esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Multiparameter Station
#### 2.5.1 Sensors  
The sensors used in multi-parameter stations are [26]:  
* Broadband Seismometer CME 4311: Electrochemical transducer designed for permanent or portable installation. It is a robust instrument that is easy to install. In addition, it does not require maintenance, mass blocking, or leveling. This sensor offers an effective solution for installations with a noise level close to the low-noise model with a response between 1/60 and 50 Hz [27].
* Non-polarizable electrode for burial Tinker & Rasor DB-A: Copper and copper sulfate (Cu/CuSO\({}_{4}\)) electrode, non-polarizable, which allows direct exposure of copper sulfate over a large contact area. It has a shelf life of up to 10 years. Its structure is made of PVC/ABS and has a low freezing point and high evaporation point, which makes it robust for most environments. Dimensions: 7 cm in diameter, 12.2 cm high, and a weight of 896 g [28].
* Magnetometer Bartington Mag648L: Low-power, low-noise triaxial magnetometer with \(\pm\)60 uT range. It has vehicular, perimeter security, and ground measurement applications [29]. Figure 2 shows the instrumental deployment corresponding to the USME station.  
Figure 1: Distribution of the stations of the Geophysical Network of the National University of Colombia in the central region of Colombia.
## 2 Design
### Optical Design and Modelling  
The optical response of metal nanoparticles can be modelled by the calculation of the scattering, radar backscattering, extinction and absorption cross-sections obtained from a Mie model (Equations (1)-(4)) [3; 24]:  
\[\sigma_{sca}=\frac{2\pi r^{2}}{x^{2}}\sum_{n=1}^{\infty}(2n+1)(|a_{n}|^{2}+|b_{ n}|^{2}) \tag{1}\]  
\[\sigma_{back}=\frac{\pi r^{2}}{x^{2}}\left|\sum_{n=1}^{\infty}(2n+1)(-1)^{n}(a _{n}-b_{n})\right|^{2} \tag{2}\]  
\[\sigma_{ext}=\frac{2\pi r^{2}}{x^{2}}\sum_{n=1}^{\infty}(2n+1)Re\{a_{n}+b_{n}\} \tag{3}\]  
Figure 2: **(a)** theoretical backscattering cross-section [24] as a function of particle diameter and probe laser wavelength using Equation (2) for a gold nanoparticle in water; **(b)** optical sensitivity defined as the change in scattered light over 1 nm change in the radius of the particle; **(c)** modulation depth (optical sensitivity divided by the backscattering cross-section); **(d)** the optical sensitivity with respect to the signal-to-noise ratio (SNR) assuming optical shot noise; **(e)** theoretical change in modulation depth per nm for a 150, 200 and 250 nm gold nanoparticles in air (lines) and water (dots), calculated by the Mie model; **(f)** theoretical breathing mode frequency for a nanoparticle of silica, silver (Ag) and gold (Au) in air (lines) and water (dots), calculated by the mechanical model.  
