esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Runoff prediction plays an extremely important role in flood prevention, mitigation, and the efficient use of water resources. Machine learning runoff prediction models have become popular due to their high computational efficiency. To select a model with a better runoff simulation and to validate the stability of the model, the following studies were done. Firstly, the support vector machine Model (SVM), the Elman Neural Network Model (ENN), and the multi-model mean model (MMM) were used for the runoff prediction, with the monthly runoff data from 1963-2007 recorded by the Pingtang hydrological station in the Chengbi River Karst Basin, China. Secondly, the comprehensive rating index method was applied to select the best model. Thirdly, the indicators of the hydrologic alteration-range of variability approach (IHA-RVA) was introduced to measure the model stability with different data structure inputs. According to the comprehensive rating index method, the SVM model outperformed the other models and was the best runoff prediction model with a score of 0.53. The overall change of the optimal model was 10.52%, which was in high stability.  
support vector machine; Elman neural network; multi-model mean model; model stability; runoff prediction; Chengbi River Karst Basin
# Retinal OCT Images: Graph-Based Layer Segmentation and Clinical Validation +
## 4 Discussion
### Validation Against Manual Segmentation  
The validation study compared the automated segmentation results with manual segmentation (MS) performed by an experienced clinician. The mean error between the two methods served as a metric for accuracy, with lower errors indicating higher accuracy. Negative mean error values for layers such as RNFL + GCL, OPL, and OS + RPE suggested that the automated algorithm tended to overestimate thickness compared to manual segmentation, whereas positive values for other layers indicated the opposite.  
The algorithm's accuracy varied across different layers, with the inner plexiform layer (IPL) showing the lowest accuracy (74.58%) and the combined outer nuclear layer and inner segment (ONL + IS) displaying the highest accuracy (98.90%). These findings underscore the potential of the automated algorithm to reliably segment retinal layers and provide accurate measurements that can be used for clinical diagnostics.
###### Abstract  
Given the improvements to network flexibility and programmability, software-defined wireless sensor networks (SDWSNs) have been paired with IEEE 802.15.4e time-slotted channel hopping (TSCH) to increase network efficiency through slicing. Nonetheless, ensuring the quality of service (QoS) level in a scalable SDWSN remains a significant difficulty. To solve this issue, we introduce the application-aware (AA) scheduling approach, which isolates different traffic types and adapts to QoS requirements dynamically. To the best of our knowledge, this approach is the first to support network scalability using shared timeslots without the use of additional hardware while maintaining the application's QoS level. The AA approach is deeply evaluated compared with both the application traffic isolation (ATI) approach and the application's QoS requirements using the IT-SDN framework and by varying the number of nodes up to 225. The evaluation process took into account up to four applications with varying QoS requirements in terms of delivery rate and delay. In comparison with the ATI approach, the proposed approach enhanced the delivery rate by up to 28% and decreased the delay by up to 57%. Furthermore, even with four applications running concurrently, the AA approach proved capable of meeting a 92% delivery rate requirement for up to 225 nodes and a 900 ms delay requirement for up to 144 nodes.  
software-defined wireless sensor networks; time-slotted channel hopping; network slicing; quality of service; application traffic isolation +
Footnote †: journal: Article  
0203 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)]([https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)) 4.0/).
# Knowledge Graph and Personalized Answer Sequences for Programming Knowledge Tracing
## 3 Method
### Knowledge Graph to Represent Exercise Vectors
#### 3.3.2 Knowledge Graph Embedding Model: RotatE-TA  
To improve the representation of exercises in our model, we build on the RotatE model, which is a knowledge graph embedding method that uses relational rotation in complex vector space to model entities and relations [31]. RotatE effectively captures the relationships between entities by treating relations as rotations in the complex plane. Building on this foundation, we propose a type-aware extension called RotatE-TA. The knowledge graph structure is incorporated into our model, while type information of KCs is also accounted for to create more accurate embeddings.  
* **Entity and Relation Definition**: We define predecessor KC as head entities \(c_{i}\) and successor KC as tail entities \(c_{j}\), with the relation denoted as \(r\). These two KCs, along with the relation, are represented as a triple \((c_{i},r,c_{j})\).
* **Scoring Function**: We define a scoring function for the triple as \(d_{r}(c_{i},c_{j})\), which evaluates the importance of a candidate triple.
* **Type Comparison**: The knowledge topics for all KCs in the knowledge graph are defined as \(T=\{t_{1},t_{2},\ldots,t_{m}\}\), where each KC \(c_{i}\) is associated with a knowledge topic  
Figure 3: A section of the constructed knowledge graph, illustrating the relationships between various KCs.  
