esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Perlite Characterization and Perlite as a Lead Ion Sorbent
Specifically, for 2, 4, 7, and 10 mg\(\cdot\)L\({}^{-1}\) of lead ion influent concentrations, retention capacities of 2.0, 2.5, 3.1, and 3.5 mg \(g_{\text{sorbent}}\)\({}^{-1}\), respectively, were obtained, as shown in Table 1. A concern, regarding the breakpoint, was detected in the case of the most concentrated solution (27 BV, 10 mg of Pb\({}^{+2}\) L\({}^{-1}\)), with respect the 2 mg of Pb\({}^{+2}\) L\({}^{-1}\) solution (100 BV). Figure 3B shows the kinetic study of lead ion retention on perlite at different influent concentrations. Quicker saturation of the column was observed at high metal concentrations, thereby decreasing the breakthrough time. Time-dependent breakthrough curves showed a reverse linear dependence of t\({}_{0.5}\) vs. C\({}_{0}\), as shown in Figure 3C, according to the film diffusion control theoretical model (Equation (2)) [41]. Figure 4A shows, the breakthrough curves for experiments carried out with 2 mg of Pb\({}^{+2}\) L\({}^{-1}\) influent solution, 4 g of perlite, and at different flow-rates (0.15, 0.2, 0.3, and 0.4 L\(\cdot\)h\({}^{-1}\)). Figure 3: (**A**) Breakthrough curves for lead ions retention at 2, 4, 7, and 10 mg of Pb\({}^{+2}\) L\({}^{-1}\) (0.3 L\(\cdot\)h\({}^{-1}\) flow-rate; 4 g perlite; 1-2 mm bead size range; T = 298 K). (**B**) Time-dependent breakthrough curves. (**C**) Correlation of the half exchange time, t\({}_{0.5}\), vs. influent concentration. Retention capacities were 2.5, 2.2, 2.0, and 1.6 mg \(\text{g}_{\text{sorbent}}\)\({}^{-1}\) for 0.15 L\(\cdot\)h\({}^{-1}\), 0.2 L\(\cdot\)h\({}^{-1}\), 0.3 L\(\cdot\)h\({}^{-1}\), and 0.4 L\(\cdot\)h\({}^{-1}\), respectively, while breakpoints were detected at 153, 130, 100, and 73 BV, as shown in Table 1. Thus, at low flow-rates, corresponding to long liquid/solid contact times, a more complete saturation of the column was detected. This result explains the increasing retentions and higher values of the breakthrough points. A typical, \"self-sharpening\" of the column breakthrough curves was observed at lower flow-rates, associated with an increase of metal sorption, as shown in Figure 4A, due to favorable retention equilibria towards the entering ions, occurring when the rates of motion decreased [41]. Figure 4B shows the kinetic study for lead ion retention on perlite, at different flow-rates. Slower elution determined a delay of the breakthrough (i.e., higher contact times), while faster elution induced an advance of the breakthrough (i.e., lower contact times). Time-dependent breakthrough curves showed an inverse linear dependence of t\({}_{0.5}\) vs. flow-rate, as shown in Figure 4C, according to film diffusion control (Equation (2)) [41]. At lower flow-rates, the film at the liquid/solid interface (Nernst film) could be assumed to be thicker, while at higher flow-rates the film could be assumed to be thinner.
# Assessment of Soybean Oil Oxidative Stability from Rapid Analysis of its Minor Component Profile
## 2 Results and Discussion
### Study by \({}^{1}\)H-NMR of the Evolution Under AS Conditions of VSO and RSO
#### 2.2.2 Formation and Evolution of Oxidation Products
With regard to major epoxides, it is worth noticing that a part of this type of epoxides, presumably monoepoxides from linolenic and/or linoleic groups, was present in the fresh refined soybean oil, before being subjected to the heating process. This is deduced from the observation of signal \"e\" in Figure 2 (see the spectrum corresponding to day 0), which in the absence of epoxides should resemble that of the right side-band of _bis_-allylic protons signal (see signal \"sb\"). However, in the case of RSO, it seems clear that there are additional compounds contributing to signal \"e\". Figure 4 reveals that, as might be expected from the evolution of hydroperoxides (see Figure 3B), the appearance of major epoxides in VSO occurs before their concentration begins to increase in RSO. Apart from the aforementioned major epoxides, other types of epoxy-compounds are also generated throughout the oil oxidation process, although in considerably lower concentrations: epoxy-keto-enes and (\(E\))-epoxystearates. These derive from the degradation of polyunsaturated and oleic groups, respectively, and their corresponding evolutions are also shown in Figure 4. As in the case of major epoxides, both types of compounds are detected later in RSO than in VSO, their evolution with time being similar in the two studied oils. As shown in Figure 5, aldehydes, including the most toxic ones (4-hydroperoxy-(_E_)-2-alkenals, 4-hydroxy-(_E_)-2-alkenals and 4,5-epoxy-2-alkenals), are also generated earlier in VSO than in RSO (after 6 and 8 days under AS conditions, respectively). Moreover, while all the different kinds of aldehydes are detected at the same time in VSO, the aldehyde appearance process in RSO is staggered, in such a way that 4-hydroxy-(_E_)-2-alkenals, 4,5-epoxy-2-alkenals and (_E_,_E_)-2,4-alkadienals emerge one day later than the first ones detected (n-alkanals, (_E_)-2-alkenals, and 4-h hydroperoxy-(_E_)-2-alkenals). This indicates a lower generation rate of this type of oxidation product in RSO. With regard to the evolution with time of the several groups of aldehydes, this is the same in both oils.
