esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### EPA and DHA
#### 4.8.3 N-3 PUFA Supplementation  
The fact that dietary intake of EPA + DHA is below recommendations in most studies, begs the question of whether supplementation or food fortification of these fatty acids would be beneficial for these age groups, using, for example, fortified milks. Indeed, [PERSON] et al., found a better adherence to n-3 FA recommendations in a group of children consuming adapted milks (including follow-on formula, toddler's milk, growing-up milk, and fortified and enriched milks) [89].  
Many observational and intervention studies explored the effects of n-3 LC PUFA among this age group and the vast majority of DHA supplementation trials to-date in children have focused on brain function outcomes, likely due to the known influence of DHA on brain development in early life [123]. A recent systematic review of n-3 PUFA supplementation in relation to cognition in children and adolescents (4-25 y) concluded that it seemed that an increase in the circulating levels of n-3 LCPUFA, reaching >6% EPA + DHA in blood erythrocytes, was required for a positive effect on cognition and that in healthy children, daily supplementation with at least 450 mg DHA + EPA was necessary for this improvement [124].  
Another area of research is the influence of n-3 PUFA supplementation on body composition and obesity risk in children, however, few studies exist beyond the pregnancy/lactation period. Even during this period, at present, evidence does not support a benefit of n-3 PUFA supplementation for later obesity risk reduction in infants and young children [125; 126]. In older children (7-12 y), dietary intakes of n-3 PUFA are positively related to lean mass accretion [127], but further research is required to determine the impact of n-3 PUFA supplementation at this stage in life on body composition and cardiometabolic health, both in the short- and long-term.
# Recent Advances in Optical Detection of Aminoglycosodies
## 1 Introduction
### Recent Advances in Optical Detection of Aminoglycos  
In recent years, many optical assays including turn-on FL, turn-off FL, ratio FL, colorimetric and chemiluminescence (CL) assays, etc., have been established for the determination of AGs (Figure 2). Compared with other analytical methods, optical analysis methods such as FL, CL, colorimetry, etc., have the advantages of their simple instrumentation, easy operation, easily realized mobile phone imaging and visualization [9; 10; 11]. Some of the proposed methods can give responses to the presence of target AG within 30 min which are convenient for POCTs for AGs. These optical methods (or sensors) are established for the determination of AGs based on the recognition by antibodies, aptamers, metal ions, and imprinted polymers [12; 13; 14; 15]. Antibodies are commonly used in the development of enzyme-linked immunosorbent assay (ELISA) kits for the detection of AGs. In recent years, the aptamer has become a powerful recognition element used in AG detection. Aptamers are single-stranded DNA or RNA selected by an in vitro systemic evolution of ligands by the exponential enrichment (SELEX) technique. Aptamers have a high affinity to their targets. Compared with antibodies, aptamers have many advantages, including a short in vitro synthesis process, good stability, easy modification, and long shelf life. In recent years, lots of aptamer-based assays have been developed for the determination of AGs. According to our literature research, about 70% of optical detection methods have employed aptamers as recognition elements for the detection of AGs.  
In addition, various nanomaterials, including metal nanoparticles (NPs) [16], metal nanoclusters (NCs) [17], carbon dots (CDs) [18], quantum dots (QDs) [19], etc., have been employed to construct optical assays for AGs. Metal NPs such as Au NPs, Ag NPs, Pt NPs, etc., have a characteristic surface plasmon resonance (SPR) absorption peak located in the visible region. The SPR absorption wavelength is affected by the size, shape, and dispersed state of metal NPs. FL-emitting nanomaterials such as QDs and CDs have size-dependent FL properties. Their FL emission wavelength is affected by their size and their FL intensity may be affected by their surface state. Based on the unique property of these nanomaterials, various simple and convenient optical sensing methods have been developed for the detection of AGs. To improve detection sensitivity, several signal amplification techniques have been successfully applied to the design of sensitive methods for AGs. These amplification techniques include target recycle-assisted signal amplification, hybridization chain reaction (HCR), rolling circle amplification (RCA), etc. [20; 21; 22].
