esa-sceva/satcom-chunk-collection huggingface dataset:
# Critical Review of Techniques for Food Emulsion Characterization
## 3 Techniques for Emulsion Characterization
### Determination of pH Value  
The pH of emulsions can significantly affect their interfacial composition, stability, and aging [150]. The impact of pH on emulsion stability is ascribed to the ionization of polar groups in charged molecules present in the emulsion, such as surfactant molecules, ions, and electrolytes, which can generate electrostatic forces able to disrupt the interfacial layer [151]. Changes in pH also directly affect the zeta potential of the emulsion by altering surface droplet charge, which has a fundamental effect on electrostatic repulsion between the droplets. Optimizing pH conditions (e.g., by fabricating emulsions at different pH values, such as 2, 4, 6, 8, and 10 [152]) allows improvement in emulsion stability by preventing droplet coalescence and flocculation. It is worth noting that even slight changes in pH may significantly alter the stability of the emulsion. For example, it has been shown that, when milk is heated to temperatures above 70 \({}^{\circ}\)C at a pH of 6.5, a large fraction of denatured whey proteins is linked to casein micelles [153]. This interaction occurs through the creation of disulfide-linked complexes with \(\kappa\)-casein located on the micelle surface. However, at a higher pH value of 6.7, the extent of this association decreases and only about 30% of denatured whey proteins are connected to the surface of the casein micelles.  
pH has a particularly pronounced effect on the stability of protein-stabilized emulsions. pH values impact the ionization degree and solubility of protein emulsifiers. When the pH of a protein-stabilized emulsion is far from the isoelectric point of the emulsifier, the protein molecules on the droplet surface exhibit a higher potential value. This results in higher zeta potential and increases repulsive force between the droplets, effectively preventing their flocculation and coalescence. Conversely, under pH conditions near the isoelectric point of the protein, the droplet surface potential becomes close to zero, leading to the destabilization of the emulsion [147].
# Sequence-Information Recognition Method Based on Integrated mDTW
###### Abstract  
In the fields of machine learning and artificial intelligence, the processing of time-series data has been a continuous concern and a significant algorithm for intelligent applications. Traditional deep-learning-based methods seem to have reached performance ceilings in certain specific areas, such as online character recognition. This paper proposes an algorithmic framework to break this deadlock by classifying time-series data by evaluating the similarities among handwriting samples using multidimensional Dynamic Time Warping (mDTW) distances. A simplified hierarchical clustering algorithm is employed as a classifier for character recognition. Moreover, this work achieves joint modeling with current mainstream temporal models, enabling the mDTW model to integrate modeling results from methods like RNN or Transformer, therefore further enhancing the accuracy of related algorithms. A series of experiments were conducted on a public database, and the results indicate that our method overcomes the bottleneck of current deep-learning-based methods in the field of online handwriting character recognition. More importantly, compared to deep -learning-based methods, the proposed method has a simpler structure and higher interpretability. Experimental results demonstrate that our proposed method outperforms existing state-of-the-art models in handwriting character recognition, achieving a top-1 accuracy of 98.5% and a top-3 accuracy of 99.3%, thus confirming its effectiveness in overcoming the limitations of traditional deep-learning models in temporal sequence processing.  
Keywords: computational intelligence; multidimensional dynamic time warping (mDTW); online character recognition; deep-learning model integration +
Footnote †: journal: Applied
## 5 Conclusions  
We described the vicarious radiometric calibration approach for multispectral imaging sensors of the KOMPSAT-3 and -3A satellites based on field campaigns. Microtops II(r) instruments were used to measure atmospheric conditions and were highly efficient in conducting field campaigns multiple times over three study sites due to their simple operation and portability. Additionally, the dual ASD FieldSpec(r) hyperspectral radiometer system provided stable reflectances for the reference tarps, even in cloudy atmospheric conditions. Therefore, we conclude that the use of these instruments could increase the efficiency of field campaign-based sensor calibration.  
