esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
To validate the improvement of the proposed method, a comparison between the PSO-BP in situ stress numerical analysis method and the conventional method is conducted in Shuangjiangkou Hydropower Station engineering.  
For comparison, the BP network was constructed the same as the PSO-BP network in this study, with a topology of 30-16-6-6 and the mean square error of the network model set to \(10^{-8}\). The BP neural network was also trained using the boundary conditions listed in Table 4 as input samples and the calculated stresses at the measured points as sample outputs. After the iterative search, the optimal combination of boundary conditions from the BP network method can be obtained. The inversed coefficients are \(k_{\text{g}}\) = 1.35, \(k_{\text{x}}\) = 1.18, \(k_{\text{y}}\) = 1.72, \(\tau_{\text{xx}}\) = 0.73 MPa, \(\tau_{\text{yz}}\) = \(-\)2.29 MPa, and \(\tau_{\text{x}}\) = 1.75 MPa. Then, the obtained coefficients are utilized in the FLAC(3D) software for additional forward analysis, which generates the computational results from the conventional method, as shown in Table 6. For comparison, the measured data and the results from the PSO-BP method are also listed in this table.  
As can be seen, the BP network method also inversed a relatively reasonable combination of boundary conditions, while the average relative error of the PSO-BP results is 3.45% lower than that of the conventional method, indicating a closer approximation to the actual in situ stress. Therefore, for in situ stress inversion using the same engineering, the PSO-BP method may evidently improve the accuracy of the inversion results compared with the conventional BP network method. It should be noted that the PSO-BP method has some limitations, one of them being the requirement of sufficient input samples. While the huge training samples result in a significant increase in computational cost, the accuracy of the results may improve as the number of samples increases. Hence, it is necessary to select a reasonable and sufficient number of training samples for different engineering problems.  
Figure 9: Stress variations along the depth (**a**) in the main powerhouse and (**b**) in the tailrace surge chambers; Stress variations in the plane of 2260 m elevation along the axis of the main powerhouse (**c**) and along the vertical axis of the main powerhouse (**d**).
## 3 Methods
### Real-Time Feedback and Dynamic Adjustment  
The real-time feedback mechanism is an important part of the system in this paper. The core is to transmit the data generated by students in the learning process, such as task completion, learning outcomes, emotional changes, etc., to the feedback module of the system in real time. After students complete a task or activity, the system collects relevant data and transmits them to the backend through an interface. The system quickly processes these data and applies them to recommendation strategies through a feedback mechanism. The specific process is as follows: feedback information, such as the completion of students' learning tasks, test scores, and participation, is transmitted to the background in real time. The system updates the model through incremental learning methods and real-time data streams, evaluates the current learning status of students, and adjusts the learning path based on their behavioral data and emotional changes.  
The dynamic adjustment mechanism is the core module for dynamically optimizing the recommended path based on real-time feedback. This ensures that the recommendation system can make real-time adjustments based on students' feedback data, thereby improving the accuracy and adaptability of personalized recommendations. Incremental learning technology is the core of this mechanism, allowing the system to update the model in real time when new data are obtained without completely retraining the model. The system in this paper adapts to new data changes by fine-tuning model parameters. When students perform poorly in a certain learning module, the system can adjust the recommendation strategy by updating the model parameters and recommending more auxiliary learning materials. As student feedback data continue to accumulate, the recommendation system can continuously optimize its recommendation strategy to ensure that the recommended content always matches the student's learning progress and needs.  
This system monitors students' learning behavior data in real time and combines GPT-4, GAN, and other technologies to generate personalized learning paths based on students' learning outcomes and emotional changes. The system collects and processes student feedback. Based on the latest feedback data, the system updates model parameters through an incremental learning mechanism, dynamically adjusts the recommendation strategy, and achieves the long-term adaptive adjustment of personalized recommendation paths.
