\) is a set representing the number of players in the coalition. \(V\) represents the characteristic function that reflects the payoff of the coalition, which is represented by the mapping relationship \(V:F(N)\to R\). For any \(S_{i}\) of \(S_{i}\in N\), \(V(S_{i})\) or \(V_{S_{i}}\) represents the overall payoff of the \(S_{i}\) coalition.  
In the cooperative game, the three players can form four different coalitions denoted by [{H, T}, {P}], [{H, P}, {T}], [{P, T}, {H}], and [{H, T, P}]. The payoff of the players is defined by the tuple \(\langle S_{n},x\rangle\), where \(S_{N}=\{S_{1},S_{2},\cdots,S_{n}\}\) is the coalition structure and \(x=\{x_{1},x_{2},\cdots,x_{n}\}\) is the payoff vector. \(\langle S_{n},x\rangle\) must meet the individual rationality  
\[x_{i}\geq I_{i},\forall i\in\{H,T,P\}, \tag{21}\]  
where \(x_{i}\) is the payoff received by the player \(i\) in the coalition, and \(I_{i}\) represents the payoff of the player \(i\) in the non-cooperative game.
# Using GPS Trajectories to Adaptively Plan Bus Lanes
## 2 Problem Formulation
### Objective Function  
In existing bus lane planning models, the objective functions are organized from two perspectives: space and time. From the spatial perspective, bus lanes should use road space effectively. From the temporal perspective, bus lanes should improve the travel times of passengers [26]. Correspondingly, two objective functions are considered in this model.
## 3 Results and Discussion
### Miscellaneous Compounds  
1-Octen-3-one and 1-octen-3-ol had previously been reported for their contributions to the mushroom note of blueberries, while methional contributes to the cooked potato note [8; 21]. Sotolon has been widely noted for providing a seasoning note in food [28] and may be formed by chemical or biochemical means [46]. Sotolon was identified for the first time in blueberries in this study. Other miscellaneous compounds were 2,3-butanedione (buttery note), 2-heptanone (green note), and \(\gamma\)-decalactone (coconut note). The odor potential of 1-octen-3-one was very high throughout storage, peaking in S2 (16,384 and 8192), while 1-octen-3-ol had the highest FD in S1 (16,384; Wax column) and decreased in later storage treatments. The FDs of these two volatiles were higher than those reported in previous research with 'Bluecrop' highbush blueberries (FDs of 128 and 256); however, the high relative contribution of these odorants agrees with this study. \(\gamma\)-Decalactone also showed a high potential aroma contribution in SC (FD = 4096, 2048), but this decreased as storage progressed. Meanwhile, for 2,3-butanedione, the FD values were relatively low, with the highest FD occurring in S1.  
The identification of 3-methylbutyric acid, responsible for a cheesy note, was in accordance with previous GC-O work on blueberry aroma in the 'Bluecrop' and 'Elliot' varieties (FD = 32 and 16, respectively) [8]. In this study, the FD for SC was 32, which declined in S1 and fluctuated thereafter in later storage.  
Even though \(\beta\)-damascenone, a norisoprenic ketone, was identified as having a honey note in this study, the FDs detected were quite low (1-16), showing the lower contribution of this compound to the overall impression of blueberry aroma. In previous studies, [PERSON] et al. (2021) indicated that this compound had an impact on 'Bluecrop' and 'Elliot' blueberry flavor, with FDs of 64-256 [8].
## 4 Development of Blockchain-Based Cloud Manufacturing System
### Part Assembly Tracking  
The assembly of a railcar follows a structured production process and therefore a QR code identification system is adopted, permitting information flow and continuous updating from the beginning to the end of the process. A railcar is manufactured with thousands of parts, hence a QR code is more applicable in comparison to an RFID, as it is sizeable and stores multiple details about a product. The QR code can easily be labelled using cryptographic and timestamped data for record-keeping based on the Fabric data, which ensures accuracy in data capture. QR codes are easily printed onto a part and/or scanned thereon, hence parts are traceable along the assembly system. This process can be applied across the manufacturer's assembly plant for various sub-assemblies for part tracing and during RCM.  