In this study, field campaigns were attempted twelve times, but only four satellite images were obtained due to weather limitations. Furthermore, the satellite images obtained from the two field campaigns in South Korea had somewhat more substantial errors due to the large viewing zenith angles of the imaging sensors. Nonetheless, when considering sensor geometry and weather conditions, the newly updated coefficients obtained from the field campaigns in Mongolia are sufficient to provide users with consistent coefficients for the KOMPSAT-3 and -3A satellite.  
[PERSON]-[PERSON] developed the research plan and supervised the work. [PERSON]-[PERSON] and [PERSON] analyzed data and prepared the manuscript and figures. [PERSON], [PERSON], C.[PERSON], and [PERSON] participated in the literature review, data acquisition, method selection, and discussions. All authors were equally involved in the editing of the manuscript.  
We are grateful to the editors and anonymous referees for their helpful comments and suggestions. This study was supported by the Korea Aerospace Research Institute (FR18720).  
The authors declare no conflict of interest.
## 4 Discussion
### Cross-Platform Generalization and Data Efficiency  
To address the practical challenge of the shortage of high-quality annotated data for agricultural remote sensing, this paper constructs a transfer learning framework that combines pre-training and fine-tuning. By conducting preliminary training on a large-scale publicly available water body dataset and then transferring the learned knowledge to the agricultural water body image data captured by UAVs, high-precision recognition of agricultural watersheds can be achieved even with a very small number of annotated samples. The experimental results show that this strategy not only significantly improves the model's cross-domain prediction ability but also effectively reduces the degree of data dependence in remote sensing application scenarios, enhancing the model's general adaptability across multiple geographical regions and multiple observation platforms. The effectiveness of this transfer mechanism indicates that LKF-DCANet also has strong generalizability in scenarios with scarce data, providing a technical reference for the monitoring of water resources in wide-area farmland.
# A Hybrid Route Selection Scheme for 5G Network Scenarios: An Experimental Approach
## 5 Results and Discussion  
Simulation results, including the packet delivery ratio, end-to-end delay, throughput, and the number of route breakages, are presented.
# A Line Segment Detector for Space Target Images Robust to Complex Illumination
## 3 Results
### Evaluation Metrics  
To scientifically and comprehensively evaluate the performance of ST_LSD, four classical indicators in the field of line segment detection are used in this paper: precision, recall, F-score, and computation time (the unit is milliseconds). Precision, recall, and F-score can be calculated by:  
\[Precision=\frac{TP}{TP+FP} \tag{20}\]  
\[Recall=\frac{TP}{TP+FN} \tag{21}\]  
\[F-score=\frac{2 PR}{P+R} \tag{22}\]  
where \(TP\) is the number of correctly detected line segments called true positive, \(FP\) is the false positive and represents the number of incorrectly detected line segments, and \(FN\) indicates false negative, that is, the number of line segments that exist in the image but are not detected by the algorithm. In addition, the computation time can reflect the operating efficiency of the algorithm.  
In the evaluation of Linelet and AG3 line, the valid detection line segment \(L_{cd}\) and its ground truth \(L_{gt}\) satisfied the following constraints: (1) the angle difference between \(L_{cd}\) and \(L_{gt}\) is smaller than \(\pi/36\); (2) the midpoint of \(L_{cd}\) is within one pixel to \(L_{gt}\); and (3) the intersection over \(L_{gt}\) is larger than the threshold:  
\[\frac{L_{cd}\cap L_{gt}}{L_{gt}}\geq\lambda_{area} \tag{23}\]  
To avoid setting the overconnection as a true positive, we follow an additional constraint added by AG3 line:  
\[\frac{L_{cd}\cap L_{gt}}{L_{cd}}\geq\lambda_{area} \tag{24}\]  
\(\lambda_{area}\) is the intersection threshold. In this paper, we set it to \(\lambda_{area}=0.5\) by following the Linelet and AG3 line methods.  
Figure 8: Typical adjacent line segment relationship. (**a**) The angle difference is too large. (**b**) The endpoint distance between line segments exceeds the threshold value. (**c**) The line segments overlap. (**d**) The ideal situation of collinear disjoint. (**e**) The parallel disjoint but overlapping situation.