\(c_{i}^{t}\in T\), and each KC belongs to only one knowledge topic. The comparison of the two knowledge topic types is calculated as follows: \[type(c_{i},c_{j})=\begin{cases}1,&\text{if}\quad c_{i}^{t}=c_{j}^{t}\\ 0,&\text{else}\end{cases}\] (1)
* **Distance Scoring with Type-Awareness**: Based on the type comparison function, we introduce type-aware weights into the distance scoring function as follows: \[d_{r}(c_{i},c_{j})=||c_{i}\circ r-c_{j}||+\lambda*||m_{c_{i}}-m_{c_{j}}||*type (c_{i},c_{j}).\] (2) In Equation (2), \(\circ\) represents the Hadamard product, \(m_{c_{i}}\) and \(m_{c_{j}}\) represent the modular lengths of the head entity and tail entity, respectively. \(\lambda\) is a hyperparameter representing the magnitude of type-aware weights.  
After embedding the knowledge graph, we obtain the vector representation of an exercise by simply adding the embedding vectors of the KCs, as shown in Equation (3):  
\[e_{t}=\sum_{k=1}^{m}\ c_{k}, \tag{3}\]  
where \(m\) represents the number of KCs involved in the exercise, \(c_{k}\) is the embedding vector for each KC, \(e_{t}\) is the final exercise vector, and this vector can capture the semantic relationships of the exercise in the knowledge graph.
## 3 Results and Discussions
### Energy Analysis of Corn Drying with the IRCC Corn Dryer  
#### 3.2.1 General View of Energy Structure  
Figure 7 describes the overall energy consumption proportions which demonstrate the whole dryer and the DC. The overall energy consumption of the whole dryer is primarily composed of five aspects including the coal, induced fan, discharging motor, hoist and the additional parts, which accounts for 97.57%, 1.88%, 0.19%, 0.28% and 0.08% of the total, respectively. The thermal energy given by fuel for heating drying medium accounts for most of the total energy consumption compared with the electrical energy given by power source for the other components. For the DC, the energy consumption in the inlet air, the induced fan and the recover radiant energy were studied, and the results indicated that the energy consumption in \(E_{ai}\), \(E_{\mathit{if}}\) and \(E_{\mathit{fir}}\), respectively, account for 91.66%, 5.86% and 2.47% of the total in the DC. Specific energy consumption is a commonly adopted index to evaluate the energy consumption in grain drying process which refers to the energy input to the drying system when 1 kg water is evaporated. It can be seen from Figure 8 that the SEC of both whole dryer and drying chamber increases continuously with drying process. Moreover, due to the binding energy as described in Section 3.1, the phenomenon that SEC of the whole dryer increases rapidly (19,844.52-42,425.59 kJ/kg) after 540 min was found obviously. Similar phenomenon has also been found in the research of a novel industrial multifield synergistic paddy dryer conducted by [PERSON] [22]. The technology of variable temperature subsection drying technology proposed by some researcher [49] may solve this problem. In the present work, the overall energy input to the whole dryer of drying process was 84,874.8 MJ, and the average SEC was 10.592 MJ/kg. The experiment of combined hot air-infrared thin layer drying of paddy studied by [PERSON] in 2015 [50] showed that the SEC of paddy drying varied from 9.4 to 50.3 MJ/kg, which indicating that the IRCC dryer of the present work has an acceptable level of energy utilization. For the drying chamber, the total energy consumption was 27,263.962 MJ while the average SEC is 3.402 MJ/kg. Figure 9 shows that the overall energy efficiency of the entire drying system and DC respectively ranges from 2.16% to 35.21% and 4.2% to 62.11%--which is higher than some previous findings, such as the paddy-fluidized bed drying with an energy efficiency of 5.24% to 13.92% [51] or potato-slices drying with an energy efficiency of 15.13% to 37% [21].  
Figure 7: Assessment of overall energy consumption of (**a**) the entire drying system and (**b**) the counterflow drying chamber (DC).
