esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Enzymatic pretreatment (EP) is widely applied to increase the extraction yield of bioactive compounds [20]. In curcuminoid extraction, EP with \(\alpha\)-amylase and glucoamylase is a selective and effective method [21]. In another study, \(\alpha\)-amylase, glucoamylase, and a commercial mixture of enzymes containing cellulase and xylanase were considered to assist curcuminoids recovery [20; 21; 22]. The amount of pectin in turmeric reaches up to 6.3% \(w/w\)[23]; nonetheless, so far, pectinases have been little considered to improve the curcuminoids recovery from turmeric. The pulsed electric field (PEF) is the most recent applicant to compare with other methods. PEF application in food has been broadly researched. [24]. The extraction yield of polyphenol is much dependent on the material matrix [25]. When applying PEF to plant tissues and cells, electroporation will enhance the membrane permeability by rearranging lipids on the cell membrane; this resulted in greater access to a solvent to bioactive compounds inside the cell cytoplasm [26]. For that reason, PEF was mostly used to enhance the sensitive bioactive extraction because of ultra-short treatment time and non-thermal effect [27]. Energy consumption is one of the most important criteria when applying a new technique to the food industry [28]. An applied technique must meet the requirement to adopt legally, product safety, cost reduction, and enhance food quality [29]. PEF was considered the promising innovation method with ultra-fast treatment time (millisecond) and low energy consumption compared to UP and EP. The current research focused on curcuminoid recovery by applying different cell disintegration techniques on fresh and dried turmeric. The cell disintegration index (\(Z_{p}\)) after pretreatment was measured by impedance measurement according to the method of [PERSON] [30]. Curcuminoids were further extracted by a rapid shaking procedure and quantification took place by high-performance liquid chromatography (HPLC) to compare the effect of different pretreatments on the extraction yield of CUR, DMC and BDMC. In order to consider the energy consumption of all pretreatments, the specific energy input was discussed to compare the efficiency of the applied techniques and applicability in the food industry.
## 2 Materials and Methods
### Sampling Area and Real Samples  
The suggested analytical approach was utilized in a \"case study\" conducted in Greece and Albania. The samples were obtained from aquatic environments that are affected by aquaculture activity, notably fish farms or mussel farms, in addition to other human activities. In Greece, specific locations were chosen from three regions within the water bodies of Epirus. These regions include the Aracththos catchment area, the Louros watershed where freshwater fish farm units are located, and the Sayada Strip, which is renowned for seawater aquaculture. Within Albania, specific locations were chosen within the Butrint Lagoon near mussel farming facilities.
# Evaluation of GNSS-TEC Data-Driven IRI-2016 Model for Electron Density
###### Abstract  
The ionosphere is one of the important error sources that affect the communication of radio signals. The international reference ionosphere (IRI) model is a commonly used model to describe ionospheric parameters. The driving parameter IG12 of the IRI-2016 model was optimally updated based on GNSS-TEC data from 2015 and 2019. The electron density profiles and NmF2 calculated by the IRI-2016 model (upda-IRI-2016) driven by the updated IG12 value (IG-up) were evaluated for their accuracy using ionosonde observations and COSMIC inversion data. The experiments show that both the electron density profiles and NmF2 calculated by upda-IRI-2016 driven by IG-up show significant optimization effects, compared to the IRI-2016 model driven by IG12. For electron density, the precision improvement (PI) for both MAE and RMSE at the Beijing station exceed 31.2% in January 2015 and 16.0% in January 2019. While the PI of MAE and RMSE at the Wuhan station, which is located at a lower latitude, both exceed 32.5% in January 2015, both exceed 42.1% in January 2019, which is significantly higher than that of the Beijing station. In 2015, the PI of MAE and RMSE compared with COSMIC are both higher than 20%. For NmF2, the PI is greater for low solar activity years and low latitude stations, with the Wuhan station showing a PI of more than 11.7% in January 2019 compared to January 2015. The PI compared to COSMIC was higher than 17.2% in 2015.  
