esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
It is stated in [21] that the Monte Carlo simulation is an efficient tool to model electrical characteristics of power systems, which makes it possible to covert the stochastic variables (e.g., outputs of wind turbines, PVs, and electric loads) to the fixed variables. Therefore, this paper develops a combined Particle Swarm Optimization--Monte Carlo (PSO-MC) algorithm to optimize the chance constrained programming, which eliminates stochastic variables and reduces operation parameters. To deal with the coupling effects of heat and power in energy system optimization, an extra thermal energy storage system is added to the microgrid to increase the system operation efficiency and the peak load regulating capacity in [22]; and in [23], a boiler and a thermal energy storage tank are installed in the microgrid to decouple heat and power, which increases the flexibility of CHP operation. Therefore, based on the existing research, to improve network efficiency, an extra heat storage tank is installed to the distributed network to decouple heat and power. Therefore, the main contributions of this paper can be summarized as follows: (1) it is the first time that a portable energy storage system is installed in the microgrid to increase power system peak load regulating capacity, taking thermal demand into consideration; (2) the combined PSO-MC algorithm is proposed to optimize power system operation, which reduces the stochastic variables and accelerates computation speed; (3) a heat storage tank is introduced to the distributed microgrid to decouple heat and power, and by doing that fossil energy efficiency is significantly improved and battery installation capacity is reduced as well.
## 4 Discussion
Firstly, while the cross-over design allowed each participant to serve as their control, minimizing between-subject variability, the use of two different intervention formats, olive leaf powder incorporated into biscuits and in tea form, could have introduced variability in participant responses due to factors such as bioavailability of the active compounds and digestion rate influenced by meal composition. Additionally, the relatively short duration of the intervention period may not have been long enough to observe significant changes in biomarkers such as lipid profiles or inflammatory markers, which often require longer periods of supplementation to manifest. Furthermore, the homogeneity of the study cohort--composed solely of metabolically healthy volunteers from a single geographic location--while enhancing internal validity and reduces potential confounding factors, may restrict the generalizability of the results to broader populations, particularly those with underlying metabolic or inflammatory conditions. Moreover, the final sample size was smaller than initially anticipated and calculated during the study design phase, which may have impacted on the statistical power and robustness of the findings. The study also lacked a more precise standardization in the preparation process of the olive leaf powder, although all participants received the same dosage of 1.25 g, which was derived from a well-mixed batch of 200 g of olive leaves collected from different trees. Despite the careful mixing, slight variations in the chemical composition of the leaves from different trees could theoretically influence the consistency of the bioactive compounds, potentially affecting the observed outcomes. Finally, the possible role of olive leaf bioactives in modulating insulin sensitivity, inflammatory cytokine production (e.g., interleukin), and oxidative stress pathways warrants further mechanistic investigation. Bioactive compounds such as oleuropein and hydroxytyrosol may exert differential effects depending on dosage, formulation, and timing of intake, which could partly explain the heterogeneity in findings across existing studies. Inter-individual variability in metabolic responses further highlights the need for personalized approaches in future research. To advance understanding and improve reproducibility, upcoming trials should prioritize standardized intervention formats, extended supplementation periods, and diverse participant cohorts. Well-designed protocols with consistent dosing will be critical for accurately evaluating the therapeutic potential of olive leaf supplementation in metabolic and inflammatory regulation.
