esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization of Chinese Chive Juice as a Functional Feed Additive
## 3 Results
### Fractional Factorial Design for Optimized CC Products  
FFD was conducted to identify optimized CC products based on the moisture content and antibacterial activities against the two dedicated pathogens. A total of 18 runs of the experiment were used to investigate the optimal drying conditions of solidified CC (Table 4). The moisture content (Y1) of each run varied from 1.6 to 28.0%, and decreased steeply during 3 to 6 h of drying but no notable changes were observed with the extended heating treatments. The moisture content was reduced with the increase in SBM content (20% to 50%). The antibacterial activities of CCPE against _E. coli_ (Y2) and _S. pullorum_ (Y3) varied in the range of 8.7 \(\pm\) 0.5 to approximately 14.0 \(\pm\) 1.4 mm and 9.3 \(\pm\) 0.6 to approximately 15.0 \(\pm\) 0.8 mm, respectively. The antibacterial activity was significantly decreased with the increase in the drying temperature, drying time, and SBM content. Analysis of the major effects of the variables (Figure 1) showed that the SBM content was the main determinant factor to impact both the moisture content and antibacterial activity against _E. coli_ and _S. pullorum_. Moreover, the moisture content and antibacterial activity against _S. pullorum_ were additionally impacted by the drying time (_p_ < 0.01) and drying temperature (_p_ < 0.05), respectively. Considering these results, an SBM content of 20% and drying temperature at 60 \({}^{\circ}\)C were selected for further analysis (Table 5). The moisture content was linearly reduced with extended heating time and was less than 5% within 8 h. No significant effect on antibacterial activities of CCPE was observed along with the drying time. Taken together, the results suggested that CC juice with 30% SBM content and 3 h drying at 80 \({}^{\circ}\)C or with 20% SBM content and 8 h drying at 60 \({}^{\circ}\)C achieve high antibacterial activity with less than 5% moisture content.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Runs**} & \multicolumn{3}{c}{**Variables (\({}^{\text{1}}\))**} & \multicolumn{3}{c}{**Responses (\({}^{\text{2}}\))**} \\ \cline{2-7}  & **X\({}_{\text{1}}\)** & **X\({}_{\text{2}}\)** & **X\({}_{\text{3}}\)** & **Y\({}_{\text{1}}\)** & **Y2 (_E. coli_)** & **Y2 (_S. pullorum_)** \\ \hline  
\hline \end{tabular} \({}^{\text{(1)}}\) Variable: X\({}_{\text{1}}\), temperature (\({}^{\circ}\)C); X\({}_{\text{2}}\), drying time (h); X\({}_{\text{3}}\), SBM content (%). \({}^{\text{(2)}}\) Response: Y\({}_{\text{1}}\), Moisture (%); Y\({}_{\text{2}}\), antibacterial activity against _E. coli_ and _S. pullorum_ (mm). The data expressed as mean \(\pm\) SEM.  
\end{table}
Table 4: Fractional factorial design (FFD) for evaluation of the effect of critical factors (drying temperature, time, and the solid content (SBM) on antibacterial activities and moisture contents of the CC products.
## 2 Materials and Methods
### Slip and Drift Velocity  
The slip velocity is defined as the velocity of the secondary phase (expressed as '\(p\)') relative to the velocity of the primary phase (expressed as '\(q\)') given by the following equation:  
\[\vec{V_{pq}}=\vec{V_{p}}-\vec{V_{q}} \tag{13}\]  
However, the mass fraction can be stated as,  
\[C_{k}=\frac{\varepsilon_{k}\rho_{k}}{\rho_{m}} \tag{14}\]  
\(\vec{V_{dr,p}}\) and \(\vec{V_{pq}}\) are related to the following equation  
\[V_{dr,p}^{\rightarrow}=\vec{V_{pq}}-\sum_{k=1}^{n}C_{k}\times\vec{V_{qk}} \tag{15}\]  
The relative velocity from literature [35], \(\vec{V_{pq}}\) is given as,  
\[\vec{V_{pq}}=\frac{\tau_{p}}{f_{d}}\frac{\left(\rho_{p}-\rho_{m}\right)}{\rho _{p}}\times\vec{a} \tag{16}\]  
where, '\(\tau_{p}\)' is the reduction time of particles, which is given as,  
\[\tau_{p}=\frac{\rho_{p}d_{p}^{2}}{18\mu_{q}} \tag{17}\]  
where, \(d_{p}\) and \(\vec{a}\) are the diameter and acceleration of secondary phase particles respectively, \"\(f_{d}\)\" is the drag force applied by the fluid on spherical particles. An interphase drag principle was introduced for the two components (multiphase) to consider the dragging-by-fluid force exerted on rigid granular particles. The Gidaspow model [16; 36] was adopted for the drag force induced between RBCs and plasma to validate the densely dispersed RBCs. The term drag force is generally expressed as below,  
\[f_{d(RBC,Plasma)}=C_{RBC}(U_{RBC}-U_{Plasma}) \tag{18}\]  
where, \(C_{RBC}\) represents the coefficient of momentum exchanges between the two phases, which is normally referred by the Equation (19),  
\[C_{RBC}=\frac{3}{4}\frac{C_{D}}{dRc}\varepsilon_{RBC}\varepsilon_{Plasma} \rho_{Plasma}|U_{RBC}-U_{Plasma}| \tag{19}\]  
\(U_{RBC}\) and \(U_{Plasma}\) are the phase velocities of RBC and Plasma respectively, '\(d_{RBC}\)' is the diameter of RBC, that has chosen to be 8 \(\upmu\)m by previously published articles [13; 14; 16]. The Gidaspow model [36] is generally used with the two separate equations for the drag coefficient for thinned and thick particles.  