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Models: Improvement Group \({}^{\bullet}\) (ImpG) versus No Change Group (NCG)**} & \multicolumn{2}{c}{**Females**} \\ \hline \multirow{3}{*}{\begin{tabular}{c} **KOOS** \\ **Subscales** \\ \end{tabular} } & \multicolumn{2}{c}{**_Independent_**} & \multicolumn{2}{c}{**_m_**} & \multicolumn{2}{c}{**_n_**} & \multicolumn{2}{c}{**_n_**} \\ \cline{3-8}  & \begin{tabular}{c} **Variables in** \\ **the Model** \\ \end{tabular} & \
\begin{tabular}{c} **(ImpG/** \\ **NCG)** \\ \end{tabular} & **OR (CI 95\%)** &
\begin{tabular}{c} **(ImpG/** \\ **NCG)** \\ \end{tabular} & **OR (CI 95\%)** \\ \hline \multirow{3}{*}{KOOSsymp} & log2(C2C) & \multirow{3}{*}{\(38/29\)} & \(1.99\) (\(0.78\)–\(5.04\)) & \multirow{3}{*}{\(1.35\) (\(0.40\)–\(4.48\))} & \multirow{3}{*}{\(24/10\)} & \multirow{3}{*}{\(1.13\) (\(0.88\)–\(1.44\))} \\  & Age & & \(1.06\) (\(0.93\)–\(1.20\)) & & \(1.01\) (\(0.86\)–\(1.18\)) & & \(1.13\) (\(0.88\)–\(1.44\)) \\  & BMI & & \(0.99\) (\(0.87\)–\(1.14\)) & & \(1.08\) (\(0.88\)–\(1.33\)) & & \(0.80\) (\(0.62\)–\(1.04\)) \\  & Sex & & \(2.47\) (\(0.80\)–\(7.66\)) & & & \\ \hline \multirow{3}{*}{KOOSpain} & log2(C2C) & \multirow{3}{*}{\(46/21\)} & \(0.99\) (\(0.44\)–\(2.22\)) & \multirow{3}{*}{\(22/11\)} & \multirow{3}{*}{\(0.52\) (\(0.14\)–\(2.02\))} & \multirow{3}{*}{\(1.23\) (\(0.26\)–\(5.81\))} \\  & log2(C2C) & & \(0.94\) (\(0.38\)–\(2.34\)) & & \(0.52\) (\(0.14\)–\(2.02\)) & & \(1.23\) (\(0.26\)–\(5.81\)) \\  & Age & \(46/21\) & \(0.95\) (\(0.83\)–\(1.07\)) & \(0.92\) (\(0.77\)–\(1.11\)) & \(0.92\) (\(0.77\)–\(1.11\)) & \(24/10\) & \(0.95\) (\(0.79\)–\(1.16\)) \\  & BMI & & \(0.86\) (\(0.74\)–\(1.00\)) & & \(0.78\) (\(0.60\)–\(1.01\)) & & \(1.01\) (\(0.82\)–\(1.24\)) \\  & Sex & & \(1.87\) (\(0.54\)–\(6.52\)) & & – & & – \\ \hline \multirow{3}{*}{KOOSadl} & log2(C2C) & \multirow{3}{*}{\(46/24\)} & \(1.58\) (\(0.72\)–\(3.48\)) & \multirow{3}{*}{\(0.97\) (\(0.34\)–\(2.79\))} & \multirow{3}{*}{\(3.46\) (\(0.8\)–\(14.98\))} & \multirow{3}{*}{\(3.03\) (\(0.60\)–\(15.3\))} \\  & Age & & \(1.35\) (\(0.56\)–\(3.25\)) & & \(0.62\) (\(0.18\)–\(2.15\)) & & \(3.03\) (\(0.60\)–\(15.3\)) \\ \cline{1-1}  & Age & \(1.06\) (\(0.94\)–\(1.19\)) & \(21/13\) & \(1.08\) (\(0.92\)–\(1.26\)) & \(23/11\) & & \(1.02\) (\(0.85\)–\(1.23\)) \\ \cline{1-1}  & BMI & & \(0.99\) (\(0.87\)–\(1.13\)) & & \(0.86\) (\(0.70\)–\(1.07\)) & & \(1.05\) (\(0.87\)–\(1.28\)) \\ \cline{1-1}  & Sex & & \(1.10\) (\(0.36\)–\(3.39\)) & & – & & – \\ \hline \hline \end{tabular}
\end{table} 
Table 11: The coefficient of improvement in different KOOS subscales by preoperative uC2C values (generalized linear models (GLMs)).