\[\sigma_{abs}=\sigma_{ext}-\sigma_{abs} \tag{4}\]  
The particle is characterized by the size parameter \(x=2\pi n_{m}r/\lambda\) and the relative refractive index \(m=n/n_{m}\), where \(\lambda\) is the wavelength of the incident wave in vacuum, \(r\) is the particle radius and \(n_{m}\) and \(n\) are the refractive index of the medium and the particle, respectively. The expressions for \(a_{n}\) and \(b_{n}\) are shown below:  
\[a_{n}=\frac{[H_{n}^{a}(mx)/m+n/x]\psi_{n}(x)-\psi_{n-1}(x)}{[H_{n}^{a}(mx)+n/x] \zeta_{n}(x)-\zeta_{n-1}(x)} \tag{5}\]  
\[b_{n}=\frac{[mH_{n}^{b}(mx)+n/x]\psi_{n}(x)-\psi_{n-1}(x)}{[mH_{n}^{b}(mx)+n/x] \zeta_{n}(x)-\zeta_{n-1}(x)} \tag{6}\]  
where \(\psi\) and \(\zeta\) are the Riccati-Bessel functions defined, for instance, in [PERSON] and [PERSON] et al. [25] and the determinants \(H_{n}^{a}\) and \(H_{n}^{b}\), defined in [PERSON] et al. [3]
## 2 Bioelectrochemical Systems' Configurations
The inclusion of microalgae in mMFCs offers additional benefits, such as CO\({}_{2}\) capture and value-added product generation, distinguishing them from other BESs. However, each system faces specific challenges, including electron transfer limitations, substrate availability, and efficiency constraints, which require further optimization to enhance scalability and performance. Other applications of BESs are related to the cathode and the ability of microorganisms to catalyze several cathodic reactions such as the oxygen reduction to water, the proton  
Figure 3: Schematics of typical BESs: Microbial Fuel Cell (MFC), microbial electrolysis cell (MEC), microbial electrosynthesis system (MES), Microbial Desalination Cell (MDC), Sediment Microbial Fuel Cells (SMFCs), Plant-Type Microbial Fuel Cells (PMFCs), Constructed Wetlands (CWMFC), and Microalgal Microbial Fuel Cells (mMFCs). reduction to hydrogen, and the nitrate reduction to nitrogen gas, among others, generating value-added products [37, 54, 55, 56]. BESs could take advantage of the current flow not only for power generation, but also to reduce organic or inorganic compounds in the cathodic chamber, using them as electron acceptors, and thus obtain reduced chemicals with high value added. Benefits of BESs in wastewater treatment include offering economic advantages by generating less sludge compared to traditional aerobic methods and converting pollutants directly into electrical energy or chemicals. This makes BESs particularly suitable for areas with limited electrical infrastructure. In the literature, it has been described that the complete oxidation of organics in municipal wastewater can generate a theoretical electricity supply of around 1.9 kWh m\({}^{-3}\) wastewater (assuming typical COD concentration of domestic wastewater as 0.5 kg m\({}^{-3}\), and energy content of 3.6 MWh kg\({}^{-1}\) COD), and direct nutrient recovery for fertilizer applications can save up to 0.79 kWh m\({}^{-3}\)[26, 57]. Anyway, additional efforts are needed to enhance energy production in BESs by developing cost-effective materials, increasing current density through the use of more efficient electrogenic bacterial strains, consortiums, or genetic engineering, and producing high-value compounds like acetate, methanol, butyrate, butanol, etc. In the literature, recent economic evaluations have shown returns surpassing those of conventional processes, highlighting the potential for viable and profitable applications [58, 59].
# Multiple PUE Attack Detection in Cooperative Mobile Cognitive Radio Networks
## 5 Results
### Application Data Results  
We identified data from telecommunication operators in Colombia by analyzing site survey measurements and experimental configuration settings about PUs and PUEs. The relevant information includes mobile country code (MCC) 732 and mobile network codes (MNCs): 101 for Claro, 102 for Movistar, 111 for Tigo, 142 for UNE, and 154 for Virgin.  
Subsequently, the application transmits key information--such as the network's short name, MCC, and MNC--to the central base station (CBS). The CBS then cross-references these data with verified operator information to determine whether the signal is associated with a legitimate primary user or an emulator. Additionally, the SUs undergo authentication within the network. The results are also transmitted to the FC for final decision-making.  
In scenarios where the system encounters a short name associated with an alternative operator, the sensors will attempt to connect and gather information concurrently with the actions executed by the motion detection algorithm. During the experiments, the default identifier for the primary user emulator (PUEA) is set to \"01-001\" or referred to as the \"range network,\" depending on the Software Defined Radio (SDR) in use. When an emulator attempts to mimic a legitimate primary user, it may present itself similarly to a genuine operator (e.g., Operator_).  
The testing process starts with a PUE designed to simulate a PU. However, if the system detects an invalid short name or authentication fails, the device is subsequently classified as a PUE, as illustrated in Figure 13.  
If the PUE mimics the operator's real short name, the algorithm detects a duplicate name and tries to authenticate both to correctly identify the PU and the PUE. The results are saved as binary (0--authentication OK; 1--no authentication).  
Each SU performs one authentication process in the detection process. The detection is valid for (905/10,000), obtaining a PD of 90% for the authentication process for one PUE. For four PUEs, there is a PD of (862/1000), 86%.  
