esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Task Allocation in Multi-Agent Systems Using Reinforcement Learning and Genetic Algorithm
## 2 Related Work
### Distributed Optimization Algorithms  
Distributed optimization algorithms decentralize decision-making, allowing agents to allocate tasks based on local information with limited communication. This structure enhances scalability, adaptability, and fault tolerance by enabling agents to act independently, without the need for global communication [29]. [PERSON] et al. [30] demonstrated the effectiveness of distributed optimization in large-scale wireless sensor networks, where agents can allocate resources based on local observations, thus improving system efficiency.  
\begin{table}
\begin{tabular}{c l} \hline
**Notation** & **Definitions** \\ \hline \(\{I\}_{j}\) & the set of agents or the group for task \(j\) \\ \(\omega_{i}\) & the work ability of agent \(i\) \\ \(h_{j}\) & the workload of task \(j\) \\ \(T_{j}\) & the completion time of task \(j\) \\ \(T\) & the max completion time of all tasks \\ \(A_{i}\) & the strategy set of agent \(i\) \\ \(A\) & the strategy space \\ \(\tau\) & the discount factor reflecting the rate of task reward change \\ \(Rad_{i}\) & the communication radius of agent i \(j\) \\ \(R_{j}\) & the reward of task \(j\) \\ \(E_{i}\) & the battery level of the agent \(i\) \\ \(v_{i}\) & the speed of the agent \(i\) \\ \(\theta\) & the elevation angle of the agent \\ \hline \end{tabular}
\end{table}
Table 1: \(Cont.\)[PERSON] et al. [31] applied similar principles to multi-robot systems, optimizing task allocation to increase operational efficiency while maintaining system scalability.  
Despite these advantages, distributed optimization faces challenges in terms of coordination and convergence. [PERSON] et al. [32] highlighted inefficiencies arising from incomplete information, where agents' local knowledge may lead to suboptimal allocations. Moreover, the absence of global coordination can cause slower convergence, as agents may not be able to adjust their strategies based on the broader system dynamics [33]. These limitations can result in inefficiencies, particularly in dynamic environments where global coordination is beneficial for maintaining optimal system performance.  
**Comparison with GA-PPO:** GA-PPO overcomes many of these challenges through the integration of genetic algorithms for broad exploration and proximal policy optimization for continuous policy refinement. This combination allows GA-PPO to adapt dynamically to changing environments, ensuring both efficient exploration and targeted optimization over time. Unlike [PERSON] et al.'s [30] distributed algorithm, which relies heavily on local communication between agents, GA-PPO does not require fixed communication patterns, providing more flexibility and robustness to network failures. Furthermore, while [PERSON] et al.'s [31] work in multi-robot systems benefits from localized decision-making, GA-PPO's hybrid framework allows it to perform effectively even in scenarios with high levels of environmental uncertainty and variable task demands, making it more adaptable and scalable in comparison.
## 2 Materials and Methods
### Chaincode Implementation Details  
To implement the proposed proof of concept, the Hyperledger Fabric blockchain was chosen. The main features and advantages of Hyperledger Fabric are as follows: a modular architecture--pluggable components, pluggable consensus mechanism (crash fault tolerance (CFT) or Byzantine fault tolerance (BFT)), permissioned network--only authorized participants can join the network, scalability and performance, private transactions, channel-based communication--enables private and confidential communication between subsets of network participants, identity management, and enterprise-focused features--addressing concerns related to compliance, regulatory requirements, and auditability. These advantages make Hyperledger Fabric a preferred choice for enterprises and consortiums looking to deploy permissioned blockchain networks, especially in sectors requiring privacy, scalability, and customizability, such as supply chain management, finance, healthcare, and more [27].  
In Hyperledger Fabric, chaincode functions equivalent to a smart contract found in other blockchain platforms. It defines the rules and logic-governing transactions within the network. Chaincode can be scripted in Go, Node.js, or Java, and it is a program that implements a predefined interface (API), provided by the _fabric-contract-api_ library. Another important library for implementing and deploying chaincode is _fabric-shim_. It contains the implementation necessary for facilitating communication with Hyperledger Fabric peers for smart contracts created by using _fabric-contract-api_.  
The proposed DPP blockchain chaincode was implemented in Node.js. The chaincode structuring is presented in the module dependency diagram (see Figure 4).  