# Algorithms for Optimal Power Flow Extended to Controllable Renewable Systems and Loads
## 2 Materials: Optimal Power Flows Considering Uncertainty Cost Functions
### Optimization Problem Statement
#### 2.1.1 Target Function  
The objective function evaluated in this proposal is shown below, in the expression (4):  
\[\begin{split} f_{obj}=\sum_{j=1}^{N_{gen,C}}f_{j}(P_{j})+\sum_{k= 1}^{N_{gen,B}}f_{k}(P_{k})+&\sum_{m=1}^{N_{gen,S}}f_{m}(P_{m})+ \\ &\sum_{n=1}^{N_{gen,E}}f_{n}(P_{n})+\sum_{r=1}^{N_{gen,H}}f_{r}(P_ {r})+\sum_{t=1}^{N_{gen,CL}}f_{t}(P_{t}).\end{split} \tag{4}\]  
The variables presented in the objective function are described below:  
\[\begin{split} N_{gen,C}&\text{Number of conventional generators,}\\ N_{gen,B}&\text{Number of batteries,}\\ N_{gen,S}&\text{Number of solar generators,}\\ N_{gen,E}&\text{Number of wind generators,}\\ N_{gen,H}&\text{Number of run-of-river hydraulic generators,}\\ N_{gen,CL}&\text{Number of controllable loads or nodes with electric vehicle connection,}\\ P_{j}&\text{Active power delivered by the conventional generator j,}\\ P_{k}&\text{Active power delivered by battery k,}\\ P_{m}&\text{Active power delivered by the solar generator m,}\\ P_{n}&\text{Active power delivered by wind generator n,}\\ P_{r}&\text{Active power delivered by the hydraulic generator r,}\\ P_{t}&\text{Active power dispatched by controllable load or electric vehicle station t.}\end{split}\]  
The functions \(f_{j}\) and \(f_{k}\) refer to the cost functions for conventional generators and batteries, respectively. On the other hand, the functions \(f_{m}\), \(f_{n}\), \(f_{r}\) and \(f_{t}\) refer to uncertainty cost functions for solar, wind, run-of-the-river hydro generators and electric vehicles/controllable loads, respectively.  
The cost functions for conventional generators correspond to quadratic polynomial functions of the form [19]:  
\[f_{Conv}(P)=aP^{2}+bP+c. \tag{5}\]On the other hand, the cost functions for batteries correspond to linear functions. In this work, only batteries are taken into account in their discharge cycle, which means, working as generators [20; 21]:  
\[f_{Cms}(P)=cs_{m}PD. \tag{6}\]  
In the expression (6), \(cs_{m}\) is the battery operation and maintenance cost and PD is the power delivered by the battery.  
The functions \(f_{m}\), \(f_{n}\), \(f_{r}\) and \(f_{t}\) refer to cost functions of uncertainty due to the variable behavior of the resources from which energy is extracted, such as solar radiation, wind speed, water flow or the number of connected electric vehicles.  
The aforementioned functions are the sum of the costs of uncertainty due to underestimation and the costs of uncertainty due to overestimation, as shown below:  
\[f_{m}(P_{m})=f_{m,u}(P_{m})+f_{m,o}(P_{m}) \tag{7}\]  
\[f_{n}(P_{n})=f_{n,u}(P_{n})+f_{n,o}(P_{n}) \tag{8}\]  
\[f_{r}(P_{r})=f_{r,u}(P_{r})+f_{r,o}(P_{r}) \tag{9}\]  
\[f_{t}(P_{t})=f_{t,u}(P_{t})+f_{t,o}(P_{t}). \tag{10}\]  
In the expressions from (7)-(10), the subscript \(u\) refers to the cost function of uncertainty due to underestimation while the subscript \(o\) refers to the cost function of uncertainty due to overestimation.
