esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments and Performance Evaluation
### Performance of the Routing Protocol  
In this section, we evaluate the proposed routing protocol in some metric of the RCC performance. Compared with conventional AODV routing protocol, the evaluation analysis includes end-to-end delay and packet delivery ratio. In this simulation, the NS-2 and IEEE 802.15.4 PHY/MAC protocol are used. All the statistics of the evaluation are acquired from the packet-trace report of NS2. We use WSN as a transparent medium that passes CBR (constant bit rate) traffic. The maximum receiving range and carrier sensing range are 1600 and 3520 m, respectively. There are 80 towers in the network. The neighbor tables of the forward and backward list are created by the link status message with one-hop broadcast.  
The end-to-end delay of AODV significantly increases to 0.3 s with the number of nodes increasing, as shown in Figure 9a. The main reasons are repeated routing request time and overlapped routing paths. On the other hand, the proposed method shows low end-to-end delay compared with AODV, since the proposed method has no other round of route discovery before transmitting data. As for the packet delivery ratio in Figure 9b, the proposed method is comparable to AODV because the network congestion causing collision and drops of the packets of AODV is comparable to the proposed routing protocol.  
Figure 10 evaluates the proposed routing protocol after the removal of some nodes. A removal of a key relay node leads to initiations of the next route discovery for AODV, which results in high end-to-end delay of the coming data packet. In this case, the proposed method can quickly recover the broken route.  
Figure 8: Effect of end-to-end delay constraint.
## 2 Materials and Methods
### Simulations  
Simulations were performed to study the effect of the spatial position of the two arrays and the scan sequence on the imaging performance of the system. The configurations were simulated in MATLAB (The MathWorks, Natick, MA, USA) by using Field II [25; 26], setting a sampling frequency of 100 MHz.  
Two different sets of simulations were performed. The first set consisted of an optimization sweep, in which the parameters were varied to evaluate the configuration where CoMTUS operates most effectively. A single point-scatterer was placed at the center of the common FOV of both arrays (see Figure 1) to simulate the system PSF for different spatial configurations, depths, and using different numbers of angled transmissions at varying angle ranges. Each parameter was allowed to vary as follows: the angle between the arrays, \(\theta\): 95\({}^{\circ}\) to 165\({}^{\circ}\); the gap between the arrays: 0 mm to 35 mm; the maximum transmitted PW angle, \(\alpha_{max}\): 0\({}^{\circ}\) to 15\({}^{\circ}\); the number of transmitted PWs per array: 1 to 11. The ranges of the parameters investigated were chosen to match feasible experimental configurations of the two arrays. In a first step, the optimum \(\alpha_{max}\) was determined as a function of depth. Like in standard PW compounding with a single array, it is expected that \(\alpha_{max}\) decreases with depth [20]. To optimize \(\alpha_{max}\), a simulation was performed varying \(\alpha_{max}\) from 0\({}^{\circ}\) to 15\({}^{\circ}\) while keeping the number of transmissions per array equal to 3, (\(-\alpha_{max}\), 0\({}^{\circ}\), \(\alpha_{max}\)), and the gap between the arrays zero. This results in a total of six PWs to be compounded in CoMTUS images. The angle between the arrays was varied according to the desired depth. In a second step, a simulation was performed varying the number of PWs for the different probe positions (changing angle and gap) while \(\alpha_{max}\) was set according to the imaging depth (resulting from the previous step).  
PSFs were used to determine two optimal configurations at 70 mm depth: a first configuration with minimum-side lobe energy, and a second configuration with a minimum main lobe width. The latter configurations were used for a second set of simulations investigating a tissue-mimicking phantom. The phantom consisted of randomly generated point scatterers (234 scatterers/mm\({}^{3}\)) with a Gaussian amplitude distribution, along with a 10-mm diameter circular empty region simulating an anechoic cyst. The tissue phantom size was 50 mm (width) \(\times\) 1 mm (depth) \(\times\) 30 mm (height) and was centered at a depth of 70 mm from the center of both arrays. This phantom was used to demonstrate the effectiveness of the imaging configuration in determining edges of regions and the impact of grating and side lobe on the final image quality. Simulations were performed using 7 transmission angles per array with a maximum steering angle of 13\({}^{\circ}\). These choices are based on the previous optimization and will be explained further in Section 3.
