esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Biosorption Isotherms  
From the experimental data obtained under the optimum conditions, defined by the experimental design in Table 2 and Figure 5, the parameters shown in Table 1 were obtained by non-linear regression. Three models show a good fit to the experimental data: Langmuir, Sips, and Redlich-Peterson, with a correlation coefficient R\({}^{2}\) = 0.987. The maximum biosorption capacity, according to the Langmuir model, was 48.14 mg Cu(II)/g dry biomass. The Langmuir model assumes that the surface has a limited number of active sites and that each adsorbate molecule binds to an active site without interacting with the adjacent sites. This model fits a type of monolayer adsorption that can generally be compatible with what is observed in the FT-IR analysis. However, in practice, these assumptions have limitations since not all active sites will have the same affinity for metal ions, and physisorption phenomena may occur on the initial monolayer that improves the retention of Cu(II) ions and give rise to complexation phenomena, as can be seen in the FE-SEM analysis. This fact fits better with what was observed in this work and could explain the good fit of the experimental data to more complex models such as the Redlich-Peterson model (R\({}^{2}\) = 0.987), which combines Langmuir and Freundlich elements and therefore does not fit an ideal monolayer adsorption model but would contemplate a more heterogeneous type of adsorption, which seems to be more in agreement with what was observed in the biosorption of Cu(II) by _S. epidermidis_. Table 3 shows the maximum biosorption capacities obtained by other authors for different types of biomass, including microbial biomass. It can be seen that the values obtained in this work are similar to or slightly better than those obtained for other types of bacteria and higher than those obtained for fungi, yeasts, or marine macroalgae. Furthermore, although in all cases these are theoretical values deduced from the adjustment of experimental data to mathematical models, in the case of _S. epidermidis_ CECT 4183, the data come from a very good fit to the Langmuir model, which indicates that it is a value very close to the reality of the Cu(II) biosorption process by the bacteria.  
Figure 5: (**a**) Response surface plot for Cu(II) biosorption by _Staphylococcus epidermidis_ CECT 4183 at 27 \({}^{\circ}\)C and initial copper concentration of 50 mg/L. Effect of the studied factors on the biosorption capacity (**q**). (**b**) Perturbation plot showing the effect of pH (A) and biosorbent dose (B) on the equilibrium copper biosorption capacity (q) for pH = 4.25 and B = 0.5 g/L.  
Likewise, Figure 6 shows the graphical representation of the adsorption isotherms studied. The Redlich-Peterson isotherm is the one that best fits the experimental results, but the Langmuir and Sips isotherms also show good fits. On the contrary, the Freundlich isotherm shows worse results.
# Effects of Vehicular Communication on Risk Assessment in Automated Driving Vehicles
## 2 Sensor Characteristics and Information Fusion
### Vehicular Communication
Cloud computing has no limitation of computing power; however, it has more likely to experience slow internet connection issues. Edge computing is much faster than cloud computing; however, the computational resource is limited or is hard to expand. Korea Telecom (KT), one of the cellular communication providers in Korea, demonstrated Level 3 autonomous driving technology with 5G communication. Two buses were demonstrated with the 5G connected technology by exchanging their driving information as well as video that shows blinded front traffic situation. This connected technology-based service is promising for the future connected automated driving since it can stabilize Internet-based service which is going to be applied for the safety related functions for the intelligent vehicle. Connected automated driving technology needs to offer less than 10 ms of latencies with less than 1% of packet error rate. This allows the autonomous vehicle to drive safely without any issue of communication error. In the future, the C-V2X solution and the convention ADAS local sensors are to be combined for 100% of guaranteed safety. Even though we use the edge computing with 5G communication, there are still chances for communication errors. This still makes us use local sensor-based safety functions in terms of emergency situations such as emergency driving situations. For normal driving situations, infrastructure computing (cloud or edge) based-based autonomous driving can be performed cooperation with other surround vehicles. This allows the vehicles to drive much safer and much smoother  
Figure 2: Experiment results at intersection: (**a**) Package Deliver Rate (PDR); (**b**) Latency of DSRC.
