esa-sceva/satcom-chunk-collection huggingface dataset:
# Target Selection Strategies for Demucs-Based Speech Enhancement
## 6 Results  
As explained beforehand, the metric with which the enhancement performance is measured is the output signal-to-interference ratio (SIR). It is this performance metric that is observed and plotted against the other evaluation parameters (number of interferences, input SIR, etc.). These plots are in the form of error plots, in which, usually, the middle point indicates the median and the vertical line indicates a type of error against another metric. However, it is very important to note that, in the case of this work, the vertical lines indicate the standard deviation of the results. This was decided to provide the reader with a way to observe the result variability in each evaluation, and should only be considered for comparative purposes (not for any statistical analysis).  
It is also important to note that each of the results shown here were obtained from a subset of the overall evaluation, to focus the discussion on what we believe is worthwhile presenting (and from which other parts of the evaluation may deviate). Thus, unless stated otherwise, these are the default values for each of the evaluation parameters when not being focused on or discussed:  
* Number of interferences : 0.
* Not applicable when there are no interferences.
* Between 0 and 7 dB (inclusive), when there are interferences.
* Input SNR : greater than or equal to 15 dB.
* Reverberation time : less than or equal to 0.2 s.
* Distance from center : 0 m.  
In the following figures and the rest of this writing, for simplicity, the embedding-based target selection strategy is referred to as \"Demucs-Embed\", the location-based target selection strategy is referred to as \"Demucs-PhaseBeamform\", and the original version of the Demucs-Denoiser model is referred to as just \"Demucs\".
# Effects of Glyphosate and Its Metabolite AMPA on Aquatic Organisms
## 2 Glyphosate (N-(phosphonomethyl)glycine)
### Acute Toxicity  
It has been already mentioned that the initial testing of GLY did not fully demonstrate its toxic effects, and therefore the amount for use was not strictly regulated. U.S. The EPA divided the toxicity of GLY into slight toxicity with concentrations ranging from 10 to 100 mg/L and almost nontoxicity with concentration higher than 100 mg/L to fish species with acute LC50 values from \(>\)10 to \(>\)1000 mg/L [15]. Lethal concentrations are various for 24, 48, and 96 h ranging from 0.295 to 645 mg/L for fish species (Table 1); from 6.5 to 115 mg/L for amphibian's species (Table 2); and from 35 to 461.54 mg/L for invertebrate species (Table 3).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Species** & **Formulation** & **Exposure** & **Concentration** & **References** \\ \hline Rainbow trout & GLY & 96 & 140 & [41] \\ (_Oncorhynchus mykiss_) & Roundup 1  & 96 & 52–55 & [46] \\ \hline \multirow{4}{*}{Common carp (_Cyprinus carpio_)} & 48 & 645 & \\  & GLY & 96 & 620 & [47] \\ \cline{2-5}  & Roundup 1  & 96 & 22.19 & [48] \\ \cline{2-5}  & GLY & 48 & 602.61 & [49] \\ \cline{2-5}  & GLY & 96 & 520.77 & [49] \\ \hline Blackhead minnow (_Pimephales promelas_) & GLY & 96 & 97 & \\ \hline Channel catfish (_Iclalurus punctatus_) & & 96 & 130 & [41] \\ \hline Bluegills (_Leponis macrochirus_) & GLY & 24 & 150 & [41] \\ \hline Guppy (_Pocilia reticulata_) & GLY & 96 & 69.83 & [50] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Acute toxicity values (LC50) of glyphosate and its commercial products on fish.
## 4 Results
### Statistical Assessment of Multiple SPPs
#### 4.1.2 Watershed-Averaged Precipitation  
The watershed-averaged 3-hourly precipitation estimates from the eight SPPs were evaluated in contrast with the gauge-based time series. Table 1 shows that 3B42 RT moderately overestimates the watershed-averaged precipitation by 11.1%, and 3B42V7 presents a minor underestimation of 4.8%. All three IMERG SPPs systematically underestimate the basin-averaged precipitation with BIAS values of \(-\)27.0%, \(-\)30.6%, and \(-\)6.8% for IMERG-E, IMERG-L, and IMERG-F, respectively, and IMERG-F demonstrates a slight underestimation. A significant underestimation of 28.7-39.0% was found in the three GSMaP SPPs. Table 1 also displays that TMPA and IMERG SPPs are in a higher correlation with the gauge-based watershed-averaged precipitation time series than GSMaP SPPs. IMERG-E has the highest CC (0.323) among all the near-real-time products, and IMERG-F presents the highest CC value (0.330) among the four post-real-time data sets. The absolute errors of all SPPs have similar RMSE magnitudes, ranging from 2.9 mm to 3.1 mm. Overall, 3B42 RT has the best performance in retrieving the basin-averaged precipitation among the four near-real-time products, followed by IMERG-E and IMERG-L, whereas GSMaP-NRT presents the most unsatisfactory performance. Regarding the post-real-time SPPs, IMERG-F demonstrates the best performance, slightly better than 3B42V7. GSMaP-GAUGE demonstrates inferior quality.  