Figure 12: Uplink signal without and with active signal (source: own).
# Phase-Matching Quantum Key Distribution with Discrete Phase Randomization
## 5 Numerical Results
Here \(p_{d}\) is the dark counts rate; \(e_{opt}\) is the misalignment error probability of the system; \(\eta_{d}\) is the detection efficiency; \(f\) is the error correction efficiency; \(\eta_{f}\) is the transmission fiber loss coefficient (dB/km). Due to there being a sifting factor \(2/D\), we know that when \(D\rightarrow\infty\), the key rate will tend to 0. In order to compare the key rate between continuous phase randomization and discrete phase randomization, we first compare the fidelity between \(\ket{j}_{\text{AB}}\) and \(\ket{\lambda_{j\
eq 4}^{D}}_{\text{AB}}\), as shown in Figure 3a. The fidelity varies slightly with the intensity. With the increase of \(D\), the fidelity gradually approaches 1. Therefore, when \(D\) is too small, the method of continuous phase randomization is not suitable; we cannot ignore the safety effect of discrete phase randomization. Then, considering finite decoy states, the key rate between continuous phase randomization and discrete phase randomization has been studied in Figure 3b. As \(D\) increases,  
Figure 1: The key rate versus the transmission distance of the PM-QKD with different number of discrete phase values; the PLOB linear bound is plotted for comparison. Figure 2: The key rate versus the transmission distance of the PM-QKD with different number of discrete phase values, infinite decoy states and vacuum and one decoy state are plotted for comparison. The dash line represents the case of vacuum and one decoy state; the solid line represents the case of infinite decoy states. the performance of a key rate between discrete phase randomization and continuous phase randomization is almost the same. This is consistent with the conclusion in Figure 3a.
## Interpretation of Results
### Cyclic and Permanent Strain
#### 5.4.2 Qualitative Evaluation
Figure 11: Vertical section in the graph of Figure 10 for values of N equal to 1, 10, 100, and 1000, corresponding to \(\rm\tau_{\rm o}/\sigma^{\prime}_{\rm ov}=0\). The empirical relation (7) is obtained by replacing (5) and (6) in (4), which expresses the cyclic strains \(\gamma_{\rm c}\) as a function of the permanent strains \(\gamma_{\rm P}\) and the number of cycles N.  
\[\gamma_{\rm c}={\rm D}\cdot{\rm N}^{\rm E}\big{(}\gamma_{\rm P}\big{)}^{\rm F\cdot In \,(N)+H} \tag{7}\]  
The empirical constants D, E, F, and H of function (7), see Table 11, were obtained in a similar way with the results of the cyclic simple shear tests in which \(\tau_{\rm o}/\sigma^{\prime}_{\rm ov}\) are equal to 5%, 10%, 15%, and 20% and \(\tau_{\rm c}/\sigma^{\prime}_{\rm ov}\) are variable between 10% and 25%, see Table 4. \begin{table}
\begin{tabular}{c c c c c} \hline \(\tau_{\rm o}/\sigma^{\prime}_{\rm ov}\) & \({\bf D}\) & \({\bf E}\) & \({\bf F}\) & \({\bf H}\) \\ \hline
0 & 2.310 & \(-\)0.082 & 0.027 & 0.687 \\
5 & 4.082 & \(-\)0.209 & \(-\)0.017 & 0.819 \\
10 & 0.917 & \(-\)0.229 & 0.052 & 0.797 \\
15 & 0.580 & \(-\)0.938 & 0.362 & 0.481 \\
20 & 0.190 & \(-\)0.930 & 0.394 & 0.452 \\ \hline \end{tabular}
\end{table} 
Table 11: Values of the constants D, E, F, and H of the empirical relation (7) for different normalized monotonic stress \(\tau_{\rm o}/\sigma^{\prime}_{\rm ov}\) in the particular case of this investigation. Figure 12: (**A**) Empirical constant A as a function of the cycle’s number, for \(\tau_{\rm o}/\sigma^{\prime}_{\rm ov}=0\); (**B**) empirical constant B as a function of the cycle’s number, for \(\tau_{\rm o}/\sigma^{\prime}_{\rm ov}=0\). In Figure 13a for \(\tau_{\text{o}}/\sigma^{\prime}_{\text{ov}}=0\), and in Figure 13b for \(\tau_{\text{o}}/\sigma^{\prime}_{\text{ov}}=5\%\), it can be seen that the empirical relation (7), with the constants of Table 11, reproduces very well the experimental data obtained from cyclic simple shear tests done for the present research.