## Acronyms  
ABAC attribute-based access control  
AG aggregator AMI advanced metering infrastructure  
AS authentication server  
BAN building area network  
CC control center  
CS charging station  
DCU data concentration unit  
DDoS distributed DoS  
DNP distributed network protocol  
DoS denial-of-service  
DR demand response  
DRTM dynamic root of trust for measurement  
DSA digital signature algorithm  
EP energy providers  
GW gateways  
HA home appliances  
HAN home area network  
HMAC hash-based MAC  
KDC key distribution center  
LTE long term evolution  
MAC message authentication code  
MITM man-in-the-middle  
NAN-GW neighboring gateway  
NIST national institute of standards and technology  
OMS open metering system  
PHEV plugged-in hybrid electric vehicles  
PKI public key infrastructure
# Using Satellite-Derived Vegetation Products to Evaluate LDAS-Monde over the Euro-Mediterranean Area
## 2 Material and Methods
### Satellite Observations
#### 2.3.4 Fluorescence  
The Global Ozone Monitoring Experiment-2 (GOME-2) is an operational scanning spectrometer [54] on board the European Meteorological Satellite (EUMETSAT) Polar System MetOp-A and MetOp-B. GOME-2 has been measuring the Earth's backscattered radiance at wavelengths between 240 and 790 nm. SIF products were derived by [55] from radiance observations at wavelengths between 734 and 758 nm (far-red SIF). The Level-3 v27 SIF product was used in this study, which is a monthly gridded product at 0.5\({}^{\circ}\) resolution estimating a daily-averaged SIF. Figure 1 shows the average SIF product for the whole 2007-2015 period. Pixels too close to the coasts and pixels where the vegetation cover was too low (less than 10% of the pixel covered by any kind of vegetation according to ECOCLIMAP) were filtered out.  
[PERSON], [PERSON] et al. [56] investigated whether the SIF observations were suitable to assess the performances of the JSBACH (Jena Scheme of Atmosphere Biosphere Coupling in Hamburg) biosphere model [57] at the regional scale of Fenno-Scandinavia and at the site scale with multiple coniferous forests in Finland. Both observations and simulations revealed that SIF can be used to estimate GPP at both site and regional scales. They also concluded that GOME-2-based SIF was a better proxy for GPP (similar slopes of regression for the different sites) than the remotely-sensed FAPAR (Fraction of Absorbed Photosynthetically Active Radiation; different slopes of regression for the different sites). In this study, SIF observations will be compared to simulated GPP.
## 4 Results and Discussions
### Discussion
However, to the best of our knowledge, this study is the first in clearly identifying the most significant parameter's base in the intelligent model for BFP prediction. Parameters abdominal C (waist circumference) followed by gender were determined as the first and second most influential variables in the list of optimal parameters that affect BFP prediction indicated by the SVR sensitivity analysis. In this study, all machine learning models were capable of predicting BFP using the considered primary dataset of 2000 people; however, the proposed hybrid model SVR-EANN with an intelligence-based feature selection algorithm outperformed other models by achieving superior results for all evaluation metrics used in this research. It was observed that selected data provide more accurate results than complete data, and it increased the prediction rates from 2.8% to 16% compared to other models. This showed that SVR could be used for regression tasks and data selection quite effectively. In addition to the success of SVR in data selection, it has been observed that the EANN, which is a new generation artificial neural network, achieves the highest results with appropriate data by imposing the hormone neurons that improve the learning ability. The obtained results demonstrate the efficacy of the proposed hybrid SVR-EANN model for BFP. Furthermore, combining the efficiency of the two models, SVR-EANN showed that the hybrid models increase the probability of achieving superior results than conventional and single models. Figure 7 presents the visualization of the obtained R\({}^{2}\) scores and rRMSE results to demonstrate the differences between the models effectively. The highest and lowest indicators in the R\({}^{2}\) score and rRMSE results represent the superior prediction rates, respectively. However, the proposed model was not applied in different fields and disciplines, which is the study's limitation.