In addition, the non-exceedance frequency distributions of the basin-averaged precipitation estimates from the eight SPPs were compared with those of the gauge-based data set on 3-h temporal scales. Figure 7 shows that all SPPs significantly underestimate the basin-averaged precipitation at the high quantile levels (\(\geq\)87.2-95.3%) and clearly overestimate the intermediate precipitation quantiles at the levels of 45.6-87.2%. The reason is that all SPPs at the grid scale tend to overestimate the occurrence  
Figure 6: Taylor diagrams showing the comparison of the eight SPPs at the locations of the seven gauges in the Chindwin River basin with respect to the gauge observations (1 April 2014–31 December 2016): (a) 3-hourly daytime precipitation and (b) 12-hourly nighttime precipitation.  
frequency of light and moderate rain events and largely underestimate the frequency of heavy rain events (Figure 5). This satellite-derived basin-averaged precipitation estimate feature would likely greatly influence the discharge simulations, particularly high-flow simulations, when these SPPs are adopted as the forcings of a hydrological model.
## 3 Results
### Validation of CAM-Based Patient-Specific Nuss Bar Fabrication Technology
#### 3.2.2 Manufacture of Patient-Specific Nuss Bar  
The patient-specific Nuss bars were fabricated with SUS and Ti-Gr5. The design of the patient-specific Nuss bar was derived using CAD-based design technology, and the product design drawings were created for symmetrical, eccentric, and unbalanced Nuss bar shapes in accordance with the morphological classification of PE [2; 7; 18; 25]. Each type of Nuss bar was designed by collecting CT data and using virtual surgery scenarios, and they represent a different patient group (symmetric, eccentric, and unbalanced group).  
Figure 20a-c shows the product design drawings of the three types.  
Figure 20: Three types of patient-specific Nuss bar drawings and products with Ti-Gr5: (**a**) symmetric Nuss bar design and (**a’**) symmetric Nuss bar product for Patient No. 1; (**b**) eccentric Nuss bar design, which was fitted for an asymmetric chest wall patient (Patient No. 2), and (**b’**) eccentric Nuss bar product; (**c**) unbalanced Nuss bar design, which was fitted for an asymmetric chest wall patient (Patient No. 3), And (**c’**) unbalanced Nuss bar product.  
The average dimension accuracies of the titanium and SUS prototypes were \(\pm\)0.85 % and \(\pm\)1.47%, respectively. These results show that the patient-specific Nuss bar fabricated with Ti-Gr5 has a higher machining accuracy.
# Techno-Economic Assessment of an Innovative Small-Scale Solar-Biomass Hybrid Power Plant
## 3 Results and Discussion  
### Simulation  
As mentioned earlier in Section 2.2, for the development of this analysis, different designs of hybrid plants were considered, varying both the SM and the BFF. Given the relatively small size of the proposed plant in this study, only two realistic values of BFF were considered. This is due to the technical difficulty of implementing biomass boilers with cooled grate burners on a very small scale. Therefore, two alternatives were analyzed regarding the size of Boiler 2, one corresponding to a BFF of 100% and another to a BFF of 50%. Regarding the size of the solar field, SM values from 0.5 to 2.5 were considered in intervals of 0.5.  
Figures 7 and 8 show the hourly evolution of both the biomass consumption and gross electric power generated for the same four representative days previously shown in Figure 4. Both figures assume SM = 1. However, Figure 7 considers BFF = 100%, while Figure 8 considers BFF = 50%. The analysis of Figure 7 (BFF = 100%) highlights the complete independence of the generated power from the solar resource, even on days with high variability such as days #172 and #355. By regulating the mass flow rate of the biomass consumed in Boiler 2, the steam turbine can maintain operation at its nominal power. It can be observed that during periods of null or low solar resource availability, biomass consumption increases, compensating for the deficit of thermal energy from the solar field. When the BFF is reduced to 50% (Figure 8), notable differences compared to Figure 7 are observed. The first difference is the inability to maintain the steam turbine operating at full load during periods of null or low solar resource availability, resulting in a decrease in gross electric power generated to 1 MW (BFF = 50%). Additionally, it is identified that when the thermal power provided by the solar field drops below a certain value, biomass consumption decreases. This decrease corresponds to the reduction in the steam turbine load to 50%, as mentioned earlier.
