esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
[29] also reported that \(cSIF_{yield\_760}\) can reasonably capture the spatial and temporal dynamics of drought and heatwave development and showed an earlier response than traditional vegetation indices (such as the enhanced vegetation index, EVI) during the early stages of drought and heatwave events. Recent studies have begun to explore the relationship between red SIF and GPP. For example, [PERSON] et al. [30] explored the apparent spectral fluorescence yields (\(ASIF_{yield\_687}\) and \(ASIF_{yield\_760}\), which were measured by SIF normalized with PAR) for wheat in correlation with GPP and found that far-red canopy  
Figure 1: Sample reabsorption and scattering inside leaves (**a**) and canopy (**c**). PAR is the photosynthetically active radiation. SIF is the solar-induced chlorophyll fluorescence. R represents the reflectance, and T is the transmittance. A represents the absorbed effects and E indicates the canopy scattering effects. Relative PSII or PSI fluorescence represents the relative magnitude of fluorescence emitted from PSII or PSI, respectively (**b**). SIF (\(cSIF_{60}\)) related more strongly to GPP than red canopy SIF (\(cSIF_{688}\)). It should be noted that \(cSIF_{60}\) carries much information relating to the canopy structure, and [PERSON] et al. [16] reported that \(NIRvP\) was a robust structural proxy for \(cSIF_{760}\) and GPP across scales. At present, there are few studies on the relationship between red SIF and GPP under stress conditions. Therefore, in order to explore the physiological link between SIF and GPP under stress conditions, changes in both red and far-red SIF in response to environmental conditions should be explored. Until now, except for ENF, the effects of temperature on the SIF-GPP relationship in most plants have not been well-explored [31]. Furthermore, some studies have found that plant function types influenced SIF-GPP relationships and that the impacts of low temperatures on photosynthetic apparatuses of woody plants (for example, ENF) and herbaceous species (for example, winter wheat) may follow a different pattern during the winter [32]. Therefore, observations on winter wheat would represent an important contribution to our knowledge of the impact of the differences between SIF and GPP in response to temperature. Here, we studied the responses of red band and far-red band SIF as well as photosynthesis to low air temperature based on continuous measurements of winter wheat during the overwintering period. Furthermore, to disentangle the structural and physiological factors, we investigated the mechanisms linking GPP to SIF by disentangling the impacts of APAR, \(\Phi_{F}\) and \(f_{esc}\), separately. Finally, we assessed the impacts of temperature on GPP estimation models based on SIF.
# A Novel System of Mixed RF/FSO UAV Communication Based on MRR and RIS by Adopting Hybrid Modulation
## 2 System and Theoretical Model
### FSO Link
\[\alpha_{i}=\left[\exp\Bigg{(}\frac{0.49\sigma_{R}^{2}}{(1+1.11\sigma_{R}^{12/5 })^{7/6}}\bigg{)}-1\right]^{-1}, \tag{5}\]  
\[\beta_{i}=\left[\exp\Bigg{(}\frac{0.51\sigma_{R}^{2}}{(1+0.69\sigma_{R}^{12/5 })^{5/6}}\bigg{)}-1\right]^{-1}, \tag{6}\]  
where \(\sigma_{R}^{2}~{}=~{}1.23C_{h}^{2}K^{7/6}d^{11/6}\) is the Rytov variance, \(C_{n}^{2}\) is the atmospheric refractive index structure parameter, \(K=2\pi/\lambda\) is the wave number, \(\lambda\) is the wavelength, \(d=(h^{\prime}-h)/\sin\theta\) is the FSO propagation distance, \(h^{\prime}\) is the height of the UAV, \(h\) is the height of the optical receiver at the destination and \(\theta\) is the angle between the FSO link and the horizontal direction. By integrating (4), the CDF of the instantaneous SNR for the FSO uplink can be obtained as [32]:  
\[F_{\gamma_{2}}(\gamma_{2})=\frac{\xi^{2}}{\Gamma(\alpha_{1})\Gamma(\alpha_{2} )\Gamma(\beta_{1})\Gamma(\beta_{2})}G_{2,6}^{5}\bigg{(}\frac{\alpha_{1}\alpha_ {2}\beta_{1}\beta_{2}}{A_{0}}\sqrt{\frac{\gamma_{2}}{\gamma_{2}}}\bigg{|} \xi^{2},\alpha_{1},\alpha_{2},\beta_{1},\beta_{2},0\bigg{.}\bigg{)}. \tag{7}\]  
