esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Biodiesel has emerged as an alternative to traditional fuels with the aim of reducing the impact on the environment. It is produced by the esterification of oleaginous seeds, animal fats, etc., with short-chain alcohols in an alkaline solution, which is one of the most commonly used methods. This increases the oxygen content (from the fatty acids) and promotes the fuel to burn faster and more efficiently. The accurate quantification of biodiesel is of paramount importance to the fuel market due to the possibility of adulteration, which can result in economic losses, engine performance issues and environmental concerns related to corrosion. In response to achieving this goal, in this work, synergy interval partial least squares (siPLS) algorithms in combination with Raman spectroscopy are used for the quantification of the biodiesel content. Different pretreatment methods are discussed to eliminate a large amount of redundant information of the original spectrum. The siPLS technique for extracting feature variables is then used to optimize the input variables after pretreatment, in order to enhance the predictive performance of the calibration model. Finally, the D1-MSC-siPLS calibration model is constructed based on the preprocessed spectra, the selected input variables and the optimized model parameters. Compared with the feature variable selection methods of interval partial least squares (iPLS) and backward interval partial least squares (biPLS), results elucidate that the D1-MSC-siPLS calibration model is superior to the D1-MSC-biPLS and the D1-MSC-iPLS in the quantitative analysis of adulterated biodiesel. The D1-MSC-siPLS calibration model demonstrates better predictive performance compared to the full spectrum PLS model, with the optimal determination coefficient of prediction (R\({}^{2}\)\(p\)) being 0.9899; the mean relative error of prediction (MREP) decreased from 9.51% to 6.31% and the root-mean-squared error of prediction (RMSEP) decreased from 0.1912% (\(\
icefrac{{p}}{{\it v}}\)) to 0.1367% (\(\
icefrac{{p}}{{\it v}}\)), respectively. The above results indicate that Raman spectroscopy combined with the D1-MSC-siPLS calibration model is a feasible method for the quantitative analysis of biodiesel in adulterated hybrid fuels.  
dulteration; fatty acid methyl esters; feature variable extraction; combined preprocessing; synergistic interval partial least squares +
Footnote †: journal: Applied Sciences
# Research on Cloud Task Scheduling Algorithm with Conflict Constraints Based on Branch-and-Price
## 5 Cloud Task Scheduling Algorithm Based on Branch-and-Price
### Column Generation Algorithm  
#### 5.2.1 Algorithmic Procedure  
One of the cores of branch-and-price algorithms is to use column generation algorithms to solve the optimal solution of the Lagrange relaxation problem for each node. The solution process for the column generation algorithm on each node is as follows:  
Step 1 Judge whether the current node is a root node. If it is a root node, the feasible columns generated by the initial solution are used as the initial columns of the node. Otherwise, use the feasible columns in the parent node that satisfy the branch constraint as the initial columns of the current node to obtain RMP.  
Step 2 Solve RMP and obtain a set of dual solutions, then transfer the dual solutions to SP.  
Step 3 Solve SP and calculate the lower bound \(m\cdot z_{i}(\mu)+\sum_{j\in\mathcal{J}}\mu_{j}\) of the Lagrangian relaxation problem. Judge whether the lower bound is equal to the upper bound, i.e., the optimal solution of RMP. If they are equal, the algorithm is terminated. Otherwise, add the columns generated by SP to RMP and skip to Step 2.
## 4 Discussion
### Involvement of Signaling Pathways
Studies consistently demonstrated that strontium-doped materials significantly stimulate osteoblast activity, crucial for effective bone healing. Furthermore, strontium's impact on osteoclast activity, reducing bone resorption, aligns with its known benefits in osteoporosis treatment, as summarized in Figure 3.  