## 4 Conclusions  
A SIW-fed printed folded dipole antenna is proposed for the MMW application. By cutting two gaps on the folded dipole, an additional resonance is generated, thus the operating bandwidth and impedance matching is greatly improved. Moreover, by appropriately adjusting the length of the DSPSL, the electric fields generated by the aperture and the connecting metallic vias are designed with an out-of-phase potential; thus the cross-polarization level of the proposed folded dipole is improved. The cross-polarization levels of the proposed antenna are approximately 9 dB lower than that of the conventional SIW-fed dipole antenna. The folded dipole antenna obtains a working bandwidth of 58.5% from 30.3 to 53.7 GHz and a very compact size. It can be used as the terminal antenna of the MMW communication system.  
Conceptualization, L.X. and K.F.; data curation, L.X.; formal analysis, Q.T.; methodology, L.X.; software, L.X. and K.C.; validation, K.F., L.X. and K.C.; investigation, L.X.; resources, K.F. and Q.T.; writing --original draft preparation, L.X. and K.C.; writing--review and editing, K.F. and Q.T.; visualization, Q.T.; supervision, K.F.; project administration, K.F.; funding acquisition, K.F. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Indoor Localization Architecture and Methodology  
In view of the disadvantages of traditional indoor positioning methods based on RSSI information or CSI information alone, we propose a novel positioning algorithm based on RSSI and CSI to make up for their shortcomings. This section focuses on the realization of the indoor ranging model. First, the structure of the model is introduced in its entirety. Then, the filtering fusion process of the extended Kalman filter is described in detail.
# Parameter Extraction of Photovoltaic Cells and Panels Using a PID-Based Metaheuristic Algorithm
## 3 Results and Discussion
### aSi Cell  
The amorphous silicon photovoltaic is the topic of discussion in the second analysis. It was evaluated at a specified 1000 W/m\({}^{2}\) irradiance and 25 \({}^{\circ}\)C temperature, and so the I-V characteristic was measured. The result observed [29], which includes the current values derived using the SDMs and DDMs, including their associated errors, can be found in Table S2. Table 7 displays the characteristics of the aSi photovoltaic cell determined from the PSA for both the SDMs and DDMs. The SDM analysis took 3.3512 s to execute, whereas the DDM took 6.5292 s. Statistical tests, RMSE, MBE, and STD were used to evaluate algorithm performance, which included algorithms such as BWOA, ISOA, and HSDA. The PSA and BWOA algorithms produced identical RMSE values for both models, showing only minor variations. A comparison of the RMSE value with ISOA for the SDM revealed a small improvement of roughly 0.01%, whereas the improvement for the DDM was more considerable at 3.88%. For both models, the RMSE produced by the HSDA was higher than that obtained by the PSA. When compared to BWOA, the RMSE findings for the SDMs and DDMs are essentially equal, showing insignificant changes in the obtained parameters. However, substantial differences arise when comparing the DDM to other algorithms such as ISOA and HSDA. Table 7 shows how the PSA beats alternative techniques for the DDM across all statistical tests. As all collected parameters for both models are analyzed using the PSA technique, substantial differences are shown when compared to other algorithms. Compared to ISOA, the DDM has more changes in reverse saturation for recombination and generation processes as well as changes in series and shunt resistance. This shows that the DDM excels at extracting parameters reliably and with accuracy. Furthermore, the evaluation of all aSi solar cell parameters and the RMSE demonstrates the PSA's capacity to find the optimal solution and avoid entering local minima. Using the PSA approach, the I-V in Figure 8 and the P-V in Figure 9 shows features of the SDMs and DDMs aligned with the actual values. Figure 8: aSi SDM and DDM I–V characteristics.