## 4 Results
### Temporal Trends in Mental Health Discourse on Reddit
#### 4.1.4 Heatmap Analysis of of Engagement Patterns  
The heatmap provides a clear illustration of these shifting trends in Figure 5. The transition from darker to lighter colors from the mid- to post-pandemic phases, without reverting to pre-pandemic levels, visually affirms the enduring effect of the pandemic on mental health discussions. The color intensities correspond to the volume of posts, with darker shades indicating higher activity. Particularly, the r/depression subreddit maintained a consistently high level of posts throughout all phases, suggesting that it remained a critical space for discourse throughout the pandemic. The r/anxiety subreddit showed a prominent peak during the mid-pandemic period, reflecting the spike in public concern and discussion related to anxiety. Interestingly, the data also showed that the discussion remained more active in the post-pandemic phase compared to the pre-pandemic phase, indicating a lasting shift in engagement patterns. The r/SuicideWatch subreddit also marked an increase in activity during the mid-pandemic period, which aligned with broader societal concerns about mental health crises during this challenging time. Again, post-pandemic activity in this subreddit did not return to pre-pandemic levels, suggesting a sustained need for crisis support and discussion spaces.  
This observation could imply that the pandemic may have permanently changed how individuals discuss and approach mental health on public forums. It also sets the stage for further research into the effectiveness of online communities in providing support, the potential changes in the stigma surrounding mental health discussions, and the broader societal acknowledgment of mental health challenges.
# Influence of Ozone on the Biochemical Composition of Birch Sap
###### Abstract  
Studies have shown that ozone is a good oxidizer and a strong disinfectant. There are many uses for ozone in the food industry, but there is relatively little information about the influence of ozone on biochemical composition and the capacity to reduce the number of microorganisms in birch sap. In this study, sap was cozonated at different intervals for 5 min (O\({}_{3}\): 0.087 \(\pm\) 0.009 mg L\({}^{-1}\)), 10 min, 15 min, 20 min, 25 min, or 30 min (O\({}_{3}\): 0.99 \(\pm\) 0.09 mg L\({}^{-1}\)). The parameters of the birch sap were studied immediately after the ozone treatment as well as during storage for seven days at 2 \({}^{\circ}\)C and for five days at 20 \({}^{\circ}\)C. The parameters of ozonated birch sap were compared with the parameters of fresh sap (control). The microbiological analysis included total bacterial count, lactic acid bacterial count, and yeast and mold count. Birch sap color, pH, titratable acidity, and \"Brix values were also determined. Evaluation of monosaccharides, sucrose, total sugars, and ascorbic acid was carried out in fresh sap as well as sap ozonated for 30 min, immediately after ozonation. The results show the statistical significance of the inactivation of microorganisms after treatment in most cases. The microorganism counts gradually reduced with increasing intervals of ozone treatment. The best results were obtained after 25 and 30 min of ozonation. Ozone treatment did not significantly influence the pH, titratable acidity, or \"Brix statistically. Values of monosaccharides, sucrose, total sugars, and ascorbic acid were influenced within the margin of error. Ozone had a significant influence on the chroma and hue angle.  
Betula pendula; biochemical composition; birch sap; ozone treatment; microbiological contamination; quality +
Footnote †: journal: Applied Sciences
# User Utility Maximization in Narrowband Internet of Things for Prioritized Healthcare Applications
## 4 Design of the PAUM System
### Interference Mitigation  
The next research concern is interference mitigation among UEs while communicating with the base station. The frequency band allocation for NB-IoT in standalone mode or in-band mode may not be simultaneous in all cells, resulting in inter-cell interference (ICI) [7; 11]. Any possible noise/interference may cause natural readings to be disrupted, resulting in incorrect identification and treatment [10]. As a result, an effective strategy for eliminating interference and providing QoS to users must be developed.  
