esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Mathematical Relations Used for Modelling STPP  
The heliostat efficiency is contingent on its location in relation to the receiver. In the central receiver system, the cosine effect depends on both the position and location of the sun. This is different from that of the parabolic trough collector, which depends only on the hour of the day. The cosine angle is computed using Equation (1) [58; 59].  
\[cos\big{(}\theta_{i,p}\big{)}=\sqrt{\frac{1+cos\big{(}2\theta_{i,\ p}\big{)}}{2}} \tag{1}\]  
The total yearly energy \(E_{s}\) (Wh/m\({}^{2}\)) for each mirror area can be estimated using Equation (2).  
\[E_{s}=\sum_{i=0}^{8760}DNI_{i}\times cos\big{(}\theta_{i,\ p}\big{)} \tag{2}\]  
where the hour is denoted by \(i\), and the location of the point is represented by \(p\).  
The heliostats are practically arranged with gaps in between them; this is to allow for maintenance and to prevent shadowing. The packing density can be expressed as the ratio of the minor area to the area of the land; this varies for different locations in the heliostat field. Therefore, in the case of an STPP, the impact of the packing density has to be taken into consideration to calculate the actual energy that is reflected. The actual yearly reflected energy for each land area for a point \(p\) at the base field, taking into consideration the impact of the packing density, can be computed using Equation (3).  
\[E_{a}=(PD)\sum_{i=0}^{8760}DNI_{i}\times cos\big{(}\theta_{i,\ p}\big{)} \tag{3}\]  
The thermal power of the HTF under design conditions, wherein the plant produces the design electric power, can be computed using Equation (4).  
\[E_{th_{dt}\ f,ds}=\frac{P_{des}\times 10^{6}}{\eta_{E_{PB}}\times\eta_{HE}} \tag{4}\]  
The thermal power that can be collected from the heliostat field is mathematically expressed as follows:  
\[E_{th_{des}}=\frac{E_{th_{dt}\ f,ds}}{\eta_{receiver}} \tag{5}\]  
Equation (6) would be obtained if Equation (4) is substituted into Equation (5).  
\[E_{th_{des}}=\frac{P_{des}\times 10^{6}}{\eta_{receiver}\ \times\eta_{HE} \times\eta_{E_{PB}}} \tag{6}\]  
Equation (7) can be used to compute the concentrating ratio (CR) [15].  
\[CR=\frac{Area\ of\ the\ receiver\ \big{(}m^{2}\big{)}}{Total\ area\ of\ the\ heliostats\ \big{(}m^{2}\big{)}} \tag{7}\]  
The capacity factor (CF) of a power plant is a key parameter that is used to evaluate the performance of the system. The CF can be estimated using Equation (8) [49; 58].  
\[CF=\frac{Gross\ annual\ electricity\ generated}{P_{des}\times 10^{6}\times 8760} \tag{8}\]The receivers nominal thermal power is determined by the solar multiple (SM). It is defined as the ratio of the receiver thermal power to the cycle thermal power. Systems without storage should have an SM close or equal to 1 [42].  
\[\textit{Receiver thermal power}\ (MWt)=SM\times\textit{Cycle thermal power}\ (MWt) \tag{9}\]
## 4 Representation of Epistemic Uncertainty Considering Uncertain Distribution Parameters  
The distribution type \(\theta\) of the epistemic uncertainty variable \(y\) is deterministic, whereas distribution parameters \(\xi\) have the characteristic of uncertainty. The insufficient data and evidence theory information are fused from two sources, so the distribution parameters \(\xi\) can be defined by the Gaussian interpolation method. The epistemic uncertainty variable \(y\) is one of the functions of the uncertain distribution parameters \(\xi\). The probability density function \(f_{y}(y)\) of epistemic uncertainty variable \(y\) is calculated by the weighted summation of multiple dispersed distribution parameters, which can be obtained from the probability density function \(f_{\xi}(\xi)\). Therefore, the uncertainty calculation complexity of the system performance can be effectively reduced.  