###### Abstract  
An experimental study on the resistance of concrete containing air-cooled slag (AS) and water-cooled slag (WS) against freeze-thaw cycles was conducted. For comparison, the durability of ASTM Type I ordinary Portland cement (OPC) concrete exposed to the same freeze-thaw environment was examined. To evaluate the durability of concrete exposed to the freeze-thaw environment, an experiment was conducted according to ASTM C 666 procedure A. Furthermore, the relative dynamic modulus of elasticity, surface electrical resistivity, and compressive strength of concrete specimens were measured after exposing them to freeze-thaw cycles for a predetermined period, and the results were compared with those of OPC concrete. The relationship between the freeze and thawing resistances of concrete and the air-void system (spacing factor and specific surface area) was identified. Furthermore, the microstructure of concrete exposed to freeze-thaw cycles was observed using scanning electron microscopy to identify the interfacial transition zone, cracks, and micropores. Experimental results showed that the resistance of blended cement concrete containing WS and AS against freeze-thaw cycles was significantly higher than that of OPC concrete. The concrete in which 10% of OPC was replaced by AS exhibited a similar durability as that of the concrete in which 40% of OPC was replaced only by WS. Therefore, it is expected that blended cement concrete containing WS and AS based on an appropriate mix proportion design will exhibit excellent durability in regions experiencing freezing temperatures.  
water-cooled slag; air-cooled slag; concrete; freeze-thaw cycles; air-void system; microstructure +
Footnote †: journal: Applied Sciences
# PUF and Chaotic Map-Based Authentication Protocol for Underwater Acoustic Networks
## 2 Preliminaries
### Chebyshev Polynomial  
**Definition 1 (Chebyshev polynomial):**_The Chebyshev polynomial can be defined as (1) or (2), where \(n\in N\), \(n\geq 2\), \(T_{0}(x)=1,T_{1}(x)=x\):_  
\[T_{n}(x) =cos(n\cdot\text{arccos}(x)),x\in[-1,1] \tag{1}\] \[T_{n}(x) =2 xT_{n-1}(x)-T_{n-2}(x),n\geq 2 \tag{2}\]  
The semi-group property and chaos property are two primary properties of the Chebyshev polynomial [33].  
**Definition 2 (semi-group property):**_The semi-group property of the Chebyshev polynomial \(T_{n}(x)\) is defined as follows:_  
\[\begin{array}{c}T_{g}(T_{h}(x))=cos(g\text{-arccos}(cos(h\text{-arccos}(x)) ))\\ T_{h}\big{(}T_{g}(x))=cos(h\text{-arccos}(cos(g\text{-arccos}(x))))\\ T_{g}(T_{h}(x))=T_{h}\big{(}T_{g}(x))\end{array} \tag{3}\]  
where \(g\) and \(h\) are positive integers and \(x\in[-1,1]\).  
**Definition 3 (chaos property):**_The Chebyshev polynomial map, \(T_{n}(x):[-1,1]\rightarrow[-1,1]\) of degree \(n>1\), is a chaotic map with invariant density as: \(\partial=\frac{1}{n\sqrt{1-x^{2}}}\), for the Lyapunov exponent \(\lambda=lnn\)._  
**Definition 4:**_Enhanced Chebyshev polynomial is expressed as:_  
\[T_{n}(x)=2 xT_{n}(x)-T_{n-2}(x)\text{ mod }q \tag{4}\]  
where \(q\) is a large prime and \(x\in(-\infty,+\infty)\). The enhanced chaotic maps still satisfy the semi-group property and chaos property.  