# A Comprehensive Literature Review on Hydrogen Tanks: Storage, Safety, and Structural Integrity
## 2 Hydrogen Tanks Classification
### Composite Tanks of Type I  
Type I tanks are widely used for hydrogen storage in industrial [49] and submarine applications [54], but their application in fuel cell vehicles is highly disadvantage. Firstly, they exhibit system gravimetric capacities of less than 0.010 kgH\({}_{2}\)/kgsystem, meaning that to store 5.6 kg of compressed gaseous hydrogen (enough for about 500 km of vehicle range), a storage system weighing 560 kg would be required. Secondly, they have fatigue resistance issues due to hydrogen diffusion into the metal, known as hydrogen embrittlement [23]. The only advantageous aspect of Type I tanks is their lower cost [56].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Type**} & \multicolumn{2}{c}{**Materials**} & \multicolumn{2}{c}{**Max. Pressure**} & \multirow{2}{*}{**Applications**} & \multirow{2}{*}{**Structural**} \\ \cline{2-2} \cline{5-6}  & **Metal** & **Composite** & **Polymer** & & **(bar)** & **Load** [51] \\ \hline \multirow{4}{*}{**III**} & \multirow{4}{*}{\begin{tabular}{c} Al/Steel liner \\ \end{tabular} } & Composite & \multirow{4}{*}{-} & Al/Glass: & \multirow{4}{*}{Vehicles} & The composite \\  & & & over-wrap & & 305 & & \\  & & (fiber glass/ & & Al/Aramid: & & metal liner \\  & & & aramid or & & 438 & & takes only \\  & & & carbon fiber) & & Al/Carbon: & & about 5\% \\  & & & & 700 & & mechanical \\ \hline \multirow{2}{*}{**IV**} & \multirow{2}{*}{-} & Composite & \multirow{2}{*}{\
\begin{tabular}{c} Polymer \\ liner \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} 350 (buses) \\ 700 \\ \end{tabular} } & \multirow{2}{*}{Vehicles} & The composite \\  & & & over-wrap & liner & 700 & & carries \\  & & & & & the load \\ \hline \multirow{2}{*}{**V**} & \multirow{2}{*}{-} & \multirow{2}{*}{Composite} & \multirow{2}{*}{-} & \multirow{2}{*}{1000} & Aerospace & The composite \\  & & & & Applications & carries \\  & & & & [36; 43; 55] & the load \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 3: Types of hydrogen tanks and their features. Grey represents the metal part of the tanks; black represents the composite over-wrap; green represents the polymer liner. Adapted from [51].
###### Abstract  
Photovoltaic (PV) water pumping systems are becoming popular these days. In PV water pumping, the role of the converter is most important, especially in the renewable energy-based PV systems case. This study focuses on one such application. In this proposed work, direct current (DC) based intermediate DC-DC power converter, i.e., a modified LUO (M-LUO) converter is used to extricate the availability of power in the high range from the PV array. The M-LUO converter is controlled efficiently by utilizing the Grey Wolf Optimizer (GWO)-based maximum power point tracking algorithm, which aids the smooth starting of a brushless DC (BLDC) motor. The voltage source inverter's (VSI) fundamental switching frequency is achieved in the BLDC motor by electronic commutation. Hence, the occurrence of VSI losses due to a high switching frequency is eliminated. The GWO optimized algorithm is compared with the perturb and observe (P&O) and fuzzy logic based maximum power point tracking (MPPT) algorithms. However, by sensing the position of the rotor and comparing the reference speed with the actual speed, the speed of the BLDC motor is controlled by the proportional-integral (PI) controller. The recent advancement in motor drives based on distributed sources generates more demand for highly efficient permanent magnet (PM) motor drives, and this was the beginning of interest in BLDC motors. Thus, in this paper, the design of a high-gain boost converter optimized by a GWO algorithm is proposed to drive the BLDC-based pumping motor. The proposed work is simulated in MATLAB-SIMULINK, and the experimental results are verified using the dsPIC30F2010 controller.  