Each manufacturing machine possesses a unique serial number which is utilized for its identification. As such, as a supplier is awarded parts for manufacturing, the cloud manufacturing \"event\" is triggered to start the manufacturing process, which is controlled through resource scheduling. The blockchain system uses the IS network to control and monitor the SCM processes, suppliers, and manufacturing process, hence it is able to trace each manufactured part incrementally. As each part is produced, it is stamped with a unique QR code based on a supplier's details, namely, the supplier, the machine, and the type of material used. Timestamping is used in determining the exact date a part is manufactured, which is critical for life cycle analysis (LCA).  
A challenge often encountered by the manufacturer is the production of parts with different materials for part manufacture. As such, inventory management using deep learning (DL) algorithms such as neural networks, were applied in order to forecast other aspects of the SC such as part failure. DL application to identifies how the varying materials perform in operation. DL is also used to simulate and optimize assembly processes by mapping them to cloud-based scheduling and planning. Lastly, the application of data analytics within the system allows for visualizations, which are used to identify supplier performance and manufacturer productivity.
## 5 Conclusions  
The results of the petrophysical inversion show that the sequential prediction approach produces a higher resolution than the simultaneous case in this Volve example. Because of their high porosities and thicknesses, the sands in the Hugin Formation window are suitable for lithofluid discrimination, according to the V\({}_{clny}\)/porosity modeling results. The impact is particularly noticeable in the in situ conditions for most of the wells in the upscaled domain. Overall, the outcomes in every Volve field well are consistent. However,  
Figure 20: (**a**) Cross plot of intercept vs. AVA gradient fluid modeling results. AVO results show a strong effect from 0 to 0.1 CO\({}_{2}\) saturation, with a sharp drop in AVA gradient (\(-0.06\) to \(-0.08\)) and intercept (0.03 to 0). A subtle effect is observed for higher CO\({}_{2}\) saturation values. (**b**) Cross plot of angle vs. reflectivity, where sharp drop in reflectivity is observed from 0 to 0.1 CO\({}_{2}\) saturation. A subtle effect is observed for higher CO\({}_{2}\) saturation values.  
separating the hydrocarbon scenarios (oil vs. gas) could be difficult. The cross plots of AI vs. PR colored with facies also demonstrate the effectiveness of discrimination in this area.  
Due to the good accuracy in the inversions and log analysis, we undertook fluid substitution studies for possible CO\({}_{2}\) sequestration. The modeling of the CO\({}_{2}\) fluids helped us to model the conditions in the area of interest and the seismic changes that occurred throughout the various stages of CO\({}_{2}\) injection (0% to 100% CO\({}_{2}\) saturation). The following could be observed in the first 10% CO\({}_{2}\) substitution: The typical decrease in P-wave velocity is 12%. The \(V_{s}\) increases in direct proportion to CO\({}_{2}\) saturation. A subtle effect is observed for higher CO\({}_{2}\) saturation values. The decrease in \(V_{p}\) and density represents a decrease in the impedance and reflectivity contrast.  
Conceptualization, D.P.-D. and R.R.S.; methodology, D.P.-D.; software, D.P.-D.; validation, R.R.S.; formal analysis, D.P.-D.; investigation, R.R.S.; resources, D.P.-D. and R.R.S.; data curation, D.P.-D.; writing--original draft preparation, D.P.-D.; writing--review and editing, D.P.-D. and R.R.S.; visualization, D.P.-D. and R.R.S.; supervision, R.R.S.; project administration, D.P.-D. and R.R.S.; and funding acquisition, R.R.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Subsea Systems Institute and Allied Geophysics Laboratory (AGL) at the University of Houston.  
Not applicable.  
Data Availability Statement: Data is available upon request from the authors.  
The authors would like to thank [PERSON], [PERSON] and [PERSON] for their technical support. We also wish to express our gratitude to Equinor for providing the data, PGS and CGG for providing the software, and the Allied Geophysics Laboratory (AGL) at the University of Houston for their support.  