**Definition 5 (chaotic map-based discrete logarithm problem, CMDLP):**_Considering \(\alpha\) and \(\beta\), it is computationally infeasible to compute an integer, \(\lambda\), such that \(T_{\lambda}(\alpha)\text{ mod }q=\beta\)._  
**Definition 6 (chaotic map-based Diffie-Hellman problem, CMDHP):**_Considering \(\alpha\), \(T_{\lambda}(\alpha)\), and \(T_{\beta}(\alpha)\), it is computationally infeasible to compute \(T_{\lambda\beta}(\alpha)\)._
## 3 Results and Discussion
### Method Validation  
To further validate the accuracy and precision of the method, tests were conducted based on the potential drug concentrations typically found in various feed categories ([PERSON] et al., 2003) [24]. The spiking levels for formulated feed and vitamin feed were set at 0.05 \(\upmu\)g/g, 0.5 \(\upmu\)g/g, and 5.0 \(\upmu\)g/g. For concentrated feed and bovine concentrate, the concentrations were 0.1 \(\upmu\)g/g, 1.0 \(\upmu\)g/g, and 10.0 \(\upmu\)g/g, respectively. The premixed feed was spiked at three levels: 0.5 \(\upmu\)g/g, 5 \(\upmu\)g/g, and 50 \(\upmu\)g/g. At each concentration level, five replicate samples were prepared, along with corresponding blank controls. The samples were processed according to the established sample pretreatment protocol and analyzed using LC-MS/MS. The concentration and recovery rates were calculated based on the peak areas of the target analytes. The results of the recovery tests are presented in Tables 3-8. The results demonstrated that the average recovery rates for the different feed categories ranged from 60% to 120%, with coefficients of variation (CV) within 15% at all spiking levels. These findings confirm that the method meets the required standards for accuracy and precision in drug residue analysis in feed (European Commission, 2002) [28]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Feed**} & \multicolumn{2}{c}{**Added Concentration (\(\upmu\)g/g)**} & \multirow{2}{*}{**Recovery Rate (\%)**} & **Intra Batch (\%)** & **Inter Batch Coefficient of Variation (\%)** \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Chicken \\ compound \\ feed \\ \end{tabular} } & 0.05 & 91.27 & 2.09 & 2.43 \\ \cline{2-5}  & 0.5 & 90.38 & 3.51 & 4.75 \\ \cline{2-5}  & 5.0 & 93.19 & 3.32 & 4.70 \\ \hline \multirow{2}{*}{Concentrated chicken feed} & 0.1 & 92.44 & 7.74 & 8.03 \\ \cline{2-5}  & 1.0 & 90.06 & 1.98 & 3.94 \\ \cline{2-5}  & 10.0 & 95.25 & 6.08 & 6.52 \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Chicken \\ premix feed \\ \end{tabular} } & 0.5 & 91.77 & 5.55 & 6.18 \\ \cline{2-5}  & 5.0 & 95.26 & 4.70 & 5.35 \\ \cline{2-5}  & 50.0 & 97.72 & 3.80 & 4.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Recovery rate and coefficient of variation in acetaminophen B1a added to feed. Figure 6: Effect of n-hexane extraction of feed sample on recovery ((**a**): formulated feed; (**b**): concentrated feed). _Remarks: the X-axis represents six macrolide drugs; the Y-axis represents the recovery rate of drugs with and without n-hexane treatment_.