In case for, \(\varepsilon_{RBC}\) < 0.2 then \(C_{D}\) is represented by an equation  
\[C_{D}=\varepsilon_{RBC}^{-1.65}\text{max}\left[\frac{24}{R_{ep}}\left(1+0.15 R_{ep}^{0.687}\right)0.44\right] \tag{20}\]where, \(R_{ep}\) is the particle modified Reynolds number and which is expressed as the following equation:  
\[R_{ep}=\frac{\rho_{Plasma}\varepsilon_{Plasma}d_{RBC}|U_{RBC}-U_{Plasma}|}{\mu_{Plasma}} \tag{21}\]  
If in case, \"\(\varepsilon_{RBC}>0.2\)\", then the momentum exchange coefficient between phases \"\(C_{RBC}\)\" is directly expressed by the equation.
# SSTNDS: A SET-Based Non-Dominated Sorting Algorithm for Multi-Objective Optimization Problems
## 3 Basic Concepts and Definitions
### Definitions  
Dominant Relationships between different solutions: Suppose solution \(p\) and \(s\) are any two members of solution set \(P\), if \(\forall j\in\{1,2,...,M\},\ A_{j}(p)\leq A_{j}(s)\) and \(\exists i\in\{1,2,...,M\},\ A_{i}(p)<A_{i}(s)\) is satisfied, we will say that \(p\) dominates \(s\), which can be expressed as \(p>s\), else \(s>p\) instead. If \(p\) and \(s\) are not dominated by each other, we will say that \(p\) is unrelated to \(s\), which can be expressed as \(p>_{d}s\) and vice versa.  
Partition of objective space: The objective values of any point in the objective space can divide the space into several parts, based on the relationships between solutions with the under-sorted solution \(s\), these parts can be named the dominant region, unrelated region and dominated region. As shown in Figure 2, the objective values of point \(s\) can divide the bi-objective space into four parts; the relationships between point \(s\) and these parts are given as follows:  
\(\Omega_{1}(s):\) located in the lower left part of \(s\), also called the dominant region--any point located here dominates \(s\), which can be expressed as \(\forall p\in\Omega_{1}(s),\ p>s\).  
\(\Omega_{2}(s)\cup\Omega_{4}(s)\): located in the upper left and lower right of \(s\), also called the unrelated region--any point located at this part is unrelated to s, which can be expressed as \(\forall p\in\Omega_{2}(s)\cup\Omega_{4}(s),\ p>_{d}s\).  
\(\Omega_{3}(s)\): located in the upper right of s, also called the dominated region, any point located here is dominated by \(s\), which can be expressed as \(\forall p\in\Omega_{3}(s),\ s>p\).  
Determination of the solution's rank: As shown in Figure 3, it is enough to only consider the dominant region \(\Omega_{1}(s)\) of \(s\) to determine the rank of \(s\). Suppose the highest rank level in \(\Omega_{1}(s)\) is \(r\) and the corresponding point is \(p\), i.e., \(F(p)=r\). Then the rank of s will be \(r+D\) (\(p\), \(s\)).