The implementation of business logic within the chaincode was divided into several modules. Most of the modules contain the implementation of the available operations for one of the entities proposed by the data model (e.g., _productContract.js_ for _Product_, _manufacturerContract.js_ for _Manufacturer_, _supplierContract.js_ for _Supplier_, etc.). These are called domain logic modules.  
The _chaincodeUtils.js_ module is used by the domain logic modules as it provides generic implementation for the following operations: _create_, _read_, _update_, _delete_, _getAll_, _saveAll_, _exists_, and _getBySelector_--for running complex queries.  
The entire business logic is exposed to the outside world through the facade module _chainCode.js_. All the public operations that are implemented by the domain logic modules are aggregated by this module. The actual implementation of this code unit is a class that extends the _Contract_ class from the _fabric-contract-api_ library.
## 2 Background and Related Work  
### Background  
Video quality evaluation has been an active research topic for a while. From a users' perspective, it is key to determine the impact of immersive video applications on the perceived QoE related to the videos consumed by the users of such applications. Signal processing applied on digital video, conversion of videos from one type to another, compression of videos for transmission purposes and errors introduced in the transmission process itself are some of the factors that have an impact on the quality of video signals, and thus, on the user QoE. Traditionally, evaluation of processed video quality has been carried out through subjective assessments, where the processed videos are displayed to a group of observers for recording their opinion on the perceived quality [13].  
Although this subjective approach to video quality evaluation offers very representative observer opinion scores, it is a very tedious, time and effort consuming process. This may lead to negative effects on the development and enhancement of emerging immersive video applications and services.  
Figure 1: Camera and potential rendering positions in a typical include 3 DTV or FTV application (CAM (real camera) and VC (desired virtual camera/view locations), which may be requested at the receiver end by the viewer).  
Video quality evaluation metrics define a link between physical parameters of the video and perceived video quality [14]. Objective quality assessment for conventional 2D video signals has been a focus of research for quite a long period. However, these assessment methods have not considered artefacts introduced by the new video processing techniques, such as the disocclusion artefacts introduced by the DIBR process in 3D immersive video rendering and viewing.  
Recent developments in video quality evaluation research, particularly targeting 3D immersive video applications [15; 16; 17; 18; 19; 20; 21; 22; 23], provided an insight into building a reliable relationship between the objective measures and their predicted subjective counterparts without having to conduct long and tedious subjective tests. These research activities investigated predicting subjective measurement using full, reduced (partial) or no-reference evaluation methods. Our work solely focuses on no-reference quality evaluation for immersive video applications, such as 3 DTV or FTV, as discussed with reasons in the previous section. Thus, the next section distils the related work into this particular topic of research interest.
# Goal Estimation of Mandatory Lane Changes Based on Interaction between Drivers
## 3 Proposed Method
### Collision Check Based on Trajectory Prediction  
To assess the collision risk during the lane-changing, the proposed method predicts the trajectories of all vehicles for a time horizon of 4.6 s. According to a previous survey, the duration of lane changes was 4.6 s, on average [17]. Then, the sinusoidal model is applied to predict the lateral movement [18]. This model generates a trajectory such as a sine curve. The acceleration in the lateral movement can be derived as follows.  
\[a_{lat}(t)=\frac{2\pi H}{t_{lat}^{2}}\sin{\frac{2\pi}{t_{lat}}t}, \tag{10}\]  
where \(a_{lat}\) represents the lateral acceleration, \(t\) is the time from the beginning of lane-changing, \(H\) is the lane width, and \(t_{lat}\) is the lane-changing duration. The proposed method determines the lane-changing duration, \(t_{lat}\), as 4.6 s. Through the above equation, the lateral movement of the target vehicle can be predicted. The sinusoidal model has no parameter which requires the optimization. Conversely, the lateral movement of the lead and rear is ignored, and it is assumed that the two vehicles move while maintaining the center position of the mainstream lane. The details are explained in our previous work [19]. For the longitudinal position, it is assumed that all drivers maintain the current acceleration until the trajectory prediction is over.  
The collision risk is assessed based on the predicted trajectories. If the future trajectory of the target is overlapped with that of the lead or rear, the proposed method decides that a collision would occur. Then, the estimated goal is reversed as \(D\) despite the previous result was \(A\), \(B\), or \(C\) in the goal estimation part. This decision lies on the assumption that drivers generally maintain the current lane until the driving safety is ensured.