## 4 Results and Discussion
### Validation of the Results  
The Wi-Fi signal strength quality was measured in %, and the corresponding signal strength values in dB. However, we found that signal strengths of 90% and above were regarded as strong signals (\(-\)55 dB), whereas the low-quality signals had an approximate RSS of 30% (\(-\)80 dB). An RSS of below \(-\)80 dB is regarded as a very weak signal. Access points are needed to boost the signal strength for vehicular ad hoc networks.  
In open road environments, heavy traffic can negatively impact V2V communications in the 2.4 GHz band. Similarly, crowded scenarios in highway settings lead to the 60 GHz frequency band experiencing higher path loss. Previous research has focused on improving the physical and MAC layer protocols, transmission power control, and channel assignment in VANETs. Field trial measurements and propagation studies highlight the importance of optimal access point placement to enhance VANET performance.  
The field measurement was repeated three times for each scenario to ensure accurate data were collected. The parking lot in the small is usually crowded seven days a week, especially during opening hours. We performed field measurements at lunchtime, when less people and cars were moving around the parking lot. Firewalls and anti-virus software were disabled. During the field measurements there were quite a few neighboring wireless networks detected. To avoid co-channel interference with the system performance, the access points (APs) were set to a different channel.
# RSVQ-Diffusion Model for Text-to-Remote-Sensing Image Generation
## 3 Methods  
The text-to-remote-sensing image generation model, RSVQ-Diffusion, proposed in this paper is an improvement of the VQ-Diffusion model. By incorporating the spatial characteristics of remote sensing images, we introduce a spatial position encoding mechanism based on pixel space coordinates. To address the issue of neglecting local information in the Transformer architecture when processing short sequences, we design a local perception mechanism for short-sequence feature extraction. The RSVQ-Diffusion model optimizes the diffusion image decoder, leading to improved performance in remote sensing image generation tasks. The RSVQ-Diffusion model significantly outperforms the original model in terms of the spatial structure, realism, diversity, and detail presentation of the generated remote sensing images.
# Development and Application of a Novel Non-Iterative Balancing Method for Hydronic Systems
## Appendix C PFM Procedure for Manifold-Based Systems  
Considering more complex systems than the case discussed, the procedure can be further refined, but there is no need to introduce additional conceptual elements.  
A case to be examined is the case of a manifold-based system or sub-system (Figure 11).  
As discussed above, the presence of the low-loss manifold makes the order of activation irrelevant, as conceptually it is as if all paths were driven by the same head pressure value. The procedure only requires an initial step to check which is the disadvantaged terminal; once that terminal with the valve fully opened is brought to its nominal flow rate value (first step of the PFM procedure), the pressure at the manifold will be taken as reference and balancing of other terminals starts (second step). In Figure 11 the position of the measuring points is shown, as well as the reference control loop and the operator's activity.  
As a further example, Figure 12 shows a system composed by a main manifold serving more risers each supplying local manifolds with several terminals.  
In this case, it is possible to start by applying the PFM balancing procedure to each zone manifold, then repeat it at branch-level and finally proceed similarly at the main manifold level for the balancing of the risers. The same pressure taps can be used as reference for all balancing levels. In the case of large systems the control point can be located far from the system portion under balancing procedure so the electrical connection required by the reference loop can be an issue, as well as the transferring the information of the value of the measured flow rate from the control point to the operator acting on the valves.  
Figure 12 also shows the adoption a multiple scalar measuring system, with a smaller instrument suitable in term of range/resolution for local (range refers to manifold/resolution refers to single terminal), a medium one (riser/local manifold) and a bigger (total pump discharge/riser).