# Efficient Task Allocation in Multi-Agent Systems Using Reinforcement Learning and Genetic Algorithm
## 3 Problem Formulation  
In this paper, we address the multi-agent task allocation problem, where agents are assigned to distinct tasks and operate either independently or cooperatively to enhance task efficiency and coverage across the environment.  
More specifically, agents have the following two tasks.  
1. Primary (Extrinsic) Task: High-quality execution of multiple operational tasks by agents, such as conducting surveillance on designated targets or performing search and rescue missions in critical scenarios. Those tasks necessitate that agents autonomously assess their environments and adapt to dynamic conditions, thereby ensuring effective and efficient performance.
2. Secondary (Intrinsic) Task: Evaluation of task allocation decisions. This task focuses on evaluating the effectiveness of agents' decisions in optimizing resource utilization and enhancing operational efficiency, thereby improving the overall performance of the multi-agent system.  
To achieve these objectives, which include the high-quality execution of operational tasks and the evaluation of task allocation strategies, each agent undertakes the following steps: state estimation, task allocation (for scenarios involving multiple tasks), and strategy, as illustrated in Figure 1 and elaborated upon in the subsequent sections.  
1. State Estimation: Each agent's state includes its position, an occupancy map, and target identifiers. At time \(t\), the state \(S_{ti}\) reflects its configuration and location, updated in real time. The transition function \(f(S_{ti},a_{i})\) models state evolution. The Actor-Critic network processes these data to optimize policies, enabling efficient navigation, obstacle avoidance, and target acquisition in dynamic environments.
2. Task Allocation: A dynamic algorithm optimizes task distribution, preventing clustering. Agents evaluate tasks based on proximity, urgency, and workload, considering completion time and expected benefits. They share status updates for real-time reallocation, ensuring balanced task coverage and efficient resource use.
3. Strategy: Agents optimize detection and mapping based on state estimates. Initially, they update policies independently via rewards. In collaborative settings, they share policy gradients to refine strategies. GAPPO, with attention mechanisms, enables autonomous and cooperative decision-making for better coordination.
# An Adaptive Routing Algorithm Based on Relation Tree in DTN
## 7 Summary  
In this paper, we proposed an adaptive routing algorithm based on the Relation Tree in DTN. We exploited the feature that nodes in DTN have stable social attributes similar to people and introduced the concept of the Relation Tree. Each node constructs its own Relation Tree based on its historical encounter frequency, and will adopt different forwarding policies based on the Relation Tree in the forwarding phase. In addition, the source node adaptively adjusts the initial maximum number of message copies according to its own cache occupancy, which enables the node to make negative feedback to the network environment changes. Simulation results show that the proposed AR-RT algorithm had the best overall performance in terms of average delay, average hop count, and delivery rate compared with existing routing algorithms, while the network overhead rate was kept at a stable low level.  
Conceptualization, D.C.; methodology, D.C.; software, D.C.; resources, H.C. and R.E.W.; data curation, D.C.; writing--original draft preparation, D.C.; writing--review and editing, H.C. and R.E.W.; supervision, H.C. and R.E.W.; project administration, H.C. and R.E.W.; funding acquisition, H.C. All authors have read and agreed to the published version of the manuscript.  
The paper was funded by the National Key Research and Development Program of China (2020 YFB1807805), National Natural Science Foundation of China (62171049).  
Not applicable.  
The authors wish to express gratitude to the State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, for the administrative and the technical support.  
Figure 6: Comparison of the results (95% CI) of various algorithms under different node caches. Subfigures (**a–d**) compare the performance of the five algorithms with the variation of the four parameters, delivery rate (**a**), average delay (**b**), overhead (**c**) and average hop count (**d**), with the node cache, respectively.
## 2 Materials and Methods
### Measurements
#### 2.4.3 HRQoL Assessment  
The SF-36 Health Survey is a valid questionnaire comprising 36 items designed to assess various aspects of perceived health status [57]. It encompasses eight domains, including physical functioning, social functioning, role limitations due to physical problems, pain, role limitations due to emotional problems, mental health, vitality, and general health perception. Each item is scored and transformed to a scale ranging from 0 to 100, where a higher score indicates better health status.
## 2 State of the Art  
### Current Technologies in Port Terminals  
Port terminals have not been immune to the technological revolution that is presently occurring globally, which is why ports have incorporated advanced technologies that seek to optimize operational management and reduce loading and unloading times. Automation using systems such as AGVs (Automated Guided Vehicles) has significantly improved operational efficiency in Ro-Ro terminals, reducing the need for human labor [9]. In addition, the use of algorithms in endpoint management systems allows for a more precise allocation of resources, improving responsiveness to peak activity [10].  