# A 1064 nm Dispersive Raman Spectral Imaging System for Food Safety and Quality Evaluation
## 4 Conclusions  
A 1064 nm dispersive Raman spectral imaging system was developed for food safety and quality evaluation. The system is capable of hyperspectral Raman imaging and spatially offset Raman spectroscopic (SORS) measurement of the food samples. The application of the hyperspectral Raman imaging system was demonstrated by detecting metanil yellow contamination in turmeric powder at five concentration levels. A hyperspectral Raman image of the mixture sample at each concentration was collected covering the sample surface area of 25 mm \(\times\) 25 mm, using a step size of 0.25 mm along the X and Y direction. The system could detect metanil yellow at concentrations as low as 1%. The detected metanil yellow pixels linearly correlated with its concentration in the mixture  
Figure 12: Spatially offset Raman spectra of turmeric-metanil yellow mixture packaged inside: two-layer (**a**); three-layer (**b**); four-layer (**c**); and, five-layer (**d**) capsule.  
sample (R\({}^{2}\) = 0.99). Application of the system for SORS measurement was demonstrated by subsurface detection of gelatin-encapsulated pure and mixed samples of turmeric and metanil yellow powder. This system is a versatile platform capable of 1064 nm hyperspectral Raman imaging and SORS measurement for a wide variety of food and pharmaceutical samples.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data, with other co-authors [PERSON], [PERSON], and [PERSON] also participating in the discussion of the results; [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest.  
Mention of specific products is for identification only and does not imply endorsement by the US Department of Agriculture to the exclusion of other suitable products or suppliers.
# A New Method of Geological Modeling for the Hydrocarbon Secondary Migration Research
## 5 Discussion
The reliability of geological modeling in deep and ultra-deep highly heterogeneous reservoirs is contingent upon the accuracy of our geological understanding [89; 90]. This understanding encompasses several key aspects that need to be comprehensively mastered during the geological modeling process. Firstly, it involves grasping the stratigraphic framework characteristics of oil and gas reservoirs, including fault and stratigraphic structures, as well as their respective inter-relationships. Additionally, it entails a thorough understanding of sedimentary characteristics such as sedimentary facies, subfacies, and microfacies. Furthermore, it encompasses reservoir characteristics such as the lithology, reservoir type, physical properties, oil and gas properties, and the relationships between the porosity and permeability. Understanding the reservoir's developmental laws, including the controlling factors, diagenesis, qualitative characteristics of reservoir heterogeneity, and spatial distribution patterns, is also crucial. Lastly, knowledge of the reservoir's spatial distribution characteristics, including the reservoir thickness, range, and the 3D prediction of reservoir parameters, is essential. In the case of mid-shallow oil and gas reservoirs, the uncertainty in reservoir parameter calculation and spatial distribution prediction based on well logging and seismic data is relatively low due to the accessibility and high quality of the data. However, challenges arise when dealing with deep and ultra-deep layers due to limited data availability and the lower quality of seismic data. This comprehensive geological understanding serves as the foundation for geological modeling, influencing the strategies employed throughout the process. It also plays a role in selecting interpretation or prediction methods for reservoir parameters using geophysical techniques like logging and seismic analysis. Ultimately, the reliability of the geological modeling results is determined by the accuracy and precision of the prior geological understanding. While geological understanding is often subjective, this new workflow provides a flexible framework allowing modelers to define their own \"unit elements\" based on their individual interpretations of the depositional environment. However, the potential for multiple valid \"unit element\" definitions highlights the need for further research. Specifically, integrating additional field observations and well data will be crucial for refining these definitions and minimizing uncertainty in the resulting models.
# Insights into Segmentation Methods Applied to Remote Sensing SAR Images for Wet Snow Detection
## 2 Data and Method
### BERA Bulletins
The resulting RGB image was then converted into HSV space. With this configuration, detecting wet snow implied identifying shades of magenta (which indicated areas where there was a drop in backscatter values compared with reference images). We fixed a magenta color variation range to detect wet snow optimally. An example of the detection results is shown in Figure 9, where wet snow extent is shown for 27 April 2018 over the Belledonne massif. The method is noted as \"Magenta\" (M) hereafter. A version of the method with Gaussian filtering was also tested and is noted as \"Magenta Filtered\" (MF) hereafter.
