esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Example Simulation and Analysis
### Basic Data  
In this paper, a wind-PV-thermal-storage power system of a north city is selected for simulation, which contains 1200 MW thermal power units, 200 MW wind units, and 400 MW photovoltaic units. The error is 20% between the actual active power output and the predicted active power output of wind power and photovoltaic power generation. In addition, their samples follow the Weibull distribution between \(P_{\text{max}}^{\text{min}}\) and \(P_{\text{max}}^{\text{max}}\), and average adjustment cost can be calculated by a two-point estimation method. According to the uncertain models of wind, solar, and system load, the wind and photovoltaic output are predicted. The predicted curves are shown in Figure 5 and the operation cost coefficients and maintain cost coefficients of wind and photovoltaic generation are shown in Table 1. Combined with the actual situation, the maximum price variation of the day-ahead price demand response virtual unit in this region is to increase or decreased 50%, and the inflection point of the electricity price change rate is \(\pm\) 0.3 [32]. The critical value and saturated value of intraday IDR virtual units are 40 $ and 55 $ respectively.  
In the existing power system, the installed number of thermal power generators is more, and the capacity of energy storage power station is limited. It is a big challenge to face the large-scale new energy connection, and there is a huge peak-valley difference caused by the user's electricity consumption behavior. Therefore, the number of started thermal power generators is too much during peak hours, but units fail to stop frequently during low hours, and they often run in depth peak load cycling. The operation cost of thermal power plants increases with peak-shaving depth. The standard coal price in calculating operation cost is 85.57 $/t, and the operating parameters of thermal power units are shown in [33]. The start-stop cost and loss cost curves of thermal power units at different peak load adjustment depths are shown in Figure 6. Parameters settings of improved bat algorithm are listed in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Capacity (MW)** & **Cost Coefficient (\$/MW-h)** \\ \hline Wind Power & 45 & 3.25 \\ \hline PV & 40 & 3.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Operation Cost Coefficient and Maintenance Cost Coefficient of Wind Power and PV Power Generation.  
Figure 5: Wind and PV forecast output curve and system load forecast curve.
## 3 Materials and Methods
### Data  
For the evaluation phase, we used two publicly available English-language datasets dedicated to the detection of the severity of depression. These datasets, namely, the depression severity dataset (DsD) [30] and the depression signs detection dataset (DepSign) [31], were collected from the Reddit social network. In the following lines, we provide detailed descriptions of these datasets and pertinent statistics drawn from their respective contents.  
* **Depression severity dataset**. This dataset was constructed by augmenting a publicly available Reddit dataset [51]. Each post was annotated by two annotators usingthe depressive DDA scheme [52] and then categorized into one depression severity level following the depression rating scale [53]. The dataset results in 3553 posts in four depression severity levels: minimal (2587 posts), mild (290 posts), moderate (394 posts), and severe forms of depression (282 posts).
* **Depression signs detection dataset**. This dataset comprises English posts from the depression and mental health Reddit subgroups. Two domain experts annotated the data based on signs of depression, assigning each post one of three severity levels. The authors of [32] eliminated duplicates to create the final dataset utilized in this article, which consists of 10,251 posts in three depression severity levels: not depressed (3503 posts), moderate depression (5780 posts), and severe depression (968 posts).  
The same pre-processing procedures were employed in all datasets: expansion of contractions (e.g., It's, we'll), conversion of chat words (e.g., AFAIK, ASAP), and spell checking. Table 1 provides an overview of the datasets used in this study, including a concise summary of the key statistics.  
The datasets differ notably in terms of size, textual characteristics, and label distribution. Posts in DsD have an average word count of 88 words and an average character count of 442 characters per post. On the other hand, DepSign posts feature a higher average word count of 171 words and an average character count of 840 characters per post. These differences in text length could affect the task of depression detection, as longer posts on DepSign might contain more detailed and subtle expressions of emotions and thoughts. Furthermore, the distribution of labels in DsD spans four levels of severity of depression, while DepSign has only three distinct labels with varying post counts for each category.