In the FSO downlink, the experienced atmospheric channel distance is the distance from the destination to the relay. Thus, the PDF of the instantaneous SNR for the FSO downlink can be represented as [34]:  
Figure 2: Schematic diagram of MRR FSO with bidirectional communication. \[f_{\gamma_{2}}(\gamma_{2})=\frac{\xi^{2}}{2\gamma_{2}\Gamma(\alpha_{3})\Gamma( \beta_{3})}G_{1,3}^{3,0}\bigg{(}\alpha_{3}\beta_{3}\sqrt{\frac{\gamma_{2}}{ \tilde{\gamma}_{2}}}\bigg{|}\xi^{2}+1\bigg{.} \tag{8}\]  
where \(\alpha_{3}\) and \(\beta_{3}\) are the atmospheric turbulence parameters for the FSO downlink. Similarly, by integrating (8), the CDF of the instantaneous SNR for the FSO downlink is derived as [35]:  
\[F_{\gamma_{2}}(\gamma_{2})=\frac{\xi^{2}}{\Gamma(\alpha_{3})\Gamma(\beta_{3})} G_{2,4}^{3,1}\bigg{(}\alpha_{3}\beta_{3}\sqrt{\frac{\gamma_{2}}{\tilde{ \gamma}_{2}}}\bigg{|}\xi^{2},\alpha_{3},\beta_{3},0\bigg{)}. \tag{9}\]
## 2 Literature Review
### Clustering Based Two-Phase Recommendation  
Existing literature has addressed the improvement of the CF algorithm through clustering analysis. For example, in Reference [41], users are grouped into clusters to find a match between the target user and each user group. Figure 1 illustrates a two-phase strategy for a recommendation based on clustering techniques, which consists of two phases, i.e., offline clustering and online recommendation. Before making rating predictions according to neighbors, users or items are aggregated first to form clusters, from which the nearest neighbors are determined through similarity calculation. Once clusters of the target user are determined, rating predictions can be made in view of the ratings of nearest neighbors of the small-scale clusters in the phase of online recommendation. In this way, the computational complexity of the CF algorithm is optimized.  
However, most of them focus on clustering analysis of items only or users only. Furthermore, due to the sparsity of the user-item rating matrix, the deviation of similarity calculation is quite large, making it difficult to guarantee the quality of recommendation services. This paper proposes a hybrid recommendation method considering both item clustering and user clustering.
# Computational-Intelligence-Based Spectrum-Sharing Scheme for NOMA-Based Cognitive Radio Networks
###### Abstract  
The integration of non-orthogonal multiple access (NOMA) technology and cognitive radio networks (CRNs) promises to enhance the spectrum utilization efficiency of 5G and beyond-5G (BSC) mobile communication systems. In this article, a NOMA-based spectrum-sharing scheme is proposed for dual-hop CRNs in which a primary transmitter separated by a long distance from the primary receiver communicates via NOMA-based CRN. In this scenario, we mathematically formulate a constrained optimization problem to maximize the sum rate of all secondary users (SUs) while maintaining the total transmit power of the system. Inspired by the effectiveness of computational intelligence (CI) tools in solving non-linear optimization problems, this article proposes three CI-based solutions to the given problem aiming to guarantee quality of service (QoS) for all users. In addition, an enhanced version of the classic artificial bee colony (ABC) algorithm, referred to here as the enhanced-artificial-bee-colony (EABC)-based power allocation scheme, is proposed to overcome the limitations of classic ABC. The comparison of different CI approaches illustrates that the minimum power required by the secondary NOMA relay to satisfy the primary rate threshold of 5 bit/s/Hz is 20 mW for EABC, while ABC, PSO and GA achieve the same target at 23 mW, 27 mW and 32 mW, respectively. Thus, EABC reduces power consumption by 13.95% compared to ABC, while 29.78% and 46.15% power-saving is achieved compared to PSO and GA, respectively.  
caputational intelligence; spectrum sharing; cognitive radio networks; non-orthogonal multiple access +
Footnote †: journal: Applied Sciences  
0
Footnote 0: Copyright** © 2023 by the author. 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/py/](https://creativecommons.org/licenses/py/)]([https://creativecommons.org/licenses/py/](https://creativecommons.org/licenses/py/)) 4.0/).