## 4 A Deep Learning-Based CSI Feedback for New Radio
### NR-CsiNet Encoder  
The structure of the encoder of the NR-CsiNet (Figure 5a) is based on the encoder designed for the CsiNet proposed in [12]. It consists of a deep convolutional neural network, where the input has size \(C\times N\times 2\), corresponding to the real and imaginary parts of the truncated matrices \(\mathbf{H}_{\text{m}}^{\text{Csi-RS};}\). The first layer of the CNN is a convolutional layer adopting  
Figure 4: Module of DFT-transformed channel matrix \(|\tilde{\mathbf{H}}_{m}|\)[32].  
Figure 3: Module of channel matrix \(|\mathbf{H}_{\text{m}}|\)[32].  
a filter of size \(3\times 3\) that generates 2 feature maps. A reshape process vectorizes the two feature maps into a vector of size \(T\times 1\), where \(T=2\cdot C\cdot N\). The last block is a fully connected layer which outputs the actual codeword \(\mathbf{s}\), one for each receiving antenna, as a vector of size \(R\). The overall compressing ratio is \(K=\frac{R}{C\cdot N}\).
# The Potential of _Phaeodactylum_ as a Natural Source of Antioxidants for Fish Oil Stabilization
## 2 Materials and Methods
### Analyses
#### 2.3.4 Secondary Oxidation Products: Volatile Compounds  
The degree of secondary oxidation was determined by analysis of specific volatile compounds with headspace solid-phase microextraction Arrow gas chromatography-mass spectrometry (HS-SPME Arrow GC-MS) (GC Trace 1300 + ISQ 7000 single quadrupole MS, Thermo Scientific, Interscience, Louvain-la-Neuve, Belgium). After incubation (60 \({}^{\circ}\)C, 15 min), the volatile compounds were extracted (60 \({}^{\circ}\)C, 15 min) on a PAL DVB/CWR/PDMS SPME Arrow 1.1 mm fiber (CTC Analytics, Zwingen, Switzerland). A subsequent 2 min injection with a 1/50 split ratio and a desorption temperature of 250 \({}^{\circ}\)C was performed on a Rxi-1 ms column (50 m length, 0.20 mm ID, 0.33 \(\upmu\)m film) (Restek, Bellefonte, PA, USA) after which a time-temperature program from 40 \({}^{\circ}\)C to 250 \({}^{\circ}\)C was followed (5 min at 40 \({}^{\circ}\)C, increase of 5 \({}^{\circ}\)C/min, 5 min at 250 \({}^{\circ}\)C). The transfer lines worked at 280 \({}^{\circ}\)C. The MS operated with electron ionization at 70 eV and the scanned mass units ranged from 40 to 250 amu. For monitoring and data processing Chromelon 7 Chromatography Data System software (Thermo Scientific, Interscience, Louvain-la-Neuve, Belgium) was used. Several volatile compounds can be considered as a marker for secondary oxidation, originating from n-3 LC-PUFA hydroperoxides formed during primary oxidation [5; 36; 37; 38; 39]. However, only the compounds with a solid identification for all time points when using the NIST MS Search 2.3 spectral library, and of which a standard was available to confirm the retention time, were further reported. These are as follows: (E,E)-2,4-heptdiagnel, (E)-2-pentenal and 2-butenal. Calibration plots were set up by spiking these standards in the matrix of interest.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**System** & _Phaeodactyllum_ Total Lipid Extract & **Fish Oil** \\  & **(Weight\%)** & **(Weight\%)** \\ \hline Pure _Phaeodactyllum_ total & 100.0 & 0.0 \\ lipid extract & 78.6 & 21.4 \\ Mixture 49/51 & 49.2 & 50.8 \\ Mixture 6/94 & 6.4 & 93.6 \\ Mixture 1/99 & 0.7 & 99.3 \\ Pure Fish oil & 0.0 & 100.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ratios (in weight%) of the _Phaeodactyllum_ total lipid extract and fish oil used for preparing the different systems of Part II of the experimental set-up.  
