esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Chemical Composition of Bio- and Petroleum-Based Modifiers on Asphalt Binder Rheology
## 3 Results
### Chemical Characterization of Modifiers  
#### 3.1.1 Elemental Analysis  
The results from elemental analysis are presented in Table 2. Modifiers A, D, and E show similar elemental composition of carbon, hydrogen, nitrogen and oxygen with additional sulfur in E (0.33%). Modifier K has slightly higher carbon (79.7%) and hydrogen (12.7%) content but is lower in oxygen (5.7%) compared to A, D, and E. Modifier K also has higher sulfur (0.98%) content than E. Modifier C has relatively higher nitrogen (3.5%) and oxygen (14.9%) and is low on Carbon (70.1%) in comparison to A, D, E, and K. Modifier G possesses very different composition compared to all other modifiers. Its elemental oxygen (33.1%) and nitrogen (9.0%) contents are the highest while the carbon (47.9%) is the lowest compared to other modifiers.
## 3 Solution Approach
### Re-Optimization Stage and VNS-TA Algorithm
#### 3.2.1 Idea and Process
Local optimization process: based on the neighbor path selection mechanism, select two paths in \(x_{neighborhood}\) for the local search and optimization successively and obtain the local optimal solution \(x_{local\_optimal}\). if\(f\Big{(}x_{local\_optimal}\Big{)}\leq f(x_{current})\) \(x_{current}=x_{local\_optimal}\), \(k=1\) if\(f(x_{current})<f\Big{(}x_{current\_optimal}\Big{)}\) \(x_{current\_optimal}=x_{current}\) endif else Change in the threshold acceptance range: \(T=f\Big{(}x_{current\_optimal}\Big{)}\)_-rho_ if\(f\Big{(}x_{local\_optimal}\Big{)}-f\Big{(}x_{current\_optimal}\Big{)}<T\) \(x_{current}=x_{local\_optimal}\),\(k=1\) else \(k=k+1\) endif endwhile Change of the tolerance coefficient: rho = rho-phi endfor //Output: Re-optimized scheme. ```  
**Algorithm 2**The VNS-TA algorithm in the re-optimization stage.
## 5 Discussion and Future Perspectives
### Future Research Prospects  
Biogas upgrading technologies require numerous improvements, as they still constitute an emerging field that needs further investigation. Future research should focus on reducing costs and enhancing the efficiency of these technologies to address economic and environmental challenges. PtM offers a valuable opportunity to store surplus renewable energy in the NG grid, thereby enhancing energy security. While industrial-scale tests and ongoing process optimization are currently underway, further investigations are necessary to substantiate and refine the viability of this innovative technology.  
The primary determinant of the implementation of a given technology will be the capital expenditure cost. These technologies have the potential to enhance the economic performance of biogas plants and, more broadly, the renewable energy sector, thereby bolstering energy security. Predicting the dominant technology in biogas upgrading is challenging, as it heavily relies on capital and operational expenditure costs, as well as incentives mandated by energy grid providers and governmental decisions. At present, these technologies are not sufficiently mature to replace traditional ones. Nevertheless, considering them as \"complementary technologies\" for use in pretreatment steps to support conventional technologies holds the potential for reducing operational costs. Future research and innovations must also guarantee the enduring viability of the processes.