The literature, however, presents certain gaps. There is a notable lack of studies evaluating the exact release rates of strontium ions from biomaterials into biological media, complicating the establishment of safe and effective dosage ranges. Additionally, the specific signaling pathways through which strontium exerts its effects--such as Wnt/\(\beta\)-catenin, BMP-2, Runx2, and ERK--have not been comprehensively explored across all relevant studies. The understanding of how these pathways interact in the presence of strontium-doped biomaterials remains incomplete. Future research should aim to address these gaps by investigating the release kinetics of strontium ions in various biological environments and focusing on the detailed mechanisms of cell signaling pathways activated by strontium. This would enable the optimization of strontium concentrations in biomaterials, ensuring both efficacy and safety. Such advancements could significantly improve the design and application of strontium-doped biomaterials for clinical use in bone repair and regeneration, ultimately leading to more effective and reliable treatment options for patients with bone-related conditions.
# Hierarchical Model Updating Method for Vector Electric-Propulsion Satellites
## 3 Method
### Least-Squares Method
The iteration form of \(\mathbf{x}\) is  
\[\mathbf{x}_{0}^{(k,1)}=\mathbf{x}_{0}^{(k,0)}+\Bigl{(}\mathbf{H}^{T}\mathbf{H}\Bigr{)}^{-1}\bm {H}^{T}\Bigl{(}\bar{y}^{(k),tele}-\bar{y}^{(k,0),sim}\Bigr{)} \tag{48}\]The root-mean-square error (RMSE) of telemetry variables and the relative error of state variables are calculated to evaluate the efficiency of the model updating method:  
\[rmse=\sqrt{\frac{1}{mf}{\sum\limits_{j=1}^{m}{\sum\limits_{i=0}^{f}{\left[{{{{\tilde \tilde{y}}}_{j}^{sim}}(t_{i})-{{\tilde{y}}_{j}^{tele}}(t_{i})}\right]^{2}}}}} \tag{49}\]  
\[x_{error}=\frac{{\left|{{x_{est}-{x_{real}}}}\right|}}{{{x_{\max}}-{x_{\min}}}} \times 100\% \tag{50}\]  
where \(x_{est}\) is estimated value, \(x_{real}\) is real value, and \(x_{\max}\) and \(x_{\min}\) define the range of \(x\). Repeat this process until \(rmse(k)\) is less than a threshold,  
\[rmse(k)<\varepsilon \tag{51}\]  
After the calculation of all groups is completed, the working condition of the earlier group has changed, so it is necessary to recalculate the \(rmse(k)\) of each group. Furthermore, the sum of the telemetry errors of each group is calculated, and the errors \(rmse_{total}\) of all the telemetry variables in the global period are calculated. Check whether \(rmse_{total}\) is within the range of the threshold \(\varepsilon_{total}\). If the accuracy is satisfied, output the result and; otherwise, revise from Group 1 again until meets the accuracy requirement.
# Automated Detection of Misinformation: A Hybrid Approach for Fake News Detection
## 3 Methodology
### Datasets
#### 3.1.1 FA-KES  
A Fake News Dataset around the Syrian War  
The FA-KES dataset focuses on the Syrian conflict, presenting a departure from typical fake news datasets [13]. It comprises articles from diverse media outlets, covering various aspects of the conflict. It was generated using a semi-supervised fact-checking approach, human contributors and crowd-sourcing techniques to match articles against a 'ground truth' reference from the Syrian Violations Documentation Center. With 804 labeled articles as true or fake, this dataset is invaluable for training machine learning models in credibility assessment. Its construction method offers a generalized framework for creating fake news datasets in other conflict-related domains.
# Lightweight CoAP-Based Bootstrapping Service for the Internet of Things
## 4 Experimental Results
### Performance Evaluation
#### 4.2.4 Memory Footprint  
In terms of the memory footprint in the Smart Object, Table 3 shows the size in bytes of several components of the bootstrapping service based con CoAP-EAP or PANATIKI. To obtain these values, we have compiled with memory optimization (-Os compiler option) a set of programs that includes incrementally different modules (EAP, network support, EAP lower-layer) to estimate the size with each new module. Clearly, the EAP state machine and the corresponding modules for enabling network connectivity are also the same.  