## 4 Discussion
### Conclusions Regarding the General Approach  
The numerical simulations show a good performance of the proposed approach in terms of the requirements of a decision-support system. In comparison to a default rolling horizon optimization, the application of the MPC scheme shows much better results due to the increased size of prediction horizons over the sampling step size (c.f. Section 3.3, cases \(P\geq 2\)). Moreover, the same results show that short sampling step sizes of only 84 h result in highly efficient plans, while an increase in the sampling step size does not lead to better plans. These short horizons enable the acquisition of good or even optimal plans with comparably short response times of only some minutes (c.f. Section 3.4). These short response times constitute a major requirement of decision-support systems, which generally require real-time capabilities. Nevertheless, the offshore-application fields regarded in this article allows for substantially longer response times. The results in Section 3.4 demonstrate that the algorithm detects better solutions if it has more time to search. In cases where the algorithm can be assigned a large amount of optimization time, users can choose to increase the prediction horizon to achieve better results. Whereas the results in Section 3.3 show that larger horizons of \(P=3\) lead to marginally better results, they massively increase the required optimization time due to the MILP's combinatorial nature. Both sampling step sizes only require a small number of recourse actions shown by the low number of required plans in Section 3.3. Only one (\(P=2\)) or two (\(P=3\)) additional plans were required for a sampling step size of 84 h, while a sampling step size of 168 h only required a single additional plan across all test cases. This low number of additional plans shows a good approximation of operation times by the proposed Markov-Chain approach during the simulation and falls in line with the evaluation given in Section 3.1.  
Consequently, it can be stated that the proposed approach and its extensions are suitable for the implementation of a decision-support system for the scheduling of offshore operations. The short response times allow transferring this approach to other applications, even when these impose higher real-time requirements. In such cases, the underlying MILP needs to be adapted to reflect the actual problem. Nevertheless, the results show that an application of control-theoretic approaches such as the MPC provides a viable alternative for short- to mid-term planning tasks in highly dynamic systems.
# Analysis of Total-Forms of Cyanotoxins Microcystins in Biological Matrices: A Methodological Review
## 2 Quantification of Total MC by the MMPB Method
### Limitations and Difficulties Faced during the MMPB Method
#### 2.5.3 Matrix Effects  
One reason that can be advanced to explain this loss of MMPB could be the matrix effect. In their study, [PERSON] et al. [64] distinguished between instrumental matrix effects and matrix effects that can occur during sample preparation. Instrumental matrix effects are often caused by co-eluting matrix components that can lead to ion suppression or enhancement phenomena at the ionization source [91], while sample preparation matrix effects can occur when an incomplete extraction recovery is achieved from the test samples [64,92]. Regarding the matrix effects related to water samples when using the MMPB method, [PERSON] et al. [64] determined instrumental matrix effects ranging from \(-59\%\) to \(-75\%\) and minimal matrix effects during the oxidation step. In addition, the analysis of environmental water samples by LDTD-APCI-MS/MS allowed for oxidation yields of 103% with a recovery signal of MMPB at 91%, showing the absence of a significant instrumental matrix effect during the analysis [66]. In another method developed by [PERSON] et al. (2015) using LDTD-APCI-HRMS on fish tissues, quite low instrumental matrix effects were observed (between 7 and 10%). Of the few studies analyzing the total forms of MCs in sediments, little information is reported on the matrix effects. The only information available indicates that there is \"an adverse impact of the sediment matrix on Lemieux oxidation yields\" without quantifying this impact [69]. In the animal matrix studies, instrumental matrix effects leading to either signal gain or loss were both observed. The study by [PERSON] and [PERSON] [72] showed a signal suppression of 41.8% in rat and chicken liver, and [PERSON] et al. [49] observed a signal loss in the serum and tissue of aquatic snail samples spiked at the final step of sample preparation (after oxidation and extraction) ranging from 63 to 84%. On the contrary, other studies report a signal enhancement effect on rainbow trout samples extracted and spiked just before injection, where the recovered signal was 122% for the intestines and 142% for the muscles [29]. Similarly, a study conducted on _Dreissena polymorpha_ tissues estimated the signal enhancement to be 300% [93]. Although two types of matrix effects (instrumental and sample preparation) have been distinguished, most of the data collected on the MMPB method's protocols seem to focus only on instrumental matrix effects.