###### Abstract  
Computed tomography (CT) imaging using low-dose radiation effectively reduces radiation exposure; however, it introduces noise amplification in the resulting image. This study models an adaptive nonlocal total variation (NL-TV) algorithm that efficiently reduces noise in X-ray-based images and applies it to low-dose CT images. In this study, an AAPM CT performance phantom is used, and the resulting image is obtained by applying an annotation filter and a high-pitch protocol. The adaptive NL-TV filter was designed by applying the optimal window value calculated by confirming the difference between Gaussian filtering and the basic NL-TV approach. For quantitative image quality evaluation parameters, contrast-to-noise ratio (CNR), coefficient of variation (COV), and sigma value were used to confirm the noise reduction effectiveness and spatial resolution value. The CNR and COV values in low-dose CT images using the adaptive NL-TV filter, which performed an optimization process, improved by approximately 1.29 and 1.45 times, respectively, compared with conventional NL-TV. In addition, the adaptive NL-TV filter was able to acquire spatial resolution data that were similar to a CT image without applying noise reduction. In conclusion, the proposed NL-TV filter is feasible and effective in improving the quality of low-dose CT images.  
low-dose computed tomography (CT); X-ray noise reduction; nonlocal total variation (NL-TV) filter; quantitative evaluation of image quality +
Footnote †: journal: applied sciences  
Article Application of Adaptive Search Window-Based Nonlocal Total Variation Filter in Low-Dose Computed Tomography Images: A Phantom Study
## 3 Data Collection  
### Density and Dynamic Modulus  
In the laboratory, volumetric properties of specimens were detected, including bulk specific gravity (G\({}_{\text{mb}}\)) which was measured according to AASHTO T166, theoretical maximum specific gravity (G\({}_{\text{mm}}\)) (AASHTO T209), and air voids (AASHTO T269). Prior to the density test, water absorption of each asphalt concrete was checked. In this study, the dry volume method was used rather than the wax seal method because the water absorption of all asphalt concretes was less than 2%.  
The dynamic modulus is the modulus of pavement material measured under the condition of simulating the dynamic load of vehicle. It is a dynamic parameter that truly reflects the comprehensive behavior of the pavement under various load conditions and also represents the stress-strain analysis of the pavement structure. In the updated Chinese specification (2017 version) of asphalt pavement, the dynamic modulus is designed at a frequency of 10 Hz and a temperature of 21.1 \({}^{\circ}\)C. Therefore, dynamic modulus tests for all four mixes were carried out at a frequency of 10 Hz and a temperature of 21.1 \({}^{\circ}\)C following AASHTO TP 62 using a UTM-25 test machine. The last five load deformation curves were collected and the averages of the cyclic load amplitude and the recoverable axial deformation were recorded. The dynamic moduli of each mix type were mathematically calculated as the maximum (i.e., peak) dynamic stress divided by the peak recoverable axial strain. The results of the densities and dynamic moduli of the AC-13, SMA-13, AC-20, AM-20 are presented in Table 1.
# TSA-GRU: A Novel Hybrid Deep Learning Module for Learner Behavior Analytics in MOOCs
## 3 Methodology
### Temporal Sparse Attention (TSA)  
The TSA Module directs its focus on the most vital temporal interactions from the information. It utilizes multi-head attention while incorporating the sparsity mechanism to ignore bleeding relationships. While the concept of sparse attention has been explored in the context of language models and transformers, our proposed TSA introduces a novel adaptation tailored to time-series educational data. Specifically, TSA applies a learnable sparsity threshold on attention weights across time steps, dynamically retaining only the most salient learner interactions. This contrasts with standard sparse attention mechanisms that often rely on fixed or predefined patterns.