We observe that the total size of CoAP-EAP solution is slightly bigger than the PANATIKI one. The main reason is the inclusion of cantcoap library, which implements CoAP. Nevertheless we argue that CoAP implementation will surely be present in the majority of the Smart Objects as a common module to be used for other services in the Smart Object, not only for the bootstrapping service. Thus, this library, which adds 4.6 kB to the overall size, is likely to be reused. In fact, it is reasonable to think that PANATIKI will also need to include this library in real deployments to support other CoAP-based services (e.g., Ohba _et al._[16] requires CoAP also for pulling cryptographic material) giving a total of 102.9 kB + 4.6 kB = 107.5 kB.  
Let us illustrate this with an example. Let us assume we have a size of \(X\) kB available in the Smart Object. Part of that space is occupied by the operating system and the IP/UDP stack for communications. Of course, it is common in both alternatives (62.7 kB + 24.9 kB in our estimation in Table 3). The rest of the available space is to deploy services and applications in the Smart Object. For this, the Smart Object will ship a CoAP implementation (e.g., cantcoap) that will be used for the specific implementation of other services (e.g., a service to obtain temperature measurements). In our case, this CoAP implementation is cantcoap and subtracts 4.6 kB from the free memory available. If we want now to support bootstrapping, we need additional source code. PANATIKI subtracts 5.9 kB but since CoAP-EAP is re-using CoAP implementation it only subtracts 3.8 kB. Thus, CoAP-EAP would save 2.1 kB with respect to PANATIKI in the available space for other services in a more realistic case.
## 2 Message Replication in OppNets for Sensed Data Collection
### Handheld User Devices and OppNets for Sensed Data Collection
#### 2.2.3 Higher User and Node Population  
The impact of node population on message transmission overhead can be observed from the scenario in Figure 1, in which more message copies are generated when more relay nodes are present. With human population growing rapidly (at an average rate of 1.2% per annum over the last 50 years) and increasingly flowing towards urban areas [1], the number of portable handheld user devices in Smart Cities is expected to increase significantly in the years to come. Other nodes that can be connected with these devices in such environments (e.g., sensors, vehicles, and appliances) are likely to increase in number as well. Consequently, OppNets for sensed data collection are likely to consist of large number of nodes--a city for instance, with some hundred thousand (handheld and static) nodes. Hence, it would be preferable if message transmission overhead does not tend to rise at a high rate with user and device population.
## 2 Related Work
### Variability of Lap Time and Pit Stop Duration  
When analyzing real lap times, it can be observed that they are scattered around a mean value, since no driver can perfectly repeat a lap. For the analysis of this effect, other influences on the lap times have to be removed as far as possible, for example, the effects of tire degradation and burned fuel mass. Therefore, quadratic polynomials of the form \(t_{\text{lap,poly}}(l)=k_{2}\,l^{2}+k_{1}\,l+k_{0}\) are fitted to the real lap times \(t_{\text{lap}}\) for every stint. This is visualized in the upper part of Figure 2. Since we only want to include \"clean\" laps, the first two laps (heavily affected by the start of the race) and all laps that are affected by pit stops or full course yellow (FCY) phases have to be removed from the process. FCY phases are used by race control to reduce speed when there is danger on the race track.  
The driver-specific lap time deviations \(t_{\text{lap,dev}}\) shown in the lower part of Figure 2 can then be calculated by  
\[t_{\text{lap,dev}}(l)=t_{\text{lap}}(l)-t_{\text{lap,poly}}(l). \tag{5}\]  
The deviations are approximately normally distributed. Accordingly, [PERSON] [7], [PERSON] [5] and [PERSON] [6] model the lap time variability by adding a sample \(t_{\text{lap,var}}\) from a Gauss distribution  
Figure 2: Visualization of the lap time variability of Hamilton in Austin 2019. The deviations are the difference between real lap times and a quadratic polynomial fitted separately for every stint. The first two laps, as well as laps affected by pit stops or full course yellow phases, are not included in this process.  
with zero mean and driver-specific standard deviation (cp. Equation (6)) to every lap time \(t_{\text{lap}}\) in Equation (1).  
\[t_{\text{lap,var}}\sim\mathcal{N}\left(0,\sigma_{\text{lap,var}}^{2}\right) \tag{6}\]  
As can be seen in Figure 3, pit stop durations vary as well. The plot indicates that the pit stop durations of Mercedes are mostly very close to the minimum duration of the races, whereas Force India's pit stops often take significantly longer.  