PV water pumping; high gain DC-DC converter; modified LUO converter; hybrid MPPT algorithm; grey wolf optimizer
## 2 LFM Signal Detection Based on FrFT  
### Definition and Properties of FrFT  
The FrFT of signal \(x(t)\) with angle \(\alpha\) is defined by  
\[X_{p}(u)=\{F^{p}[x(t)]\}(u)=\int_{-\infty}^{+\infty}x(t)K_{p}(t,u)dt, \tag{1}\]  
where \(K_{p}(t,u)\) is the  
\[K_{p}(t,u)=\left\{\begin{array}{ll}\sqrt{(1-\mathrm{j}\cot\alpha)}\exp{j \pi\big{[}\big{(}t^{2}+u^{2}\big{)}\cot\alpha-2 tu\csc\alpha}\big{]},&\alpha\
eq n \pi,\\ \delta(t-u),&\alpha=2n\pi,\\ \delta(t+u),&\alpha=(2n+1)\pi,\end{array}\right. \tag{2}\]  
where \(\alpha\) is the rotation angle and represents the \(\alpha\)-th power of the ordinary Fourier transform operator, \(p\) is the fractional order, \(\alpha=p\pi/2\), \(F^{p}[\cdot]\) denotes the fractional Fourier transform operator, \(t\) represents the integral variable and the variable \(u\) has different physical meanings according to different fractional order \(p\). When the rotation angle \(\alpha=\pi/2\) (the order \(p=1\)), the transformation kernel is  
\[K_{1}(t,u)=e^{-j2\pi ut} \tag{3}\]  
The FrFT of \(x(t)\) with \(\alpha=\pi/2\) is  
\[X_{1}(t,u)=\int_{-\infty}^{\infty}e^{-j2\pi ut}x(t)dt. \tag{4}\]  
\(X_{1}(t)\) is the Fourier transform of \(x(t)\). Thus, FrFT is a generalization of FT.  
The inverse FrFT is  
\[x(t)=F^{-p}\big{[}X_{p}(u)\big{]}=\int_{-\infty}^{\infty}X_{p}(u)K_{-p}(t,u)du. \tag{5}\]  
Equation (5) indicates that signal \(x(t)\) can be characterized by a set of orthogonal basis functions \(K_{-p}(t,u)\) with weight coefficient \(X_{p}(u)\). These basis functions are complex exponential functions of LFM.  
In the following, we list some useful properties of FrFT, which will be applied to the mathematical derivation of proposed detection method.  
(1) Linear property:  
\[\{F^{p}[ax(t)+by(t)]\}(u)=aX_{p}(u)+bY_{p}(u) \tag{6}\]  
(2) Time shift property:  
\[\{F^{p}[x(t-\tau)]\}(u)=X_{p}(u-\tau\cos\alpha)\exp\Big{(}\mathrm{j}\tau\tau^ {2}\sin\alpha\cos\alpha-\mathrm{j}2\pi u\tau\sin\alpha\Big{)} \tag{7}\]In (6), \(a\) and \(b\) are arbitrary constants. Moreover, \(\tau\) is the time shift value. More properties of FrFT refer to [6].
# Performance Characterization of GNSS/IMU/DVL Integration under Real Maritime Jamming Conditions
## 5 Results  
Within the first part of this section, the results of the lab jamming experiment are shown. The experiment was used to investigate the validity of the adaptive range noise models in  
Figure 4: BALTIC TAUCHER II with the mounting of the antennas.  
Figure 3: Power spectral density of the used PPD jammer.  
jamming scenarios. In the second part of the section, we demonstrate the performance of the hybrid IMU/DVL/GNSS navigation system under real jamming conditions using both the classical GNSS measurement noise model as well as the adaptive noise model, as described in Section 5.1.
## 2 Modeling of a DFIG  
Based on the actuator disk theory, the power captured by the wind turbine from the wind is depicted by [2]:  
\[P_{m}=0.5\rho Av_{w}^{3}c_{P}(\lambda,\beta), \tag{1}\]  
where \(P_{m}\) is the mechanical power. \(\rho\) and \(A\) indicate the air density and swept area by the turbine, respectively; \(\beta\) and \(\lambda\) are pitch angle and the tip-speed ratio, respectively; \(c_{p}\) and \(v_{w}\) indicate the power coefficient and wind speed, respectively.  
In this paper, the power coefficient \(c_{p}\) is given as [4]:  
\[c_{P}(\lambda,\beta)=0.645\{0.00912\lambda+\frac{-5-0.4(2.5+\beta)+116\lambda_ {i}}{e^{2\Pi\lambda_{i}}}\}, \tag{2}\]  
where:  
\[\lambda_{i}=\frac{1}{\lambda+0.08(2.5+\beta)}-\frac{0.035}{1+\left(2.5+\beta \right)^{3}}, \tag{3}\]  
\[\lambda=\frac{\omega_{r}R}{v_{w}}, \tag{4}\]  
As in [21], the dynamic mode of one drive train is employed as follows:  
\[2H_{wt}\frac{d\omega_{r}}{dt}=T_{m}-D_{r}\omega-T_{e}, \tag{5}\]  
where \(H_{wt}\) is the inertia time constant of the wind turbine. \(T_{m}\) and \(T_{e}\) are the mechanical torque of wind turbine and electromagnetic torque of the DFIG, respectively. \(D_{r}\) indicates the rotor damping coefficient.  