## 3 Results and Discussion
### Antioxidant Activity  
Bioactive compounds with antioxidant activity play a significant role in protection against ROS and reactive nitrogen species. The major anti-oxidant compounds are bioactive compounds, whose production increases when a particular factor in the medium is changed. The factor causes stress, which leads to improved accumulation of bioactive compounds, such as GA, phenols, and flavonoids. Previous results reported changes in radical scavenging activity (57.10%), reducing potential (absorbance at 0.15) and antioxidant capacity (17.54 mg/g) in _G. sylvestre_ leaf extracts [50]. The phenolic and flavonoids were significantly greater in CSC treated with CuO NPs, which correlated with their antioxidant activity (Figure 4). The free radical scavenging activity of the CSC extracts was confirmed through the 2,2-diphenyl-1-picryl-hydrazyl-hydrate (DPPPH) assay. Of the different concentrations of CuO NPs used to treat CSC, 3 mg/L CuO NPs treated CSC extracts displayed the highest antioxidant activity of 86.15% as compared to the control (72.62%) (Figure 4a). The CuO NPs elicited CSC extracts exhibited the highest reducing potential compared with non-elicited CSC (Figure 4b). The reducing potential is commonly connected with the number of reductones in the test samples [4]. The chelating capacity is essential to determine the antioxidant power of crude extract or single compound because it decreases the number of metal ions by catalyzing malondialdehydedehyde [51]. The antioxidant activity of the CuO NPs (3 mg/L) elicited CSC extracts was higher (80.55 mg/g) than non-elicited CSC extracts (69.50 mg/g) (Figure 4c). The present results are consistent with the previous report that the elicitation of polyunsaturated fatty acids triggered the antioxidant defense system and the marked production of GA and PC in hairy root cultures of _G. sylvestre_[4]. Correspondingly, the antioxidant potential was improved in CuO NPs elicitation in hairy roots of _Brassica rapa_ spp. _pekinensis_[29] and regenerated shoots of _Stevia rebaudiana_[42]. Similar to the present study, CuO NPs were associated with the development of intracellular oxidative stress by the release of metal ions (e.g., Cu\({}^{+2}\)) or free radicals. The oxidative damage that occurred at higher concentration impaired growth reduced the quantity of bioactive compounds in steviol glycoside and mitigated antioxidant activities [42].
## 2 Various PPF Strategies
### PPF of NAS Based Monoliths
Residual activated esters lead, under well-defined conditions, to carboxylic groups enabling generation of electroosmotic flow (EOF). Adapted with modification [28]. **Scheme 18. ** Reaction scheme for the surface immobilization of gold nanoparticles (GNPs) on the poly(NAS-_co_-EDMA) interface via free radical UV-initiated copolymerization, surface grafting of propargylamine and subsequent photo-initiated thiol-yne functionalization. Bis-cysteineing addition on alkyne units is assumed to be the major reaction mechanism. Taken with permission from [29]. In addition, a triphenylmethylamine-functionalized poly(NAS-_co_-EDMA) was prepared as new monolithic capillary column for RPC [30]. The generic monolith of poly(NAS-_co_-EDMA) was subjectedto the post-polymerization modification with three ligands (i.e., tritylamine, 3,3-diphenylpropylamine, or benzylamine) as shown in Schemes 19 and 20. The monolithic columns thus obtained were evaluated with PAHs, phenols and anilines in terms of retention factor, selectivity, resolution and efficiency. **Scheme 19. ** Schematic representation of two of the synthetic steps applied to prepare poly(NAS-_co_-EDMA) monolith functionalized with tritylamine or benzylamine: (ii) NAS and EDTA are photopolymerized in the presence of toluene as porogen and AIBN as initiator, (iii) aromatic amine is grafted on the surface of the macroporous monolith via nucleophilic substitution of N-hydroxysuccinimide esters. Residual activated esters lead, under well-defined conditions, to carboxylic groups enabling the generation of cathodic EOF. Taken with permission from [30]. The preparation of a monolithic precursor and its PPF with alkyl ligands, trypsin and lectins for RPC, protein enzymatic digestion and lectin affinity chromatography, respectively, was recently reported (see Scheme 21) [31]. In this report, a precursor monolith consisting of poly(NAS-_co_-EDMA) (so-called NNASM) was synthesized by an in situ thermal polymerization in a narrow bore stainless-steel column. After the polymerization was completed, the obtained poly(NAS-_co_-EDMA) was modified with three types of ligands via the process of post-polymerization modification. Firstly, octadecyl ligands were attached to the surface of the poly(NAS-_co_-EDMA) monolith by passing octadecylamine through the column. This nonpolar monolith was used for the RPC separations of alkylbenzenes, PAHs and proteins yielding high resolution. Secondly, the surface of \(N\)-acryloxysuccinimide monolith (NASM) was immobilized with trypsin in order to use it for the online digestion of proteins. Thirdly, lectins immobilized on the surface of NASM was demonstrated in the selective isolation of glycoproteins from human serum by lectin affinity chromatography.
