esa-sceva/satcom-chunk-collection huggingface dataset:
# Capacity-Operation Collaborative Optimization for Wind-Solar-Hydrogen Multi-Energy Supply System
## 4 Case Study
### Pareto Front  
The optimization method proposed in this study utilizes the predicted annual hourly load and weather to optimize the capacity and operating schedule of the critical equipment  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**DNI (W/m2)**} & \multicolumn{2}{c}{**GHI (W/m2)**} & \multicolumn{2}{c}{**Wind Speed (m/s)**} & \multicolumn{2}{c}{**Temperature (\({}^{\circ}\)C)**} \\  & **RMSE** & **MAE** & **RMSE** & **MAE** & **RMSE** & **MAE** & **RMSE** & **MAE** \\ \hline LSTM & 178.22 & 82.78 & 97.07 & 49.39 & 1.39 & 0.98 & 0.88 & 0.61 \\ Persistence & 199.62 & 87.53 & 105.31 & 60.23 & 1.42 & 1.00 & 1.02 & 0.74 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Prediction model performance evaluation.  
Figure 7: The original and predicted values of user load.  
in the WSH-MES system. The convergence stage of the optimization process is depicted in Figure 8.  
As illustrated in Figure 8, the Pareto front curve for this optimization problem (represented with the orange curve in the figure) is achieved after 80 generations of optimization. The rightmost point on this Pareto front curve is (3.26, 0.0059), which indicates the optimal capacity allocation point that minimizes the CO2 emissions of the WSH-MES system.  
The corresponding capacity configurations are 776 loops, 96 MW CSP capacity, 19 h TES capacity, 150 MW PV capacity, 66 WT, 37 MW PEME capacity, 9.024 m\({}^{3}\) HST capacity, and 27 MW PEMFC capacity. All six capacity parameters fall within their corresponding optimization interval, but three parameters (SF size, PV capacity, and WF capacity) are on the boundary of the optimization interval. These three variables determine the amount of energy input into the system, and in order to meet the user load as much as possible, it is necessary to increase the energy input to the system as much as possible. Consequently, to minimize the power purchase from the grid, the optimized installed capacity is larger than the actual local installed capacity. Therefore, there exists a relative optimal capacity allocation point within the optimization constraint interval for CO2 emissions.  
Regarding the economic index, the corresponding optimal capacity allocation point for the economic index is the leftmost point of the Pareto front curve, which is (0.1364, 2.6113). The corresponding capacity configurations for this point are 165 loops, 13 MW CSP capacity, 13 h TES capacity, 13 MW PV capacity, 13 WT, 0 MW PEME capacity, 0 m\({}^{3}\) HST capacity, and 0 MW PEMFC capacity. Four of these capacity allocations are located on the boundary, while the installed capacity of PV and WP is also lower. This is because the SF and hydrogen energy network (PEME, HST, and PEMFC) have high costs, and with lower renewable energy generation, the average energy cost is lower. Therefore, there is no corresponding optimal capacity configuration for economic indicators.