# Big Data Collection in Large-Scale Wireless Sensor Networks
## 5 Data Transferring in LS-WSNs  
The data captured by the nodes must be transferred to a collection point in the network called a sink. Routing is the process of finding a cost-effective route in terms of power consumption in order to transfer the data from the sensor nodes to the sink. The sink can be connected to a remote user via Wimax, LTE, a satellite, or another wide transmission system. The user can address requests to other nodes in the network, specifying the type of required data and harvesting them through the sink node. Routing in LS-WSNs remain a very active area of research, and several routing protocols have been proposed [12; 105; 106; 107; 108; 109; 100; 112; 103]. Recently, routing protocols for WSNs have been the subject of  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Architecture**} & \multirow{2}{*}{**Reliability**} & **Number** & **Energy** & \multirow{2}{*}{**Latency**} & \multirow{2}{*}{**Lifespan**} & \multirow{2}{*}{**Scalability**} \\  & & **of Sinks** & **Consumption** & & & & \\ \hline \multicolumn{7}{l}{_Static sensors and sinks_} \\ [PERSON] et al. (2018) [85] & high & many & low & medium & low & yes \\ [PERSON] et al. (2018) [100] & high & 1 & low & medium & medium & yes \\ [PERSON] et al. (2018) [82] & medium & 1 & medium & high & medium & yes \\ [PERSON] et al. (2016) [36] & high & many & low & medium & low & yes \\ [PERSON] et al. (2012) [101] & low & 1 & strong & high & low & difficult \\ [PERSON] et al. (2011) [25] & medium & many & medium & low & medium & yes \\ [PERSON] et al. (2009) [81] & high & many & medium & low & high & yes \\ [PERSON] et al. (2006) [47] & low & 1 & strong & high & low & difficult \\ \hline \multicolumn{7}{l}{_Mobile sensors and static sink_} \\ [PERSON] et al. (2019) [97] & medium & many & low & medium & & no \\ [PERSON] et al. (2009) [95] & medium & many & low & medium & no \\ [PERSON] et al. (2005) [96] & medium & 1 & medium & high & medium & no \\ \hline \multicolumn{7}{l}{_Mobile sink and static sensor nodes_} \\ [PERSON] et al. (2018) [91] & medium & many & medium & medium & high & yes \\ [PERSON] et al. (2018) [99] & medium & 1 & medium & high & medium & yes \\ [PERSON] and [PERSON] (2018) [98] & low & many & medium & medium & medium & no \\ [PERSON] et al. (2017) [26] & high & 1 & medium & medium & high & yes \\ [PERSON] et al. (2013) [102] & high & 1 & medium & medium & yes \\ [PERSON] et al. (2012) [103] & high & many & medium & low & medium & yes \\ [PERSON] et al. (2005) [104] & low & 1 & medium & medium & high & no \\ [PERSON] et al. (2002) [84] & low & 1 & medium & low & medium & no \\ \hline \multicolumn{7}{l}{_Hybrid_} \\ [PERSON] et al. (2002) [84] & & depends & weak & & & no \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of different data-collection architectures.  
several studies, and various studies have been published. These protocols can be classified according to two main axes [45; 79]: the structure of the network and the type of operation to be performed (see Figure 17).
## 4 Natural Antioxidants and Their Role in Cosmetics
### Hormones  
#### 4.6.1 Melatonin  
Melatonin (N-acetyl-5-methoxyryptamine) (Figure 5) is a hormone that effectively inhibits the oxidation processes of proteins, lipids, and DNA and can also mediate the stimulation of antioxidant activity of enzymes. Studies have shown that combining this hormone with antioxidants (glutathione, vitamin C and E) significantly reduces this process. What is more, vitamin C can contribute to the regeneration of melatonin because they interact synergistically [153,154].  
Melatonin has a positive effect in the fight against Alzheimer's disease. In the aging process, the repair potential of damaged neurons in the body is reduced. Increased oxidative stress is generated by excess free radicals. With age, there is also a decrease in the secretion of melatonin, which is an effective antioxidant that plays an important role in protecting the cells of the nervous system [154].  
Melatonin directly neutralizes reactive oxygen species. It has the ability to capture the hydroxyl radical, singlet oxygen, nitric oxide, peroxynitrite, hypochlorite, and peroxyl radicals. The direct role of this hormone in the body's defense system is to eliminate ROS from the cellular environment and metabolize their inactive forms. Indirect antioxidant action is to stimulate the synthesis of prooxidant enzymes [155].  