## 3 Results and Discussion
### Optimization Process
#### 3.4.2 Analysis of the Response Surfaces  
The effects of the two independent variables (amounts of RPC and SAFA) on antioxidant potential of biscuits extracted by classical extraction and analyzed by the DPPH assay as well as their five sensory characteristics (color, odor, flavor, overall acceptability, and purchase intent) were illustrated using surface response and contour plots of the quadratic polynomial models (Figure 2).  
As can be seen, the generated shapes of the response surfaces for AC results differ from those obtained for sensory scores. The DPPH of baked biscuits significantly increased with the increasing concentration of RPC in the dough (Figure 2a). The parabolic shape of the response surface for DPPH was caused by the positive values of the quadratic terms of RPC amount and content of SAFA in the fat used.  
On the contrary, scores for color, odor, flavor, and overall acceptability and purchase intent of the biscuits analyzed were the highest when RPC was not added to WF and rapeseed oil or coconut oil were used for dough preparation (Figure 2b-f). The surface plots of odor, flavor, overall acceptability, and purchase intent indicate that the negative linear of RPC content and positive quadratic term of SAFA content were significant (Figure 2c-f). The elliptical contour of the odor plot confirms that there was an interaction between independent variables (Figure 2c), whereas the significant negative linear and quadratic effects of RPC amount resulted in a decrease of the score for biscuit color after the fortification with a higher RPC amount (Figure 2b).  
Figure 2: Response surfaces and contour plots for: (**a**) DPPH results of extracts obtained by conventional extraction (DPPH\({}_{\rm CE}\)), (**b**) color, (**c**) odor, (**d**) flavor, (**e**) overall acceptability, and (**f**) purchase intent of functional biscuits expressed as a function of the rapeseed press cake (RPC) content and saturated fatty acids (SAFA) content.
## 3 Results
### Results for the IEEE 57-bus system  
Figure 10 illustrates the results (\(Pa\) on the left and \(Pr\) on the right) obtained by the artificial neural network (ANN) during network training and validation. Figure 10 shows training with 80% of the samples (158 samples). Validation is demonstrated in Figure 10, with 20% of the samples (44 samples) that were not used in training, providing estimated \(Pa\) and \(Pr\) data. Finally, Figure 10 shows the results for all samples (202 samples).  
Figure 8: \(Pa\) (\(\mathbf{Y_{des}}\) and \(\mathbf{Y_{ob}}\)) \(Pa\) (total real power losses) and \(Pr\) (total reactive power losses) as a function of the samples from the IEEE 30-bus system: (**a**) training (80% of the samples); (**b**) validation (20% of the samples); (**c**) 100% of the samples (2 network phases).  
Figure 9: Obtained curves from the IEEE 30-bus system: (**a**) \(Pa\) (\(\mathbf{Y_{des}}\) and \(\mathbf{Y_{ob}}\)) vs. \(\lambda\), i.e., total real power loss curve (\(Pa\)) vs. loading factor (\(\lambda\)); (**b**) \(Pr\) (\(\mathbf{Y_{des}}\) and \(\mathbf{Y_{ob}}\)) vs. \(\lambda\), i.e., curve total reactive power loss (\(Pr\)) vs. loading factor (\(\lambda\)).  
Figure 11 shows the total real and reactive power losses (_Pa_ and _Pr_) in relation to the increase in load (\(\lambda\)). The similarity of the values obtained via ANN is noticeable when compared to the desired outputs. This is due to the high correlation between the outputs, highlighting the robustness of the ANN developed for this application.