## 4 Discussion
### ILCA 7 Sailors Somatotype  
The lack of scientific research on the body structure of ILCA 7 sailors is also evident with respect to somatotype. Both studies that determined somatotype and this study found that the mesomorphic component is a dominant feature in ILCA 7 sailors [19; 20]. The somatotype category \"endomorphic mesomorph\" was found across Spanish ILCA 7 sailors [19], while Croatian ILCA 7 sailors were classified as \"balanced mesomorph\" [20].  
In our study, more than 75% of elite ILCA 7 sailors have a dominant mesomorphic somatotype component and can be categorized as \"_endomorphic mesomorph_\" somatotypes. Similar distributions were also observed in elite Finn class sailors [18]. Generally, the mesomorphic component is dominant for sailors in all sailing classes where somatotype was calculated: Finn [18; 19], ILCA 6 [20], Formula [11], Optimist [36], RSX [19], and Olympic classes 470 & 49 er [19]. The _endomorphic mesomorph_ somatotype is common to ILCA 7 and Finn class sailors [18], while the _balanced mesomorph_ somatotype is more common in ILCA 6, Formula, and 49 er classes [11; 19; 20], and the _ectomorphic mesomorph_ somatotype in RSX and 470 classes [19]. The dominant mesomorphic somatotype component of ILCA 7 sailors could be explained by the theory that size only confers an advantage if extra weight does not add more to the resistance of the yacht than it contributes to the propulsive forces of the dinghy; therefore, optimal leverage (stature) and ballast mass (body mass) are needed to be an efficient ILCA 7 sailor.  
A mesomorphic somatotype component is dominant in various high-performance athletes in sports such as soccer [37], rowing [38], table tennis [39], track cyclist [40], canoe and kayak paddlers [15], 100 m sprimters [10; 41] track and field [41], swimming [41], and basketball [12]. However, ILCA 7 sailors share the same _endomorphic mesomorph_ somatotype category with sprint-track cyclists [40], rowers [42], kayakers [12], and water polo players [43].
# Smart Meter Gateways: Options for a BSI-Compliant Integration of Energy Management Systems
## 6 Assessment of EMS Integration Options
### Discussion of EMS Integration Option 1a  
In _Option 1a_, the EMS makes use of the HAN interface of the SMGW, more precisely the _Consumer Interface_, since it is the only defined local SMGW interface that provides measurement data in version 1.0 of the technical guideline [7]. To access the interface, the credentials of the consumer are required. In future versions, SMGWs may be equipped with dedicated CLS interfaces providing local access to meter measurements. The frequency in which these measurements are received or requested is determined by the respective _Meter Profile_ using an interval in seconds. Considering solely the BSI technical guideline in version 1.0, the SMGW only needs to update the stored meter measurement according to the interval configured in the _Meter Profile_. As the _PTB-A 50.8_ is not mandatory for non-billing-relevant purposes, an SMGW is theoretically able to provide full access to the measurements provided by the meters via the _Consumer Interface_.  
There is one major disadvantage regarding data privacy in cases where the local EMS uses the _Consumer Interface_ and also communicates with external third parties (such as a remote prediction service for photovoltaic generation) via the Internet. In such cases, the _Smart Metering System_'s architecture cannot guarantee that the EMS does not forward the fine-grained meter measurements available via the _Consumer Interface_ to these third parties. Although possible, the usage of the _Consumer Interface_ for such purposes is not intended by the BSI technical guideline as it poses a serious threat for data privacy.