However, in many cases, terminals face challenges related to a lack of technological interoperability and the need to upgrade infrastructures to support new technologies [11].  
Blockchain has been successfully implemented in port logistics to improve data integrity and automate administrative processes. For example, the Port of Antwerp has launched pilot projects that leverage blockchain for real-time cargo tracking and customs clearance, demonstrating its potential in Ro-Ro operations. These innovations have transformative implications not only for Roll-on/Roll-off (Ro-Ro) terminals but also for other critical areas such as containerized cargo handling, urban freight systems, and multimodal logistics chains. The IoV facilitates seamless communication between vehicles, infrastructure, and port systems, optimizing traffic management and reducing inefficiencies in port operations. Blockchain, on the other hand, enhances transparency and security in cargo tracking, enabling real-time data sharing across stakeholders. Ref. [12] emphasizes that ports adopting advanced digital technologies, such as IoV, can significantly improve operational performance and reduce bottlenecks caused by limited infrastructure. Globally, leading ports such as Rotterdam and Singapore are leveraging IoV and blockchain to pioneer smart transportation systems that integrate maritime, road, and rail logistics, setting benchmarks for innovation in the industry.
## 2 Materials and Methods
### Soil-Vegetation-Atmosphere Transfer Model
#### 2.3.2 Longwave Radiation Emitted by the Atmosphere  
Longwave radiation emitted by the atmosphere was calculated through the Stefan-Boltzmann law, in which the emissivity of a body is proportional to the fourth power of the absolute temperature of said body. Thus,  
\[L\downarrow\ =\ \varepsilon_{a}\ \sigma\ T^{4} \tag{5}\]  
where \(\sigma\ =\ 5.67\ \times\ 10^{-8}\) W m\({}^{-2}\)K\({}^{-4}\) is the Stefan-Boltzmann constant, and \(\varepsilon_{a}\) is the atmospheric emissivity, which was calculated through two empirical models proposed by [PERSON] [36] (Equation (6)) and [PERSON] [37] (Equation (7)), respectively.  
The choice of these two models was based on previous analyses carried out in the Caatinga and Cerrado biomes [38; 39; 40]. Although these studies used different methods, they all agreed in indicating the models by [PERSON] [36] and [PERSON] [37] as appropriate in order to better represent atmospheric emissivity in the region. Specifically, a study [40] was carried out using in situ eddy covariance data measured in a preserved Caatinga segment. The same dataset was used in the calibration of the SITE model [31]. The quality of these data was previously analyzed and verified in other studies [21; 24; 30].  
[PERSON]'s model [36] determines emissivity through observed air temperature data and estimated vapor pressure (Equation (6)). Similarly, [PERSON]'s model [37] determines emissivity based on air temperature and partial water vapor pressure (Equation (7)).  
\[\varepsilon_{a,6}\ =\ 0.70\ +\ 5.95\ \times\ 10^{-7}\ \times\ e\ \times\ exp\ \bigg{(}\frac{1500}{T}\bigg{)} \tag{6}\]  
\[\varepsilon_{a,7}\ =\ \Big{\{}1\ -\ (1\ +\ \xi)exp\Big{[}-(1.2\ +\ 3.0\xi)^{0.5} \Big{]}\Big{\}},\ with\ \xi\ =\ 0.465\Big{(}\frac{e}{T}\Big{)} \tag{7}\]  
Although both models performed well in representing observed data, the model by [PERSON] was slightly more advantageous regarding the mean absolute error [40]. Thus, we attributed weights to the emissivity computed by each model, with a 0.6 weight to [PERSON]'s model and a 0.4 weight to [PERSON]'s model. Finally, we calculated longwave radiation emitted by the atmosphere as follows:  
\[L\downarrow\ =\ (0.4\varepsilon_{a,6}\ +\ 0.6\varepsilon_{a,7})\sigma\ T^{4} \tag{8}\]  
Since measured \(L\downarrow\) data at the Ibimirim and Cabrobo sites are not available, the weighed emissivity solution (Equation (8)) was used in order to minimize errors inherent to the empirical nature of the formulations used for the calculation of the emissivity parameter.