## 4 Model Validation
### Validation Data Results  
The maximum torsional strength of the specimens was calculated using the ANN algorithms with two preprocessors, PCA and autoencoder, and current four design codes. In Figure 4, the validation results of the algorithms were compared with the prediction of design codes. The \(x\)-axis represents the specimen name and the \(y\)-axis shows the ratio of the test to calculated torsional strength. In Figure 4, the circular, diamond, triangular, and cross markers represent ACI 318-19, EC2-04, CSA-14, and JSCE-07, respectively, whereas in Figure 4, the circular and cross markers indicate the ANN algorithm results with PCA and autoencoder, respectively. The mean value, standard deviation, and CV of the validation set are presented in Table 4. As shown in Figure 4 and Table 4, the ANN model accurately predicted the maximum torsional strength compared to the predictions of design codes, even if the models were untrained for this data set. The ANN models resulted in the best mean and CV values of 0.96 and 2.27%, respectively, for PCA, and 1.05 and 11.55%, respectively, for the autoencoder. In contrast, ACI 318-19 and EC2-04 overestimated, and JSCE-07 and CSA-14 mainly underestimated, the maximum torsional strength of RC beams. The mean and CV values for ACI 318-19, EC2-04, CSA-14, and JSCE-07 were 1.09, 1.13, 0.701, 0.88 and 22.32%, 22.95%, 23.57%, 21.55%, respectively.
## 2 Materials and Methods
### Tissue-Simulating Phantom Validation
#### 2.3.1 Phantom Composition  
Two heterogeneous phantoms, A and B, were constructed, both consisting of a 12.5 cm diameter cylindrical background material with a 2.6 cm diameter cylindrical inclusion in the center. The cylindrical inclusion is positioned in both phantoms such that the base of the inclusion is 2 mm from one side of the phantom. The base of the inclusion is 7 mm and 9 mm from the other end for phantoms A and B respectively. These exact depths were determined by the custom apparatus that was built to fabricate the heterogeneous phantoms, the mold, and the silicone volume poured. Figure 4 illustrates the geometry of the phantoms A and B in x-z plane.  
Phantoms A and B were designed to have the same absorption contrast between the inclusion and the substrate. Phantom A was designed to show similar scattering, whereas Phantom B shows a scattering contrast between the inclusion and the substrate. This was done in order to test the ability of the hybrid DOI method to provide better scattering estimates to improve the CW reconstruction.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Normal** & **Tumor** \\ \hline \multicolumn{3}{c}{**Absorbing Chromophores**} \\ HbO (\(\upmu\)M) & 8.6 & 17.5 \\ HHB (\(\upmu\)M) & 6.3 & 9.8 \\ H\({}_{2}\)O (\%) & 21.8 & 31.3 \\ Lipids (\%) & 80.6 & 66.3 \\ NGB (g/L) & – & 0.05 \\ \hline \multicolumn{3}{c}{**Scattering Parameters**} \\ \hline Scattering amplitude (a) (mm\({}^{-1}\)) & 2.29 & 3.11 \\ Scattering power (b) & 1.2 & 1.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Normal versus tumor parameters used for simulations [37].  
Figure 4: Heterogeneous phantom geometry in x–z plane.  
Both phantoms contained a P4 silicone rubber base and P4 silicone activator (Eager Polymers, Chicago, IL, USA). Anatase titanium (IV) oxide and water-soluble Nigrosin ink (Sigma-Aldrich, St. Louis, MO, USA) was added to the phantoms for scattering and absorption features [38]. The inclusion also contained NGB ink (Sigma-Aldrich, St. Louis, MO, USA) as an additional absorbing component.
## 2 Governing Equation of Stay Cable with Two HDR Dampers  
Figure 1 shows the considered taut cable model with two HDR dampers. The \(x\)-axis coincides with the longitudinal axis of the stay cable. The combination of two HDR dampers is located at \(l_{1}\) and \(l_{2}\) distances from each cable end. The mass per unit length of taut cable, cable length, bending stiffness and tension force in the stay cable is denoted as \(m\), \(L\), \(EI\), and \(S\), respectively. Since the rubber pads in the HDR dampers have the hysteretic characteristic, so the damping force does not depend on the frequency [14] and can be displayed as:  
\[f_{j}(x,t)=K_{j}.(1+i.\lambda_{j}).u\left(l_{j},t\right),\,(j=1,2) \tag{1}\]  
where \(K_{j}\) is the spring factor of \(j\)th damper; \(\lambda_{j}\) the material loss factor of \(j\)th damper; \(i^{2}=-1\); \(u(l_{j},t)\)--the displacement of stayed-cable in location attached \(j\)th damper at time \(t\).  