The lower limit and upper limit of the \(i\)-th distribution parameter \(\xi_{i}\) are referred to \(\xi_{i}^{\min}\) and \(\xi_{i}^{\max}\), which are determined with \(\xi_{i}^{\min}=\Gamma^{-1}(0.005)\) and \(\xi_{i}^{\max}=\Gamma^{-1}(0.995)\), where \(\Gamma^{-1}\) is the inverse probability density function \(f_{\xi_{i}}(\xi_{i})\) of \(\xi_{i}\). Then, the interval \(\left[\xi_{i}^{\min},\xi_{i}^{\max}\right]\) is decomposed by the inverse cumulative distribution function. Therefore, after \(\xi_{i}^{j}=\Gamma_{\xi_{i}}^{-1}(0.1j),j=1,\cdots,9\) is calculated, the design space of \(\xi_{i}\) can be decomposed into 10 Bayesian evidence intervals, \(\left[\xi_{i}^{\min},\xi_{i}^{1}\right]\), \(\left[\xi_{i}^{j},\xi_{i}^{j+1}\right]\)(\(j=1,\cdots,8\)) and \(\left[\xi_{i}^{9},\xi_{i}^{\max}\right]\), which have the same BPA of \(m=0.1\). If the interval length \(\xi_{i}^{j+1}-\xi_{i}^{j}>0.2(\xi_{i}^{\max}-\xi_{i}^{\min})\), then the interval is decomposed to \(\left[\xi_{i}^{j},\left(\xi_{i}^{j}+\xi_{i}^{j+1}\right)/2\right]\) and \(\left[\left(\xi_{i}^{j}+\xi_{i}^{j+1}\right)/2,\xi_{i}^{j+1}\right]\), and the corresponding BPAs are calculated using the cumulative distribution function of \(\xi_{i}^{j}\).  
Therefore, the uncertain distribution parameter \(\xi_{i}\) is decomposed into \(nm_{i}\) sub-interval \(\left[\xi_{i}^{j-1},\xi_{i}^{j}\right]\)(\(j=1,\cdots,nm_{i}\)), where \(\xi_{i}^{0}=\xi_{i}^{\min}\) and \(\xi_{i}^{nm_{i}}=\xi_{i}^{\max}\). Thus, \(\xi_{i}\) is represented using discrete points \(\left(\xi_{i}^{j-1}+\xi_{i}^{j}\right)/2\) (\(j=1,\cdots,nm_{i}\)) with the BPA of interval \(\left[\xi_{i}^{j-1},\xi_{i}^{j}\right]\).  
Every uncertain distribution parameter \(\xi\) can be represented by the method mentioned above, so the epistemic uncertain variable \(y\) is probabilistically calculated by a weighted sum of probability variables \(\mathbf{u}^{j}\) in Equation (12):  
\[y\sim\sum\Theta\Big{(}\xi^{j}\Big{)}\mathbf{u}^{j}. \tag{12}\]  
The distribution type and distribution parameters of \(\mathbf{u}^{j}\) are \(\theta\) and \(\frac{\xi^{j-1}+\xi^{j}}{2}\), respectively.  
\[\mathbf{u}^{j}\sim\theta\Bigg{(}\frac{\xi^{j-1}+\xi^{j}}{2}\Bigg{)}. \tag{13}\]
## 2 Overview of the IMBPC HVAC Solution Hardware and Software
### Software
Taking for example Equation (4), the inside air temperature \(TA_{i}\), at instant \(k\), depends on delayed values of the same variable, and delayed values of the exogeneous variables inside relative humidity, external air temperature, solar radiation, HVAC reference temperature and movement. The models above are also RBFNN models, whose design is achieved by a MOGA model design framework. The MOGA-based model design framework is composed of an evolutionary algorithm and a derivative-based algorithm. The evolutionary part searches the admissible space of the number of neurons and the number of inputs (which in this case are lags for the modelled and exogenous variables) for the RBFNN models. Before being evaluated by the genetic algorithm, each model has its parameters determined by a Levenberg-Marquardt algorithm [21; 22], minimizing an error criterion that exploits the linear-nonlinear relationship of the RBF NN model parameters [23; 24]. For more details on the MOGA model identification framework, please see, for instance [12]. Models (4) to (6) are iterated over \(PH\) to obtain the forecasts of these three variables. Notice that, dependent on the prediction step and the delays for each exogenous variable, forecasted variables may be used as inputs instead of measured variables. The last predictive model that needs to be referenced is the one used for movement detection, denoted by \(f_{M}(. )\), used to estimate the influence of occupation in Equations (4) and (6). Assuming the existence of schedules, the forecast of movement throughout PH at each instant will be a constant value, obtained as the exponential weighted average value of the movement signal, from the start of the schedule until the corresponding sample. Figure 1 illustrates, schematically, the IMBPC HVAC approach. As can be seen, it needs measurement (thin lines) of weather variables (orange enclosure) and inside climate variables (blue enclosure). The intelligent weather station computes the forecasts (thick lines in dark blue, red and grey) of the weather variables over \(PH\). The inside climate variables, as well as the movement signal, are acquired by the SPWS, and a local computer is used to obtain the forecasts of these variables (thick lines, blue, dark red, black and green). The forecast of the climate variables is applied to the static PMV model, to obtain the forecasts of \(\Theta\) (thick, yellow). These will used as restrictions for the optimization problem (1), which will compute the optimal reference temperature sequence (thick brown line), that minimizes the economic cost of all admissible sequences, for the specified schedule, using for that the branch-and-bound algorithm.