The UEs are assigned to a transmission queue to prioritize event-influenced access to the base station. Currently, the system is working on making the most efficient use of available resources while addressing interference mitigation. For an OFDMA (Orthogonal Frequency Division Multiplexing)-based cellular network, where cells are separated into discrete areas with different frequencies, frequency reuse methods are the best interference management strategies. The signal to interference and noise ratio (SINR) is the most essential factor in determining the amount of inter-cell interference and evaluating the interference management technique's performance.  
The SINR formula, in general, is as follows:  
\[SINR=\frac{\rho\times h}{I_{z}+N_{0}}. \tag{1}\]  
Figure 3: Preemptive multi-level priority queue scheduling.  
Here, \(\rho\) is the received power of the desired user, \(I\) is the total interference, \(h\) is the fading channel gain, and \(N_{0}\) is Additive white Gaussian Noise.  
Now, we can define, the interference:  
\[I_{z}=\sum_{k\in B}\rho_{z}^{k}\times h_{z}^{k}. \tag{2}\]  
Equation (2) implies that \(I_{z}\) is the inter-cell interference received by the user \(i\) of Base station \(b\) in the uplink transmission scheme from the users of neighboring Base stations using the same resource, where \(\rho_{z}^{k}\) is the transmit power from \(z\) the downlink/uplink user of neighboring cell \(k\), \(h_{z}^{k}\) is the channel gain between the neighboring BS \(k\), and \(j\) is the uplink user of Base station \(b\).  
The data rate in Equation (3) can be defined for uplink users as follows:  
\[r_{z}^{b}=\log_{2}(1+SINR_{z}^{b}). \tag{3}\]
# Prediction of Total Organic Carbon Content in Shale Based on PCA-PSO-XGBoost
###### Abstract  
Total organic carbon (TOC) content is an important parameter for evaluating the abundance of organic matter in, and the hydrocarbon production capacity, of shale. Currently, no prediction method is applicable to all geological conditions, so exploring an efficient and accurate prediction method suitable for the study area is of great significance. In this study, for the shale of the Qingshankou Formation of the Gulong Sag in the Songliao Basin, TOC content prediction models using various machine learning algorithms are established and compared based on measured data, principal component analysis, and the particle swarm optimization algorithm. The results showed that GR, AC, DEN, CNL, LLS, and LLD are the most sensitive parameters using the Pearson correlation coefficient. The four principal components were also identified as input features through PCA processing. The XGBoost prediction model, established after selecting the parameters through PSO intelligence, had the highest accuracy with an \(R^{2}\) and RMSE of 0.90 and 0.1545, respectively, which are superior to the values of the other models. This model is suitable for the prediction of TOC content and provides effective technical support for shale oil exploration and development in the study area.  
s 2025 15 344 +
Footnote †: journal: applied sciences  
Article
## 2 Results
### Lag Response of the NPP to Precipitation  
Figure 4 shows the results of the lag analysis between the NPP and precipitation in Northeast China, with the first column showing the maximum correlation coefficients between precipitation and the NPP. The areas with a maximum correlation coefficient greater than 0.3 (\(p<0.05\)) were defined as areas where the NPP was correlated significantly with precipitation. In May, the NPP showed a significant correlation with precipitation, but only accounting for a tiny proportion over Liaohe Plain, Daxing'anling, and Songnen Plain. Between June and August, the significant correlation between NPP and precipitation mainly occurred in the eastern part of Hulunbeier, Chifeng City, and the agropastoral areas of the Songnen, Liaohe, and Sanjiang plains, as well as the southwestern part of Daxing'anling and the middle part of Xiaoxing'anling. The proportion of the significant correlation between NPP and precipitation decreased in September, which was predominant in the grassland in the northwest of the study area.  
The spatial distribution of the lag periods in the response of the NPP to precipitation is shown in the second column of Figure 5. The lag period in the response of the NPP to precipitation increased with time. During July and August, the lag periods of most of the forested areas were 2 to 3 months, which was generally longer than those of the grass and crop areas.  
The third column in Figure 4 shows the spatial distribution of the cumulative lag months in the response of the NPP to monthly precipitation. The percentage of one cumulative lag month was over 50% in May, whereas the proportions of other cumulative months were small. The NPP became more significantly correlated with the precipitation of the current month during June and July, when the proportion with five cumulative lag months dominated due to the abundant precipitation. The results manifested a decreased correlation of the NPP with the monthly precipitation, but an increased correlation with the cumulative precipitation of several months. Lastly, the distribution of the cumulative lag months of the vegetation NPP was even in August. Owing to decreased precipitation at the end of the growing season, vegetation growth relies more heavily on soil moisture and water storage in the roots. Therefore, the lag in the response to the cumulative precipitation of four or five months was obvious.  