# A Novel Multi-Agent-Based Approach for Train Rescheduling in Large-Scale Railway Networks
## 5 Case Study
### Scenario Design  
Most of the past research used historical delay data as the test scenario in case studies; however, it would be challenging to reflect all potential delay scenarios in practice. In that case, we use a sensitive testing approach to design the delay scenarios in this paper, as such, to evaluate the effectiveness of the proposed algorithm.  
In this paper, three classes of test scenarios were designed: individual train entrance delays, multiple train entrance delays, and minor perturbations in station areas [29].  
**Class I**: Individual Entry Delay  
In this class, a delay is introduced at the point where a train enters the simulation. This could occur either at the edge of the network, where a train enters the simulation area, or at a station within the network, where a scheduled journey begins. The purpose of this scenario is to observe how a single train's delay propagates across the entire network under different dispatching strategies. These scenarios are labeled _I_Si_, where _Si_ represents the delayed train. Each scenario includes 25 cases, with input delays ranging from 0 to 1440 s, in increments of 60 s.  
**Class II**: Multiple Entry Delays  
In Class II, delays are introduced to two individual services at their entry points into the simulation. These scenarios are labeled _M_Si_Sj_, where _Si_ and _Sj_ represent the delayed services. Each scenario includes 156 cases, testing various dispatching rules. _Si_ is delayed from 0 to 1440 s in increments of 120 s, while _Sj_ is delayed from 60 to 1380 s, also in increments of 120 s.  
**Class III**: Minor Perturbation Delays  
In this class, delays are introduced at each scheduled stopping point for every service, affecting the departure time. The delay is added as follows:  
1. the scheduled departure time and
2. the arrival time + the required dwell time.  
The value of delays is drawn from the Weibull distribution stated in Equation (25):  
\[f(x;\lambda,k)=\left\{\begin{matrix}0,&x<0\\ \frac{k}{\lambda}\left(\frac{x}{\lambda}\right)^{k-1}e^{-(x/\lambda)^{k}},&x \geq 0\end{matrix}\right. \tag{25}\]  
where \(\lambda\) and \(k\) are the scale and shape parameters, respectively. We developed three kinds of cases with different parameter settings, as shown in Table 3.
## 6 Discussion
### First-Arrival Time Picking Methods  
In this study, the first-arrival phase-difference picking method relied on cross-correlation calculations. However, under high-noise conditions, the cross-correlation results can interfere, leading to inaccurate first-arrival time picking. In such cases, incorrect seismic trace selection may introduce misleading velocity updates, thereby affecting the accuracy of inversion results. Therefore, future research should explore more precise first-arrival time extraction methods combined with noise suppression techniques to enhance the reliability of the first-arrival information extraction.
# Valorization of Agri-Food Waste into PHA and Bioplastics: From Waste Selection to Transformation
## 2 Production of Valeric- and Propionic-Rich VFA Streams from Agri-Food Wastes
### Agri-Food Waste Typically Used for VFA Production
#### 2.2.1 Production of VFA, Rich in Valeric Acid and Propionic Acid, from Cheese Whey
A distinct study was conducted by [PERSON] and [PERSON] [83], which showed that propionate is favored during the acidogenesis of dairy wastewater (simulated dairy wastewater, prepared from full-cream powdered milk) at pH 4.0-4.5. * Organic loading rate (OLR). The organic loading rate indicates the amount of material used in the reactor feed, and can be expressed in terms of COD or volatile solids (VSs). [PERSON] et al. [80], no clear trend was observed between changes in organic loading and VFA yield and production; [PERSON] et al. [77] observed that an increase in organic loading had a negative effect on VFA production when whey was used as substrate. The increase in OLR from 3 to 12 g COD L\({}^{-1}\) d\({}^{-1}\) means a decrease in the acidification degree from 94% to 65%. Regarding the VFA profile, in the same study, it was concluded that a lower organic load improves the production of propionic and valeric acids; the highest ratio of both acids occurred at an OLR of 3 g COD L\({}^{-1}\) d\({}^{-1}\). The effect of OLR on the distribution of the different VFA is illustrated in the figure below (Figure 2). * Temperature. The mesophilic condition (35 \({}^{\circ}\)C) is considered the most efficient and economically favorable for VFA production [80]. However, the influence of temperature on the type of VFA produced is less than that of pH [84]. In conclusion, according to the literature reviewed, whey is a good raw material for VFA production due to its high content of fermentable organic matter. The SBR operating parameters such as pH, SRT, and OLR have a direct influence on the VFA distribution [77].