## 2 Preliminary Background
### ECQV Implicit Certificate Scheme  
As mentioned above, the KEM requires a public-private key pair before the encapsulation operation. In public key cryptography, the Diffie-Hellman key exchange is the common method for two parties to generate their key pairs and exchange the public parts. This key exchange requires explicit signatures from both participants to authenticate the exchange materials. However, generating these certificates can be computationally intensive, which is not ideal for IoT node devices. The ECQV scheme leverages the mathematical properties of ECC to associate IoT nodes' identities with their public keys, forming implicit certificates. This characteristic reduces the computational effort on authentication from one party in the communication channels in contrast to traditional public key cryptography methods, such as RSA, ECDSA, or EdDSA, which require both parties to compute and directly verify each other's certificates. In the context of a WSN, the ECQV scheme provides a passive approach, ensuring that only the deployed sensor nodes can derive the correct key pair from the implicit certificate issued by the trusted server. This scheme effectively mitigates the computational overhead associated with the key pair generation and authentication on the node devices. Moreover, the scheme reduces the quantity and size of the exchange messages in the network.
# Optimization Environment Definition for Beam Steering Reflectarray Antenna Design
###### Abstract  
Reflectarray antennas are low-profile high-gain systems widely applied in the aerospace industry. The increase in their application is leading to the problem of getting more advanced performance while keeping the system as simple as possible. In these cases, their design cannot be conducted via analytical methods, thus evolutionary optimization algorithms are often implemented. Indeed, the design is characterized by the presence of many local minima, by high number of design variables, and by the high computational burden required to evaluate the antenna performance. The purpose of this paper is to develop, implement, and test a complete Optimization Environment that can be applied to achieve high scanning capabilities with a reflectarray. The design of the optimization environment has been selected to be flexible enough to be applied also with other different algorithms.  
Evolutionary Algorithms; reflectarray optimization; Particle Swarm Optimization; Genetic Algorithm; Social Network Optimization +
Footnote †: journal: _mathematics_
# Architectural Simulation of Hybrid Energy Harvesting: A Design Experiment in Lanzarote Island
## 2 Research Scope and Subjects  
The object of this study is Lanzarote Dynamic Square located in the city of Arrecife (28.9\({}^{\circ}\) N, 13.4\({}^{\circ}\) E, UTC+1), the capital of the Spanish territory of Lanzarote island (this research subject is a project with which the researcher has won the Honorable Mention at the Lanzarote Dynamic Square Architecture Competition hosted by Cabildo de Lanzarote in 2018 [21]). The location of the research object to be installed in the square is currently used as a parking lot, and the goal is to create a versatile space where residents can freely entertain themselves. The research object has the shape of a square with a side width of 38 m. There is a four-story building to the south of the research object, a three-story building to the east, and a three-story and a one-story building to the north (Figure 1). Notably, there are roads on all sides of the study object, except for the east side, so it has excellent accessibility. In the year of 2020, Lanzarote Island had the lowest temperature of 14 \({}^{\circ}\)C in January and the highest temperature of 24 \({}^{\circ}\)C in August.  
The average annual temperature is 20 \({}^{\circ}\)C. The average annual rainfall is 16.25 mm, with the precipitation in October, November, and December constituting 40% of the total annual precipitation. The average annual wind speed is 6.6 m\({}^{-1}\cdot\)s\({}^{-1}\)[22]. An analysis of the climate shows that the research area, surrounded by the North Atlantic and under the influence of the oceanic climate, tends to have warm temperatures, low precipitation, and high wind speeds throughout the year.  
In recognition of the value of the magnificent natural environments of the island of Lanzarote, it was registered as a World Heritage Site in 1993 by UNESCO. This study aims to plan a green building design for future Lanzarote Dynamic Squares to preserve the excellent natural environments of Lanzarote Island. Specifically, a green building design applies energy harvesting technology to architectural design to generate renewable energy.  
