esa-sceva/satcom-chunk-collection huggingface dataset:
# Leveraging Energy Harvesting and Wake-Up Receivers for Long-Term Wireless Sensor Networks
## 3 Energy Management for an Energy-Harvesting Sensor Node
### TC Design  
The TC aims to compute the throughput of the node over a time slot to consume the amount of energy specified by the EBC. As wireless communications are usually the most consuming task over all the other tasks such as sensing and computing [6], the throughput of the node given an energy budget is strongly tied to the MAC protocol.  
To transmit a single packet, a given MAC protocol typically requires many steps, such as receiving/sending a beacon frame, sending a data frame, receiving an Acknowledgment (ACK) frame, etc. The number of states in which the node can be when communicating using a given protocol is denoted by \(N_{S}\). Each state is defined by the combination of the different components' states (MCU, radio chip, sensors). The time spent in the state \(i\in\{1,\ldots,N_{S}\}\) during a single packet transmission is denoted by \(\tau_{i}\), and the corresponding power consumption of the node is denoted by \(P_{i}\). The energy cost of the whole process of performing a measurement and sending a single packet is therefore:  
\[e_{T}=\sum_{i=1}^{N_{S}}\tau_{i}P_{i}. \tag{5}\]  
and the energy consumed by the node over one time slot \(k\) is:  
\[e_{C}[k]=\frac{T}{T_{WI}[k]}e_{T}+\left(T-\frac{T}{T_{WI}[k]}\tau_{T}\right)P _{S}. \tag{6}\]  
where \(P_{S}\) is the power consumption of the node when all the components are in the sleep state and \(\tau_{T}\) is the total time required to perform a measurement and send a packet and is equal to \(\tau_{T}=\sum_{i=1}^{N_{S}}\tau_{i}\). Therefore, in order for the consumed energy \(e_{C}[k]\) to be equal to the energy budget \(e_{B}[k]\), the wake-up interval is set to the following value:  
\[T_{WI}[k]=\frac{e_{T}-\tau_{T}P_{S}}{\frac{e_{B}[k]}{T}-P_{S}}. \tag{7}\]  
This equation is obtained by replacing \(e_{C}[k]\) by \(e_{B}[k]\) in Equation (6). The associated throughput, in packets per minute, is thus:  
\[THR[k]=\frac{60}{T_{WI}[k]}. \tag{8}\]  
If we assume a low data-rate application, typical in EH-WSNs, usually MAC protocols are based on pseudo-asynchronous approaches, which makes the estimation of the \(\tau_{i}\) values challenging. Indeed, rendezvous schemes incur high variability of the time spent in the idle state and receive state for different packet transmissions. As a consequence of an inaccurate estimation of these values, the energy consumed by the node can be significantly different from the energy budget calculated by the EBC, which can lead to power failures or energy waste. Therefore, in Section 4, a new protocol reducing the energy consumption variability of packet transmission is proposed.
# Stabilization of Underactuated Surface Vessels: A Continuous Fractional Power Control Method
## 1 Introduction  
### Background Material  
Dynamic positioning of underactuated surface vessels (USV) plays an important role in many offshore applications such as cable laying, mine sweeping, platform supplying, rock dumping and, especially, oil field operations like drilling, pipe-laying and diving support. Critical to the dynamic positioning problem of an USV is its capability for accurate and reliable control subject to environmental disturbances as well as to configuration related changes. It is an underactuated control problem, since the number of independent actuators of the system is less than that of the degree of freedom. In this paper, we consider the stabilization problem of an USV that has no side thruster but only two independent main thrusters to provide surge force and yaw moment. The challenging problem is how to design a feedback control law that stabilizes both the position and the orientation of the vessel using only two available control inputs.
# Enhanced Learning Behaviors and Ability Knowledge Tracing
## 5 Experiments
### Baseline Models  
To assess the performance of the proposed LBAKT model, we conduct a comparison with five well-known state-of-the-art models (namely, DKT, DKT+, DKVMN, SAKT, and LPKT) to demonstrate its effectiveness. The details of these models serving as baselines are as follows:  
DKT [5]: DKT stands out as the pioneering model that introduced the use of deep neural networks for knowledge tracing. By employing recurrent neural networks, DKT adeptly captures the intricate sequential patterns inherent in student interactions and learning trajectories. The model facilitates more precise predictions of student performance and proficiency levels, revolutionizing the realm of educational data analysis and personalized learning experiences.  