international reference ionospheric model; ionospheric electron density; F2 layer peak electron density; ionosonde; COSMIC
## 4 Proposed Method
### Proposed Method: AAD
Suppose that the shortest path connecting \(s_{1}\) and \(s_{6}\) is \(s_{1}\to s_{2}\to s_{3}\to s_{6}\), and the shortest path connecting \(s_{1}\) and \(s_{7}\) is \(s_{1}\to s_{4}\to s_{5}\to s_{7}\). The network topology in this example is shown in Figure 8. Then, since \(s_{1}\) has to transmit \(E_{7}(5)\) and \(z_{1}^{r}\) to \(s_{6}\) and \(s_{7}\), \(s_{1}\) will send \(\Omega_{1}^{r}=\langle s_{1},1,1,E_{k_{1}^{r}}(5),z_{1}^{r},s_{6}\rangle\) and \(\Omega_{1}^{r}=\langle s_{1},1,1,E_{k_{1}^{r}}(5),z_{1}^{r},s_{7}\rangle\) to \(s_{2}\) and \(s_{3}\), respectively. Since the sensors on these two paths work similarly, we only discuss the path \(s_{1}\to s_{2}\to s_{3}\to s_{6}\). When \(s_{2}\) receives \(\langle s_{1},1,1,10,8,s_{6}\rangle\), it first check its \(\lambda\)-hop proximity relationship to \(s_{1}\). Here, since we assume that \(\lambda=3\), the check can be passed. Thus, assuming that \(p_{2}=3\), \(s_{2}\) forwards \(\langle s_{1},9,2,10,h_{k_{2}^{r}}(8),s_{6}\rangle\) to \(s_{3}\). Similarly, if we assume that \(p_{3}=7\), after receiving \(\langle s_{1},9,2,10,h_{k_{2}^{r}}(8),s_{6}\rangle\), \(s_{3}\) sends  
Figure 7: The algorithm of verification procedure. \(\langle s_{1},3087,3,10,h_{k_{6}^{r}}(h_{k_{3}^{r}}(8)),s_{6}\rangle\) to \(s_{6}\). When \(s_{6}\) receives \(\langle s_{1},3087,3,10,h_{k_{3}^{r}}(h_{k_{2}^{r}}(8)),s_{6}\rangle\), it stores \(\langle s_{1},3087,10,h_{k_{6}^{r}}(h_{k_{3}^{r}}(h_{k_{2}^{r}}(8)),s_{6})\) in its local memory. Now consider that the sink wants to obtain \(d_{1}^{r}\). In our method, the sink has to perform two verifications on \(s_{6}\) and \(s_{7}\). Basically, because the verification performed on \(s_{6}\) is the same as the one performed on \(s_{7}\), we only describe the one performed on \(s_{6}\). From \(s_{6}\), the sink can obtain \(\langle s_{1},3087,10,h_{k_{6}^{r}}(h_{k_{3}^{r}}(h_{k_{2}^{r}}(8)))\rangle\). After performing the prime number factorization of the value 3087, the sink can know that \(3087=3^{2}\times 7^{3}\), which means that the second hop sensor and the third hop sensor on the path connecting \(s_{1}\) and \(s_{6}\) are \(s_{2}\) and \(s_{3}\), respectively. The sink also extracts \(\bar{d}_{j}^{r}=5\) from \(E_{k_{1}^{r}}(5)=10\). Then, the sink checks if \(h_{k_{6}^{r}}(h_{k_{3}^{r}}(h_{k_{2}^{r}}(8)))\) is equal to the value obtained by sequentially applying three keyed hash functions \(h_{k_{2}^{r}}(\cdot)\), \(h_{k_{3}^{r}}(\cdot)\), and \(h_{k_{6}^{r}}(\cdot)\) on \(z_{1}^{r}=8\). \(\bar{d}_{j}^{r}\) will be temporarily stored if the above check is passed and dropped otherwise. Finally, when all the co-authenticators are visited, the sink checks the consistency of \(\bar{d}_{j}^{r}\)'s obtained from different co-authenticators. Note that for the notational simplicity, we do not put additional subscript on \(\bar{d}_{j}^{r}\) to distinguish different \(\bar{d}_{j}^{r}\)'s obtained from different co-authenticators. \(\bar{d}_{j}^{r}\) is deemed to be genuine if they are consistent and bogus otherwise.