Human skin is characterized by significant levels of melatonin. The MT1 and MT2 receptors are present in fibroblasts, keratinocytes, hair follicles, excrine glands, and blood vessels. The skin is exposed to various external factors, mainly solar radiation and environmental pollutants, which cause oxidative stress and inflammation, thus accelerating the photoaging process. Melatonin easily penetrates the skin, where it can exhibit its antioxidant and anti-inflammatory properties. As an endogenous substance, it is practically devoid of allerggenic potential. Topically applied melatonin has been studied in clinical trials for its effectiveness in protecting the skin from radiation, reducing skin aging, and supporting hair growth [156].  
The production of melatonin for cosmetic use encompasses traditional chemical synthesis and emerging biotechnological methods. While chemical synthesis remains the primary approach due to its established processes and scalability, biotechnological production offers a promising alternative that aligns with the growing demand for sustainable and natural ingredients in cosmetics. Ongoing research and technological advancements continue to optimize these production methods, ensuring the availability of high-quality melatonin for cosmetic applications [157].
## 2 Methods
### Calculation Method  
Figure 2 shows the flow chart of the calculation method in this paper. Firstly, the dark noise was subtracted from the original interference signal, and a standard mirror was used to eliminate the influences of the light source and optical system. Second, the calibrated signal was transformed into the wavenumber domain. Third, the Lomb-Scargle periodogram power spectral density estimation algorithm was used to process the unevenly sampling data in the wavenumber domain, and calculate the power spectral density \(P\) and frequency \(f\). Finally, the peak value of the power spectral density function was extracted by the quadratic linear fitting method to calculate the film thickness.  
Due to the wavelength drift of the spectrometer and the reciprocal transformation from the wavelength to the wavenumber domain, the non-uniform sampled characteristic of \(\sigma\) made \(I_{\sigma}\) unable to be directly analyzed by the conventional spectral analysis method. The Lomb-Scargle periodogram (LSP) [31; 37] is a widely used algorithm for detecting and characterizing periodicity in unevenly sampled data. It is particularly suitable for extracting the period or frequency of unevenly sampled interference signals in the wavenumber domain. The LSP function applied in the wavenumber-phase factor domain (\(\sigma\)-\(z\)) is defined as  
\[P_{LS}(z)=\frac{1}{2}\left\{\frac{\left[\sum_{n}I_{n}\cos(2\pi z(\sigma_{n}- \tau))\right]^{2}}{\sum_{n}\cos^{2}(2\pi z(\sigma_{n}-\tau))}+\frac{\left[ \sum_{n}I_{n}\sin(2\pi z(\sigma_{n}-\tau))\right]^{2}}{\sum_{n}\sin^{2}(2\pi z (\sigma_{n}-\tau))}\right\} \tag{5}\]  
where \(\tau\) is specified for each \(z\) to ensure time-shift invariance,  
\[\tau=\frac{1}{4\pi z}\tan^{-1}\biggl{(}\frac{\sum_{n}\sin(4\pi z\sigma_{n})}{ \sum_{n}\cos(4\pi z\sigma_{n})}\biggr{)} \tag{6}\]  
The power spectral density estimate of the spectral interference signal is discrete and sparse. Limited by the sampling rate of the spectrometer, the minimum frequency interval  
Figure 2: Flow chart of the film thickness calculation method. \(I_{D}\), dark noise signal; \(I_{M}\), signal for a mirror; \(I_{a}\), denoised signal of the sample; \(I_{b}\), calibrated signal of the sample.  
converts to an actual thickness of about 30-60 nm. Therefore, there is an inherent error in extracting film thickness directly by finding the frequency corresponding to the maximum power spectral density. To further improve the calculation accuracy of film thickness, data with an amplitude greater than 50% of the peak value of power spectral density were used to calculate the final film thickness by quadratic linear fitting. Therefore, using the built-in complete method to calculate the film thickness can greatly eliminate the false-frequency interference caused by environmental noise and improve the accuracy of the system.