## 4 Results and Discussion
This shows a better tripping time than the model without the relay setting, as seen in Figure 4. In addition, the fault current trip time was 0.05 s, while the voltage trip time was 0.35 s, as opposed to 0.04 s in Figures 4 and 5. The reduction of the tripping time was due to the introduction of instantaneous overcurrent and voltage relay settings with the help of the threshold voltage and current of the model. Figure 4: Fault at bus A without the relay. Figure 5: Application of relay at bus B. Figure 7 represented the initial condition when the trip time was 0.35 s without setting the instantaneous overvoltage relay at bus B. However, it reduced to 0.05 s when the model was implemented, as shown in Figure 5 above. In Figure 8, the reference tripping time of the three-phase-to-ground fault at relay 2 shows that the line tripped at 0.2 s without applying the proposed model. The performance of the proposed model is shown in Table 7 with a percentage increase in the tripping time without the application of the model compared to the proposed model, and it shows that at bus A, the double line to ground fault shows 99.87% with a difference of 0.33 s. The same also applies to three phase-to-ground faults and to single phase-to-ground faults. Figure 8: Three phases to ground fault at relay 2. Figure 6: The overcurrent and voltage relay function at relay 1 before setting. Figure 7: Overvoltage. Trip at Bus B. The differences in the various fault condition tripping times were computed to find the percentage increment of the tripping time of the normal system without the threshold value. The proposed model is shown in Equation (10) below. \[P_{i}=\frac{T_{A}-T_{B}}{T_{A}}\times 100, \tag{10}\]  
where \(P_{i}\) is the percentage increment of the tripping time of the relay, \(T_{A}\) is the tripping time without the threshold value, and \(T_{B}\) is the tripping time using the proposed model. At bus B relay 1, there were significant changes in the tripping time with the double-line-to-ground fault of 84.38% and a difference of 0.27 s, as seen in Table 8. The proposed model improved the tripping time and reduced the time delay of the relay to sense a fault signal and trip, as compared to [16], which focused on the selection and reliance of backup relays to trip in a fault condition.
## 4 Result Analysis and Discussion  
### Angles of Transmitted Waves  
It is important to observe how the angle of the transmitted waves changed when the angle of the incident waves varied because it allows us to understand what happened inside the detected RC wall. Based on the Snell-Descartes law, the evolution of the angles of the refracted waves (\(\theta_{\mathrm{i}}\)) in the function of the direction of the microwave antenna used in the experimental campaigns for the proposed methodology is shown in Figure 5.  
The evolution of the transmitted waves depends also on the dielectric constant of the concrete. As the real value of the dielectric constant of the tested concrete is unknown, the graphs for six different values of the dielectric constant of concrete (\(\varepsilon_{2}\)) are proposed. The proposed values of \(\varepsilon_{2}\) are for common concrete, which vary from about 4.5 to 15 [31]. According to the graphs in Figure 5, we see that when the angle of the incident waves increases from 15\({}^{\circ}\) to 60\({}^{\circ}\), the angle of the refracted waves also increases. However, the angles of the refracted waves are always smaller than the angles of the incident waves: they increase from 4\({}^{\circ}\) to 13\({}^{\circ}\) (the minimum angles) when \(\varepsilon_{2}=15\), and from 7\({}^{\circ}\) to 24\({}^{\circ}\) (the maximum angles) when \(\varepsilon_{2}=4.5\). The small values of the angles of the refracted waves also allow us to know that the shifts' values (\(\Delta_{\mathrm{i}}\)) to be determined for the proposed methodology are very small.
# Recent Developments in Modulation Spectroscopy for Methane Detection Based on Tunable Diode Laser
## 4 Application of Modulation Spectroscopy for Methane Detection
### Wavelength Modulation Spectroscopy Applied for Methane Detection
#### 4.1.5 Optimization of Methane Detection Based on WMS  
Apart from high detection sensitivity, the capacity of suppressing 1/\(f\) noise makes WMS a widely used technique [89; 96]. However, optical interference fringe, the primary cause of background fluctuation, has not been effectively suppressed in WMS. Many methods, including the adoption of post-detection filtering and the improvement of the detection system, have been attempted to optimize the optical interference fringe in methane sensors [97]. For example, recently, a method of combining dual tone modulation (DTM) with vibration reflector (VR) was introduced, which decreased the standard deviation (STD) value of the background signal to 0.0924 ppm [98].  
Other than the optical interference fringe, some kinds of MPCs, as well as the modulation spectroscopy technique, have also been under study to improve the detection sensitivity, by increasing the effective optical path while at the same time keeping the small size of methane sensor. White cells [99], Herriott cells [100] and Chernin [101] are the three most common MPCs applied for detecting gas, and variations on these have been developed. [PERSON] et al. [2] studied a novel compact dense-pattern multipass cell (DP-MPC). The cell was used to detect atmospheric methane in TDLAS with WMS. Then, a confocal MPC was developed to build a compact and portable methane sensor [102]. The MPC was mainly comprised of confocal mirrors, of which the radii of curvature were 500 mm. Compared to the reported sensors, the sensitivity of ambient methane was improved. Additionally, many other MPCs are also used for the detection of atmospheric trace species, such as a multiple-reflection optical cell with three mirrors [103], the multipass cell formed by two twisted cylindrical mirrors [104] and circular multireflection cell [105]. Some parameters of novel MPCs are listed in Table 1.  
