esa-sceva/satcom-chunk-collection huggingface dataset:
# Passive UHF RFID Tag for Multispectral Assessment
## 2 Materials and Methods
### Fabrication Process  
The tag pattern is directly printed on polyimide (PI) Kapton(r) HN (DupontTM, Wilmington, DE, USA), a flexible polymeric substrate. The PI used has a thickness of 75 um, a relative permittivity (\(\epsilon_{\mathrm{r}}\)) of 3.5, and a loss tangent (tan8) of 0.002. The screen-printing machine is a Serfix III (Seglevint SL, Barcelona, Spain). The screen used to fabricate the single-layer screen-printed tag has a mesh density of 120 nylon threads per centimette (T/cm). The conductive silver-based ink is CRSN 2442 (Sun Chemical Corporation, Parsippany, NJ, USA). Under these conditions, the printed layer thickness is 13.3 um and the resistivity is 39 \(\pm\) 4 um\(\cdot\) cm [52]. After printing, the tag is cured at a constant temperature of 150 \({}^{\circ}\)C for 20 min to enable the formation of the conductive pattern and obtain the desired resistivity. To assemble the chips and the external components to the substrate, a three-step process was carried out. First, we interconnected the chips and silver pads by using the conductive resin H20E (Epoxy Technology, Inc., Billerica, MA, USA). After this, a double layer of 50-um-thick dry adhesive, AR Clear  
Figure 3: Screen-printed RFID tag on flexible substrate with all components.  
8932 (Adhesives Research, Inc., Glen Rock, PA, USA) was attached to the bottom of the chips to fix them to the substrate. Finally, a heating process was performed in an oven at 120 \({}^{\circ}\)C for 20 min to cure the conductive resin. The E5071C Vector Network Analyzer (VNA) (Keysight Tech., Santa Clara, CA, USA) was used to characterize the fabricated antenna. Considering the differential character of the antenna measurement, we defined the S-parameter differential port between port-1 and port-2 through a test fixture simulator included in the VNA. To connect the antenna to the VNA, we used Ultra Miniature Coaxial connectors (U.FL) (Hirose Electric, Tokyo, Japan) attached at the antenna feed point along with SMA to U.FL wires. For calibration purposes, a full custom Short-Open-Load-Thru (SOLT) calibration kit was used [35].
# Direct Spread Spectrum Technology for Data Hiding in Audio +
## 3 Materials and Methods
### Indicators of Effectiveness of Hiding Messages  
In order to evaluate the efficiency of the steganosystem, it is necessary to evaluate the distortions in the recovered messages, as well as the distortions of the original container. Therefore, we experimentally estimated the bit error rate (BER) in recovered messages. We also estimated audio cover distortions by mean squared error (MSE) and peak signal-to-noise ratio (PSNR). We did not use other indicators of distortion of the audio cover (for example, percentage residual deviation, autocorrelation analysis of stegodata with original data, etc.), as this can be obtained after processing our results.  
These are the most common metrics used in many related works.  
1. Bit Error Rate, BER The bit error rate is calculated using the equation: \[BER=\frac{k_{error}}{k}\] (12) where \(k_{error}\) is the number of erroneously recovered bits, \(k\) is the total number of bits in the information message.
2. Mean squared error, MSE In order to estimate the container distortion, the root mean square error (MSE) is used, i.e., the averaged square of the difference between the original multimedia data and the cover data obtained after the message was hidden. For an audio signal represented by a set of \(n\) discrete samples, \(MSE\) is calculated by the equation: \[MSE=\frac{1}{n}\sum_{j=0}^{n-1}\left[Mix_{i}-N_{i}\right]^{2}\] (13)
3. Peak signal-to-noise ratio, PSNR A more visual characteristic of cover distortion is the ratio of the maximum possible signal power to the power of the distorting noise. Usually, this characteristic is expressed on a logarithmic scale and calculated by the equation: \[\begin{array}{l}PSNR=10\cdot\log_{10}\left(\frac{N_{\max}^{2}}{MSE}\right)=20 \cdot\log_{10}\left(\frac{N_{\max}}{\sqrt{MSE}}\right)=\\ =20\cdot\log_{10}(N_{\max})-10\cdot\log_{10}(MSE),\end{array}\] (14) where \(N_{\max}\) is the maximum possible value of the signal \(N\).  