# Bio-Inspired Sleep Control for Improving the Energy Efficiency of a MEC System
## 2 Related Works
### Energy Saving in a MEC System
Subsequently, they solved the subproblems using the alternating direction method of multipliers in a distributed manner. In [26], a multi-state stochastic problem was formulated to model the energy-aware application placement problem in a MEC system. A parallel sample average approximation (SAA) algorithm was proposed to solve the formulated problem. Because SAA approximates the recourse function based on the sample average, SAA experiences a tradeoff between the complexity and accuracy of the algorithm. In this study, we devised a MECS sleep control method that can reduce the total energy consumption of a MEC system. We followed a bio-inspired approach and devised the lightweight microscopic behavior of each MECS that can result in a globally desirable system property. This property is the energy saving of a MEC system that can be achieved if each MECS repeats the microscopic behavior using the local information obtained by interactions with only the local neighboring MECSs.
## 2 Materials and Methods
### Data Preprocessing  
The objective of this study was to predict the occurrence of physical activity and psychological stress individually or concurrently in different subjects. All data were collected in a clinical setting. Data quality may be compromised due to factors such as sensor detachment, loss of communication, outliers, and missing information. Therefore, preprocessing of all signals was needed to improve the signal-to-noise ratio as well as to keep the valuable information from being filtered out.  
The processed variables from the Empatica E4, namely, time between individual heartbeats (IBI) and HR values estimated by the E4 during physical activity and intense body movement, are contaminated by noise and motion artifacts because the photoplethysmography sensor is noise-sensitive. Hence, these variables were not taken into account when developing NN models. Instead, we used the BVP signal and processed it to generate time- and frequency-domain features that were then used in the training of the classification models.
## 1 Introduction: Inorganic Polyphosphate and Polyphosphate Kinase 1 (PPK1)  
Inorganic polyphosphate (polyP) is an ancient and evolutionarily conserved biopolymer consisting of phosphate monomers linked together via high-energy phosphoanthydride bonds. PolyP chains can range in length from three to over one thousand inorganic phosphate (P\({}_{i}\)) residues [1]. Insoluble phosphate-containing granules--what we now know to be polyP--have been documented in bacteria since the late 1800s [2]. However, the source of this mysterious phosphate polymer remained unclear until [PERSON] and colleagues isolated an enzyme from _Escherichia coli_ that could synthesize polyP, which they termed polyphosphate kinase (PPK; later disambiguated as PPK1) [3]. It was not until 1990 that _E. coli_ PPK1 was finally purified to homogeneity [4], which allowed for the identification and cloning of the \(ppK\) gene, followed by overexpression and deletion studies [5; 6; 7]. The newfound ability to manipulate the \(ppK\) gene was a boon for polyP research. _E. coli_ mutants lacking \(ppK1\)--and thus generating no detectable polyP--exhibited profound defects in stationary phase survival upon nutrient downshift [7; 8; 9; 10; 11]. PolyP has also been shown to protect bacteria from a variety of external stressors, such as heat [12], ultraviolet irradiation [13], antibiotics [14; 15], metal ions [16], and oxidative stress [17; 18]. Although it is not yet completely understood, at least some of these protective effects may be a result of chaperone-like function, whereupon polyP binds partially unfolded or denatured proteins to facilitate refolding once stress has abated [19].  