It is assumed that the stay cable tension is much greater than its self-weight and the frictional force between the air and stay cable is negligible. The governing equation of in-plane cable motion can be expressed by:  
\[m.\frac{\partial^{2}u}{\partial t^{2}}+EJ.\frac{\partial^{4}u}{\partial x^{4}}-S.\frac{\partial^{2}u}{\partial x^{2}}=\ f_{j}(x,t).\delta(x-l_{j}) \tag{2}\]  
where \(\delta(.)\): the Dirac delta function is to simulate the concentrated damping force \(f_{j}(t)\) (\(j=1,2\)) located at \(x=l_{j}\).  
By using the variables separation of Fourier, the result of Equation (2) can be described by:  
\[u(x,t)=X(x)T(t) \tag{3}\]  
The frequency equation of a stay cable with two HDR dampers can be derived by applying the boundary conditions and assuming equilibrium in the vertical direction at the positions where the dampers are attached, as shown in Equation (4). In which, \(\eta\) is the stay cable frequency at \(j\)th mode; setting \(k\) as a cable eigenvalue with complex form, \(k\) can be defined as Equation (5).  
\[\begin{array}{l}\left(\frac{EJm}{S^{2}}\eta^{2}-1\right)^{2}\left[\begin{array} []{c}\cot(\eta\sqrt{\frac{m}{S}}l_{1}).\cot[\eta\sqrt{\frac{m}{S}}(L-l_{1}-l_ {2})\Big{]}+\cot(\eta\sqrt{\frac{m}{S}}l_{1}).\\ \cot(\eta\sqrt{\frac{m}{S}}l_{2})+\cot(\eta\sqrt{\frac{m}{S}}l_{2}).\cot[\eta \sqrt{\frac{m}{S}}(L-l_{1}-l_{2})\Big{]}-1\end{array}\right]\\ -\left(\frac{EJm}{S^{2}}\eta^{2}-1\right)\left[\begin{array}{c}\left(\cot[ \eta\sqrt{\frac{m}{S}}(L-l_{1}-l_{2})]+\cot(\eta\sqrt{\frac{m}{S}}l_{2})\right) \frac{K_{1}(1+i\lambda_{1})}{\eta\sqrt{mS}}\\ +\left(\cot[\eta\sqrt{\frac{m}{S}}(L-l_{1}-l_{2})]+\cot(\eta\sqrt{\frac{m}{S} }l_{1})\right)\frac{K_{2}(1+i\lambda_{2})}{\eta\sqrt{mS}}\end{array}\right]\\ =-\frac{K_{1}(1+i\lambda_{1})}{\eta\sqrt{mS}}.\frac{K_{2}(1+i\lambda_{2})}{ \eta\sqrt{mS}}\end{array} \tag{4}\]  
\[k_{j}=\sigma_{j}+\phi_{j}.i=\left(-\xi_{j}+i.\sqrt{1-\xi_{j}^{2}}\right)\frac {\eta_{j}}{\omega_{1}^{0}} \tag{5}\]  
\[\xi_{j}=\sqrt{1+\frac{\phi_{j}^{2}}{\sigma_{j}^{2}}} \tag{6}\]  
where \(\omega_{1}^{0}\) is the cable natural frequency without damper devices; \(\xi_{j}\), is the cable damping ratio at \(j\)th mode.  
Figure 1: A stay cable with two HDR dampers.
## 4 Results and Discussion
### Results Analysis
#### 4.2.4 Dataset 3  
When running the FIS with the last dataset (Figure 12), there are some false alarms during the first half of the acquisition (before 21:06), specially for the Flow Restriction, which was constantly alarming. Those alarms are mainly due the CP set point being defined as 7900 psi, which is an accurate value for the second half of the acquisition, but not for the first half, where the pressure was higher due to a higher pump flow rate. This indicates that this FIS will be more accurate if also adjusted by the flow rate of the fluid being pumped inside the CT string and through the PDM, which could not be done due to unavailability of accurate pump rate data in the available data. During the second half of the acquisition (21:06 onwards) the only stall event that occurred in reality was successfully detected by the FIS. There was an increase in pressure at 21:14; however, this event did not activate the PDM Status as no _WOB_ was applied, and the Flow Restriction output was activated as expected.  
The FIS detected 3 out of 3 stalls present in this data.