This study attempts to create a new, future-oriented concept of a place. Since the energy consumption of buildings accounts for 40% of the total energy consumption, it is a legal requirement in the European Union to reduce the energy consumption of architectural structures and to generate renewable energy with these buildings [23]. In that sense, this study, which develops an architectural design based on the generation of renewable energy in Europe, is a very timely piece for the reality of architecture in many European countries, where buildings consume a high energy level.
## 4 Modeling of Acoustic Target Tracking
### Geometric Solution for Acoustic Target Tracking
#### 4.3.2. Pencil Condition for Intersection Planes of Target Tracking  
The intersection planes of each three sensing cones that are constructed from the sensed information of motes make a pencil.  
Assume that our three sensing cones are \(i,j,k\). We denote their paired intersection planes as \(\pi_{ij},\pi_{ik},\pi_{jk}\). The first equation in Equation (25) represents the intersection plane \(\pi_{ij}\) and the second equation represents the intersection plane \(\pi_{ik}\):  
\[\begin{array}{l}2(x_{j}-x_{i})x+2(y_{j}-y_{i})y-2(t_{j}-t_{i})v^{2}t-(({t_{i }}^{2}-{t_{j}}^{2})v^{2}+({x_{j}}^{2}-{x_{i}}^{2})+({y_{j}}^{2}-{y_{i}}^{2})) =0\\ 2(x_{k}-x_{i})x+2(y_{k}-y_{i})y-2(t_{k}-t_{i})v^{2}t-(({t_{i}}^{2}-{t_{k}}^{2 })v^{2}+({x_{k}}^{2}-{x_{i}}^{2})+({y_{k}}^{2}-{y_{i}}^{2}))=0\end{array} \tag{25}\]  
To obtain the equation of the third intersection plane of a pencil, we substitute the equations of planes \(\pi_{ij}\) and \(\pi_{ik}\) from Equation (25) in Equation (24) and derive the following:  
\[k_{1}(2(x_{j}-x_{i})x+2(y_{j}-y_{i})y-2(t_{j}-t_{i})v^{2}t-(({t_{i }}^{2}-{t_{j}}^{2})v^{2}+({x_{j}}^{2}-{x_{i}}^{2})+({y_{j}}^{2}-{y_{i}}^{2})))\] \[+\ k_{2}(2(x_{k}-x_{i})x+2(y_{k}-y_{i})y-2(t_{k}-t_{i})v^{2}t-(({t _{i}}^{2}-{t_{k}}^{2})v^{2}+({x_{k}}^{2}-{x_{i}}^{2})+({y_{k}}^{2}-{y_{i}}^{2} ))) \tag{26}\] \[=0\]  
If we set \(k_{1}=-1\) and \(k_{2}=\ +1\) in Equation (26) and simplify it, we get Equation (27), which is the equation of intersection plane \(\pi_{jk}\) based on Equation (20).  
\[\begin{array}{l}2(x_{k}-x_{j})x+2(y_{k}-y_{j})y-2(t_{k}-t_{j})v^{2}t-(({t_{ j}}^{2}-{t_{k}}^{2})v^{2}+({x_{k}}^{2}-{x_{j}}^{2})+({y_{k}}^{2}-{y_{j}}^{2}))\\ =0\end{array} \tag{27}\]  
So the intersection planes of three sensing cones that are constructed from sensed information of motes make a pencil.  
We call common line of a sheaf of planes as axis of pencil. We denote a pencil that is constructed from intersection planes of three sensing cones \(i,j,k\) as \(\omega_{ijk}\) and axis of this pencil as \(\xi_{ijk}\).  
The equation of two planes of a pencil is sufficient for computing axis of pencil. Furthermore, we saw that the equation of a third intersection plane \(\pi_{jk}\) is a linear combination of intersection planes \(\pi_{ij}\) and \(\pi_{ik}\). This means that the equation of the third intersection plane \(\pi_{jk}\) does not provide more information and it is thus redundant. Figure 6 shows the three vertical right circular cones of sensing information of three sensor nodes. This figure also shows the three planes of a pencil, which are the intersection planes of each pair of three cones.