## 2 Liquefaction Fragility
### Fragility Assessment Method  
If the damage index of liquefaction is denoted by the random variable \(X\), the probability that \(X\) exceeds a specified value of \(x\) within \(t\) years can be assessed using the total probability theorem to incorporate the contribution from all possible values of intensity measure (\(IM\)) of the earthquake (e.g., peak ground acceleration, PGA) as:  
\[P\left[X>x\right]_{t}\ =\int P\left[X>x|IM=a\right]\left[-\frac{d\mathrm{H}_{ \mathrm{t}}(a)}{da}\right]\,da \tag{1}\]  
where  
\[\mathrm{H}_{\mathrm{t}}(a)=P\left[IM>a\right]_{t} \tag{2}\]  
\(\mathrm{H}_{\mathrm{t}}(a)\) is the hazard curve that describes the probability of exceeding \(a\) in \(t\) (for example, 50) years. This curve can be constructed using probabilistic models of the earthquake source, path, and site, based on available regional seismicity information.  
The item of \(P\left[X>x|IM=a\right]\) in Equation (1) is now defined as the seismic fragility of liquefaction:  
\[F(x;a)=P\left[X>x|IM=a\right] \tag{3}\]  
For example, if three types of damage state (DS) are assumed, \(\mathrm{D}_{3}\) (light to no damage), \(\mathrm{D}_{2}\) (medium damage), and \(\mathrm{D}_{1}\) (extreme damage), the probability mass function for these three damage states of liquefaction of a soil layer subjected to an earthquake can be obtained from the fragility function as follows:  
\[P\left[DS=\mathrm{D}_{3}|IM=a\right]=F(x_{3};a) \tag{4}\]  
\[P\left[DS=\mathrm{D}_{2}|IM=a\right]=F(x_{2};a)-F(x_{3};a) \tag{5}\]  
\[P\left[DS=\mathrm{D}_{1}|IM=a\right]=1-P\left[DS=\mathrm{D}_{3}|IM=a\right]-P \left[DS=\mathrm{D}_{2}|IM=a\right] \tag{6}\]
# Using Smartwatches in Stress Management, Mental Health, and Well-Being: A Systematic Review
## 3 Smartwatches as Stress Assessment Tools
### Smartwatches for Mental Health Monitoring
Methodologically, researchers have begun addressing such issues through innovative means: synchronizing data from multiple sensors and contexts, experimenting with data processing choices (for instance, using longer monitoring windows of around 2 min, which significantly boosts detection accuracy compared to 15 s snapshots), and even generating synthetic data to augment limited training datasets. These efforts reflect a maturing intersection of biomedical engineering and data science, where improving signal quality and algorithmic robustness go hand in hand. Still, certain limitations are sobering--for example, one study found that popular smartwatches' heart rate and HRV data did not reliably reflect users' reported stress during complex multitasking scenarios, a caution that even core biometric signals may fail to capture the nuances of psychological stress. Taken together, these findings advocate for a careful empirical approach to algorithm development and validation, ensuring that stress-detection models are trained on diverse, ecologically valid data and tested against meaningful benchmarks of mental well-being. Encouragingly, numerous interventions and clinical studies have demonstrated that when these technological and human factors align, the use of smartwatches can yield tangible health benefits. In clinical mental health care, for example, continuous monitoring via wearables has shown potential for early intervention. Preliminary case studies of patients with severe mental illness revealed that subtle changes in smartwatch-recorded metrics--such as deteriorations in sleep patterns and spikes in electrodermal activity--preceded clinical relapse events and hospitalizations. These behavioral gains illustrate how real-time feedback from wearable sensors can translate into healthier routines in daily life. Integrating smartwatches with therapeutic interventions also amplifies their impact. One study combining a smartwatch app with cognitive-behavioral therapy for anxiety found over 80% improvement in patient-reported outcomes, with clinicians praising the system's utility in promoting mental well-being. In a related vein, patients undergoing digital therapy for depression accepted smartwatch mood monitoring enthusiastically, noting that it felt more private and less burdensome than manual mood diaries--a convenience that improved their engagement and adherence to treatment. Even in high-stress medical situations, wearables prove beneficial: for instance, cancer patients wearing a smartwatch during radiotherapy sessions showed elevated heart rates correlating with anxiety spikes, indicating these devices can effectively monitor patient stress and guide timely stress-reduction strategies during care.