## 3 Results
### Net Radiation, Energy Balance, Bowen Ratio and Water Balance
The seasonal NEP in our Gmelin larch ecosystem appeared to be the weakest among Siberian boreal ecosystems and other boreal forest ecosystems reported in the literature, except one that was also on permafrost in central Yakuttia [53]. If to conclude from extremely low maximum net CO\({}_{2}\) uptake reaching \(\sim\)5 umol m\({}^{-2}\)s\({}^{-1}\) and \(\sim\)0.1 mol m\({}^{-2}\)d\({}^{-1}\) in the mid-summer, a similar Gmelin larch forest in East Siberia [53] looks very much of the same metabolism and grows in a similar environment to our Gmelin larch forest located in Central Siberia. In the pine forest, NEP was between 212 and 251 g C m\({}^{-2}\) season\({}^{-1}\), with a 3-year average of 228 g C m\({}^{-2}\) season\({}^{-1}\) (Figure 6 lower, Table 2). The NEP patterns for all observation years showed a strong signal of both studied ecosystems being a C-sink for the growing season. On average, in West and Central Siberia, NEP in middle taiga pine forests was measured by inventory methods as 160-225 g C m\({}^{-2}\) yr\({}^{-1}\)[54]. Cumulative ecosystem respiration for the growing season in the Gmelin larch forest was 106 g C m\({}^{-2}\) on average and did not change from year to year. In the pine forest, it was 3 times greater--335 g C m\({}^{-2}\). In both our forests, R\({}_{\rm eco}\) appeared to be lower, especially in the larch forest, compared to 25 various forest ecosystems in West Europe [55] and to 8 ecosystems in Northern Asia [56] that differed by composition. [PERSON] (2006) [57] calculated R\({}_{\rm eco}\) = 400 g m\({}^{-2}\) for the 2001 growing season in a Cajander larch forest in Yakuttia on permafrost (with a deep active layer, though). R\({}_{\rm eco}\) summed to NEP yielded 167-210 g C m\({}^{-2}\) of gross primary production (GPP) for the growing season in the larch forest and 542-592 g C m\({}^{-2}\) in the pine forest (Table 2). Compared to the GPP of the Cajander larch forest [10], the GPP of our Gmelin larch forest \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Larch Ecosystem**} & \multicolumn{3}{c}{**Pine Ecosystem**} \\  & **2005** & **2007** & **2008** & **1998** & **1999** & **2000** \\ \hline GPP, gCm\({}^{-1}\) & 167 & 186 & 210 & 543 & 592 & 552 \\ R\({}_{\rm eco}\), gCm\({}^{-1}\) & 104 & 106 & 104 & 324 & 341 & 340 \\ NEP, gCm\({}^{-1}\) & 63 & 80 & 106 & 219 & 251 & 212 \\ \hline \hline \end{tabular}  
* May of 1998 was approximated by the May average of 1999–2000.  
\end{table} 
Table 2: NEP, R\({}_{\rm eco}\) and GPP for the growing season in the Gmelin larch and pine ecosystems.
# Pullout Response of Ultra-High-Performance Concrete with Twisted Steel Fibers
## 5 Conclusions  
In this paper, a theoretical formula for pullout force was first derived according to the force analysis of twisted steel fiber (TSF) of rectangular section and triangular section. The model assumptions were explicitly made based on the experiment data obtained from the existing studies. Then the experiment on the ultra-high-performance fiber reinforced concrete (UHPFRC) with TSF at three embedment lengths was performed to gain insights into the strain hardening characteristics due to untwisting mechanism. A simplified numerical study was also performed to understand the untwisting behavior inside the concrete. The microscopic X-ray tomography was used to compare the internal microstructure of the UHPC matrix before and after pullout. The following conclusion can be made:  
* The pullout process of a single TSF in UHPFRC can be divided into a linear debonding stage, plastic untwisting stage and a dropping stage, so the pullout force versus displacement curve can be simplified as a trilinear curve.
* A significant strain hardening characteristic occurred due to the plastic deformation of TSF and its untwisting deformation during the pullout process. This untwisting deformation occurred on the fiber (within the effective untwisting fiber length) close to the exterior concrete surface.
* The fiber channel inside the matrix was not damaged after pullout, indicating a strong pullout resistance of matrix-fiber interface due to the high mechanical strength of matrix and strong bonding between matrix and TSF.
* The pullout force is independent of the embedment length of TSF and dependent of the geometry of fiber cross-section as well as the friction at matrix-fiber interface and its adhesive bond.
* The developed analytical formula was verified by the experimental data and thus can be used to gain insights into the mechanism of pullout process and to understand the parameters that influence the mechanical property of UHPFRC with TSFs.  
Conceptualization, J.Y. and G.L.; methodology, J.Y. and G.L.; software, J.Y.; validation, J.Y. and G.L.; formal analysis, J.Y. and G.L.; investigation, J.Y.; resources, G.L.; data curation, J.Y.; writing--original draft preparation, J.Y.; writing--review and editing, G.L.; visualization, J.Y.; supervision, G.L.; project administration, G.L.; funding acquisition, J.Y. and G.L.  