## 5 Conclusions  
In this paper, a behavior recognition method is proposed based on BPF for the application scenario of a fixed thermal imaging camera. Taking the indoor scene of the elderly living alone as an example, the practicality and effectiveness of this method are verified. The method first detects human targets using the improved YOLOv4 target detection algorithm. Secondly, a BPF model is designed to characterize human abnormal behavior.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test Set** & **Number** & **A\({}_{cc}\)/\%** & **FNR/\%** & **FPR/\%** \\ \hline all & 56 & 94.64 & 5.56 & 5 \\ crouch down & 5 & 80.00 & 0.00 & 20.00 \\ sit down & 7 & 85.71 & 0.00 & 14.29 \\ bend over & 3 & 100.00 & 0.00 & 0.00 \\ walk & 6 & 100.00 & 0.00 & 0.00 \\ fall down & 18 & 94.44 & 5.56 & 0.00 \\ stay still & 17 & 94.11 & 5.88 & 0.00 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of behavior detection.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Strategy** & **A\({}_{cc}\) (Normal Behavior)/\%** & **A\({}_{cc}\) (Abnormal Behavior)/\%** \\ \hline Direct update strategy & 89.79 & 91.46 \\ Voting update strategy & 91.27 & 94.02 \\ Similarity update strategy & 91.42 & 94.28 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation experiments of three strategies.  
Then, the abnormal behavior recognition task is accomplished by a self-update learning strategy and a series of detection rules.  
The work still has limitations, and future research can be conducted in the following aspects. (1) The current BPF method is mainly used for fixed thermal imaging camera scenes. The method can be extended to spherical thermal imaging cameras with rotatable viewpoints to establish a panoramic BPF model. (2) The features of the BPF model at this stage are extracted manually, and other effective features can be further explored in the future by considering using neural network learning to establish the BPF. (3) The idea of transfer learning can be applied to improve the skeleton extraction effect of the method based on skeletal keypoints on thermal imaging video, according to the unique imaging characteristics of thermal imaging. The skeleton and behavioral parameter field can be combined to obtain better recognition accuracy.  
Conceptualization, B.W., Z.D. and J.L.; Data curation, B.W., X.J. and J.L.; Formal analysis, B.W., X.J., Z.D. and J.L.; Funding acquisition, J.L.; Investigation, B.W. and J.L.; Methodology, B.W. and J.L.; Project administration, J.L.; Software, B.W.; Supervision, Z.D. and J.L.; Validation, B.W., X.J., Z.D. and J.L.; Visualization, B.W. and X.J.; Writing--original draft, B.W., Writing--review & editing, B.W., X.J., Z.D. and J.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Department of Science & Technology of Shandong Province (2017 CXGC0810) and Shandong Natural Science Foundation (ZR2021 QF043).  
The authors declare no conflict of interest.
## 4 Experimental Setup
### Dataset
#### 4.3.1 CoLA  
The CoLA (corpus of linguistic acceptability) [47] consists of 10,657 sentences from 23 linguistic publications, professionally annotated for acceptability (grammaticality) by their original authors. The public version presented here contains 9594 sentences from the training and development sets, excluding 1063 sentences from the retention test set. The goal is to classify the sentences into either acceptable or unacceptable categories based on their grammaticality. Due to its carefully curated and annotated nature, CoLA is a valuable resource for evaluating the performance of various NLP models and techniques in the domain of language understanding.