## 2 Target Detection Method
### Faster R-CNN Network Structure and Detection Steps  
The network structure diagram of faster R-CNN is shown in Figure 1. The detection process is described as follows:  
1. Input the image to be tested;
2. Use the VGG16 network to extract feature maps from the entire input image. The feature maps are shared for the subsequent RPN layer and fully connected layer;
3. The RPN network is used to generate region proposals. This layer uses softmax to determine whether the anchors are positive or negative, and then uses bounding box regression to correct the anchors to obtain accurate proposals.
4. The RoI pooling layer collects the input feature maps and proposals, combines the information to extract the proposal feature maps, and sends them to the subsequent fully connected layer to determine the target category;
5. Proposal feature maps are used to calculate the category of the proposal, and at the same time again, bounding box regression are used to obtain the final precise position of the detection frame.  
Figure 1: Faster R-CNN network structure diagram.
# Optical Element Surface Defect Size Recognition Based on Decision Regression Tree
## 4 Results and Discussion  
### Simulation of MSDI System  
To collect the far-field simulation light intensity distribution as the training data set of the DRT, an electromagnetic simulation model of MSDI is built in the FDTD solution software, as illustrated in Section 3.1. Here, we take the width recognition of scratch as an example. Since some research [21; 22] has reported that the shape of the scratch cross-section is usually v-shaped in practice, a series of v-shaped scratches are designed in FDTD solution with the width \(w\) in the range of 0.5 \(\upmu\)m to 5 \(\upmu\)m and the depth \(d\) below 1 \(\upmu\)m. A TFSF source with the wavelength \(\lambda\) in the range of 0.4 \(\upmu\)m to 0.75 \(\upmu\)m is taken as the white light source. The refractive index of air is set to 1, and the material of the optical element is set to SiO\({}_{2}\). The SiO\({}_{2}\) refractive \(n(\lambda)\) is a built-in parameter in the FDTD solution, which is a function of the wavelength \(\lambda\). The magnification \(\beta\) of the objective lens is set to 9.7\(\times\). All settings of model parameters are shown in Table 1.  
A series of the simulation light intensity distribution is obtained from the simulation model, as shown in Figure 5. The scratch width of 0.5 \(\upmu\)m is close to the wavelength (0.4-0.75 \(\upmu\)m), which will cause the scattered light diffraction. Thus, its distribution will be unimodal, as can be seen in Figure 5a. When the width of the scratch is slightly larger than the wavelength, the bimodal distribution of some scratch occurs, as shown in Figure 5b, which suggests that the diffraction caused by scratch weakens. In Figure 5c-f, the width of the scratch is in the range of 2-5 \(\upmu\)m. The light intensity distribution of scratches is bimodal, and the distance between two peaks increases with the increase in the scratch width.
## 4 Simulation and Analysis
### Double-Lane Change Condition  
Under this condition, the vehicle speed is set to 60 km/h, and the road surface has an adhesion coefficient of 0.85 in this operating condition. Figure 4 shows the observer's corresponding system input, and Figure 5 compares the simulation results of the SCKF and IMM-SCKF algorithms.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Parameter Name** & **Value** \\ \hline m & Vehicle mass & 750 kg \\ a & Distance from front axle to the & 1.1 m \\  & center of gravity & \\ b & Distance from front rear to the & 1.25 m \\ w & Wheel track & 1.415 m \\ h\({}_{\text{cog}}\) & Height of the center of gravity & 0.54 m \\ I\({}_{\text{zz}}\) & Yaw moment of inertia & 750 kg/m\({}^{2}\) \\ R & Effective radius & 0.284 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation vehicle parameter settings.  