This project was supported by the funding from the National Natural Science Foundation of China (Grant No. 51379185) and National Basic Research Program of China (973 Program, 2013 CB036901).
###### Abstract  
Because the development of the Internet of Things (IoT) requires technology that transfers information between objects without human intervention, the core of IoT security will be secure authentication between devices or between devices and servers. Software-based authentication may be a security vulnerability in IoT, but hardware-based security technology can provide a strong security environment. Physical unclonable functions (PUFs) are hardware security element suitable for lightweight applications. PUFs can generate challenge-response pairs(CRPs) that cannot be controlled or predicted by utilizing inherent physical variations that occur in the manufacturing process. In particular, the pulsewidth-based memristive PUF (pm-PUF) improves security performance by applying different write pulse widths and bank structures. Bloom filters (BFs) are probabilistic data structures that answer membership queries using small memories. Bloom filters can improve search performance and reduce memory usage and are used in areas such as networking, security, big data, and IoT. In this paper, we propose a structure that applies Bloom filters based on the pm-PUF to reduce PUF data transmission errors. The proposed structure uses two different Bloom filter types that store different information and that are located in front of and behind the pm-PUF, reducing unnecessary access by removing challenges from attacker access. Simulation results show that the proposed structure decreases the data transmission error rate and reuse rate as the Bloom filter size increases; the simulation results also show that the proposed structure improves pm-PUF security with a very small Bloom filter memory.  
physical unclonable function; bloom filter; hardware security; PUF; memristor 2020 10 6692; doi:10.3390/app10196692
## 4 Conclusions  
In this work, nanocarriers based on polystyrene containing sulfonate groups and the surfactant oleic acid diethanolamide (OADA) were developed. These particles presented controlled surfactant release when in contact with the oil phase, causing a great water/oil IFT decrease to very low values. Experimental investigations were performed to evaluate the influence of some polymerization reaction parameters, such as crosslinking agent, stabilizer and sulfonated comonomer amounts, on the main properties related to EOR application. Properties such as IFT, the retained surfactant amount in the nanocarrier and particle size were evaluated. Based on these results, the reaction conditions were optimized to an OADA concentration of twenty times CMC, 0.012 mol % of sodium 4-sulfonate-styrene, used as comonomer with styrene, and 3 (_v/v_) % of divinylbenzene, used as a crosslinking agent, obtaining the 12 sulf20 SPSNP with 66 nm of particle size. These nanoparticles showed the best performance, since they reduced the water/oil IFT to 0.07 mN\(\cdot\)m\({}^{-1}\), improved stability in aqueous media, decreased the surfactant loss during the flooding on the sand surface and were responsible for a relevant gain in oil recovery when compared to free-surfactant solution. Overall, these partially sulfonated polystyrene nanoparticles have shown a potential to be used as surfactant nanocarriers for controlled release in enhanced oil recovery, since they were able to permeate through the unconsolidated porous sand medium, carrying a great amount of surfactant, increasing significantly the oil recovery by up to about 13% in relation to the water flooding oil recovery.  
Conceptualization, R.S.V.N. and S.P.C.C.; methodology, R.S.V.N., S.P.C.C. and A.D.S.F.; investigation, S.P.C.C., T.B.G.S. and A.D.S.F.; writing--original draft preparation, S.P.C.C.; writing--review and editing, R.S.V.N. and E.R.L.; project administration, R.S.V.N.; funding acquisition, R.S.V.N.  
This study was financed in part by CNPq (Conselho Nacional de Desenvolvimento Cientifico e Tecnologico), and also by Petrobras (Petroleo Brasileiro S.A.).  
The authors are thankful to CNPq (Conselho Nacional de Desenvolvimento Cientifico e Tecnologico) for the scholarship for [PERSON], and also to Petrobras (Petroleo Brasileiro S.A.) for the funding. The authors are also thankful to PGQu-Programa de Pos-Graduacao em Quimica do Instituto de Quimica, UFRJ, for support.  
The authors declare no conflict of interest.