Despite the absence of either PPK1 or PPK2 homologues in higher eukaryotes [20], it is worth noting that polyP has been detected in various mammalian cell types at concentrations typically ranging from 25 to 120 \(\upmu\)M [21], though this can reach approximately 1 mMin platelets [22]. While the source of this mammalian polyP is unclear, it has been postulated that the mitochondrial F\({}_{0}\)F\({}_{1}\) ATPase is involved [23]. PolyP has been linked to diverse aspects of mammalian physiology, including blood clotting [24], bone formation [25], and protein polyphosphorylation [26; 27; 28]. PolyP also serves as a nucleation source for amyloid proteins ranging from human \(\alpha\)-synuclein and Tau to the _E. coli_ biofilm protein CsgA [1; 29]. Finally, several recent studies have demonstrated that bacterial polyP can modulate the mammalian immune response [30; 31], suggesting an intriguing potential for host-pathogen polyP crosstalk during infection [32].
## 3 Results and Discussion
### Results of Photocatalytic Degradation under Different Conditions
#### 3.4.5 Repeated Experimental Results under the Optimum Conditions  
From the above conditional experiments, the optimum conditions for the treatment of MB simulated wastewater with composite photocatalyst are obtained, the best GO composite ratio is 15 wt%, the most reasonable amount of composite catalyst is 200 mg/200 mL, the most suitable initial pH is better under mild alkaline conditions, and the composite photocatalyst is suitable to treat lower  
Figure 6: Degradation results of MB synthetic dye wastewater at different initial pH values.  
Figure 7: Degradation result on different concentrations of MB dye wastewater treated by composite photocatalyst under UV irradiation.  
concentrations of synthetic wastewater. In order to study the reproducibility of the resultant composite photocatalyst, the cycling number of the photocatalysis was record which is shown in Figure 8.  
When each run of the photocatalysis test was fixed at 150 min for the composite photocatalyst, it can be seen from the results that composite photocatalyst can keep the degradation rates more than 85% at less for 15 cycles. There was slight decrease of the degradation rates during the cycles, which we think might due to the loss of the smaller nanoparticles which could not be collected from the centrifugation. Besides, in the process of photocatalytic wastewater treatment, it is possible to have some reactants or intermediate products remaining on the surface of the catalyst to cover the active site, resulting in a gradual decrease in photocatalytic activity. However, even after the 15 th run, the degradation rate could also be more than 85%. These results indicate that the composite photocatalyst is a stable photocatalyst with good cycling life and could be used as the organic pollutants degradation
# Distributed Localization with Complemented RSS and AOA Measurements: Theory and Methods
## 4 Performance Analysis
### Analysis of Localization Accuracy
One can see from the figure that LLS and GTRS methods are biased for small \(R\) (m), since they outperform CRLB in this setting. Furthermore, even though it is intuitive that all methods improve as \(R\) (m) is increased, Figure 14 shows that this is not exactly the case. For instance, LLS and MCMC-MH methods worsen with the increase of \(R\), while GTRS improves until a certain point after which it seems to get saturated and its performance worsens slightly after that point. The only method that continuously benefits  
Figure 12: Average root mean square error (ARMSE) (m) versus \(N\): \(M=50\), \(R=10\) m, \(M_{c}=1000\). CRLB = Cramér-Rao lower bound (CRLB). Figure 13: ARMSE (m) versus \(M\): \(N=15\), \(R=10\) m, \(M_{c}=1000\). from the increase of \(R\) is SOCP. This can be explained to some extent by the fact that its computational complexity is the highest among the considered methods, which might give it more robustness when processing the additional information caused by increasing \(R\).
### Threat Model  
In [PERSON] et al.'s scheme, they adopt the Dolev-Yao (DY) threat model [14] to evaluate the security of the protocols. According to this model, an adversary can intercept, eavesdrop, insert, delete, and modify all messages transmitted between the communicating entities including user equipment (UE), machine-type communication (MTC) devices, and serving network (SN) because they communicate over a public (insecure) channel. The key generation center (KGC) is a fully trusted entity because it generates and manages the secret key for UEs and MTC devices (MDs). However, UEs and MDs are not physically protected and an adversary can obtain the data in the memory of UEs and MDs using power analysis attack [10; 15; 16].