The as such obtained semi-quantitative data (expressed in \(\upmu\)g/mg) were again expressed as delta values, representing the relative increase or decrease compared to day zero, to focus on the oxidation trend.
## 4 Experimental Results and Discussion
### Study on the Impact of Imbalance Rate  
In this section, the performance of different algorithms at different imbalance rates is analyzed to evaluate their robustness. The experiments were conducted using the Cora, Citeseer, and PubMed datasets with well-defined constraints. The undersampling neighbor count K was set to 3, the loss weight was set to 0.1, and the imbalance ratios varied as {0.1, 0.2, 0.4, 0.6}. Each experiment was repeated three times, and the results were averaged.  
From the experimental results in Figures 3-5, it can be concluded that the ESA-GCN model, which incorporates undersampling and attention mechanisms, is more adaptable to different imbalance rates compared to the original GraphSmote model. This indicates that the ESA-GCN model exhibits better robustness under different imbalance rates and is capable of handling imbalanced node classification tasks more effectively.  
Figure 2: Changes in AUC-ROC and F1-Macro with varying undersampling neighbor counts.
## 3 Results and Discussion  
Preliminary experiments involved the exploration of the effects of the kind of extraction solvent and the salt on the extraction performance for both analytes. These experiments were carried out using the one-variable-at-a-time approach. In the next stage, a two-step experimental design was adopted for screening and optimizing the microextraction-based method. Method optimization was performed using artificial urine spiked with both analytes at a concentration of 5 \(\upmu\)g mL\({}^{-1}\).
# An Adaptive Hierarchical Hybrid Multicast Based on Information-Centric Networking
## 5 Evaluation
### Link Load  
Thirdly, we evaluated the link load in each multicast approach. We counted the number of flows passing through each link and compared the maximum load among all links in different multicast methods. The experimental results are shown in Figure 10.  
Figure 10a shows that the link load in the AHHM is basically unchanged as the threshold increases. This is because the threshold can affect the mode used when establishing multicast communication but cannot reduce traffic. Figure 10b,c shows that the maximum link load in AHHM, SSM, and BIER all increase with the number of receivers and groups. Figure 10d shows the CDF of a load of each link in each multicast approach. These experi  
Figure 10: The comparison of link load. (**a**) The comparison of the link load with different thresholds; (**b**) The comparison of the link load with different numbers of receivers; (**c**) The comparison of link load with different numbers of groups; (**d**) The CDF of the link load in each multicast method.  
mental results indicate that AHHM can significantly reduce the link load compared to SSM multicast and BIER multicast.  
By analyzing the experimental data, we can find that the maximum link load in AHHM using the nearest selection strategy is reduced by 18.4% on average compared to SSM and 58.9% on average compared to BIER. In BIER and SSM, multicast flows are uniformly transmitted along the shortest path tree from the source to all receivers, which leads to reuse and overload of some links. Especially when the number of egress routers is greater than the length of the bit string, BIER needs to divide egress routers into different SIs and copy a packet for each SI. Packet redundancy leads to greater link load in BIER. In contrast, AHHM selects a suitable MJN for each receiver to join the group according to the given strategy, so it can make full use of the low-load links to avoid link overload and traffic aggregation. Moreover, AHHM can alleviate the problem of multicast packet redundancy because the number of egress routers in each stateless sub tree is small.  
We can also find that the maximum link load in the minimum cost selection strategy is 31.8% smaller on average than that in the nearest selection strategy. Since the factor of link load is considered when selecting MJN, the minimum cost selection strategy can make full use of low-load links and reduce the link load.
# Comparative Analysis of BERT and GPT for Classifying Crisis News with Sudan Conflict as an Example
## 4 Methodologies
### Evaluation Matrix  
#### 4.2.1 BERT  
The BERT-based and BERT-large classification models were evaluated using different metrics tailored to the requirements of SL-MLG and MLC tasks. These evaluation metrics ensured a comprehensive assessment of model performance and provided insights into the impact of hyperparameter tuning on classification accuracy.  