## 1 Introduction
Most of the existing literature focuses on the optimization of the energy consumption in electric vehicles; however, achieving real-time prediction and optimization of energy consumption is a challenging task, necessitating the need for the development of DPR-based EMS. DPR may enable driving suggestions and the identification of traffic congestion patterns along with suggestions of energy-efficient routes. It may also lead to the implementation of an optimized EMS to control the vehicle speed and realize eco-driving. Minimizing the energy consumption of an EV considering a given route and traffic situation is one of the goals of eco-driving. However, due to several technical, computational, and operational challenges, the realization of eco-driving becomes difficult. As decisions when driving must be made within milliseconds, real-time-applicable DPR and EMS are required. In the present work, real-time-applicable EMS is integrated with DPR to overcome the challenges faced in achieving optimality in real-time scenarios. The novelty of the contribution lies in the development of the two-layer DPR, which is capable of organizing unknown driving patterns into known representative driving patterns (RDPs) by recognizing the patterns in terms of road and traffic conditions. Longer and shorter sampling horizons have been chosen for road and traffic conditions, respectively. RDPs are used to represent road and traffic conditions for a certain driving route. The characterization of real and standard driving patterns has have been performed in terms of four features, namely average speed, positive and negative acceleration, and number of stops. Unknown patterns are classified into the known RDPs by employing a multi-layer perceptron neural network (MLPNN) with six classes, namely highway high and low traffic, sub-urban high and low traffic, and urban high and low traffic. A sequential quadratic programming (SQP)-based energy management strategy (EMS) is considered for the optimization of energy consumption of an EV. The feasibility of DPR is verified by subjecting the classifier to an unknown mixed pattern generated as a result of combining different standard cycles. The DPR is also evaluated for two test route data points to evaluate the classification accuracy with changing routes and data collection timings. The impact of road and traffic on energy consumption prediction has been evaluated on the different test data. A comparison analysis of results has also been performed to indicate that an EMS with DPR can lead to more energy savings. This paper is organized as follows. In Section 2, DPR is developed for roadway type and traffic recognition. Section 2 discusses EMS problem formulation using sequential quadratic programming (SQP). The results and discussion are presented in Section 3, followed by the conclusion in Section 4.
## 3 Results
### Effective Radiation Coefficient
For this purpose, an approach based on the Fourier number (\(Fo\)) according to Equation (21) is assumed  
\[Fo=\frac{\lambda_{S,r}}{(1-\varepsilon)\rho_{S}c_{S}}\frac{\tau}{l_{c}^{2}} \tag{21}\]  
whereby the characteristic length \(l_{c}\) as defined in Equation (22) represents through its novel formulation all relevant geometric aspects of such a storage system  
\[l_{c}=r_{2}-r_{1}=\frac{2}{a_{V}}\sqrt{\varepsilon}\bigg{(}\frac{1}{\sqrt{x}}- 1\bigg{)} \tag{22}\]  
The Fourier number as well as the characteristic length defined in this way include all relevant geometric-, process- and material-relating aspects of the honeycomb structure with integrated heating wires. With this dimensionless quantity a comparison between the iteratively determined effective radiation coefficient \(k_{rad}\) to the pure radiation coefficient \(C_{rad}\) are conducted. Results based on the variation matrix from Table 2 are shown in Figure 4.  
The resulting ratios show clearly a correlation between the effective to pure radiation coefficients and pointed out the usability of the novel formulation regarding the characteristic length inside the Fourier number. As can be seen at Fourier numbers \(>10^{2}\) the heat transport is dominated by thermal radiation without influence of thermal conduction within the honeycomb structure. However, for lower Fourier numbers higher iteratively determined effective radiation coefficients \(k_{rad}\) are resulting compared to the pure radiation coefficient \(C_{rad}\). This is due to the fact, that the heat transport in the porosity model refers to a homogeneous honeycomb temperature, whereas the detailed model calculates the geometry-resolved temperature distributions in the honeycomb structure and thus captures effects of local thermal resistances. Within the porosity model, this leads especially for thick-walled honeycomb structures or configurations with low heating wire assignments to an underestimation of the heat transport only on the basis of the pure radiation coefficient \(C_{rad}\) resulting in higher \(k_{rad}/C_{rad}\) ratios at \(Fo<10^{2}\). Based on the presented results, it is obvious that a detailed capture of the heat transport mechanisms is achieved by the effective radiation coefficient \(k_{rad}\). For implementation inside the porosity model, a power law approach according to Equation (23) is used  
\[\frac{k_{rad}}{C_{rad}}=1+0.07276\;Fo^{-0.903} \tag{23}\]  
Figure 4: Ratio of effective to pure radiation coefficients (\(k_{rad}/C_{rad}\)) depending on the Fourier number: results from variation study vs. regression function. which allows through its parameterized formulation via the Fourier number a wide consideration of different configurations. This novel correlation regarding the effective heat transport opens up time-efficient calculations for such electrical heated honeycomb structures without losing accuracy. Therefore, only the pure radiations coefficient must be determined and adapted according Equations (22) and (23) towards the investigated honeycomb-heating-wire configuration.