DKT+ [6]: DKT+ is an extended variant of DKT, which aims to solve two major problems in DKT. The first problem is that the model fails to reconstruct the observed input. Second, the predicted performance for knowledge components across time-steps is not consistent.  
DKVMN [7]: DKVMN is a cutting-edge deep learning model that represents a recent advancement in the field of educational data analysis. This state-of-the-art model utilizes an auxiliary memory component to enhance the neural networks' ability to infer and establish students' knowledge states. By incorporating this innovative model, DKVMN significantly improves the accuracy and efficiency of knowledge tracing, thereby contributing to the optimization of personalized learning experiences and educational outcomes.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Datasets** & **Students** & **Concepts** & **Exercises** & **Answer** & **Interval** & **Hint** \\  & & & & **Time** & **Time** & **Times** \\ \hline ASSIST2012 & 29,018 & 265 & 53,091 & 26,747 & 29,748 & 11 \\ ASSISTChall & 1709 & 102 & 3162 & 1326 & 2839 & 41 \\ Algebra0607 & 1840 & 523 & 91,759 & 2394 & 6109 & 105 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dataset statistics.  
SAKT [21]: The self-attentive model for knowledge tracing (SAKT) identifies the knowledge components (KCs) from the students' past activities that are relevant to a given KC. This approach predicts the student's mastery based on a relatively small number of KCs that it selects. As predictions are made based on a limited number of past activities, the model better addresses the data sparsity problem compared to models reliant on recurrent neural networks (RNNs).  
LPKT [17]: The learning process-consist knowledge tracing (LPKT) model monitors students' knowledge states through directly modeling their learning process. By integrating both answer time and interval time, the model captures the learning progression of learners by quantifying the learning gain and forgetting that occur between consecutive time steps.
## 7 Conclusions  
This research proposes a novel approach for task scheduling and load balancing using the DSC and MSC algorithms to increase response time for user task. Incoming tasks are clustered using the DSC algorithm on the basis of priority. Priority value is computed according to the top and bottom levels of the tasks and is used to form clusters for task scheduling. The MHEFT algorithm is adopted to rank scheduled tasks via rank value computation, in which the average execution and communication times for each task are considered. The highest priority task is selected and assigned first for upcoming processes. Resource utilization in our work is increased by such task clustering and ranking compared with that in existing methods. In load balancing, the MSC algorithm, which is a hill-climbing algorithm that shifts the kernel for each iteration to a higher-density region until convergence, clusters VMs. The mean shift defines every shift in this algorithm, and the kernel approaches the centroid or mean of the points within it in every iteration. The kernel that approaches the highest density function serves as the basis for computing the mean shift vector. The WLC algorithm, which assigns weight to each VM on the basis of its client connectivity, CPU idle rate, and CPU speed, is used for load balancing after clustering. The weight value for each VM in a cluster is computed by considering CPU idle rate, CPU speed, memory idle rate, and memory size. The highest weight VM is selected for task allocation, which reduces the response time of the user task. Response time, makespan, resource utilization, and service reliability are considered in our proposed work. The simulation results indicate that the performance metrics of our proposed work are better than those of the TTA method, particularly in decreasing response time. Our proposed method realizes 98% resource utilization, which is higher than that of TTSA. In the future, we plan to integrate a new intelligent optimization algorithm to select the highest priority task and to adopt an efficient load balancing algorithm to improve execution time in our future work.  
Conceptualization, A.S. and R.A.; methodology, A.S.; software, A.S.; validation, A.A., A.M.; formal analysis, A.A.; investigation, R.A.; resources, A.S.; writing--original draft preparation, A.S.; writing--review and editing, A.A.; visualization, A.M.; supervision, A.S.; project administration, R.A.  
This research received no external funding.  
The authors thank the anonymous reviewers for their insightful comments that helped improve the quality of this study.  
The authors declare no conflict of interest.