The data demonstrate that a symmetric Gauss distribution would not fit for modeling this variability. Therefore, [PERSON] (1995) uses a log-logistic distribution, also known as Fisk distribution \(\mathcal{F}\), to model this effect, cp. Equation (7). The distribution is fitted individually for every team using the three parameters _shape_, _loc_, and _scale_[PERSON] et al. (2014). A sample from the distribution is then added to \(t_{\text{pit,in-lap/out-lap}}\) of Equation (1) when a driver performs a pit stop. [PERSON] et al. [PERSON] et al. (2014) state that they included the effect, but do not describe it any further.  
\[t_{\text{pit,var}}\sim\mathcal{F}\left(shape,loc,scale\right) \tag{7}\]
# Robotics Applications in the Hospital Domain:
## 4 Robotic Systems in Hospitals Settings
### Cleaning and Disinfection Robots  
Hospitals are places where infection prevention is of paramount importance, as the presence of pathogens can lead to nosocomial infections (hospital-acquired infections), which represent a significant risk to patient health. The problem of cleaning and disinfection in hospital environments is complex and multifaceted, requiring an integrated approach that combines strict protocols and ongoing training [47; 48]. There are many ultraviolet (UV) germicidal systems available that incorporate specific technological advances. However, the effectiveness and operational status of traditional UV germicidal irradiation (UVGI) systems and robots require continuous improvement, which includes improved UV-C efficiency, better coverage and penetration, automation and mobility, and the incorporation of security features [49]. These robots are widely used in hospital environments for disinfection according to standard procedures and play a crucial role in reducing nosocomial infections [49]. Due to the risks associated with exposure to UV light, it is essential to optimize these robots to ensure safety in areas occupied by patients and healthcare professionals. Although safe operation in the presence of humans requires guaranteeing the secure operation of UVGI robots in areas where patients and healthcare professionals are present, several measures are being implemented with the aim of optimizing the design and functionality of these devices. These include the incorporation of advanced safety mechanisms, including zones of inhibitions, scheduling Incidence analysis, implementation of a personal behavior monitoring system and alerts, and the development of enhanced UV-C control technologies [49]. The use of UVGIs is expected to increase significantly in the post-pandemic era. Remarkable advances have been made to improve the design and use of UVGI systems and robots in hospital and healthcare environments. These advances ensure effective and safe disinfection through technological improvements such as safety mechanisms, 'contactless' disinfection methods, and operational improvements such as Bluetooth and wireless communication. In addition, the possibility of incorporating UVC sensor modules for human-robot co-localization during the UVGI process has been studied, which could lead to the advancement of adjacent UVC sensors to reduce reflections and background radiation [50]. This growth will require more advanced designs for effective and safe disinfection, making hospital environments healthier and more resistant to pathogens [49]. Cleaning and disinfection robots have become essential tools for maintaining safe and sanitized hospital environments, especially in the post-pandemic era, where efficient disinfection is vital for preventing nosocomial infections [49; 51; 50]. Table 5 provides a comprehensive overview of the robots used in cleaning and disinfection systems, comparing them in terms of characteristics, advantages and disadvantages, and stage of development.