# Biosensors Based on Bivalent and Multivalent Recognition by Nucleic Acid Scaffolds
## 3 Biosensors Based on Multivalent Binding Modes  
Because nucleic acids, especially DNA, can form diverse 2D or 3D nanostructures that serve as powerful scaffolds for multiple ligand arrays, many types of multivalent binding systems have been constructed [1, 7, 99]. Therefore, biosensors based on multivalent interactions have been developed. Compared with bivalent interaction-based sensors, multivalent-based sensors are relatively complex systems with the aim of displaying ligands on nucleic acid scaffolds rather than producing signals. In this section, strategies for designing two types of multivalent biosensors are described: biosensors based on (1) DNA chain elongation and (2) DNA nanostructures.
## 2 Instrumentation and Methods
### Flux Data Processing and Data Selection  
To ensure reliable processing of flux data, ChinaFLUX developed standard methodologies for processing flux data, including data quality control; coordinate rotation; Webb, Pearman and Leuning density correction (WPL) correction (correction for density fluctuations); canopy storage calculation; gap filling; and flux component partitioning [61]. These methods were applied to process the flux data from the Qianyanzhou site using the software program MATLAB R2014a (MathWorks Inc., Natick, MA, USA). The raw data were corrected using double coordinate rotations [62] and WPL correction [63]. Abnormal or missing data were mainly caused by rainfall, dew, frost, power failure or instrumental malfunction. The Lloyd-Taylor and Michaelis-Menten equations were used for gap filling of carbon flux at nighttime and in daytime, respectively [64; 65]. The observed nighttime carbon flux equals ecosystem respiration (Re), which represents the CO\({}_{2}\) released by vegetation and soil respiration. The Lloyd-Taylor equation was also used to extrapolate the daytime Re. The directly observed daytime carbon flux was net ecosystem CO\({}_{2}\) exchange (NEE), whose absolute value equals net ecosystem productivity (NEP, NEP \(=-\)NEE). After the calculations of daytime Re, GPP was determined by the equation GPP = Re + NEP and regarded as the observed GPP in this study. Based on the continuous daytime and nighttime datasets, GPP was obtained [66]. The threshold method was used to remove spurious environmental data, and the data gaps were filled using linear interpolation or mean diurnal variation methods [67].
# Process-Oriented Stream Classification Pipeline: A Literature Review
## 5 Stream Classification Algorithms and Architectures
### Algorithms
#### 5.2.5 Frequency-Based  
The Naive Bayes algorithm (NB) is an incremental algorithm, which is by nature able to handle data streams [121]. Decisions are made based on a frequency scheme (see Figure 7e). Concretely, NB is based on the Bayes Theorem, which assumes that the explanatory variables are independent conditionally of the target variable [146]. Thus, Naive Bayes works intrinsically incremental by updating relevant entries in its probability table and deriving predictions based on posterior probabilities whenever a new training instance arrives. The algorithm is well-known for its simplicity and can predict class memberships with low computational costs. The traditional algorithm deals with a discretized feature space. However, numerical feature spaces occur often, e.g., in sensor data. Bayesian classification can be combined with an initial discretization step as preprocessing, e.g., via the PiD method [102]. An option of using the algorithm on continuous data arises from the Gaussian Bayes models [123]. Nevertheless, the two main drawbacks of Bayes classifiers are the independence assumption of the features, such as the inability to handle multimodal distributions.