In our case, by \(N\) we mean multimedia data, i.e., an audio signal represented by a set of discrete samples. If each discrete sample is coded in \(B\) bits, then (in linear pulse code modulation, PCM) the maximum possible value of \(N_{\max}=2^{B}-1\). In our experiments, we used the simplest audio signals with \(B=8\), i.e., \(N_{\max}=255\). Equation (14) for this value takes the form:  
\[PSNR=20\cdot\log_{10}(255)-10\cdot\log_{10}(MSE).\]
## 4 Applications and Discussions
### Obtaining First-Order Stiffness Matrix  
For the special condition of a Timoshenko beam with no axial load, the first-order Timoshenko beam element stiffness matrix (without considering geometric nonlinearity) (i.e., Equation (4)) can be derived from the stability stiffness matrix formulated in Equation (15). Considering the relationship in Equation (12), the element stability functions \(T_{\text{TB}}\), \(Q_{\text{TB}}\), \(S_{\text{TB}}\), and \(C_{\text{TB}}\) in Equation (15) are functions of the axial force factor \(\lambda\) and the dimensionless factor \(\varphi\). By taking the limit of Equation (15) as \(\lambda\) approaches zero (which corresponds to zero axial load), the element stiffness matrix of the Timoshenko beam without considering geometric nonlinearity can be derived to the same form as Equation (4). This finding also verifies the correctness of the derived stability stiffness matrix (Equation (15)) in the previous section.
# A Hierarchical Mission Planning Method for Simultaneous Arrival of Multi-UAV Coalition
## 4 Path Estimation and Simultaneous Arrival Path Planning  
### Pythagorean Hodograph Curve  
In this paper, the PH curve was used in the path estimation and path planning processes, so a brief overview of PH curve is stated here.  
In the two-dimensional plane, the Pythagorean hodograph (PH) curve \(r(q)=[x(q),y(q)],q\in[0,1]\) is a parameterized polynomial curve whose hodographs \(x^{\prime}(q)\), \(y^{\prime}(q)\) satisfy a Pythagorean condition:  
\[x^{\prime 2}(q)+y^{\prime 2}(q)=\sigma^{2}(q) \tag{20}\]  
The PH curve can also be expressed in the Bezier form of degree \(n\):  
\[r(q)=\sum_{k=0}^{n}C_{n}^{k}(1-q)^{n-k}q^{k}P_{k},\quad q\in[0,1] \tag{21}\]  
where \(P_{k}=(x_{k},y_{k}),k=1,\ldots,n\) are control points. The length of a PH path parameterized with parameter \(q\) is calculated by:  
\[L(q)=\int_{q_{1}}^{q_{2}}\left\|r(q)\right\|dq \tag{22}\]  
In order to obtain the PH curve with continuous curvature, the quantic PH curve was used as the path for each UAV in this paper. The theory and calculation method of the PH curve were not repeated here, relevant contents can be found in [29; 30]. It should be noted that the kinematics constraint of a UAV is satisfied by bounding the curvature of the PH curve in a two-dimensional plane, so the maximum curvature on a flyable PH curve should satisfy:  
\[\max(\kappa(q))=\frac{\left\|r^{\prime}(q)\times r^{\prime\prime}(q)\right\|} {\left\|r(q)\right\|^{3}}\leq\kappa_{\max}\,\quad q\in[0,1] \tag{23}\]  
where \(\kappa_{\max}\) represents the maximum curvature of the UAV.  