As in [4], during MPPT control, \(c_{p}\) has a maximum value (\(c_{P,\max}\)) when \(\lambda\) is the optimal \(\lambda\) (\(\lambda_{opt}\)). Hence, the reference for MPPT control is derived via substituting (4) in (1), as follows:  
\[\begin{array}{rl}P_{MPT}&=\frac{1}{2}\rho\pi R^{2}(\frac{\omega_{r}R}{\lambda _{opt}})^{3}c_{p,\max}\\ &=k_{g}\omega_{r}{}^{3}\end{array} \tag{6}\]  
where \(k_{g}\) is the constant coefficient of MPPT control.  
As illustrated in Figure 1a, the DFIG controller system includes a pitch controller, rotor side converter (RSC) controller, and grid side converter (GSC) controller. The aim of the pitch controller is to avoid \(\omega_{r}\) from the maximum value and achieve de-loading operation. The objective of the RSC controller is to adjust torque (active power) of the DFIG, as displayed in Figure 1b. The aims of the GSC controller are to keep DC-link voltage at reference values and regulate the reactive power injection to the grid, as displayed in Figure 1c [22].  
Figure 1: Diagram configuration and control of the DFIG: **(a)** Diagram configuration of a DFIG; **(b)** RSC controller; **(c)** GSC controller.  
To draw realistic results, this study implements the maximum limiter and rate limiter for the active power control loop. The former is the torque limit referred to power of a DFIG with the rotor speed and minimum value of the power. The latter is 0.45 p.u./s [4; 23].
## 7 Conclusions  
A modified Godard timing estimation algorithm that allows for fast and efficient timing estimation has been introduced. It enables operation with less than two samples per symbol, which is not possible with the original Godard technique. The algorithm outperforms the multiplier-free Godard by, e.g., showing an improvement of more than 10 dB in jitter tolerance for a roll-off of 0.1. When compared to an implementation of the Gardner algorithm with the signal power, the newly introduced algorithm comes at the price of a slightly higher jitter for small roll-off factors, but pays off by allowing operation without any multiplications, an overall complexity reduction by more than a factor of two, and a factor of 22 and 6 times less LUTs and flip-flop operations, respectively.  
We acknowledge financial support by the European Commission under the FP7 program, project FOX-C (grant No. 318415), by the Xilinx University Program (XUP), and Oclaro for lending the optical modulators. It is a pleasure to acknowledge stimulating discussion with [PERSON], [PERSON], and [PERSON].  
[PERSON] conceived the concept, performed the simulations and the measurement, analyzed the data, and wrote the manuscript. [PERSON] developed the signal processing architecture, the hardware architecture, and the measurement, and helped with the manuscript. [PERSON] and [PERSON] realized the VHDL implementation of the algorithms. [PERSON] organized the required hardware and wrote the manuscript. [PERSON] conceived the concept, designed the experiment, and wrote the manuscript.  
The authors declare no conflict of interest.
## 1 Introduction
This creates a multi-objective optimization problem where communication and sensing are both optimized such that each UAV chooses a trajectory that gives the best balance between its communication and sensing. A trajectory only maximizing sensing capabilities may not guarantee effective communication performance, while a trajectory that optimizes communication may result in poor sensing. Thus, the co-optimization of communication and sensing is reflected in the tradeoffs between maximizing information gained and improving communication reliability. In this paper, we study the problem of multiple UAVs cooperative tracking a moving target. The main contribution of this paper is to develop a motion planning algorithm in which communication and sensing are both optimized. Our novel motion planning approach properly integrates the communication and sensing objectives to accomplish the target tracking mission of multiple UAVs, while maintaining proper connectivity to the remote base station. The structure of this paper is organized as follows. In Section 2, the mission scenario and the problem formulation is provided. Section 3 discusses the extended information filter (EIF) and the sensor fusion algorithms for moving target tracking in realistic communication environment. This section also introduces the Fisher Information Matrix (FIM), which is the inverse of Cramer-Rao Lower Bound (CRLB), as a tool to measure estimation performance. By identifying the measure of estimation performance, an observer trajectory quality metric is developed which can be used to determine the optimal trajectories of the UAVs for tracking a moving target. This trajectory quality metric combines the communication and sensing into a utility function. In Section 4, we propose our motion planning algorithm based on co-optimization of communication and sensing. The motion planning is performed in a distributed fashion, i.e., each UAV solves local rolling time domain optimization problem and obtains its own trajectory. We present our simulation and experimental results in Section 5, followed by summary and conclusions in Section 6.