Figure 3: The experimental setup used for the simultaneous measurement of CH\({}_{4}\) and CO. Reproduced with permission from [95], Optical Society of America, 2016.
# The Use of Modern Technologies in Post-COVID-19 Cardiopulmonary Rehabilitation
## 1 Introduction  
Due to the COVID-19 pandemic and the extremely rapid increase in the number of infected people [1], the medical world has had to consider adapting medical services by adopting new strategies for patient care and monitoring [2, 3].  
The COVID-19 disease took the medical world by surprise because, although this disease mainly affects the respiratory tract, the sequels also affect the heart, the brain and the digestive tract [4]. At this point, we can say that COVID-19 is a disease of the whole body [5].  
Post-COVID-19 syndrome is a condition that is marked by the presence of symptoms (such as fatigue, cough, shortness of breath, headache or brain fog) which persist for an extended period of time (such as weeks or months) following a person's initial recovery from a COVID-19 infection [6].  
The need for cardiopulmonary rehabilitation has increased with the COVID-19 pandemic due to the sequels developed as a result of this pathology [7; 8; 9; 10]. Some studies have suggested that exercise should be applied in the acute phase for early and effective rehabilitation of COVID-19 patients with respiratory failure, while other studies propose the application of a three-stage exercise protocol, beginning in the inpatient phase, continues in the post-acute and continuous phase and at home after discharge for the good physical condition of the patients [11].  
At discharge, some studies have shown that most subjects with COVID-19 have cardiopulmonary sequels with reduced functional capacity, exercise tolerance and muscle strength, regardless of previous health condition/level and other pre-existing disabilities [12; 13]. Increasing the exercise tolerance, decreasing the intensity/control of symptoms and improving the level of daily activity are the most documented benefits of cardiopulmonary rehabilitation [14; 15]. In this case, cardiopulmonary rehabilitation in post-COVID-19 patients with cardiopulmonary sequels is necessary for them to have a normal life [16; 17; 18].  
Readers should exercise caution in applying rehabilitation with exercise training because of the risk of the post-exercise fatigue reported in some patients [19]. Physiotherapists working with people with post-COVID-19 syndrome should determine the patient's level of exercise tolerance to avoid exacerbation of symptoms after exertion [20; 21].
# The Influence of UV on the Production of Free Trepenes in _Vitis vinifera_ cv. [PERSON]
## 2 Materials and Methods
### Experimental Design and Sampling  
Two transparent polymer screens were applied to grape bunches to attenuate UV. Polyethylene terephthalate glycol (PETG) film (Mulford Australia, Dandenong South, Australia) was used to exclude UV-B, but allow the transmission of UV-A, which has been proven in a previous study [17]. Polycarbonate (PC) films (Suntuf(r), Palram Australia, Derrimut, Australia) were used to remove approximately 99.9% UV transmission, including both UV-A and UV-B. UV attenuation films were applied to grape bunches at the different phenological stages based on the E-L system [23]. Phenological stages E-L 31, E-L 35, E-L 36, E-L 38 were selected, representing berries in pea-size (at pre-veraison), at full veraison, intermediate ripening and harvest, respectively. Treatments were performed in the same way over two vintages: (i) control represented naturally exposed grape bunches throughout the whole stages; (ii) TW treatments (TW PETG/TW PC) were enclosed by PETG or PC film from pre-veraison to harvest; (iii) TV treatments (TV PETG/TV PC) were enclosed by PETG or PC film from