## 3 Experiments
### Experimental Results and Analysis
#### 3.3.3 Single Frequency Band Emotion Recognition  
In this paper, we further explore the effect of using single frequency band EEG signal features for emotion recognition based on the Capsule-Transformer method. For each subject, the frontal, temporal, parietal, and occipital lobes were used as inputs to the model using only a single band of DE features with the shapes (2400, 1, 7), (2400, 1, 10), (2400, 1, 10), (2400, 1, 5), respectively. The results, as shown in Figure 5, show that the emotion recognition effect of high-frequency EEG signal features is better than that of low-frequency EEG signal features, and the Gamma band reaches 90.59%, 89.79%, and 90.28% on the dimensions of valence, arousal, and dominance, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Label** & **Method** & **Accuracy (\%)** &
\begin{tabular}{c} **Precision** \\ **(\%)** \\ \end{tabular} & **Recall (\%)** & **F1 (\%)** \\ \hline \multirow{3}{*}{Valence} & without & 89.52 & 89.44 & 88.79 & 89.46 \\  & division & **96.75** & **97.22** & **96.64** & **96.59** \\ \hline \multirow{3}{*}{Arousal} & without & 88.46 & 88.59 & 88.25 & 89.26 \\  & division & **96.88** & **96.75** & **97.18** & **96.63** \\ \hline \multirow{3}{*}{Dominance} & without & 89.64 & 89.27 & 88.53 & 89.29 \\  & division & **96.25** & **95.93** & **96.36** & **96.18** \\ \hline \hline \end{tabular}
\end{table}
Table 2: The average recognition results with and without the brain region division strategy.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
###### Abstract  
Under the background of global warming, seasonal drought has become frequent and intensified in many parts of the world in recent years. Drought is one of the most widespread and severe natural disasters, and poses a serious threat to normal sugarcane growth and yield. However, a deep understanding of sugarcane responses to drought stress remains limited, especially at a large spatial scale. In this work, we used the traditional vegetation index (enhanced vegetation index, EVI) and newly downscaled satellite solar-induced chlorophyll fluorescence (SIF) to investigate the impacts of drought on sugarcane in a major sugarcane-planting region of China (Chongzuo City, Southwest China). The results showed that Chongzuo City experienced an extremely severe drought event during the critical growth periods of sugarcane from August to November 2009. During the early stage of the 2009 drought, sugarcane SIF exhibited a quick negative response with a reduction of approximately 2.5% from the multiyear mean in late August 2009, while EVI was not able to capture the drought stress until late September 2009. Compared with EVI, sugarcane SIF shows more pronounced responses to drought stress during the later stage of drought, especially after late September 2009. SIF anomalies can closely capture the spatial and temporal dynamics of drought stress on sugarcane during this drought event. We also found that sugarcane SIF can provide earlier and much more pronounced physiological responses (as indicated by fluorescence yield) than structural responses (as indicated by the fraction of photosynthetically active radiation) to drought stress. Our results suggest that the satellite SIF has a great potential for sugarcane drought monitoring in a timely manner at a large spatial scale. These results are important for developing early warning models for sugarcane drought monitoring, and provide reliable information for developing measures to relieve the negative impacts of drought on sugarcane yield and regional economics.  
solar-induced chlorophyll fluorescence; drought; sugarcane; enhanced vegetation index; remote sensing 2023
# Coupled Vibration Analysis of Submerged Floating Tunnel System in Wave and Current
## 2 Modeling of the SFT System
### Governing Equations  
Based on the previous simplified model and the corresponding assumptions [5; 10; 26], the governing equations of the two motion components (i.e., tube vertical displacement and cable cross-flow vibration) of the SFT system in wave and current are established by using the Hamilton principle [27].  
Figure 1: A typical submerged floating tunnel (SFT) tube-cable system in wave and current.  