# Novel MIA-LSTM Deep Learning Hybrid Model with Data Preprocessing for Forecasting of PM2.5
## 2 Related Work
### Missing Values, Imputation, and Forecasting
The missing values were found by building a model based on a complete instance of the dataset excluding missing values; the nonparametric iterative imputation algorithm (NIIA) method as an extension to the solution of imputation using incomplete instances of the dataset was proposed [25]. Ref. [15] compared six imputation models and showed that various KNN imputation methods were superior to simple imputation techniques, such as mean or median imputation techniques. A hybrid imputation method proposed in [26], called KI, is a combination of KNN and iterative imputation and obtained good results compred to a simple KNN method. For NOx prediction, an LSSVM-based iteration strategy was utilized, which improved the accuracy of pollutant prediction while reducing time complexity and ensuring prediction speed and accuracy [27]. The missing values in the simple LSTM model were filled up by zeros, and the author proposed another LSTM model where the missing values were interpolated by [PERSON]'s interpolation [28]. It was proved by imputing missing attribute values that the suggested spatial-temporal (CNN BILSTON-IDW) prediction approach may successfully tackle data imputation challenges for air quality modeling, hinting that further interpolation can be improved using a multivariate dataset [29]. The missing values were replaced with a linear interpolation method in preprocessing stage followed by the prediction of multiple pollutants using the M-ConvLSTM model, which performed better compared with single output models [30]. With the use of the Keras development library, the complexity of RNN implementations has been extensively reduced, enabling noncomputer scientists to use DL without coding overhead [31]. The LSTM model shows satisfactory results and applies to time series challenges, such as forecasting wide area pollution from multiple stations and multiple pollutants. It could effectively predict individual source emissions or model source apportionment under different criteria.
# A Heterogeneous Robotic Swarm for Long-Term Monitoring of Marine Environments
## 3 Communication
### Surface Wireless Communication  
Primary surface information exchange between aPads and aMussels is based on WiFi communication. Since the goal of the system is long-term monitoring, robustness plays a key role in maintaining the usability of the system. Usually, WiFi communication is set up in an Access Point--Client configuration, meaning that several clients connect to one available access point. This introduces a single point of failure in which surface communication in the swarm could fail if the access point were to stop working. Additionally, in order to communicate over WiFi, all agents would need to be in the communication range of the single access point, thus reducing the operating area of the monitoring system.  
For these reasons a decentralised approach to mobile networking using a wireless mesh network (WMN) solution was chosen and implemented. WMN is a form of wireless ad-hoc network (WANET), meaning that it does not rely on a fixed network infrastructure [15] such as the aforementioned access points. Instead, it relies on each node in the network routing the network traffic for itself as well as all other nodes.  
Custom firmware based on OpenWRT 17.01.04 was implemented on the aPad's dual-interface WiFi router. OpenWRT is an operating system targeted for embedded devices allowing custom user configuration easily tailored for any application. Since the aMussel does not have the capability of using mesh routing protocols, each aPad is configured to operate as an access point on the 2.4 GHz interface, giving the aMussels access to the network. Meanwhile, the 5 GHz interface is kept separate and reserved for mesh routing for the aPads, providing better throughput. All traffic between agents which  
Figure 10: Kinect sensor and pan mechanism mounted on the aPad platform-design (**left**) and realisation (**right**).  
are not connected to the same access point needs to be routed over the mesh interface. The implemented network topology is shown in Figure 11.  
Routing--specifying how wireless routers communicate and establish routes that packets take on their way to their destination--is governed by the mesh routing protocol, and the implementation used on the aPad is open80211s, chosen by analysing the results of authors in [16]. With a smaller mesh network size as present here with only five aPad nodes, the proposed routing protocol noticeably outperforms similar routing protocols.
# Simulation Study on Nanofluid Heat Transfer in Immersion Liquid-Cooled Server
## 3 Data Processing and Analysis
### Analysis of the Effect of Nanoparticle Volume Fraction \(\alpha\) on Heat Transfer in Fluid Flow  
The thermal conductivity of the Al-FC40 nanofluid with a nanoparticle volume fraction \(\alpha\) of 1-5% was analyzed, and the results are shown in Figure 9, where the growth rate of \(\lambda_{f}\) relative to the base fluid FC-40 gradually increases with increasing \(\alpha\). The thermal conductivity of the Al-FC40 nanofluid increased by 3.03%, 6.12%, 9.27%, 12.49%, and 15.77% compared to the base fluid with \(\alpha\) of 1-5%, respectively.  