For SL-MLG, accuracy served as the primary evaluation metric, reflecting the model's ability to select the single label that best describes the primary issue from multiple ground-truth categories. Predictions were generated using a softmax activation function, which converted the model logits into probability distributions across all categories. The category with the highest probability was selected as the final classification.  
To assess alignment with expert labeling, accuracy was calculated by verifying whether the predicted category matched any of the ground-truth labels assigned by the experts. This metric emphasized the model's ability to prioritize the most relevant category while adhering to the strict single-label requirement of the SL-MLG framework.  
For MLC, model performance was measured using accuracy, precision, recall, and F1-score, calculated on a per-category basis and averaged using a weighted scheme. These metrics were chosen to assess both precision (the proportion of correctly predicted labels among all predicted labels) and recall (the proportion of correctly predicted labels among all actual labels assigned to an article), with F1-score providing their harmonic mean, ensuring a balanced evaluation of prediction correctness and completeness.  
Predictions were generated by applying a sigmoid activation function to the model logits, converting them into probability scores for each category. A threshold of 0.5 was used to determine whether a category was assigned to an article. The evaluation pipeline compared these binary predictions against the ground truth labels from the test dataset, computing accuracy, precision, recall, and F1-score accordingly. The results were analyzed for both the standard BERT model with fixed hyperparameters and the hyperparameter-tuned BERT model optimized using Optuna, highlighting the effects of parameter tuning on classification performance.
# Design of a Chaotic Trajectory Generator Algorithm for Mobile Robots
## 1 Introduction
### Motivation  
One of the main characteristics of a chaotic system is its unpredictability. Applying such a feature to a mobile robot's trajectory may bring several benefits and possible applications; some of them are as follows: border patrol, dangerous object search (for example, explosives), rescue, and unknown territory exploration, among others.  
For example, let us suppose there is a valuable object located in a work area (such as a table or a room). A differential drive mobile robot is used to patrol the area, serving as a security guard for said object. The robot may move in a \"predefined\" manner, using predictable movement previously programmed (see Figure 1a). Any trespasser could easily predict the path the robot would follow, enabling him to reach the valuable object without being detected by it. However, by using unpredictable trajectories (Figure 1b), it would be technically impossible for the trespasser to reach the object without being detected.  
In order to achieve these unpredictable (chaotic) trajectories, some recent studies [9; 10] have followed [PERSON]'s work [2], combining the mathematical model of the mobile robot with the state equations of chaotic systems, such as [PERSON]'s and [PERSON]'s. Other work [11] uses other chaotic systems, such as the logistic map, to generate way-points that the mobile robot should follow to move through the work area.  
Unlike the previously mentioned works, the algorithm proposed in this article (Section 4) does not require modification of the mathematical model of the mobile robot, nor does it generate way-points. Instead, the chaotic trajectories emerge from the behavior of the robot inputs (as described in Section 7). In this case (using a Khepera III differential drive mobile robot, as described in Section 7.1), those inputs are the desired speed of each wheel of the robot. The main advantage of this methodology is the easiness to apply the algorithm to an actual mobile robot.  
The main contribution of this work is the way to generate chaotic trajectories in mobile robots; that is, the application of [PERSON]'s map to give velocity values to a differential drive mobile robot, in order to generate a chaotic trajectory that allows it to patrol a work area in an effective and unpredictable manner to outside spectators (for example, intruders). To our knowledge, no similar methodology has been reported in the literature before.  
The rest of the paper is organized as follows. In Section 2 the state model of the chaotic Henon map used to generate unpredictable trajectories in mobile robots is presented. In Section 3 the mathematical model of a differential drive mobile robot is described. The proposed chaotic trajectory generator algorithm for a differential drive mobile robot is presented in Section 4. The numerical results are reported in Section 5. In Section 6, the case of multiple robots moving simultaneously in the same work area is presented. Section 7 describes the experimental results. Finally, conclusions and future research directions are stated in Section 8.