\[\left\{\begin{array}{l}m_{ci}\frac{\partial^{2}v_{i}}{\partial t^{2}}+c_{i}\frac{ \partial v_{i}}{\partial t}-(T_{i}+\Delta T_{i})\frac{\partial^{2}v_{i}}{ \partial x_{i}^{2}}=F_{iL}-F_{iD}\\ E_{i}I_{i}\frac{\partial^{2}v_{i}}{\partial x^{2}}+m_{i}\frac{\partial^{2}z}{ \partial t^{2}}+c_{t}\frac{\partial z}{\partial t}+2\sum\limits_{i=1}^{n} \Delta T_{i}\sin\alpha_{i}\delta(x-x_{i})= F_{iW}\end{array}\right. \tag{1}\]  
where \(m_{ci}\) is the mass per unit length of the \(i^{\#}\) cables in air; \(m_{t}\) is the mass per unit length of the tube in air; \(E_{i}I_{i}\) is the bending stiffness of the tube around the \(Y\) axis; \(c_{i}\), \(c_{t}\) are the damping coefficients of the cables and tube; \(T_{i}\) is the initial tension force of the \(i^{\#}\) cable; \(\Delta T_{i}\) is the dynamic tension variation in the cable; \(F_{iL}\) is the vortex-induced lift force on the cable; \(F_{iD}\) is the drag force on the cable; \(F_{iW}\) is the vertical wave force acting on the tube; \(\delta\) is the Dirac function.  
In Equation (1), \(\Delta T_{i}\) reflects the coupled two DOFs motions of the tube-cable system, and comes from the VIV of itself and the vertical motion of the tube. It can be calculated as [28]:  
\[\Delta T_{i}=\frac{E_{ci}A_{ci}}{l_{i}}\int_{0}^{l_{i}}\varepsilon_{i}dx_{i}= \frac{E_{ci}A_{ci}}{l_{i}}\left[z(x=x_{ti})sin\alpha_{i}+\int_{0}^{l_{i}}\frac {1}{2}\left(\frac{\partial v_{i}}{\partial x_{i}}\right)^{2}dx_{i}\right] \tag{2}\]  
where \(E_{ci}\) is the elastic modulus of the \(i^{\#}\) cables; \(A_{ci}\) is the sectional area of the \(i^{\#}\) cables; \(\varepsilon_{i}\) is the cable axial strain; \(z(x=x_{ti})\) is the vertical displacement of the tube at the position of the \(i^{\#}\) cables.
## 3 Results and Discussion
### Figure of Merit (ZT)  
In order to determine the measuring temperature at which the maximum ZT value is recorded, sample measurements were performed in the temperature range between 30 \({}^{\circ}\)C and 150 \({}^{\circ}\)C. As seen in Table 3, the optimum measuring temperature was 130 \({}^{\circ}\)C (as discussed in more detail in Section 3.7), since, at this temperature, the maximum ZT value (2.93 \(\times\) 10\({}^{-3}\)) was determined by employing a 75 min processing time, an 80 \({}^{\circ}\)C processing temperature and a 2-ton processing pressure. Since the thermoelectric properties recorded at different temperatures showed similar trends under variable experimental parameters, including the BST content and processing time, temperature and pressure, it is assumed that the thermoelectric behavior at 130 \({}^{\circ}\)C is representative for the entire temperature range under investigation. For the determination of the figure of merit (ZT) (using Equation (1)), the thermal conductivity \(\kappa\) was calculated based on Equation (2):  
\[\kappa_{\text{total}}=\alpha\times\text{Cp}\times\rho \tag{2}\]  
where \(\rho\) is the pellet density, \(\alpha\) is the diffusivity and Cp is the heat capacity of the sample.
# Task Scheduling Mechanism Based on Reinforcement Learning in Cloud Computing
## 5 Experimental Results
### QMTSF Comparison with RR and RANDOM  
RR scheduling is a fair scheduling method that assigns tasks in GQ to each server in order. Random assignment, on the other hand, randomly assigns tasks to each server. A performance comparison of these two methods is shown in the figures below.  
From Figure 3, it can be observed that when the task number is small, QMTSF has a slightly better makespan compared to RR and Random, though the advantage is not very significant. However, as the number of tasks increases, the makespan of QMTSF increases only slowly, while RR and random experience a noticeable increase in time. Random assignment performs the worst, resulting in a large number of tasks failing to meet their deadlines. From Figure 4, it can be seen that QMTSF maintains a consistently uniform average task processing time as the number of tasks increases, indicating that the task processing time in QMTSF is only dependent on the VM processing speed and task data length, with minimal impact from the task number. While the average task processing time for RR increases with the number of tasks, this increase is within a reasonable range, while random assignment exhibits a significantly higher increase in the average task processing time.  
This is mainly because the objective of the RR policy is to balance the load of all VMs. As the number of tasks increases, achieving a balanced task allocation becomes increasingly difficult. On the other hand, the Random policy schedules tasks to servers randomly without considering the current workload of the servers, which can lead to some servers being idle while others are congested with excessive queued tasks. In contrast, QMTSF takes into account both the characteristics of tasks and the performance of servers when allocating tasks. Additionally, it considers task deadlines, which further influences the acquisition of reward values in the UQRL algorithm. RR and random assignment do not consider this aspect, which prevents them from obtaining more optimized Q-values when constructing the Q-table in the second stage.