# Prediction Method for Mine Earthquake in Time Sequence Based on Clustering Analysis
###### Abstract
In the selection of clustering algorithm, considering that microseism events include space and time information, the ST-DBSCAN algorithm [21] is selected to cluster the microseism events occurring in the working face. After clustering, there are no large number of microseism events in a single cluster, so SVR [22], which is suitable for a small sample data set, is used to predict mine earthquakes in sequence [23, 24]. As a comparison experiment, the number of microseism events included in the working face is large before clustering, so Long Short-Term Memory (LSTM) [25] with a good prediction effect of time series on a large sample data set is selected to predict mine earthquake [26]. The main contributions of this paper are summarized as follows. (1) A time sequence prediction method for mine earthquake based on ST-DBSCAN using SVR is proposed. Clustering analysis can source microseism clusters and enhance the correlation of microseism data. This data preprocessing method can improve the accuracy of subsequent machine learning prediction models. (2) Contrast engineering experiments in the H Coal Mine are conducted to evaluate the performance of the proposed method. The results show that, compared with the classical time sequence prediction method (LSTM) on unclustered microseism data, the SVR model has a better prediction effect of mine earthquake on clustered microseism data. The remainder of this paper is organized as follows. The related machine learning algorithms are introduced in Section 2. The proposed approach is described in Section 3. A real case study is illustrated in Section 4. Conclusions and future work are summarized in Section 5.
# Electro-Thermal and Aging Lithium-Ion Cell Modelling with Application to Optimal Battery Charging
## 5 Design of the Fast Charging Robust Controller
### Design of a CRONE Controller for Fast Charging  
The frequency responses of the linear models obtained through the linearization of model (24) (see Section 4.2) for various operating conditions (temperature, aging, SOC) are shown on Figure 5. This figure highlights that, in low and high frequencies respectively, the gains of the linear models are of order 1 and 0. Thus, for a low-pass filter behaviour of the controller in high-frequencies and as an integrator in low-frequencies, the open-loop orders \(n_{l}\) and \(n_{h}\) are defined such that \(n_{l}=2\) and \(n_{h}=1\). The minimization of the complementary sensitivity function resonance peak variation according to Relation (32) is acheived under the design specifications that follows:  
* a sensitivity function \(S(s)\) resonance peak lower than 6 dB to reach a good stability degree;
* a nominal resonance peak \(M_{Thom}\) of function \(T(s)\) equal to 1.7 dB for a small overshoot of the nominal response to a step of the reference signal of \(J_{sr}\);
* a closed loop bandwidth close to 0.2 rad/s;
* a control effort sensitivity less than 10 A (\(I_{ch}\)) for a variation of \(J_{sr}\) of 10 \(\upmu\)A in high-frequency.  
The CRONE control toolbox [27; 28] was used to compute the open-loop transfer function optimal parameters that meet the above specifications. The optimal parameters obtained are the following: \(Y_{r}=3.8\) dB, \(\omega_{r}=1.5\) rad\(\cdot\)s\({}^{-1}\), \(\omega_{0}=0.1\) rad\(\cdot\)s\({}^{-1}\) and \(\omega_{1}=7.5\) rad\(\cdot\)s\({}^{-1}\), leading to the following values for the fractional integration orders: \(a_{0}=1.42\) and \(b_{0}=-0.46\). The resulting open loop transfer function Nichols chart for the nominal behaviour and the associated uncertainty domains is shown in Figure 7.  
The values of \(\omega_{\rm r}\), \(\omega_{0}\) and \(\omega_{1}\) appear on this figure. This figure demonstrates that the closed-loop system is stable, as all the behaviours are far from the instability point (\(-180^{\circ}\), 0 dB) area. Figure 8 shows the gain diagram of the four sensitivity functions \(T(s)\), \(S(s)\), \(GS(s)\) and \(CS(s)\) for the nominal behaviour of the plant and for two extreme behaviours of the plant. The user defined constraints (dotted lines) are also represented. As the gain of all the sensitivity functions does not exceed the constraints defined, it can be concluded that the design specifications are met. The closed loop robustness is also demonstrated by the small resonance peaks of the functions \(S(s)\) and\(T(s)\). An order 4 rational controller \(C(s)\) is finally obtained using the method described at the end of Section 5.2. As the controller obtained contains an integrator, an anti-windup configuration is included in the control system. It cancels the charge current increase due to the output integrator saturation, thus meeting the current limits for the considered battery (a safety charge current limit of 3.5 C).