Figure 10 shows the maximum and minimum temperature data of the GPU surface of the server component at different volume fractions of Al nanoparticles. In the process of increasing \(\alpha\), the maximum and minimum GPU temperatures continue to decrease. \(\alpha\) = 1%, the maximum GPU surface temperature is 370.1 K, and the minimum temperature is 327.8 K; \(\alpha\) = 5%, the maximum GPU surface temperature is 364.3 K and the minimum temperature is 325.4 K. The decrease in the maximum GPU temperature from 1% to 5% of \(\alpha\) is 1.6%; the decrease in the minimum GPU temperature is 0.7%, which shows that the increase in \(\alpha\) can effectively reduce the surface temperature of server components.  
Figure 8: \(Nu\) and \(f\) under flow and heat transfer of five types of nanofluid.  
Figure 9: Changes in \(\lambda_{f}\) with \(\alpha\).
# Influence of Water Addition on the Latent Heat Degradation of Sodium Acetate Trihydrate
## 3 Aging Study of Sodium Acetate Trihydrate  
### Experimental Validation of the Empirical Equations  
The validity of the previous equations was verified by the application of heating and cooling cycles on a fully solid (between 20 and 28 \({}^{\circ}\)C) and fully liquid (between 63 and 70 \({}^{\circ}\)C) SAT60% sample. During these tests, the supercooling state was not observed in this sample as the necessary conditions were not reached. However, the supercooling degree in the SAT58% was increased by the addition of water (reduction of the temperature of solidification) as noted by [PERSON] et al. [4]. Experimentally, the SAT58% sample did not crystallize even at a temperature as low as 5 \({}^{\circ}\)C. As the solidification was not obtained by a temperature reduction, it was decided to trigger it by an external mean (insertion of a copper welding rod), as presented in Figure 4.  
Figure 3: Method for crystallization onset temperature determination.  
The crystallization of the supercooled SAT was observed after the introduction of this additional metal rod. In addition, it continues appearing in the following cycles even after the rod had been removed. As SAT may corrode copper [26], some particles were assumed to be spread in the liquid PCM. These particles may act as nucleating agents as a new surface was provided from where the nuclei would grow as lower energy is required [3]. Therefore, the addition of a small number of particles can strongly promote the solidification of the SAT. This repeatable crystallization allows performing preliminary thermal cycles with a phase change. The values of the latent heat, completed by the specific heats measured for the SAT60% (solid and liquid) and the SAT58% (supercooled), are given in Table 2. Indeed, the characterization of SAT58%, which was performed before the triggering of the solidification by a metal rod, only gave properties for the supercooled state. The uncertainties were calculated to include 99.7% of the values (\(\pm\) 3\(\sigma\)). The number of experiments for each measurement was also added.  
The measured specific heat was found consistent with the literature data [25] as a difference of less than 10% was found regarding Equation (1). The same observation was done for the latent heat determined from Equation (2) (maximal difference 2% regarding Equation (3) from the literature). Nevertheless, only a few experiments could be performed, as they may induce the aging of the sample.
# Gaslike Social Motility: Optimization Algorithm with Application in Image Thresholding Segmentation
## 4 GSM Algorithm Application to Threshold Segmentation
### Minimum Cross Entropy  
The concept of directed divergence, also known as cross-entropy (CE), was introduced by [PERSON] in 1968 [PERSON] (1968). CE is an information-theoretic (IT) measure used to quantify the information difference between two probability distributions (PDs). The following expression defines CE:  
\[D(B,C)=\sum_{k=1}^{N}b_{k}log\frac{b_{k}}{c_{k}} \tag{7}\]  
From Equation (7), let \(B=\{b_{1},b_{2},\ldots,b_{N}\}\) and \(C=\{c_{1},c_{2},\ldots,c_{N}\}\) represent two distinct probability distributions (PDs) of the same set. The information-theoretic (IT) distance between \(B\) and \(C\) is denoted by \(D\). The Minimum Cross-Entropy (MCE) quantifies the statistical difference in uncertainty related to the experimental outcomes transmitted from \(C\) to \(B\). In image segmentation, the prior distribution \(B\) represents the available knowledge about the \"correct\" solution. The Minimum Cross-Entropy Thresholding (MCET) method [PERSON] (1968) applies MCE to image thresholding by minimizing the cross-entropy between the original image and the thresholded image. A lower cross-entropy value indicates reduced uncertainty and greater homogeneity [PERSON] (1968). In image thresholding and segmentation, a set of thresholds \(B=\{th_{1},th_{2},\ldots,th_{nt}\}\) is selected from the image histogram \(h\). The threshold values \(th\) are chosen to minimize the MCET result between the thresholded image \(I_{th}\) and the original image \(I_{or}\). For example, in the case of single-threshold segmentation (\(th=th_{1}\)), the thresholded image \(I_{th}\) is processed as follows:  