Figure 4: Lag responses of the NPP to the precipitation over Northeast China.
## 7 Application on Engineering Design and QoS-CSC Problem
### Welded Beam Design  
Minimizing costs is the primary aim of the welded beam design problem, which involves optimizing four design variables and complying with seven constraints. These four variables are the welding thickness (\(h\)), the steel bar length (\(l\)), the bar height (\(t\)), and the bar thickness (\(b\)) as depicted in Figure 8. The design of the welded beam is based on the following four variables. The formal mathematical framework is provided below:  
\[Consider:\vec{x}=[x_{1},x_{2},x_{3},x_{4}]=[h,l,t,b]\] \[Minimize:f(\vec{x})=1.10471x_{1}^{2}x_{2}+0.04811x_{3}x_{4}(14.0+ x_{2})\] \[Subject\ to:g_{1}(\vec{x})=\tau(\vec{x})-\tau_{\max}\ \leq 0,\quad g_{2}(\vec{x})= \sigma(\vec{x})-\sigma_{\max}\leq 0\] \[g_{3}(\vec{x})=\delta(\vec{x})-\delta_{\max}\leq 0,\quad g_{4}( \vec{x})=x_{1}-x_{4}\leq 0\] \[g_{5}(\vec{x})=P-P_{C}(\vec{x})\leq 0,\quad g_{6}(\vec{x})=0.125-x _{1}\leq 0\] \[g_{7}(\vec{x})=1.10471x_{1}^{2}+0.04811x_{3}x_{4}(14.0+x_{2})-5.0\leq 0\] \[Variable\ ranges:\quad 0.1\leq x_{1}\leq 2,0.1\leq x_{2}\leq 10,0.1 \leq x_{3}\leq 10,0.1\leq x_{4}\leq 2\] \[Where:\tau(\vec{x})=\sqrt{\left(\tau^{\prime}\right)^{2}+2\tau^{ \prime}\tau^{\prime\prime}\frac{x_{2}}{2R}+\left(\tau^{\prime\prime}\right)^ {2}},\tau^{\prime}=\frac{P}{\sqrt{2}x_{1}x_{2}},\tau^{\prime\prime}=\frac{MR} {J},M=P\Big{(}L+\frac{x_{2}}{2}\Big{)}\] \[J=2\Bigg{\{}\sqrt{2}x_{1}x_{2}\Bigg{[}\frac{x_{2}^{2}}{4}+\left( \frac{x_{1}+x_{3}}{2}\right)^{2}\Bigg{]}\Bigg{\}},\sigma(\vec{x})=\frac{6 PL}{ x_{4}x_{3}^{2}},\delta(\vec{x})=\frac{6 PL^{3}}{Ex_{3}^{2}x_{4}}\] \[R=\sqrt{\frac{x_{2}^{2}}{4}+\left(\frac{x_{1}+x_{3}}{2}\right)^{ 2}},P_{C}(\vec{x})=\frac{4.013E\sqrt{\frac{x_{3}^{2}x_{4}^{6}}{36}}}{L^{2}} \Bigg{(}1-\frac{x_{3}}{2L}\sqrt{\frac{E}{4G}}\Bigg{)}\] \[P=6000 lb,L=14\ \text{in}\,\delta_{\max}=0.25\ \text{in}\] \[E=30\times 1^{6}psi,G=12\times 10^{6}psi\] \[\tau_{\max}=13600 psi,\sigma_{\max}=30000 psi\]  
Drawing from the information in Tables 14 and 15, in tackling the design issue of welded beams, MIARO surpasses the competing algorithms and provides the most efficient solution.  
Figure 8: Welded beam.
## 3 Methodology
### Proposed Framework
#### 3.1.3 Fog Layer  
This layer's primary responsibility was to deal with decision making, to control the activity of the edge layer, and to communicate related data to the cloud layer for use by farmers. A machine learning algorithm created the decision-making system via many processing phases, starting from the data generated by edge-layer sensors:* Data gathering by IoT devices, particularly sensors, which could collect data in real time or in small batches (temperature, humidity, camera vision, light intensity, etc.);
* Data collection and aggregation in a target database;
* Filtration of stored data: Algorithms could be used to clean and correct the data in this step;
* Data classification depended on its intended purpose;
* Computing: During this phase, calculations were performed on the classified data (e.g., the amount of water to pump);
* Making decisions based on predictions and visualizing data in the form of reports or dashboards.