# A New Method of Geological Modeling for the Hydrocarbon Secondary Migration Research
## 1 Introduction
To construct a comprehensive three-dimensional subsurface reservoir model for oil and gas exploration, available data such as lithological well logs, seismic horizon or fault interpretations, and net-to-gross interpretations shouldbe incorporated. Presently, reservoir modeling primarily focuses on the facies or subfacies scale of architecture elements, while smaller-scale architectures play a crucial role in controlling the distribution and development of residual oil within reservoirs [25]. In previous research, a novel geological modeling software called _DMatlas_ was developed [26]. This software enables modelers to manually create diverse sand bodies based on sedimentary facies, adhering to the depositional process and fundamental geological principles. It allows for the creation of more than three types of microfacies within a sedimentary environment. However, given the existing data conditions, the previous methodology proposed by [PERSON] et al. [26] falls short in effectively characterizing the spatial structural characteristics of strongly heterogeneous reservoirs. This study centers around the fundamental characterization theory of significant reservoir architecture unit elements within diverse depositional environments. In particular, it provides detailed insights into three types of unit architecture elements, emphasizing their formation process and the principles governing sedimentary evolution. Subsequently, the study introduces the methodology and process of 3D reservoir modeling using unit architecture elements. To validate the approach, a case study is presented, employing a novel object-based stochastic modeling technique. The resulting reservoir model is illustrated through cross-sections showcasing the distribution of sand bodies, accompanied by statistical analysis to evaluate the modeling outcomes.
# Removal of Cochineal Dye Color through Atmospheric Pressure Plasma Discharge Jet
## 3 Results and Discussion
### Kinetic of Cochinal Dye Color Remotion  
The kinetic process of color removal can be divided into two distinct stages, as illustrated in Figure 5. The initial stage commences at the onset of the experiment and continues until the 2 min mark, while the subsequent phase begins at 2 min and persists until the 10 min mark. Based on the study reported by [PERSON] [13], it is believed that the removal of cochineal dye occurs through an oxidative process induced by the interaction with reactive species (RONS) generated by the APPJ discharge.  
The observed kinetics parameters showed in Table 2 indicate that, in the initial stage, a rapid color removal reaction occurs until reaching a critical point of dye exhaustion within 2 min. Subsequently, in the second stage, the color removal reaction proceeds more slowly due to the reduced number of dye molecules remaining. A study conducted by [PERSON] et al. [38] reported that color removal follows first-order kinetics. Accordingly, the experiment's data were fitted to the first-order color removal model using Equation (1). The model parameters obtained from the fitted equation are shown in Table 2. The kinetic parameter (k) transitions from a rapid rate (0.966) in the first stage to a slower rate (0.039) in the second stage, attributed to dye depletion and oxidative degradation induced by RONS. According to [PERSON] et al. [44]. (2020), the performance of FeSiBP amorphous alloys in degrading the dye Methyl Blue (MB) through a Fenton-like process was analyzed. The study found that the pH of the solution affects the degradation efficiency of the dye. It was observed that the degradation of MB was more effective in acidic conditions (pH 3), with almost 95% of the dye being degraded. However, under neutral conditions (pH 7), the degradation reaction barely occurred within the first 11 min, and only 17% of the dye was degraded.  
These findings have important implications in the field of textile dye wastewater treatment, as APPJ treatment shows promise as an efficient and environmentally friendly  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Exposure Time** & **Kinetic Parameters k (min\({}^{-1}\))** & **R2 (adj.)** \\ \hline
0–2 min & 0.96645 \(\pm\) 0.10845 & 0.9636 \\
2–10 min & 0.03919 \(\pm\) 0.00427 & 0.97684 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of first-order kinetic model for cochineal (woodlouse) dye degradation.  
Figure 5: The color removal of cochineal dye through atmospheric pressure plasma discharge jet (APFJ) follows a kinetic process consisting of two distinct stages.  
method to reduce dye pollution. However, this study has some limitations. Further research is required to explore the effects of different dye properties and experimental conditions on the removal process. Additionally, the study did not evaluate the toxicity or potential environmental impact of the oxidation products. Future research should address these limitations to provide a more comprehensive understanding of the dye removal process and its implications.