## 2 Study Area and Data
### Data Description  
#### 2.2.1 HY-2A Microwave Scatterometer  
Microwave scatterometers can offer wind data of the sea surface (wind direction and wind speed). Quick Scatterometer (QuikSCAT) This has proven to be a very effective scatterometer that can obtain sea surface wind speeds with high accuracy [27, 28], but this has not been available since 2009. Haiyang-2A (HY-2A) is the first altimetry and scatterometry satellite project of the China National Space Administration (CNSA), and it is dedicated to the studies of physical oceanography and geodesy. The Ku-band scatterometer is carried on HY-2A. Quantitative analysis shows the effectiveness of HY-2A sea surface wind field observations, and its advantages for typhoon monitoring [29]. Wind speeds and directions that are derived from HY-2A are accurate under medium and low wind speeds [30], though it is easily saturated at high wind speeds (>30 m/s). HY-2A satellite microwave scatterometer data can be obtained through the application of the National Satellite Ocean Application Service (NSOAS). Figure 2a is a HY-2A observation of Typhoon Fitow.
# Determination of Point-to-Point 3D Routing Algorithm Using LiDAR Data for Noise Prediction
## 4 Results and Discussions
### Accuracy for Determined Principal Routes
#### 4.1.1 Route over the Top Accuracy  
From the discussed algorithm for the route over the top, we got a route for a building as shown in (Figure 8b) as the blue line indicates the desired route. It got two points on building that create a route over the top (_Source_, _1 st_, _2 nd_, _destination_). Now, to check the accuracy change in the y value, change in the 1 st point is done with (\(\pm\)0.5, \(\pm\)1, \(\pm\)1.5) m, where (+) means left side of desired point and (-) means right side of desired point. Then, calculate the distance of the new route created with these changes. The blue line below and above the red line shows the changed route (Figure 14a). The changes in route length with deviation in right and left side of desired point is shown in Figure 14b. It is clear from the calculation that the route calculated by \"Routes over the top algorithm\" is the shortest route.
## 3 Our Approach
### Applying Tensor Decomposition with Different Rank Methods
#### 3.3.4 Comparative Analysis of Decomposition Methods  
Our approach utilizes a heuristic method grounded in layer sensitivity analysis, wherein the sensitivity of layers is evaluated across the following six distinct ranks: 1, 5, 20, 40, 60, and 80. We applied three tensor decomposition techniques, namely CP, Tucker, and Tensor Train. A comparative analysis revealed negligible accuracy disparities between Tucker and Tensor Train decomposition. However, Tensor Train decomposition demonstrated a superior compression ratio relative to Tucker, as detailed in Table 1.
## 2 Experimental Setup
### Molecular Tagging Velocimetry Configuration
To capture the slower flow around the Venturi (\(|y|>9\) mm), images with a longer \(\Delta t=\)32 us were also recorded at the bottom location.The _read_ laser pulse before the jet starts was used as the reference \(Y\)-position of the tagged tracers. The underformed MTV lines (Figure 3) were 380 um wide. The velocity was reconstructed from the \(\Delta t\) intervals and recorded displacement. The latter was found with the following algorithm: the intensity profile across the MTV lines is locally fitted with a Gaussian profile whose maximum is used as the local (sub-pixel) position of the line. The reconstructed velocity values are determined based on the displacement of the continuous lines of molecular tracers from the seeded location to the new location after the \(\Delta t\) interval. The imaged continuous MTV lines are displacement profiles directly visible on the camera frames. For the very short \(\Delta t\) intervals used in this study, molecular diffusion effects are negligible [14]. The timing unit offers a resolution of 250 ps, and the lasers exhibit a jitter of less than 0.5 ns with pulse durations under 10 ns, resulting in a negligible overall timing error. The MTV line position is detected with sub-pixel accuracy corresponding to 11 um, which translates to a velocity uncertainty of 1.3 to 2.2 m/s across the three measurement stations; this is less than 0.45% of the local peak velocity. As discussed in Section 4, the combination of shadowgraphy and MTV data allows us to divide the temporal evolution of the gas pulse into three regimes: an initial transient phase, a quasi-steady phase, and a declining jet phase. This segmentation provides a comprehensive view of the pulse dynamics. However, the primary focus of this work is the validation of a CFD simulation that models only the quasi-steady regime. Furthermore, although the jet is turbulent and our dataset does not permit full statistical convergence with respect to turbulence, velocity profiles obtained from different runs at the same phase, as well as from successive phases within the quasi-steady regime, show a strong overlap. This suggests robust repeatability in the time-averaged flow features. Our analysis focuses on the primary jet structure (especially the first Mach disk) and on capturing key trends and characteristic magnitudes rather than resolving fine-scale turbulence features. Confidence intervals and quantitative error bars are not included in the MTV-derived velocity plots, as the variability is clearly illustrated through the inclusion of multiple overlaid profiles.