###### Abstract  
Precise Point Positioning (PPP), as a global precise positioning technique, suffers from relatively long convergence times, hindering its ability to be the default precise positioning technique. Reducing the PPP convergence time is a must to reach global precise positions, and doing so in a few minutes to seconds can be achieved thanks to the additional frequencies that are being broadcast by the modernized GNSS constellations. Due to discrepancies in the number of signals broadcast by each satellite/constellation, it is necessary to have a model that can process a mix of signals, depending on availability, and perform ambiguity resolution (AR), a technique that proved necessary for rapid convergence. This manuscript does so by expanding the uncombined Decoupled Clock Model to process and fix ambiguities on up to three frequencies depending on availability for GPS, Galileo, and BeiDou. GLONASS is included as well, without carrier-phase ambiguity fixing. Results show the possibility of consistent quasi-instantaneous global precise positioning through an assessment of the algorithm on a network of global stations, as the 67 th percentile solution converges below 10 cm horizontal error within 2 min, compared to 8 min with a triple-frequency solution, showing the importance of having a flexible PPP-AR model frequency-wise. In terms of individual datasets, 14% of datasets converge instantaneously when mixing dual- and triple-frequency measurements, compared to just 0.1% in that of dual-frequency mode without ambiguity resolution. Two kinematic car datasets were also processed, and it was shown that instantaneous centimetre-level positioning with a moving receiver is possible. These results are promising as they only rely on ultra-rapid global satellite products, allowing for instantaneous real-time precise positioning without the need for any local infrastructure or prior knowledge of the receiver's environment.  
precise point positioning; decoupled clock model; ambiguity resolution; multi-GNSS; multifrequency; kinematic Article
# Short-Term Travel Demand Prediction of Online Ride-Hailing Based on Multi-Factor GRU Model
## 6 Conclusions  
The research in this paper has significance for optimizing the supply relationship of online ride-hailing, reducing urban road traffic pressure, and reducing traffic pollution. Based on the predicted travel demand for online ride-hailing in the future, we can reasonably arrange the number of online ride-hailing opportunities, reduce unnecessary vehicle waste, and reduce traffic pollution, which is of great significance to the sustainable development of the city.  
This paper fills the gap in online ride-hailing travel demand prediction considering multiple factors. Based on the big-data analysis of online ride-hailing travel based on the SQL database, the time weight is reasonably set, and the impact of rainfall intensity on travel demand is considered at the same time. Combining the rainfall intensity and time travel characteristics with the deep learning GRU model, and analyzing the prediction results through various evaluation indexes, we can show that the prediction accuracy of the GRU model considering multiple factors proposed in this paper is better than earlier models.  
Additionally, there are some future developments proposed in this article:  
(1) Weight parameters can be set more accurately through big-data analysis.  
(2) In this paper, the travel characteristics of morning and evening peak hours and late-night hours have not been taken into account. These two factors can be taken into account in the weight-setting in the future.  
(3) In the future, geographic coordinates, such as setting the weight according to the travel demand of the travel area, can also be taken into account in the model.  
Conceptualization, Q.Q. and R.C.; methodology, Q.Q.; software, H.G.; validation, Q.Q., H.G. and R.C.; data curation, Q.Q.; writing--original draft preparation, Q.Q.; writing--review and editing, Q.Q.; visualization, H.G.; supervision, R.C.; project administration, R.C.; funding acquisition, H.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Program of Humanities and Social Science of Education Ministry of China (Grant No. 20 YJA630008) and the Ningbo Natural Science Foundation of China (Grant No. 202003N4142), the Natural Science Foundation of Zhejiang Province, China (Grant Nos. LY22G010001, LY20G010004), and the K.C. Wong Magna Fund in Ningbo University, China.
In recent years, the number of research articles focused on magnetic ferrite nanoparticles has increased significantly. Figure 1 shows the number of documents published since 1975 up to this date, with a growing interest in this research area. Among all ferrites without heavy metals in their structures, cobalt ferrite exhibits superior performance in wastewater treatment due to its high magnetic saturation and coercivity, which facilitate efficient separation and recovery using external magnetic fields [56,57]. This capability is particularly useful for wastewater treatment processes, as it allows the easy retrieval of cobalt ferrite-based adsorbents or catalysts after they have been used, reducing secondary pollution and enabling material reuse [58]. Cobalt ferrite's catalytic activity also sets it apart from other ferrites. In advanced oxidation processes, cobalt ferrite serves as a heterogeneous catalyst to generate reactive oxygen species (ROS) from oxidants like hydrogen peroxide or persulfate [59, 60, 61]. These ROS degrade organic pollutants in wastewater, such as dyes, pharmaceuticals, and pesticides. The catalytic efficiency of cobalt ferrite is higher due to the redox cycling of cobalt ions (Co\({}^{2+}\)/Co\({}^{3+}\)), which enhances the generation of ROS and accelerates pollutant degradation [62, 63, 64, 65]. In contrast, other ferrites often lack comparable catalytic activity or require additional modifications to achieve similar performance. This review provides an overview of the latest trends in the preparation of magnetic ferrite nanoparticles using the most common synthesis methods, with a focus on the resulting properties of the nanoparticles. The most promising strategies for modification and composite formation are presented, along with their effects on ferrite properties and applications in wastewater treatment. It is highlighted how the target pollutants depend on different ferrites and their modifications. Moreover, it is emphasized how ferrites can be used both as adsorbents and visible light-induced photocatalysts to decompose organic materials. Finally, the outlook and future trends for ferrite nanoparticle applications are discussed.