# Analysis of GNSS Displacements in Europe and Their Comparison with Hydrological Loading Models
## 3 Results
### Annual Signal
There are also some isolated and randomly distributed stations with large anomalies (phase or amplitude), meaning that the choice of a particular solution  
Figure 8: (**a–d**) Difference in annual solar cycle between _cats_d_ and _cats_. Panels (**b,d**) represent, respectively, the number of stations where _cats_ is in phase advance (blue) or delay (red) with respect to _cats_d_ and the number of stations with larger (blue) or smaller (red) amplitude than _cats_d_. (**e–h**) are the same for the difference between _cats_d_ and _tias_d_. significantly impacts the results very locally. These stations should be considered as outliers or studied individually to determine the cause of these differences.
# The Effect of Integrative Training Program on Youth Soccer Players' Power Indexes
###### Abstract  
Integrative neuromuscular training (INT) is commonly employed for preventing injuries, yet there is a scarcity of studies examining its impact on the physical capabilities of young athletes. This study sought to explore the influence of a brief, in-season INT intervention on the performance of adolescent soccer players. The participants included thirty-one U15 (under 15 years old) soccer players who were randomly assigned to either the control group (CG) (engaged solely in soccer training, n = 14, 14.6 \(\pm\) 0.6 years) or the exercise group (EG) (where INT was incorporated into traditional soccer training, n = 17, 14.1 \(\pm\) 0.6 years). Early or late maturvers were excluded from the study. The INT program lasted for 5 weeks (two sessions per week) and mainly included single-leg exercises. At the commencement and conclusion of the intervention program, measurements were taken for acceleration (10 m), speed (30 m), jumping ability (squat jump, SJ), and countermovement jump, CMJ), and change of direction ability (COD) (Illinois agility test). The data analysis employed a two-way repeated-measures ANOVA. The INT program resulted in enhanced performance for the EG in SJ (28.4-32.3, \(p\) < 0.001, \(\eta^{2}\) = 0.463), CMJ (30.6-35.3, \(p\) < 0.001, \(\eta^{2}\) = 0.426), and COD ability (18.11-17.64, \(p\) = 0.003, \(\eta^{2}\) = 0.545). No changes in performance were observed in the CG. The results suggest that the addition of a short-duration in-season INT program in U15 soccer players can induce positive adaptations in their performance.  
functional training; adolescence; physical performance; single-leg strength exercises +
Footnote †: journal: selected topics
# Mobile Network Performance and Technical Feasibility of LTE-Powered Unmanned Aerial Vehicle
## 2 Methodology
### Field Trial Measurement  
Figure 8 illustrates the concept of the drive test conducted. The measurement results were collected in a commercial LTE network with a carrier frequency of 2.6 GHz and a system bandwidth of 20 MHz. The drive test was conducted in two steps, first, at ground level and second, at higher altitudes. To do so, a Huawei STK-L22 smartphone with the preinstalled G-net Track Pro application was used. Once the application started to record, it was carried along while driving a car for the entire planned route to record the data needed to evaluate the network performance at ground level. The phone was placed inside a car without any external antenna and only powered by its own battery. Since the GNet Track Pro application's reliability has been tested before, just a single test was conducted for each track. This test was repeated four times by using the Subscriber Identity Module (SIM) card of four different telecommunication service providers that are widely available in Malaysia: Maxis, Umobile, Celcom, and Digi. During the test, the car speed maintained at a speed around 30 km/h. The ground level measurement was performed as the benchmark. Next, higher altitude drive tests were conducted at three different altitudes: 40 m, 80 m, and 170 m on average. The height of 170 m was chosen to investigate the impact of serving drones by the sidelobes of BSs' antennas. The intermediate height of 80 m was selected because that is close to the BS antenna height in suburban areas. Finally, the height of 40 m was considered since that is the most commonly used flying altitude for many drone applications, such as survey, inspection, and monitoring, and the drones are more likely to be served by the main lobes of BSs' antenna. For the aerial drive test, the smartphone was attached to the developed hexacopter drone and the drone flew within the same routes as the car with an average speed of 35 km/h.