When the position and attitude of the starting point and ending point are given, the shape of the PH curve is determined by the modulus of the direction vectors of the starting and ending points. Increasing the modulus value can reduce the curvature of the PH curve, but it will increase the length of the curve. Therefore, it is necessary to select the appropriate modulus values to make the generated PH curve satisfy the kinematics constraints of the UAV, obstacle avoidance and some other coordination constraint.
# Quantum Time-Memory-Data Tradeoff Attacks on the SNOW-V Stream Cipher
## 1 Introduction
This paper presents the first TMDTO attacks and quantum TMDTO attacks on the stream cipher SNOW-V. We combine TMDTO attacks with the BSW sampling technique, to analyze the security of SNOW-V. The results show that SNOW-V is secure against BS-TMDTO attacks, while the attacker can mount a BG-TMDTO attack, where none of the online time complexity, the memory complexity and the offline time complexity is bigger than \(2^{256}\), if the keystream sequences generated by the secret key, together with different IVs, are provided to the attacker. Furthermore, we analyze the security of SNOW-V against quantum TMDTO attacks, and the results show that a quantum TMDTO attack offers, strictly, better online time complexity than [PERSON]'s algorithm, when the available memory space is bigger than \(2^{170,67}\). These results are helpful in evaluating the security of SNOW-V against (quantum) TMDTO attacks. The rest of the paper is organized as follows. In Section 2, a brief description of SNOW-V is given. In Section 3, we briefly introduce a TMDTO attack and a quantum TMDTO attack. The security of SNOW-V against TMDTO attacks with BSW sampling is analyzed in Section 4. In Section 5, we analyze the security of SNOW-V against quantum TMDTO attacks. Concluding remarks are given in Section 6.
## 2 Isolated Single Tree Radio Channel Characterization  
In this section, the radio channel characterization at 2.4 GHz when an isolated single tree is blocking the communication path is presented. First, wireless channel estimation methodology based on deterministic 3D Ray Launching algorithm is described and employed. For that purpose, a simulation scenario is implemented, including dimensions and electromagnetic frequency dispersive properties of the employed materials. Then, measurements in a real scenario have been performed in order to complete the obtained simulation-based radio channel model.
# Adsorption Separation of Various Polar Dyes in Water by Oil Sludge-Based Porous Carbon
## 4 Discussion
### The Adsorption Models and Mechanisms of OSC
During the adsorption process, the three dyes initially migrate through the liquid film surrounding the porous carbon surface of the oily sludge-based adsorbent, ultimately reaching the outer surface. The mass transfer rate across this liquid film significantly influences the adsorption process. The incoming dye molecules bind to the active sites on the porous carbon surface, while the remaining molecules diffuse along the pores into the interior of OSC700, with their transport governed by the molecular diffusion rate. As shown in Figure 6b, the concentration of organic dyes in the liquid decreases gradually with increasing adsorption time, leading to a corresponding decrease in molecular diffusion rate until adsorption equilibrium is achieved. The larger the C value in the intraparticle diffusion model, the more pronounced the boundary effect, indicating a greater contribution of surface adsorption to the overall adsorption process. During the adsorption process, the adsorption of porous carbon and the desorption of solvent molecules occur simultaneously (Figure 8f, and Table 6). The desorption process leads to a greater increase in entropy than the decrease in entropy caused by the adsorption of the three dyes by OSC700. When \(\Delta\)G \(<\) 0, it indicates that the adsorption of MG, MB, and CV by OSC700 is thermodynamically favorable and spontaneous. Furthermore, the positive value of \(\Delta\)S indicates an increase in the disorder or randomness at the solid-liquid interface during the adsorption process, which enhances the entropy of the system and suggests a more random and disordered interaction between the three dyes and the active sites on OSC700. This demonstrates the strong affinity of OSC700 for MG, MB, and CV. Due to the large size of the dye molecules, their adsorption on OSC700 is accompanied by the release of a significant number of water molecules. This release occurs because the incoming dye molecules replace the water molecules initially bound to the hydrophilic functional groups on the OSC700 surface through hydrogen bonding, thereby facilitating the adsorption process [52]. As the temperature rises, the number of water molecules on the OSC700 surface decreases, exposing more adsorption sites for dye molecules. However, the changes in Gibbs free energy (\(\Delta\)G) decrease with increasing temperature, from \(-\)20,045.2574 kJ/mol, \(-\)14,027.34 kJ/mol, and \(-\)16,194.66 kJ/mol to \(-\)21,436.9136 kJ/mol, \(-\)14,203.13 kJ/mol, and \(-\)16,452.18 kJ/mol, respectively. This suggests that increasing the temperature does not enhance the adsorption performance of OSC700, indicating that the entire adsorption process is a spontaneous endothermic process.