# Predicting Phubbing Through Machine Learning: A Study of Internet Usage and Health Risks
## 3 Materials and Methods
### Random Forest Regression  
The random forest (RF) algorithm is a prominent method within the realm of supervised classification algorithms; it finds application in both regression and classification tasks. Its primary objective is to enhance the accuracy of classification outcomes by generating multiple decision trees. This algorithm functions by aggregating numerous decision trees, each operating independently, and subsequently selecting the most optimum result from among them [34]. As the quantity of trees escalates, the likelihood of achieving an accurate outcome simultaneously rises. A principal distinction between the decision tree algorithm and the RF algorithm lies in the latter's utilisation of randomness during the selection of the root node and the division of the nodes. The RF algorithm incorporates an added layer of randomness into the model when cultivating trees. Rather than identifying the most significant feature for node division, it evaluates the best feature from a randomly chosen subset of features. Thus, this algorithm considers a limited, randomly selected set of features for node splitting, fostering significant diversity that typically enhances model performance. Furthermore, the RF algorithm necessitates minimal data preparation, streamlining its implementation process [35].  
A significant issue with traditional decision trees is their tendency to overlearn or, in other words, overfit the data. To address this, the RF model employs a strategy of randomly selecting numerous subsets from both the dataset and the feature set for training. This approach generates hundreds of decision trees, each of which makes individual predictions [36; 37].
## 3 Some Numerical Results and Discussions
### Time Responses of Deflection and Stress  
In Figures 2-7, the horizontal and vertical axes have units, e.g., in Figure 2, the unit for \(w(a/2,b/2)\) in the vertical axis is \"mm\", and the unit for \(t\) in the horizontal axis is \"sec\". In Figure 3, the unit for \(\sigma_{x}\) in the vertical axis is \"GPa\", the unit for \(t\) in the horizontal axis is \"sec\". In Figure 4, the unit for \(w(a/2,b/2)\) in the vertical axis is \"mm\", the unit for \(T\) in the horizontal axis is \"K\". In Figure 5, the unit for \(\sigma_{x}\) in the vertical axis is \"GPa\", the unit for \(T\) in the horizontal axis is \"K\". The time responses of deflection and stress are computed with the \(\gamma\) value decreasing from \(\gamma=15.707964/\)s at \(t=0.1\) s to \(\gamma=0.523601/\)s at \(t=3.0\) s. Figure 2 shows the time response of \(w(a/2,b/2)\) (unit mm) for \(a/h^{*}=5\) and \(10\), respectively, with \(c_{1}=0.925925/\)mm\({}^{2}\), \(R_{n}=1\), \(T=600\) K and \(\overline{T_{1}}=100\) K. The maximum absolute value of \(w(a/2,b/2)\) is \(0.006138\) mm at \(=0.1\) s found for \(a/h^{*}=5\) with advanced \(k_{a}=-3.535402\) and \(\gamma=15.707964/\)s. The time responses of \(w(a/2,b/2)\) are converging at around \(0.0\) for \(a/h^{*}=5\) and \(10\).  
Typically the stress values vary through the plate thickness by considering the effect of advanced nonlinear varied \(k_{a}\) values. Figure 3a,b show the time responses of the dominated stresses \(\sigma_{x}\) (unit GPa) at the center position of lower surface \(z=-0.5h^{*}\) as the analyses of deflection case in Figure 2 for \(R_{n}=1\), \(a/h^{*}=5\) and \(10\), respectively. The absolute value of maximum stresses \(\sigma_{x}\) is \(-9.685218\times 10^{-4}\) GPa found at \(t=2.4\) s for thick \(a/h^{*}=5\). The time responses of stresses \(\sigma_{x}\) are oscillating around \(-9.50\times 10^{-4}\) GPa for \(a/h^{*}=5\) and around \(-9.035\times 10^{-4}\) GPa for \(a/h^{*}=10\).  
values of \(w(a/2,b/2)\) for \(R_{n}=0.5\), 1 and 2 can proceed in higher \(T=1000\) K of temperature condition. The center deflection amplitude values are almost keep constant versus \(T\) for \(R_{n}=10\). Figure 4b shows the \(R_{n}\) curves in \(a/h^{*}=10\); the value of maximum \(w(a/2,b/2)\) is 0.000119 mm found at \(T=1000\) K for \(R_{n}=2\). The absolute values of \(w(a/2,b/2)\) for \(R_{n}=2\) can proceed in higher \(T=1000\) K of temperature condition.  