# An Asynchronous Parallel I/O Framework for Mass Conservation Ocean Model
## 4 Design and Algorithm
### Reordering and Parallel Outputting Data in I/O Processes  
The workflow for reordering and parallel outputting data in I/O processes is shown in Figure 5. First, the I/O primary process receives data from the corresponding computing processes through the mpi_igatherv function in step 1. Second, the I/O primary process waits for communication to be completed in step 1 by calling mpi_wait of the MPI APIs. Third, the I/O primary process calls a self-defined function of data_reorder to adjust the data into a continuous order for output. The method of data reordering is shown in the following section of data optimization. Next, the I/O primary process partitions the output data equally and continuously based on the number of I/O worker processes, and scatters these data to the I/O worker processes in step 4. Finally, all I/O worker processes use data_output in step 5 to parallel write the output data into a NetCDF file by calling nf90_put_var of the NetCDF APIs or nf90 mpi_put_var_all of the PnetCDF APIs according to the user's preference.  
To output data into a file in step 5, there are two methods: independent file access and combined file access. The independent file access method executes the functions of opening and closing a file for outputting each variable output. In the combined file access method, the function of opening a file is only executed once at the beginning of outputting all variables, and the function of closing a file is also only executed once at the end of outputting all variables. With the combined file access method, the cost of frequently opening and closing a file can be avoided.
# The Functions of Chloroplastic Ascorbate in Vascular Plants and Algae
## 7 Reactive Oxygen Species Management by Ascorbate in the Chloroplast  
The best-known and most extensively discussed role of Asc is participation in ROS management. Excellent recent reviews are available on this topic [14; 129; 130]; therefore, only a few aspects related to photosynthesis are mentioned here.  
Asc is essential for the enzymatic scavenging of ROS in the so-called Mehler reaction or the water-water cycle (reviewed by e.g., [14; 157], Figure 1). This reaction becomes particularly relevant at high light intensities and/or when the Calvin-Benson cycle cannot work at high speed, for instance under drought, cold, or salt stress. Under such conditions, the outflow of electrons at the PSI acceptor side is inhibited, leading to a surplus of electrons; therefore, ferredoxin reduces O2, and superoxide is produced. It is then reduced to H2O2 by superoxide dismutase, which is reduced by APX to water. MDA can be directly reduced back to Asc by PSI, and/or in the Asc-glutathione cycle in which MDAR and DHAR use NADPH as reducing power [158]. Asc thereby participates in the mitigation of ROS. In this respect, it is to be considered that ROSs are key signaling molecules that enable cells to respond to changes in environmental conditions rapidly, and they integrate different environmental signals to activate stress-response networks and defense mechanisms [130].  
In addition to its function as a powerful antioxidant, Asc is also considered as being a major player in redox homeostasis and part of the redox signaling network that regulates plant responses to biotic stress [14; 159; 160]. Interestingly, however, an approx. 80% decrease in Asc content led to only a minor increase in glutathione level when plants were grown under standard laboratory conditions [35; 81], though cellular glutathione levels are redistributed and an approx. two-fold increase in chloroplastic glutathione concentration was observed [161]. In addition, Asc peroxidase activity and the redox state of Asc are also unaltered in _vtc2_ mutants, just as well as the photosynthetic activity and carotenoid content, and only moderate changes occur at constant high light in these parameters [33; 35]. Thus, an approx. 80% reduction of cellular Asc content does not lead to photooxidative stress, and apparently, Asc is in large excess in vascular plants, or its deficiency, are compensated via various yet unexplored mechanisms.
# Bistatic Forward ISAR with DVB-T Transmitter of Opportunity
## 2 BFISAR Geometrical and Kinematical Equations  
Consider a BFISAR scenario with a DVB-T stationary transmitter \(\mathbf{T}\), receiver \(\mathbf{R}\), and moving object presented in a Cartesian coordinate system \(Oxyz\), as shown in Figure 1. The object's geometry depicted in its own coordinate system \(O^{\prime}XYZ\) is presented as an assembly of reference points placed in nodes of a regular grid. The reference points, whose intensities describe the object's geometry, are scattering points. Intensities of the remaining reference points are zero or noise-defined. The object is moving near the base line, while the transmitter and receiver can be placed on hills, mountain heights, or roofs of high buildings near the area of surveillance.  