# Energy-Aware Wireless Sensor Networks for Smart Buildings: A Review
## 3 Hardware Layer
### The Hardware Platform (Microcontroller and CPU)
As the Microcontroller can operate with lower voltage input, the CPU frequency is a function of the input voltage. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Microcontroller Features**} & \multirow{2}{*}{**Architecture**} & \multirow{2}{*}{**CPU**} & \multirow{2}{*}{**RAM**} & \multirow{2}{*}{**Storage**} & \multicolumn{2}{c}{**Input**} & \multirow{2}{*}{**Power Consumption**} & \multicolumn{2}{c}{**Wake-Up**} \\  & & & & & & & & & **Time** \\ \hline MSP430F5529 & \begin{tabular}{c} Mixed (von-Neumann and RISC \\ \end{tabular} & 25 Mhz & 10 KB & 128 Kbytes & 3.6 v & \begin{tabular}{c} Active: 404 uA/1 MHz \\ Standby: 2.5 uA/1 MHz \\ \end{tabular} & 3.5 ns \\ \hline MSP430F5514 & \begin{tabular}{c} Mixed (von-Neumann and RISC \\ \end{tabular} & 25 Mhz & 6 & 64 & 3.6 & \begin{tabular}{c} Active: 404 uA/1 MHz \\ Standby: 2.5 uA/1 MHz \\ \end{tabular} & 3.5 ns \\ \hline Atmega128 & \begin{tabular}{c} AVR (modified \\ Harvard) \\ \end{tabular} & 16 Mhz & 4 Kbytes & 128 Kbytes & 5.5 v & \begin{tabular}{c} Active: 19 mA/1 MHz \\ Standby: 11 mA/1 MHz \\ Power save mode: \\ Power save mode: \\ Power save mode: \\ Power save mode: \\ Power save mode: \\ \end{tabular} & 65 ms \\ \hline PIC16F877A & Harvard & 20 Mhz & \begin{tabular}{c} 368 \\ Bytes \\ \end{tabular} & 8 Kbytes & 5.5 v & \begin{tabular}{c} Active: 15 mA/1 MHz \\ Standby: 7 nA/1 MHz \\ Power save mode: \\ Power save mode: \\ \end{tabular} & 72 ms \\ \hline PIC18F46J50 & Harvard & 48 Mhz & \
\begin{tabular}{c} 16 \\ Kbytes \\ \end{tabular} & 4 Kbytes & 5.5 v &
\begin{tabular}{c} Active: 19 uA/1 MHz \\ Standby: 1.5 uA/1 MHz \\ Power save mode: \\ \end{tabular} & 105 ns \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Microcontrollers used in sensor nodes.
## 3 Results
### Phenology Extraction and Mapping  
Based on the decision tree constructed in Figure 8, spatial distributions of rice phenology were mapped with multi-temporal SAR images (Figure 9). Pixels for the phase of transplanting, vegetative, reproductive, and maturity were colored blue, green, yellow, and red, respectively. Pixel statistics of each phase were annotated behind the legends, indicating a significant growth trend of rice paddies.  
It is obvious that the spatial distribution of rice phenological phases varies on four dates, which shows a general trend of phenology evolution over time. On 15 May, almost all of the pixels are labeled as transplanting and vegetative phases, and transplanting pixels account for nearly 67%, showing that in most fields at this time, rice seedlings were just transplanted to the water and some earlier transplanted rice plants entered the vegetative phase. On 8 June, vegetative plants continued with the vegetative growth and prepared for the coming reproductive phase. Late seedlings were also extensively vegetative. These can be seen in map (b), where the pixels of the vegetative phase increase to an overwhelming proportion of 74%, while the transplanting ones sharply fall to 15%. On 2 July, about 58% of rice plants were in the reproductive phase. This ratio dropped to 17% on 26 July. In the last period, harvest pixels became the majority, with a proportion of 67%. All of these statistics are consistent with field survey observations, which demonstrates a feasibility of the decision tree classification.  
Figure 9: _Cont._
## 2 Materials and Methods
### Reliability Distribution Law Types  
The description of system reliability by means of a distribution law and related numerical characteristics represents a global approach to the problem in the spirit of classical statistics [12; 13; 14; 15; 16; 17]. The analytical expression of the reliability function is a fundamental problem of reliability theory. In general, a distribution law expresses the dependence between the values of the investigated characteristic and the related probability. Distribution laws specific to mathematical statistics are adopted in reliability theory to the extent that they imply a reliability function of suitable form, to which a certain physical interpretation can be associated.  