## 3 Materials and Methods
### Breast Ultrasound Report Classification Model
#### 3.4.2 Channel Attention Module  
After the extraction operation of the parallel feature extraction module, the feature mapping images of two input images were obtained. Now, we need to extract important channel information from the two feature maps. Firstly, we concatenate these two feature maps. The formula is represented as Equation (3):  
\[r_{con}=Concatenate(r_{s},r_{g}) \tag{3}\]  
Among them, \(r_{s}\) and \(r_{g}\) correspond to the feature vectors extracted by ResNet50 from the original low-quality breast ultrasound images and the generated high-quality breast ultrasound images, respectively. The two vectors are concatenated using the concatenate function. Then, we use the Squeeze operation and global average pooling (GAP) operation to compress the feature map, with Formula (4):  
\[z=F_{seq}(r_{con})=\frac{1}{H\times W}\sum_{i=1}^{H}\sum_{j=1}^{W}r_{con}(i,j) \tag{4}\]  
\(H\) denotes the height of the feature image, while \(W\) corresponds to the width of the feature map. The GAP operation performs average pooling on the spatial dimensions until each spatial dimension becomes 1, while keeping the other dimensions unchanged. We need to utilize the correlation between channels rather than spatial distribution. Using GAP to mask spatial distribution information can make calculations more accurate.  
The next step after the Squeeze operation is the Excitation step. The Excitation step is implemented through two fully connected layers. Let \(z_{c}\) be the number of input channels and \(m\) be the channel scaling parameter. The first fully connected layer scales the input channel to \(\frac{z_{c}}{m}\), which is then passed through a ReLu layer and outputted to the second fully connected layer. The second fully connected layer restores the input channel number to and finally passes through a Sigmoid layer to obtain attention scores. The formula for the above steps is Formula (5):  
\[s=F_{exc}(z,W)=\sigma(W_{2}\delta(W_{1}z)) \tag{5}\]  
\(W_{1}\) and \(W_{2}\) represent the weights of the fully connected layer, and \(z\) represents the input. In this step, the scaling parameter is used to reduce the number of channels in order to reduce computational complexity, usually set to 16. After obtaining the attention score \(s\), it is necessary to multiply it by \(r_{con}\) to enhance important feature channels and weaken unimportant ones, thereby increasing the weight of important feature channels, the formula for this step is Equation (6):  
\[x=F_{Scale}(r_{con},s)=s\cdot r_{con} \tag{6}\]  
\(s\) represents the attention score and \(r_{con}\) is the concatenated feature map. Upon computing the channel attention module, we acquire the final feature maps. The classification module uses these feature maps to derive the ultimate classification outcome.
## 2 Materials and Methods  
### Procedure for Determining SRS  
#### 2.1.1 Subjects  
We utilized speech audiometry results of patients who visited our hospital for hearing aid fitting to assess the relationship between \(\tau_{\text{e}}\) and audibility of monosyllables in patients with general sensorineural hearing loss. These data were used as samples to determine the accuracy of perceived articulation.  
The subjects were patients who visited the Nara Medical University Hospital, Kashihara, Nara, Japan, for hearing aid fitting between September 2005 and August 2007. A total of 75 adult patients (40 women and 35 men) with sensorineural hearing loss visited the Nara Medical University Hospital in that period. Patients with sensorineural hearing loss were defined as those who had no obvious air bone gap on pure-tone audiometry.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline /a & /ka/ & /sa/ & /ta/ & /na/ & /ma/ & /ya/ & /ra/ & /ga/ & /da/ \\ /i/ & /ki/ & /si/ & /ti/ & /ni/ & /mi/ & /mi/ & /ri/ & /zi/ & /ai/ \\ /u/ & /ku/ & /su/ & /ti/ & /hu/ & /mu/ & /yu/ & /ru/ & & /zu/ \\ /e/ & /ke/ & /se/ & /te/ & /ne/ & /me/ & /re/ & & /de/ \\ /o/ & /ko/ & /so/ & /to/ & /no/ & /mo/ & /yo/ & /ro/ & /go/ & /do/ \\ \hline \end{tabular}
\end{table}
Table 1: Japanese monosyllables included in the 57-S monosyllable word list.  
We provided the participants with the opportunity to opt out, and this information was highlighted on the website of the Department of Otolaryngology-Head and Neck Surgery, Nara Medical University. This study was conducted in accordance with the principles of the Declaration of Helsinki and was approved by the Ethics Committee of Nara Medical University (No. 2245).  