\[I_{th}(i,j)=\begin{cases}\mu(1,th_{1})&if\,I_{or}(i,j)<th_{1}\\ \mu(th_{1},L+1)&\,I_{or}(i,j)\geq th_{1}\end{cases} \tag{8}\]  
The term \(L=255\) represents the maximum intensity value in the histogram for an 8-bit grayscale image. Equation (8) can be extended to accommodate multiple thresholds(\(th\)). Given the histogram \(h\) of the original image, after normalization, the value \(u\) for a specific range bounded by \(a\) and \(b\) is calculated as follows:  
\[\mu(a,b)=\frac{\sum_{i=a}^{b-1}ih(i)}{\sum_{i=a}^{b-1}h(i)},i=1,2,\ldots,L \tag{9}\]  
To ensure accurate image segmentation, the Minimum Cross-Entropy Thresholding (MCET) method must be applied to digital images. The MCET is computed for a single threshold using the approach developed by [22], as shown in the following equation:  
\[D(th)=\sum_{i=1}^{th_{1}-1}ih(i)log\frac{i}{\mu(1,th_{2})}+\sum_{i=th_{1}}^{L} ih(i)log\frac{i}{\mu(th_{1,L+1})} \tag{10}\]  
This aims to seek the best set of thresholds that minimizes the cross-entropy.  
\[th_{opt}=argmin_{(th)}(D(th)) \tag{11}\]
# Modeling Population Dynamics and Assessing Ecological Impacts of Lampreys via Sex Ratio Regulation
## 4 Adaptive Sex Ratio Impact Model (ASRI Model)
### Results and Analysis of Modeling the Role of Sex Change in the Food Chain  
We use the predator-prey model established previously, and the experimental results are shown and analyzed.  
The first is an experimental simulation of a scenario in which lampreys serve as prey, and the results obtained are shown in Figure 10.  
The left picture of Figure 10 shows the trend of the male proportion of lampreys over time after predation, which corresponds to the trend of the seven-gill lampreys and predator populations over time shown in the right picture of Figure 10. It can be seen that, as the number of lampreys increases, the male proportion of the lampreys shows a gradual decline and the number of predators rises with a time lag; then, the increase in the number of predators results in a large number of lampreys being preyed upon. There was  
Figure 9: Number transfer relationships between lampreys as predator and prey.  
a significant drop in lampreys populations and a rise in male lampreys numbers, and over time, predator numbers also fell swiftly due to reduced food availability.  
Subsequently, there was a simulated experiment where lampreys functioned as predators, with the outcomes depicted in Figure 11.  
The left panel of Figure 11 shows the trend in the proportion of male sea lampreys as predators over time, which matches the trend in the number of sea lampreys over time shown in the right panel of Figure 11. Moreover, it can be observed that as the number of prey increases, the proportion of males in sea lampreys gradually decreases, while the number of sea lampreys themselves rises. There is the same time lag, and then the increase in the number of sea lampreys leads to a large number of prey being preyed upon and a  
Figure 11: Interactions between lampreys and their prey.  
Figure 10: Interactions between lampreys and predators.  
sharp decrease in numbers, while the proportion of male sea lampreys rises. After a period of time, the number of prey also gradually increased due to the decrease in the number of sea lampreys.  
In summary, we have experimentally obtained conclusions about the effects of variable sex in lamprey populations on the ecosystem. Changes in lamprey sex ratios have important effects on the food web, and changes in the sex ratio of lamprey populations indirectly affect species upstream and downstream of their food chain by altering reproduction rates and population sizes. For example, population growth due to an increase in the proportion of females may increase predation pressure on downstream prey species while providing more food resources for upstream predators when food conditions become more abundant.  
In addition, population dynamics due to changes in sex ratios may affect other species, such as parasites and species in symbiotic relationships. These important effects will be discussed in the subsequent sections, and in the next section, we will examine their implications for ecosystem stability.