Figure 5 shows the dominated stresses \(\sigma_{x}\) (unit GPa) vs. \(T\) (unit K) and \(R_{n}\) at center position of \(z=-0.5h^{*}\) for \(a/h^{*}=5\). The values of \(\sigma_{x}\) for \(R_{n}=0.1\) and 0.2 can proceed in higher \(T=1000\) K of temperature condition. The absolute value of maximum \(\sigma_{x}\) is \(-1.001130\times 10^{-3}\) GPa found at \(T=1000\) K for \(R_{
## 3 The Proposed Algorithm  
This paper proposes a clustering algorithm based on detecting the dimension density peaks in dimensions and measuring the degree of interaction between clusters (CPDD-ID). The algorithm consists of two main phases: (1) Firstly, in the partitioning phase, using kernel density estimation to calculate the local density of the points in each dimension and take the local densities of all dimensions to construct the kernel density curves, the curves themselves react to the density distribution of the data in each dimension. Secondly, the coordinates of the peaks of the kernel density curves are marked in each dimension, and a Kd-Tree is established to quickly and accurately assign the data points to their nearest-neighbors peaks. Lastly, the intersection of all dimensions is taken to adaptively divide reasonable subclusters. (2) In the merging phase, the interaction degree of shared neighbors between subclusters is calculated to define a novel similarity metric, and subclusters with the highest similarity are iteratively merged until the desired number of clusters is reached. The main clustering process is illustrated in Figure 1.  
Figure 1: Clustering process of the CPDD-ID algorithm (developed by authors).
# Enhanced Photocatalytic Performance of Bi2O2 CO3 Loaded Activated Carbon for Toluene Removal in Air
## 3 Results and Discussion
### Characterization and Propertiesof BOC/AC Composites
#### 3.2.2 Structure and Composition Analysis  
The XRD patterns of the BOC/AC and AC are presented in Figure 6. All BOC/AC samples show similar XRD patterns (Figure 6a). In Figure 6b-d, the enlarged local XRD patterns clearly show that the peak intensity increased for BOC/AC_50 and BOC/AC_100 near 20 = 30.2\({}^{\circ}\) and 32.7\({}^{\circ}\), which are the characteristic peaks of Bi\({}_{2}\)O\({}_{2}\)CO\({}_{3}\) (JCPDS: 41-1488), corresponding to the (013) and (110) facets, respectively. The energy dispersive spectroscopy (EDS) elemental mapping images (Figure 7) of BOC/AC_100 sample further proves the successful growth of Bi\({}_{2}\)O\({}_{2}\)CO\({}_{3}\) on the surface of AC and the homogeneous elemental distribution of Bi, C, and O elements in the Bi\({}_{2}\)O\({}_{2}\)CO\({}_{3}\) nanosheets. To quantify the composition of different chemical elements in the composite, XRF analysis was performed, which showed that the ratio of C: O: Bi was about 94.3: 4.0: 1.7 wt.%.  
Figure 4: The photocatalytic property of Bi\({}_{2}\)O\({}_{2}\)CO\({}_{3}\) for toluene removal in air. The blank control experiment was performed under light illumination in the absence of catalyst.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **AC** & **BOC/AC\_50** & **BOC/AC\_100** & **BOC/AC\_150** & **BOC/AC\_200** \\ \hline Surface area (m\({}^{2}\)/g) & 953.8 & 931.8 & 955.3 & 951.2 & 851.4 \\ Single point adsorption total pore volume (cm\({}^{3}\)/g) & 0.34 & 0.33 & 0.36 & 0.34 & 0.32 \\ Average pore diameter (4V/A by BET) (nm) & 1.44 & 1.43 & 1.52 & 1.45 & 1.48 \\ \hline \end{tabular}
\end{table}
Table 1: Surface area, pore volume, and pore diameter of BOC/AC.  
Figure 5: SEM images of BOC/AC\_50 (a), BOC/AC\_100 (b), BOC/AC\_150 (c), BOC/AC\_200 (d), and activated carbon (e,f).  
Figure 6: XRD patterns (**a**) and enlarged local XRD patterns (**b**–**d**) of the as-synthesized BOC/AC and AC.  
Figure 7: EDS element mapping images of BOC/AC_100.