# Trust and Mobility-Based Protocol for Secure Routing in Internet of Things
## 7 Discussions and Findings
### Computational Complexity  
With reference to computational overhead and in comparison, with MRHOF, the overall complexity of SMTrustOF is increased. The computational complexity of MRHOF is O(1). For SMTrustOF, the objective function is modified, basically for trust-based parent selection and attacks detection. The complexity of the parent selection algorithm is O(1) except the trust calculation for neighbouring nodes required as input for parent selection, which is O(n). Similarly, the computational complexity for attack detection algorithms is O(1) in the best case and O(n) in the worst case. Therefore, it can be deduced that the overall complexity for the added procedures in SMTrustOF is O(n). For SMTrust, the computational complexity increases with the calculation of trust metrics and aggregation.  
Similarly, in terms of memory overhead, additional memory is required for storing and aggregating the trust values. The procedures introduced for SMTrust include individual trust metrics calculation, as explained by the equations in Section 4.2, which are finally aggregated to calculate the trust index. This trust index is used in the parent selection algorithm for comparison of potential parents. The attack detection procedures are called on during the parent selection to validate the selected preferred parent. Undoubtedly, the proposed SMTrustOF increases the complexity of RPL in terms of computation, memory, and runtime. For space complexity, basically the trust metrics calculation, storage of historical observations, and recommended trust values from neighbours, are the additional memory consuming tasks involved in SMTrustOF. The data types of int, unsigned int, and float have been used to accomplish the above, and each occupies 4 bytes space size.
## 4 Results and Discussion
### Tests of the Tracker  
After receiving a quantity of light using a lamp, the prototype tracker starts tracking the solar radiation (Figure 13a). Figure 13b shows that the tracker is in the perpendicular position (90\({}^{\circ}\) almost at noon).  
To validate our results, we performed two tests on the prototype of the single-axis solar tracker: the photovoltaic module connected to the prototype of the tracker and checking if it is in its fixed position. Figure 14a,b show the variation of the motor angle (Tracker angle) with respect to the sun angle for the two tests. It can be seen that the solar tracker reaches its reference position (sun angle) in a time of response of less than a second.  
Figure 12: Temporal variation of the sun angle during a day.  
Figure 13: Tests of the tracker position: (**a**) start position of the solar tracker realized to start tracking of the light. (**b**) Perpendicular position (90\({}^{\circ}\)) of the solar tracker.  
Several studies have analyzed the efficiency of energy production using single-axis trackers compared to fixed panels [35; 36; 37; 38]. [PERSON] et al. [37] determined that the increase in solar production of a PV plant with single-axis trackers compared to a fixed-panel system is around 17.22-31.23% [39; 40; 41]. That fluctuation in energy production can be reduced depending on the tracker distribution in the PV plant [42; 43; 44].  
To validate our design, we compared the motor angle and the sun angle over an entire day if the panel is fixed and if the panel is attached to our prototype single-axis tracker. As the sun begins to shine at 5:00 a.m., our system records data until sunset. Figure 15 shows that the solar tracker device maximizes the sun's irradiance by keeping the solar module's active surface vertical at the sun's angle. From noon until sunset, we find that the fixed panel loses a large amount of solar radiation during the day.  
Figure 14: Variation of the motor angle with respect to the sun’s angle: (**a**) for test 1. (**b**): for test 2.  
Figure 16a-d show the values variation of illuminance, voltage, current, and power of a PV module for two cases (with and without the tracker), which are compared for the period spanning from sunrise to sunset. When the curves are examined between 8:00 a.m. and 11:00 a.m., the voltage, current, and power values are a little close for the two cases. On the other hand, in the afternoon, between 12:00 p.m. and 7:00 a.m., we observed a large difference in the quantity of the voltage, current, and power produced. We concluded that a fixed panel's energy loss was recorded from noon until sunset.  
Figure 16: _Cont._  
Figure 15: Comparison between sun angle and sun tracker angle.  
Figure 16: Values of (**a**) solar radiation, (**b**) voltage, (**c**) current, and (**d**) power produced by a PV, panel bound with a solar tracker compared to a fixed PV panel.