Vectors \(\mathbf{R}^{s}\) and \(\mathbf{R}^{\prime}\) define the positions of the transmitter and receiver, respectively, in a coordinate system \(Oxyz\). The line \(\mathbf{TR}\) is the baseline. The distance vector \(\mathbf{R}_{ijk}\) defines the position of the \(ijk\)-th scattering point in the coordinate system \(O^{\prime}XYZ\). The vector \(\mathbf{R}_{00^{\prime}}(p)\) defines the position of the object's mass-center at the \(p\)-th-emitted DVB-T segment. The geometrical and kinematical ratios in Figure 1 can be described by the vector equations as follows.  
The vector distance of the transmitter-object's mass center:  
\[\mathbf{R}^{s}(p)=\mathbf{R}^{s}-\mathbf{R}_{00^{\prime}}(p)=\mathbf{R}^{s}-\mathbf{R}_{00^{ \prime}}(0)-\mathbf{V}.p.T_{p} \tag{1}\]  
where \(\mathbf{V}\) is the object's vector velocity, \(p=\overline{0}\), \(N-\overline{1}\) is the index of the emitted segment, \(N\) is the total number of DVB-T segments emitted during bistatic inverse aperture synthesis, and \(T_{p}\) is the segments' time repetition period for aperture synthesis.  
The vector distance of an object's mass center-DVB-T receiver:  
\[\mathbf{R}^{r}(p)=\mathbf{R}^{r}-\mathbf{R}_{00^{\prime}}(p)=\mathbf{R}^{r}- \mathbf{R}_{00^{\prime}}(0)-\mathbf{V}.p.T_{p} \tag{2}\]  
The vector distance of a DVB-T transmitter-_ijk_-th object's scattering point:  
\[\mathbf{R}^{s}_{ijk}(p)=\mathbf{R}^{s}(p)+\mathbf{A}\mathbf{R}_{ijk}=\mathbf{R }^{s}-\mathbf{R}_{00^{\prime}}(0)-\mathbf{V}.p.T_{p}+\mathbf{A}\mathbf{R}_{ijk}, \tag{3}\]  
where \(\mathbf{A}\) is the Euler transformation matrix.  
The vector distance of an _ijk_-th object's scattering point-DVB-T receiver:  
\[\mathbf{R}^{r}_{ijk}(p)=\mathbf{R}^{r}(p)-\mathbf{A}\mathbf{R}_{ijk}=\mathbf{R }^{r}-\mathbf{R}_{00^{\prime}}(0)-\mathbf{V}.p.T_{p}-\mathbf{A}\mathbf{R}_{ijk} \tag{4}\]  
The total distance of a DVB-T transmitter-_ijk_-th object's scattering point-DVB-T receiver is defined by:  
\[\left|\mathbf{R}_{ijk}(p)\right|=\left|\mathbf{R}^{s}_{ijk}(p)\right|+\left| \mathbf{R}^{r}_{ijk}(p)\right| \tag{5}\]  
Expression (5) is applied in the determination of the signal time delays from the object's scattering points and synthesis of the BFISAR signal.
# Demand Forecasting in Supply Chain Using Uni-Regression Deep Approximate Forecasting Model
## 5 Results and Discussions
### Comparative Methods
#### 5.3.6 Comparative Analysis of Daily Demand Forecasting Orders Based on K-Fold  
The evaluation's Figure 9a demonstrates the UDAF model's exceptional performance in demand forecasting, outperforming NARX with the lowest MAE of 2.35.  
Figure 9b further showcases the UDAF model's effectiveness in demand forecasting, surpassing NARX by 51.55% with an MSE of 2.5 for a K-fold 10.  
When it comes to demand forecasting, the UDAF model, as seen in Figure 9c, excels in comparison to NARX. It achieves an improvement of 30.39% and has an RMSE of 1.58 for a K-fold 10, surpassing previous techniques.  
Furthermore, Figure 9d demonstrates the UDAF model's proficiency in demand forecasting through RMSSE, outperforming NARX by 3.125 % with a 0.031 RMSSE for a K-fold 10.  
Regarding demand forecasting, Figure 9e showcases the superior performance of the UDAF model compared to NARX with an R-squared of 0.90 for a K-fold 10, which is 1.11% improved over NARX.
## 4 Study and Implementation
### The Test Suite Implementation  
The chosen device for test suite implementation was a Lenovo Phab 2 Pro. This device offers hardware depth sensors and supports the Google Tango AR platform [9]. Google Tango is a precursor to the current Google ARCore [11], and it provided a stable platform for our markerless AR test suite implementation. Although Tango and Phab2 Pro have been discontinued, the presented ideas and results are valid in general for any mobile markerless-AR-capable device.  