# Demystifying MLOps and Presenting a Recipe for the Selection of Open-Source Tools
## 4 Workflow Stages in MLOps
### ML Training Phase
#### 4.4.3 Algorithm and Hyperparameter Selection  
The selection of the most appropriate ML algorithm for a specified problem (e.g., neural networks, vector machines, and random forest) is carried out by the data scientists. On top of this, each algorithm has parameters that must be tuned for achieving good performance. These parameters are called algorithms hyperparameters, for example, the number of layers or neutrons within a neural network. Tuning these hyperparameters and selecting an optimal algorithm is one of the most resource-intensive and tedious tasks in an ML workflow. AutoML aims to simplify this and many other manual tasks in modeling by making decisions in an automated way. AutoML aims to decrease the human effort required for building efficient ML systems. It advocates for fairness and reproducibility. It supports the data scientist such that new experiments could be tried rapidly and helps the enterprise to quickly develop new ML systems through automating much of the modeling activities. A comprehensive guide on AutoML is provided in [21]. Regarding the automation of CASH which is a sub part of AutoML, different strategies (such as, for example, generating a grid of configurations and evaluating all of them or applying random search for each hyperparameter) exist [22].
## 2 Materials and Methods  
### Materials and Instrumentation  
Disodium 3-cyano-2-(dicyanomethylene)-2H-pyrrole-4,5-bis(olate) with a minimum purity of 98% was obtained from CK Chem and used without further processing. Other reagents, also of at least 98% purity, were sourced from suppliers such as Sigma-Aldrich, Tokyo Chemical Industry (TCI), and Alfa Aesar and were used directly. Proton and carbon nuclear magnetic resonance (\({}^{1}\)H and \({}^{13}\)C NMR) spectra were recorded on a Jeol JNM-ECZ400S/L1 spectrometer (Tokyo, Japan). Fourier-transform infrared spectroscopy (FT-IR) was performed using a JASCO FT-IR 4100 (Tokyo, Japan) to identify the characteristic peaks of the synthesized compounds, and qualitative chemical analysis was carried out. The fast atom bombardment mass spectra (FAB-MS) were recorded on a Jeol JMS-700 (Tokyo, Japan), Agilent 6890 Series (Santa Clara, CA, USA). UV-Visible absorption spectra were acquired using a Shimadzu UV-1900i UV/Vis/NIR spectrophotometer (Kyoto, Japan). Thermal decomposition temperatures (T\({}_{\text{d}}\)) were evaluated by thermogravimetric analysis (TGA) with an SDT Q600 instrument from TA Instruments (Lukens Dr, New Castle, DE, USA), with samples heated to 700 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min under nitrogen. Density functional theory (DFT) calculations, including geometry optimization and electronic structure analysis, were performed using ORCA software (version 5.0.4), utilizing the B3 LYP-D3 hybrid functional with a def2-TZVPP basis set [21] (see Supporting Information). Cyclic voltammetry experiments were carried out on a WizEQCM-1000 system (Wizmac, Daejeon, Republic of Korea), employing a platinum wire as the counter electrode, a glassy carbon as the working electrode, and an Ag/AgCl electrode as the reference electrode. The voltammograms were obtained over a potential range of \(-\)0.2 to 1.4 V, with an electrolyte solution of 0.1 M tetrabutylammonium hexafluorophosphate in acetonitrile.