Speech audiometry was performed on a total of 75 patients. Clinically, speech audiometry is a standard test for hearing aid fitting in Japan and is performed according to the Japanese Society of Audiology's Methods of Audometry (2003) [22]. For 6 of the 150 ears of the 75 patients, speech audiometry could not be performed, as these patients could not hear at all. For 3 of the 150 ears, speech audiometry was performed, but the patients were nearly unresponsive (the percentages of response were 0%, 0%, and 2%). Therefore, these nine ears in total were excluded, as they were not suitable for analyses. Finally, 141 ears of 75 patients were included. The mean age of the patients was 69.9 \(\pm\) 13.7 years. The average hearing levels of 500, 1000, and 2000 Hz in pure-tone audiometry (PTA) was at 49.1 \(\pm\) 7.2 dB.
# Active Flow Control of a Supercritical Airfoil and Flap with Sweeping Jets
###### Abstract  
To provide sufficient lift during takeoff and landing, large aircraft are equipped with complicated high-lift devices. The use of simple flaps coupled with active flow control (AFC) can achieve lift improvement while reducing mechanical structure and weight. The present study focuses on verifying the feasibility and effectiveness of simple flaps combined with sweeping jet flow control. An experimental study on the AFC of flaps, using sweeping jets, was carried out using a NASA SC(2)-0410 supercritical airfoil wind-tunnel model at \(Re=2.0\times 10^{5}\) (with velocity \(V=10\) m/s). In the experiment, the wing angle of attack (\(\alpha\)) ranged from 3 to \(18^{\circ}\), and the flap deflection angle (\(\delta\)) ranged from 0 to \(30^{\circ}\); the aerodynamic characteristics and surface pressure characteristics of the wing at typical working conditions were analyzed. Using sweeping jets to control the flow on the flaps, the momentum coefficients (for three actuator groups) of the jet are 0.8%, 3.6%, and 8.2%, respectively, and the maximum lift coefficient was increased by approximately 33%. The influence of the sweeping jet flow rate on the aerodynamic performance of the airfoil is analyzed. There are two main reasons for the lift coefficient increase caused by sweeping jet flow: an extra suction peak near the flap and a suction peak increase near the leading edge area caused by induced flow.  
active flow control; flap deflection; flow separation; sweeping jet 2023
## 3 Numerical Example 1
### Numerical Optimization Results--Comparison of the GA and SA Heuristic Methods  
Both heuristic methods do not guarantee finding an exact solution, which is the consequence of the very specificity of the methods. The optimization procedures GA and SA were validated on the 1 TMD, 2 TMD, and 4 TMD models. The parameters of stiffness and damping ratio obtained from both GA and SA optimization methods did not differ. Additionally, both methods were compared for the case of 10 TMD (Zuo and Nayfeh [20])  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**TMD Number** & \(m_{k}{}^{t}\) & \(k_{k}{}^{t}\) & \(\zeta_{k}{}^{t}\) \\ \hline  & [**kg]** & [**kh/m]** & [–] \\ \hline
1 TMD & 2000 & 2.00 & 0.05 \\
2 TMDs & 1000 & 1.00 & 0.05 \\
4 TMDs & 500 & 0.50 & 0.05 \\
8 TMDs & 250 & 0.25 & 0.05 \\
20 TMDs & 100 & 0.10 & 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mass values for individual TMDs and \(k_{k}{}^{t}\) and \(\zeta_{k}{}^{t}\) starting values.  
Figure 4: (a) Finite Element Method (FEM) model of the chimney and the first three mode shapes; (b) normalized eigenvectors for the first three eigenvalues.  
attached to an undamped primary system. Figure 5 shows the optimal TMD parameter values obtained using both methods. Compared to SA, the GA method did not provide results convergent with the results presented in the work by [PERSON] and [PERSON] (2018), while the obtained values of TMDs' parameters form SA were completely convergent. Unfortunately, with a greater number of optimization variables, the GA algorithm was less stable than the SA algorithm. The obtained results are discussed in greater detail in Section 5. Only the SA optimization method was applied in the further part of the optimization calculations.