## 2 Background
ICA was applied over the processed signal in order to extract the independent components, and they were reconstructed by applying the inversion of a compressive sensed sparse binary matrix. Finally, the beats were classified into mother or fetal originated, according to their similarity to the dictionary words. Another algorithm to solve this problem is presented in [13], where the signal was firstly processed to reduce the noise, then the mother ECG was enhanced using ICA, and the mother QRS complexes were detected and cancelled from the original signal in order to reduce the interference with the fetal ECG. Then, the fetal ECG was enhanced using ICA over the signal without mother ECG, and the fetal QRS complexes were detected. This kind of algorithms stands for another application in which the physiological meaning of the components is specifically dealt, however, they explode the mother and fetal QRS complexes similarity, and the fact that the two kinds of QRS are not too overlapped. The fourth group of works is compounded by algorithms which use PCA or ICA to extract the respiratory signal from the conventional ECG. In [14], an algorithm was proposed to extract this signal from the ECG, which was firstly filtered to reduce the noise, each QRS was then detected and aligned to create a matrix containing every beat, and then ICA or PCA were applied in order to extract the respiratory signal. The component containing this signal was selected by computing the power spectral density using the Welch method. Then, the extracted signal was interpolated to get the complete respiratory signal. Another algorithm with the same objective is presented in [15], where the signal was firstly filtered to reduce the presence of noise, and then the QRS complexes were detected. Wave matrices were then created using the detected beats, namely, one containing all the P waves, one containing all the T waves, and one containing all the QRS complexes. Finally, PCA was applied over each matrix to extract the three first principal components, which where compared with a reference signal to select the best respiratory signal. This kind of algorithms corresponds to an alternative application in which the physiological meaning of the components is specifically dealt, however, their focus is not the detection of a wave inside the ECG, but rather of a wave modulating the whole ECG. In addition to these sets of algorithms, PCA and ICA are widely used in several different applications in the field of ECG processing. Examples of this include the feature extraction for bio-metric characterization [16; 17; 18], as well as to dimensionality reduction and beat classification [19; 20], or ECG compression [21; 22], among many others [23]. A number of applications take into account the physiological meaning of the PCA/ICA components applying the whole ECG, such as AF detectors or respiratory signal isolators, or the decoupling of originally separated overlapping signals, as fetal ECG, but none of them considers the localized isolation of elements that are present at individual waves in the existing ECG, as fragmentation-revealing requires.
## 3 Material and Methods
### ML-MC Propaganda Features
#### 3.3.1 Coding Analysis  
To effectively train our propaganda detection model, we conducted a structured coding analysis to identify and label various propaganda techniques present on online social media platforms. This process was critical to creating a robust and high-quality dataset tailored to the study's objectives. Unlike prior studies that broadly classified misinformation, fake news, and rumors [26], our analysis specifically focused on delineating propaganda techniques within the context of COVID-19-related discourse. This targeted approach enables our model to address a significant gap in the literature by advancing the detection of fine-grained propaganda.  
The coding analysis followed a rigorous, multi-phase methodology adapted from established qualitative research frameworks [58]. The process consisted of six systematic steps: (a) initial examination and planning, (b) open and axial coding, (c) development of a preliminary codebook, (d) pilot testing of the codebook, (e) final coding procedures, and (f) iterative review to derive final themes. These steps were instrumental in ensuring the reliability and validity of the dataset used for model training.  
During the preliminary stage, a random sampling algorithm was employed to select representative records from the complete dataset for analysis. Each sampled record was thoroughly examined and categorized based on a coding scheme designed to capture the nuances of propaganda techniques. The scheme was informed by prior research while being customized to meet the specific needs of this study. A detailed overview of the propaganda types, their definitions, and examples is provided in Table 4.  
The second phase of coding involved collaboration with five expert annotators who were extensively trained in the coding scheme. A sample dataset of 12,470 records was annotated, with each record reviewed by three annotators to ensure consistency. Annotators were assigned 2494 records each, selected randomly to minimize bias. Training sessions provided comprehensive instructions and examples for each propaganda category, ensuring uniform interpretation and application of the coding scheme.  
To assess the reliability of the annotations, we calculated inter-coder reliability using Krippendorff's alpha, a robust statistical measure suited for datasets with multiple observers and categories [59]. This ensured a high degree of agreement among annotators,confirming the reliability of the coding process. Final labels were consolidated using a majority-vote mechanism, further enhancing the quality of the annotated dataset.  
This rigorous coding process not only provided a foundational dataset for model training but also contributed to advancing the understanding of propaganda in online discourse. By focusing on fine-grained classification, our study lays the groundwork for developing sophisticated detection methods tailored to address real-world challenges in misinformation and propaganda.