The authors declare no conflicts of interest.
## 7 Discussion and Concluding Remarks  
The superior performance of ATLAS actually comes at the cost of some protocol overhead, which are also seen in previous WBAN MAC protocols such as LDTA-MAC [3] and IEEE 802.15.4. First, the MAC operation in ATLAS depends solely on the time synchronization of the sensor in order to maintain simultaneous superframes. Thus, it requires regular timing information, which is actually piggy-backed at the initial beacon of the gateway. Second, ATLAS uses the data-Ack beacon to announce the mode of operation based on the traffic load of the network in each superframe. Finally, the proposed scheme has a few limitations in terms of its implementation in WBAN. The proposed protocol is described considering only a single-path and single gateway-rooted network topology, even though a WBAN might consist of multi-path, multi-gateway, and multi-tree topology. Moreover, ATLAS lacks in making a compatibility study with any clustering algorithm in terms of energy, which seems to be very challenging for existing energy-efficient sensor MAC protocols as well. However, we leave the implementation of these multi-concepts and the clustering compatibility study for future investigations.  
The traffic load-aware ATLAS MAC protocol for body area sensor networks uses the advantageous properties of contention access period (CAP), contention free period (CFP) and inactive period (IP) of a superframe according to the IEEE 802.15.4 standard. The simulation results demonstrate that the proposed scheme performs better than the existing WBAN MAC protocols in terms of average duty-cycle, energy consumption, throughput, delivery ratio, and delay. Therefore, ATLAS is suitable for use in dynamic and diverse traffic load-oriented WBANs.  
Figure 9: End-to-end delay over different number of nodes.
# Investigating Models for the Transcription of Mathematical Formulas in Images
## 2 Challenges in Reading and Transcribing Mathematical Formulas  
Ref. [9] explains that the challenges in reading mathematical formulas lie fundamentally in the use of complex structures, symbols, numbers, relational signs, and different notations.  
Figure 2 illustrates the five main challenges a human has to deal with when reading a mathematical formula. These are the same challenges our machine learning models have to deal with in order to retrieve the transcription of the formula. The challenges are as follows:  
1. Retrieve the principal symbols (e.g., \(\sigma\), \(=\), \(\prime\), \(\longrightarrow\), \(\sum\)), even those that are not displayed (e.g., multiplier \(\cdot\)).
2. For each principal symbol, find the corresponding sub-symbols that deliver additional information (e.g., \(\frac{1}{n}\), \(\sum_{i=1}^{n}\), \((x_{i}-\mu)^{2}\)).
3. A symbol may have several semantics in different positions, resulting in various possible transcriptions. For each symbol and its corresponding sub-symbol, find the correct transcription (e.g., \"_one divided by \(n\)_\" vs. \"_one over \(n\)_\" _\"sum of \"_ vs. \"_sum from_\").
4. Find the correct order for the individual transcriptions (e.g., \"the sum from i is one to n of xi minus mu\" vs. \"the sum of xi minus mu from i is one to n\").
5. Retrieve a complete transcription that covers the whole formula, \"_sigma is equal to the square root of one divided by \(n\) times the sum from i equals one to n of xi minus mu squared_\").  
Figure 1: Processes for transcribing mathematical formulas.  
In the automatic transcription of mathematical formulas, _challenge 1_ and _challenge 2_ are usually handled using MER models, which convert the image of the formula into LaTeX code [2; 3; 4; 5; 6]. As demonstrated in Figure 3, LaTeX code usually contains commands such as _lim_, _sum_, and _frac_. These commands still have to be converted into natural language. NLP models have the potential to perform this conversion.  
Alternatively, VL models may be able to deal with _challenge 1_--5 as they connect vision and language in a generative way. Therefore, these models support tasks such as image captioning, which translates an input image into a textual description [10; 11]. Consequently, we investigate VL models' potential for the transcription of mathematical formulas in images.