As shown in Figure 5a-d, in the observation of longitudinal vehicle speed, both algorithms have high observation accuracy, but the IMM-SCKF algorithm is able to eliminate certain steady-state errors at the end of the double-lane change condition. When the vehicle steering angle gradually increases into the limit condition, the tire enters the nonlinear region. At this point, the IMM-SCKF algorithm is able to maintain high observation accuracy at the limit value for the lateral vehicle speed and yaw rate, whereas the SCKF and UKF algorithm has a 20% error. The IMM-SCKF algorithm can adjust the model probability in real-time based on the state and measured value of each sub-model, and the output results are always kept to track the model output with less error via the Markov state transfer matrix, as shown in the above results.  
Figure 4: Double-lane change condition (**a**) observer input, including front wheel angle and four-wheel speed; (**b**) vehicle trajectory.  
Figure 5: Simulation results of vehicle state under double-lane change condition: (**a**) longitudinal velocity; (**b**) lateral velocity; (**c**) yaw rate; (**d**) probability of each IMM’s model.
# Adaptive Navigation Based on Multi-Agent Received Signal Quality Monitoring Algorithm
## 4 Results and Discussion
It is noted from the figure that there is a significant increase in CCD in the highly degraded environment. CCD values are below 2m in the clear open sky environment; it increased to 6m in the degraded environment and rose to 10m in the highly degraded environment. The same pattern was followed in Range Residuals (RR), as shown in Figure 14a-c. Most of the RR points lie from \(-1\) m to 1 m in clear open sky environments. Figure 11: Satellite Signal Characteristics in Urban Canyon. (**a**) CNR (dB-Hz), (**b**) CCD (m) and (c) RR (m) for all three candidate sites clear open sky, moderately degraded and severe degraded. Figure 12: Histogram and normal distribution of CNR for all the environments (**a**) Clear open sky, (**b**) Degraded Environment and (**c**) Highly degraded environment. It is noted that there is a substantial increase in the RR at the degraded site to 5 m, and in the highly degraded environment, RR rises to 10 m. The results show that high CNR and reduced CCD and RR are the indications of a clear open sky, having negligible obstructions. But in a congested environment, the receiver faces severe interruptions produced due to multipaths and NLOS, which results in reduced navigation accuracy. Although the GNSS receiver can achieve the required navigation performance level under the clear environments, its signal quality is greatly influenced by the surroundings in the obstructed environments, such as urbanized areas.
# A Framework for Mobile-Assisted Formative Assessment to Promote Students' Self-Determination
## 1 Introduction
### Background  
With few exceptions [10], not many frameworks exist that focus on the development of motivation in mobile-based learning and assessment activities. Moreover, the majority of these frameworks address motivation either indirectly or partially [17]. The conceptual framework in [18], based on the activity theory, acknowledged the role of mobile-supported collaborative learning activities in enhancing motivation. The pedagogical framework in [19], based on the transactional distance theory, addressed the role of social interactions in supporting motivation, especially for activities with low transactional distance. Interactivity, as a means of supporting motivation, has been proposed in [20]. The framework in [21] acknowledged the inherent relation between self-regulation and mobile learning, and suggested that the student-centered, personal, and ubiquitous characteristics of mobile learning can enhance learners' self-regulation and motivation. Context-awareness, defined as the detection of the situation of the learner in the real world and the provision of adaptive support or guidance [22], is another feature of mobile technologies that has been linked to motivation. In [23], the context-aware personalized mobile self-assessment framework suggested that learning adapted to the learning context can positively influence motivation. Based on the framework of participatory simulation for mobile learning [24], augmented reality scaffolding can also support motivation.  
To shed more light on the motivational impact of mobile learning and assessment, the current study proposes a framework that utilizes the capacity of mobile technologies to support formative assessment activities having the potential to promote student motivation by enhancing their autonomy, competence, and relatedness.