The times, as random variables or other variables such as the distance traveled or the number of request cycles, are distributed according to a statistical distribution law, highlighted by means of the distribution function F(t) or the distribution density function (frequency), f(t). As the random variable takes discrete or continuous values, the distribution is discrete or continuous. Several types of distributions are used for reliability:  
* Discrete: binomial or Bernoulli, polynomial, Poisson, hypergeometric, etc.;
* Continuous: uniform, normal, lognormal, exponential, gamma, Rayleigh, Weibull, etc.;
* Specific: \(\chi^{2}\) (chi-square, Pearson), t (Student), F (Fisher-Snedecor), mixture, etc. [18; 19; 20; 21; 22; 23].  
To analyze the reliability of the components that presented the most defects, we used the Relyence Weibull 2020 software distributed by Relyence Corporation (Greensburg, PA, USA).
# Halloysite and Laponite Hybrid Pigments Synthesis with Copper Chlorophyll
## 3 Results and Discussion
### Uiv-Vis-Nir
#### 3.2.1 Total Solar Reflectance %  
The total solar reflectance calculation requires taking raw reflectance data and applying the solar weighting factors for each collected wavelength. These factors and calculations are included in the respective norms ASTM G173-03 [37]. The total solar reflectance coefficient TSR (%) calculation subtraction (1 - TSR) corresponds to the degree of total solar absorption of the nanoclay/hybrid pigment. Figure 6 depicts how there were no significant differences between the TSR of the original nanoclays. However, the hybrid pigments from halloysite had higher TSR values than the hybrid pigments from laponite.  
In addition, differences appeared between the synthesis factors using TSR (%) as a response to maximize. According to the ANOVA analysis, to maximize the TSR values the optimal levels were obtained for the halloysite nanoclay with no surfactant and mordant, and neither pH nor the silane modifier factors had any influence (Figure 7). However, significant interactions took place. No influence was noted for the laponite clay in the presence of the surfactant, while halloysite obtained the highest results without this modifier.  
At an acidic pH, the presence of silane had a significant influence because the TSR of the hybrid pigments abruptly lowered. However at a natural pH, the effect of silane-treated nanoclay was the exact opposite because the TSR values of the hybrid pigments increased (Figure 8). The functional role of the chemical silane treatment on the hydrophilic halloysite surface contributed with rearranged and delaminated HNT particles, which improved their exfoliation in the polymer matrix and could reflect solar radiation [38]. For instance, we found that this effect in the interaction analysis depended on the pH level, which also affected the nanoclay sheets arrangement.  
Figure 6: TSR (%) values from halloysite (HA), laponite (LAP), and chlorophyll hybrid pigments GLAP (1–8) and GHA (1–8).  
Figure 8: Interactions plot for TSR (%) with the pH and CLAY factors.  
Figure 7: Mean plots of the LSD 95% confidence intervals from the TSR (%) response analysis.
# High Reliable Uplink Transmission Methods in GEO-LEO Heterogeneous Satellite Network
## 3 Performance Evaluation  
In this section, we evaluate the transmission reliability in terms of the outage probability.
# Self-Reporting Technique-Based Clinical-Trial Service Platform for Real-Time Arrhythmia Detection
## 4 Discussion  
### Principal Achievements  
In this study, a new ICT-based clinical-trial service platform with a self-reporting technique was developed. Recent advancements in ICT have led to the development of wearable devices and smartphones, which have changed the communication paradigm for measuring, transmitting, processing, and saving patients' health data. Multiple vital signs and biosignals are easily measurable with user-friendly wearable devices at any time and any location. The newly proposed platform, encompassing wearable devices, mobile apps, cloud computing, databases, and data broadcasting, capitalizes on the advantages of the latest technologies. The most remarkable feature of this platform, a self-reporting technique, was realized effectively with the mobile app. Live streaming via SFU and on-demand playback-reviewer systems were implemented to help clinicians to readily access up-to-date acquired measurements. In terms of potential clinical impact, this platform improves convenience and efficiency for monitoring different types of medical data and  
Figure 4: Real-time ECG obtained from healthy volunteers: (**a**) Single-channel ECG on the mobile app; (**b**) Streamed ECG data of four participants in real-time on the web client.  
patient diagnosis. Long-term monitoring and further investigation are also possible with the data-storage function.