pre-veraison to full veraison; (iv) TI treatments (TI PETG/TI PC) were enclosed by PETG or PC film from full veraison to intermediate ripeness; v) TH treatments (TH PETG/TH PC) were enclosed by PETG or PC from intermediate ripeness to harvest (Figure 1). Five field replicates were performed for each treatment. Each replicate was conducted on one panel of five grapevines with one panel of grapevines between every replicate as a buffer zone. Transparent UV materials were fixed on the stem of the vine by zip ties and covered the whole grape bunch. Grapes were harvested on 14 March 2016 and 18 April 2017. Ten grape bunches were picked from different grapevines of each experimental replicate and immediately frozen at -20 \({}^{\circ}\)C in the winery before being transferred on dry ice back to the laboratory at the University of Melbourne. Then, impurities, including stems, rachis, leaves, soil and insects, were removed from the grapes and the frozen grape berries were stored at -20 \({}^{\circ}\)C until analysis. Grape juice pH was measured by a pH meter (Model HIS221, Hanna Instruments, Inc., Keysborough, Australia), TA was measured by alkaline titration, and \({}^{\circ}\)Brix was measured by a visual portable refractometer (Model HI5221, Hanna Instruments, Inc.). A standard method was used to estimate grape total phenolic content by homogenising the frozen grape sample, followed by extraction with ethanol (50% _v/v_, pH = 2.0), then centrifuged with supernatant collected and transferred to a flat bottom microplate, which was measured at 280 nm using a spectrophotometer (Multiskan(tm) GO Microplate Spectrophotometer, Thermo Fisher Scientific Inc., Waltham, MA, USA) [24]. This method is widely used in the wine industry for a quick comparison of total phenolics in grape and wine. Technical duplicates were performed for all samples.
# Evaluation of AI-Assisted Telemedicine Service Using a Mobile Pet Application
## 3 Methods
### Hypotheses  
To establish the hypothesis of this study, we first considered the relationship with the adoption period of companion animals. In other words, we believed that people who have been with animals for a long time will be more positive in their perceptions of telemedicine services. Therefore, we set the first hypothesis as follows:  
**Hypothesis 1 (H1).**_Positive perceptions about pet telemedicine services differ depending on the period of adoption of companion animals._  
In addition, we hypothesized that people's perceptions of telemedicine services will differ according to the health status of companion animals. In other words, it is highly likely that the owners of healthy animals did not seriously consider telemedicine services. Therefore, the second hypothesis was set as follows:  
**Hypothesis 2 (H2).**_Positive perceptions about pet telemedicine services differ depending on the health status of the companion animal._  
In addition, in order to establish a hypothesis on the usability of the technology, we tried to examine the difference in perceptions of telemedicine services according to the degree of ease of using mobile apps, which varies from one person to another. Moreover, we set the third hypothesis by subdividing it in order to examine the difference in perceptions of both pet and human telemedicine services.  
**Hypothesis 3a (H3a).**_Positive perceptions about pet telemedicine services differ depending on the degree of ease of use of mobile apps._  
**Hypothesis 3b (H3b).**_Positive perceptions about telemedicine services for people differ depending on the degree of ease of use of mobile apps._  
Finally, we divided the perception of telemedicine services into groups with positive and negative tendencies. We set the following hypotheses to evaluate the positive and negative tendencies for pet and human telemedicine services.  