# Multi-Graph Assessment of Temporal and Extratemporal Lobe Epilepsy in Resting-State fMRI
## 4 Discussion
### Limitations and Future Work  
When interpreting the results of this study, certain limitations need to be acknowledged. Initially, it is important to note that the size of the cohort, particularly in the ETLE group (16 patients), reduces the statistical power of our results. Further studies with larger cohorts would narrow the confidence intervals in the evaluation of hub probabilities. Secondly, our study does not differentiate between left and right TLE and ETLE patients. This decision was made because of the small sample size and after ensuring there is no statistical significance in the metrics within the TLE and ETLE groups with left/right seizure onset hemispheres. However, graph-theory metrics may vary, and future investigation is encouraged to explore whether hub localization depends on the hemisphere of the epilepsy onset. Lastly, our evaluation focuses solely on static connectivity using linear and non-linear metrics to quantify correlation and causality between ROIs. Future research incorporating dynamic analysis of hubs could enhance our understanding of the stability of nodes, which play crucial roles in information flow within different types of graphs.
# Managing Redundancy in a Multiple-Robot Orienteering Problem for Equilibrium and Robustness
## 1 Introduction
### Contributions  
This paper, for the first time, simultaneously considers robustness and load balancing in orienteering tasks in multi-robot systems, presenting a comprehensive approach that enhances the resilience and efficiency of these systems. Our method is validated in a high-fidelity simulation environment. A sample of task allocation results is shown in Figure 2, wherein the environment is explored by six UAVs with the proposed method.  
Figure 2: An instance of the proposed multi-UAV task allocation, where a ground station assigns respective tasks to 6 UAVs in an urban environment using RMOPWB. Based on a given topological map of the urban environment, the Ground Control Station (GCS) first performs trajectory planning to estimate the travel costs between task points. Our proposed method is then used to allocate these points to the drone swarm. The different colors distinguish the target assignments for each UAV.  
Our principal contributions are highlighted as follows:  
* _Path Replanning for Workload Management_. We introduce a Path Replanning stage, specifically designed to optimize the total workload of multi-robot systems, which we mathematically formalize as Problem 2.
* _Load Balancing for Equitable Distribution_. We introduce a workload balancing stage, which we formulate as Problem 3. The complete framework, comprising three sequential stages (Problem 1, Problem 2, and Problem 3), is termed RMOPWB. This framework has been empirically validated through numerical simulations, demonstrating its effectiveness in achieving balanced workload distribution.
* _Efficient Algorithm for Complex Optimization_. To address the complexity of this optimization problem, we propose an efficient algorithm that combines heuristic methods with Monte Carlo sampling. This approach dramatically reduces redundant operational paths while preserving essential redundancy to enhance system robustness.  
The remainder of this paper is organized as follows. Section 2 establishes the theoretical foundation for our work. We begin in Section 2.1 with a formal review of the existing RMOP. In Section 2.2, we introduce our novel problem formulation, the RMOPWB, which explicitly addresses load distribution among agents. Section 2.3 is then dedicated to detailing our proposed algorithm for solving the RMOPWB. Subsequently, Section 3 presents the results of extensive numerical simulations designed to validate the performance and efficiency of our proposed method. Finally, Section 4 concludes the paper with a summary of our key findings and a discussion of potential avenues for future research.
# Uncertainty-Aware Time Series Anomaly Detection
## 3 Proposed Approach
### Loss Function  
The model output consists of two values through two decoders: (1) the reconstruction of the input data, and (2) the estimate of aleatoric uncertainty. We use these two values in a modified version of the mean squared error (MSE) loss function, introduced by [8]. Reconstruction is used as usual in MSE loss. Special is the usage of the aleatoric uncertainty estimate which attributes to the MSE a loss resulting in direct influence on algorithm training. For an input vector \(X\), we define the loss in Equation (1).  
\[\mathcal{L}(\theta)=\frac{1}{T}\sum_{i}\frac{1}{2}\delta_{i}^{-2}||x_{i}-\hat {x}_{i}||^{2}+\frac{1}{2}\log\delta_{i}^{2}, \tag{1}\]  
where \(T\) is the number of Monte Carlo samples, \(\hat{x}_{i}\) is the reconstruction of the input value \(x_{i}\), and \(\sigma_{i}^{2}\) is the variance representing aleatoric uncertainty (the second output of the model).  