## 3 Results and Discussion
### Addressing the Issue of Plagiarism
[PERSON] very rightly pointed out that the prevention of plagiarism must not be seen as a war between academic staff and students, but as an opportunity for intercultural interactions and growth. He further said that preventing plagiarism therefore needs to be based on the principles of good teaching [55]. It was further suggested that rather than scolding the students for poor work, teachers should provide them with academic support and help in building confidence and gaining knowledge. [PERSON] and [PERSON] also said that caring behavior exhibited by teachers in the classroom can help students not only with academic improvement, but also psychological, ethical, and moral improvements, which can lead to positivity and motivation amongst those students [56]. Some students also wrote about more active roles in student counseling centers in overcoming those psychological barriers. All these steps can greatly reduce the aforementioned emotional and psychological issues that have been some of the key factors of plagiarism. The last dimension of the model is prevention. Despite creating awareness and providing supportive measures for students, some may still opt for foul play and cheating. Thus, the last dimension becomes pivotal. Universities must develop strict policies about plagiarism and enforce them in an active manner for both teachers and students. Students committing plagiarism and teachers involved in fostering plagiarism should be handled such that others will be deterred from that behavior. Most of the time, students plagiarize when they find that the benefits outweigh the risks [17]. If the risks (e.g., the punishments) are greater than the benefits, students are more likely to stay away from it. Along with punitive actions, universities must also provide teachers with access to modern plagiarism-detection software to prevent plagiarism. Institutions should make software use mandatory for students' assignments, as well as for research work. Some students wrote that there was no culture of using plagiarism-detecting software for students' assignments or even research proposals. As one post-graduate student wrote,  
_\"Even [for a] synopsis (research proposal), many students copy and paste, and they [pass]. Students don't worry about plagiarism at early stages, [in class assignments, course papers, or synopses] and then they are asked to come up with original work at thesis writing. It is not fair. Nobody cares about plagiarism and then suddenly they expect us to produce original work when thesis work starts. [Universities] should check [for] plagiarism all the time so that students learn about it, and are careful about it.\"_  
The AS&P model appears to be quite comprehensive in eliminating or at least mitigating plagiarism in three different ways: creating awareness, providing support, and taking preventive measures. These measures are not only for students, but also for teachers. Plagiarism has become quite a menace in the current era, and it can only be controlled through a comprehensive approach.
# PVPBC: Privacy and Verifiability Preserving E-Voting Based on Permissioned Blockchain
## 8 Performance Analysis  
In this section, we investigate the performance of the PVPBC voting system. To carry out this investigation, we implemented a prototype system. Development of this prototype is initially described. We evaluate the gas and time required to deploy and register a smart contract as part of the registerVoter() function. We then evaluate the gas and time associated with the voter-authorisation phase, where the voter submits an access request to the front-end system. We provide a measurement of the authorisation time, which is the time that elapses between when an access request is submitted to the front-end system and when the request is acted upon. We investigate the impact of different voter population sizes on the authorisation time. The access-request transaction latency within the authentication DLT network is also investigated.
# Search of the Shortest Path in a Communication Network with Fuzzy Cost Functions
## 5 Application to a Communications Network. Fuzzy Functions and Strategies  
### Description of the Communications Network  
We carry out our study on two well known communication networks: the Nippon Telegraph and Telephone (NTT) and the National Science Foundation's Network (NSFNET). We have set the capacity of their links to 1 Gbit/s and 0.5 Gbit/s, respectively. The connections use Multi-Protocol Label Switching (MPLS) [29], so that once the path between two nodes is selected, it remains unchanged during the connection time. This practice is widely used in networks relying on quality of service (QoS) techniques to avoid the network to become unstable when applying dynamic routing. On the other hand, the connections are established without resource reservation. This implies that the connection is never rejected, although there can be a loss of information in the links when their capacity is exceeded. This way of proceeding is usual in IP networks since the management necessary for the resource reservation reduces the maximum throughput that the network can achieve.  
In the simulated networks, each node is identified by an index \(i\) and the link between nodes is determined by the source-destination index pair \(ij\). The capacity (or bandwidth) of a link \((i,j)\), (\(C_{ij}\)) and its _Used Bandwidth_ at time \(t\), (\([BW]_{ij}[t]\)) will be used as the two primary magnitudes.