**Hypothesis 4a (H4a).**_People with positive perceptions about pet telemedicine services also have positive perceptions about telemedicine services for people._  
**Hypothesis 4b (H4b).**_People with negative perceptions about pet telemedicine services also have negative perceptions about telemedicine services for people._
## 4 Results
For better comparison with the previous method (Figure 5a, Section 4.1) and a detailed investigation of the prevailing correlations (Section 3.2), two more UQs have been performed taking into account only ND-related uncertainties in the depletion or criticality calculations, respectively. For each of these UQs, again 200 BUCSS-R executions were carried out at each burnup level and for 0 years or 50,000 years of decay. The nomenclature used is denoted and explained as follows:  
* \(\sigma_{ND}^{CASMO}(BU)\): Standard deviation in keff values due to nuclear data-related uncertainties associated with the spent fuel composition. These uncertainties are propagated through SNF compositions from depletion calculations with CASMO5, employing perturbed ND sets produced by SHARK-X. * \(\sigma_{ND}^{MCNP}(BU)\): Standard deviation in keff values caused by nuclear data-related uncertainties in the criticality calculations with MCNP6. This uncertainty component is quantified using perturbed ND-sets in ACE-file format provided by NUSS. * \(\sigma_{ND}^{TOTAL}(BU)\): Standard deviation in keff values due to nuclear data-related uncertainties in both depletion as well as criticality calculations themselves. All these quantities are burn-up dependent, but for the sake of brevity the argument notation '(BU)' is omitted hereafter. A detailed breakdown of the computational expense for an UQ based on 200 BUCSS-R executions is shown in Table 5. The depletion step of the UQ, for instance, requires a 30 min lasting CASMO5 calculation for each of the axial meshes times the number of samples (30 min \(\times\) 41 \(\times\) 200 = 170 d). For the criticality step, on the other hand, on average 17 h are needed for every considered BU value and each decay time, i.e., 0 or 50,000 years in our study (17 h \(\times\) 5 \(\times\) 2 \(\times\) 200 = 1420 d). Compared to the other steps, depletion and criticality calculations with CASMO5 and MCNP6, respectively, consist in the computationally most expensive parts of the UQ. Nevertheless, by means of massive parallelization of the entire process, the effective time needed for the UQ can be reduced to a few weeks. Figure 4: Keff values obtained with the consistent ND-UQ approach. Data points show the average of 200 BUCSS-R executions performed for every BU step. Error bars span over the entire range of calculated keff values.
## 1 Introduction
### Motivation and Contributions of Paper  
Although deep learning-based tool wear prediction methods have made notable progress, there remain critical limitations that need to be addressed. Firstly, manual feature engineering in time, frequency, and time-frequency domains relies on domain knowledge and limits the generalizability of prognostic methods. Secondly, previous models struggle with capturing long-term spatial-temporal dependencies. For example, GRU-based methods result in a mean absolute error (MAE) of 8.5 \(\upmu\)m and root mean squared error (RMSE) of 11.4 \(\upmu\)m due to their limited ability to model long-range dependencies [15]. Additionally, feature fusion with spatial and temporal dimensions from multi-sensor data is mostly overlooked in previous research. The integrated model combining a GRU and CNN with the attention mechanism can achieve a better prediction accuracy, which results in an MAE of 3.32 \(\upmu\)m and RMSE of 5.33 \(\upmu\)m [39]. However, it still does not consider the fusion between different dimensions of the sensor data.  
To address these limitations, this paper proposes a novel spatial-temporal adaptive graph convolutional network (STAGCN), which integrates an adaptive graph convolution network (AGCN), bidirectional Mamba (Bi-Mamba), and multi-scale triple linear attention (MTLA) to enhance tool wear prediction. Specifically, the AGCN dynamically learns spatial correlations, Bi-Mamba captures long-term temporal dependencies, and MTLA fuses multi-scale features across spatial, temporal, and channel dimensions to extract intricate interactions. The main contributions of this paper are as follows:  
1. With the limitation of a predefined graph structure that cannot effectively capture spatial variations, the AGCN is designed for capturing hidden spatial dependencies and improving feature aggregation.
2. To capture temporal dependencies, Bi-Mamba is used to capture both forward and backward contextual information with long-term dependencies, and the channel independence strategy allows for different features to be learned in each channel, which provides a more comprehensive feature representation.
3. To enhance the feature extraction capability while fusing multi-dimensional features, MTLA is designed for capturing multi-level local and global information from multi-scale features, and the attention mechanism can dynamically adjust weights based on input features, focusing on crucial information and improving information utilization efficiency.