The minimization objective consists of two parts: (1) the first regression part is obtained through the stochastic sampling of model parameters (MC Dropout) which represents the difference of reconstructed output and input (weighted by aleatoric uncertainty), and (2) the second part is used as an uncertainty regularization term.  
Labels to learn the uncertainty are not necessary. Through the main task of learning the regression, the variance \(\hat{\sigma}_{i}^{2}\) is implicitly learned from the loss function.  
In practice, the network is trained to predict the log variance \(v_{i}:=\log\hat{\sigma}_{i}^{2}\). The minimization objective changes accordingly as exemplified in Equation (2):  
\[\mathcal{L}(\theta)=\frac{1}{T}\sum_{i}\frac{1}{2}\exp{(-v_{i})}||x_{i}-\hat {x}_{i}||^{2}+\frac{1}{2}v_{i}. \tag{2}\]  
By predicting the log-variance instead of variance, the training process is more numerically stable, as we avoid potential division by zero.  
Additional prediction of uncertainty helps to improve the robustness of the network. Please note that \(\frac{1}{2}\hat{\sigma}^{-2}\) (the red curve in Figure 2) has a tempering effect on the residual  
Figure 1: Model Architecture.  
discouraging to predict very high or very low uncertainties. On the contrary, for high learned uncertainty, the residual gains lower importance. This serves as an automatic mechanism to involve data uncertainty in the model training and makes it less prone to noise. The model is also discouraged from either predicting only high or low uncertainty for all data points. Figure 2 helps explain this interplay. Predicting high uncertainty will cause the term \(\frac{1}{2}\log\hat{\sigma}^{2}\) to be large which, in turn, penalizes the model. On the other hand, predicting low uncertainty leads to a higher value of the residual term \(\hat{\sigma}^{-2}\). And again, it would penalize the model by exaggerating the contribution of very low uncertainties.
## 1 Introduction
Under such conditions, the frictional heat on the tooth surface increases the temperature near the contact point, which leads to thermal deformation that can deform the gear tooth and affect the TE of the system significantly. In particular, recent years have witnessed the growing use of polymer-based gears [17; 18; 19; 20; 21]. As typical polymer composites have greater thermal sensitivity than steel materials, it is important to examine their thermal deformation in the context of gear design. In this study, we establish a comprehensive algorithm for UQ of a spur gear pair by considering both mechanical and thermal uncertainties under 2D simplification. We assume that the spur gear has a constant profile in the longitudinal direction and focus on the contact analysis of tooth surface with uncertainties using 2D simplification. The main contributions of this study can be summarized as follows. First, we present a method for realizing smooth cutter profiles with machining uncertainties. It generates a gear tooth profile machined with an arbitrary cutter shape, which is analyzed using the analytical method. Second, we estimate the thermal deformation on the basis of the uncertainties in the gear material properties. To calculate the TE, the displacement due to thermal deformation estimated using the FEM is considered for tooth contact analysis. The influence of the uncertainties in the gear material properties on the TE are statistically analyzed using Monte Carlo simulation. Moreover, we compare the results for steel and plastic spur gear pairs. Accordingly, we clarify that in plastic systems, the thermal deformation has a dominant effect on the machining and pitch errors, whereas the opposite effects are observed in the case of steel. Finally, we analyze the combined influence of three types of uncertainties, i.e., machining errors, pitch errors, and thermal deformation, on the TE. It is worth noting that although thermal deformation alone does not change the TE of steel significantly, it can produce a noticeable difference when it exists with the other type of uncertainties. The remainder of this paper is organized as follows. Section 2 reviews the analytical method for gear design and TE analysis. Section 3 introduces the proposed method for de signing a spur gear pair with mechanical uncertainties and thermal deformation. Section 4 describes the TE estimation via loaded tooth contact analysis and presents an algorithm for Monte Carlo simulation. Section 5 summarizes and analyzes the UQ results and discusses the effects of each uncertainty on the TE. Finally, Section 6 concludes the paper.