Figure 1 depicts the system support for the test suite implementation. While the Android Software Development Kit (SDK) provided system-level support, the 3D virtual object interactions were based on the Unity game engine framework. The Google Tango SDK depended on both the Android SDK and the Unity game engine framework as it presented the interface for markerless-AR-specific functionality. The software application for this study was implemented on top of all three libraries.  
Figure 2a shows the test runtime where the user could select from one of the five methods to be tested: 3 DTouch, IVI, HOMER-S, hybrid, and MOD IVI. Figure 2b shows a user interacting with a test case (discussed in the next section).  
Figure 1: System support for the suite implementation.  
Figure 2: The test suite implementation. (**a**) Initial user interface screen; (**b**) user running a test case.
## 2 Materials and Methods
### Location Selection  
According to the literature examined in the literature review section, the performance of PCMs is sensitive to many factors, including their type, latent heat of fusion, melting point, PCM layer configuration with respect to the other components inside the external walls, the PCM layer thickness, and the outside climatic conditions, including the ambient air temperature, the sky clearness, and the solar radiation of any particular location. Therefore, the PCM-incorporated building envelope predominantly depends upon the climate of the site where the building is situated.  
To evaluate the site-specific performance of PCM in detail, three locations were selected in Jordan based on the three types of climates prevailing in the country, namely, the capital city of Amman (31\({}^{\circ}\)57\({}^{\prime}\)47.3688\({}^{\prime\prime}\) N, 35\({}^{\circ}\)55\({}^{\prime}\)49.2924\({}^{\prime\prime}\) E) for temperate climate, Irbid governor (32\({}^{\circ}\)33\({}^{\prime}\)5.2020\({}^{\prime\prime}\) N, 35\({}^{\circ}\)51\({}^{\prime}\)5.3244\({}^{\prime\prime}\) E) for Mediterranean climate, Aqaba governorate (29\({}^{\circ}\)31\({}^{\prime}\)36.01\({}^{\prime\prime}\) N, 35\({}^{\circ}\)00\({}^{\prime}\)28.01\({}^{\prime\prime}\) E) for hot and dry climate, and the city of Oradea in Romania (47\({}^{\circ}\)47\({}^{\prime\prime}\) N, 21\({}^{\circ}\)51\({}^{\prime\prime}\) E) for cold and wet climate. Table 1 shows the average daytime and nighttime temperature during summer and winter for the selected locations.
# Effect of Ultrasonic Treatment on the Quality of Pumpkin Juice Fermented by Yeast
## 3 Results
### Influence of Ultrasonic-Assisted Yeast Fermentation on the Carotenoids in Pumpkin Juice  
The influence of ultrasonic-assisted yeast fermentation on the carotenoids in pumpkin juice is shown in Figure 2K. The results indicated that the content of carotenoids exhibited an obvious increasing trend as the ultrasonic power increased. However, when the ultrasonic power reached 400 W, the content of carotenoids decreased slightly. When treated at a power of 300 W, the measured carotenoid contents were 133.77%, 158.39%, 156.33%, 158.38%, and 86.45% in the control group (untreated group). This increase was mainly because ultrasonic treatment accelerated the rupture of the pumpkin cell wall. Breaking the barrier that previously restricted the release of carotenoids promoted the dissolution and release of carotenoids ([PERSON] et al., 2018). It is worth noting that, compared with the untreated group, the overall content of carotenoids still showed an upward trend. This finding aligned with the results reported by [PERSON] et al. (2018), who discovered that although carotenoids underwent chemical degradation, their content remained higher than in the untreated juice. Based on the experimental results, ultrasonic-treated pumpkin juice has certain advantages for subsequent processing. When the ultrasonic power was below 400 W, as the power increased, the dissolution rate of carotenoids rose. This might have been due to two main factors. First, the previous cell wall rupture had a cumulative effect. Second, the physical environment within the juice changed. Mechanical permeation enhanced the permeability of the solvent ([PERSON] et al., 2018), and ultrasonic cavitation caused cell rupture, increasing the amount of substances inside the cells released into the solvent ([PERSON] et al., 2018), which in turn raised the content of carotenoids.