# Application of Long-Short-Term-Memory Recurrent Neural Networks to Forecast Wind Speed
###### Abstract  
Forecasting wind speed is one of the most important and challenging problems in the wind power prediction for electricity generation. Long short-term memory was used as a solution to short-term memory to address the problem of the disappearance or explosion of gradient information during the training process experienced by the recurrent neural network (RNN) when used to study time series. In this study, this problem is addressed by proposing a prediction model based on long short-term memory and a deep neural network developed to forecast the wind speed values of multiple time steps in the future. The weather database in Halifax, Canada was used as a source for two series of wind speeds per hour. Two different seasons spring (March 2015) and summer (July 2015) were used for training and testing the forecasting model. The results showed that the use of the proposed model can effectively improve the accuracy of wind speed prediction.  
forecasting; long-short-term memory; multiple time series; RNN; wind speed Article
## 2 Numerical Simulation Algorithm and Constitutive Model
### Finite-Rate Chemical Reaction Model  
A finite-rate chemical model is used, and the detailed chemical reaction mechanism of methane deflagration is considered to accurately simulate the shock wave of methane explosion. The following equation is used for the system involving \(n_{s}\) species and \(n_{r}\) basic reactions [33]:  
\[\sum_{k=1}^{n_{s}}v_{kl}^{\prime}X_{k}\Leftrightarrow\sum_{k=1}^{n_{s}}v_{kl}^ {\prime\prime}X_{k}\quad l=1,2,3\dots,n_{r} \tag{1}\]  
Figure 1: Coupling mechanism between the chemical solver of CESE and structure. Here, \(v^{\prime}{}_{kl}\) and \(v^{\prime\prime}{}_{kl}\) are the forward and reverse stoichiometric coefficients of species \(k\) in the reaction \(l\), respectively, and \(X_{k}\) is the chemical symbol of this species \(k\). The net molar formation rate \(w_{k}\) of species \(k\) can be obtained from the following Equation (2):  
\[\dot{w}_{k}=\sum_{k=1}^{n_{s}}\left(v_{kl}^{n_{s}}-v_{kl}^{\prime}\right)\left( k_{fl}\prod\
olimits_{k=1}^{n_{s}}\left[X_{k}\right]-k_{hl}\prod\
olimits_{k=1}^{n _{s}}\left[X_{k}\right]\right) \tag{2}\]  
Here, \([X_{k}]\) is the molar concentration of species \(k\), and \(k_{fl}\) and \(k_{fl}\) are the forward and reverse reaction rate coefficients, respectively; they are calculated from Equations (3) and (4), as follows:  
\[k_{fl}=A_{l}T^{\beta l}\exp\left(-\frac{E_{al}}{RT}\right) \tag{3}\]  
\[k_{bl}=\frac{k_{fl}}{k_{cl}} \tag{4}\]  
In the above equation, \(A_{l}\) stands for the pre-index factor, \(\beta_{l}\) is the temperature index, \(E_{al}\) is the activation energy per unit mole, \(R\) is the general gas constant, and \(T\) is the temperature. \(K_{cl}\) is the equilibrium constant, which is obtained from the following Equation (5):  
\[K_{cl}=\left(\frac{P_{atm}}{RT}\right)^{\sum_{k=1}^{n_{s}}v_{kl}}\exp\left( \sum_{k=1}^{n_{s}}v_{kl}\left(\frac{S^{0}_{k}}{R}-\frac{H^{0}_{k}}{RT}\right)\right) \tag{5}\]  
\(S^{0}_{k}\) and \(H^{0}_{k}\) in Equation (5) represent the entropy and enthalpy in the standard state, respectively, and the EOS of gas adopts the ideal gas state equation:  
\[P=\sum_{k=1}^{n_{s}}\left[X_{k}\right]RT \tag{6}\]  
The thermal conductivity, viscosity, and diffusion coefficients of species \(k\) required by the Navier-Stokes solver can be obtained through the transfer data files in the reference literature [34]. The calculation cost is expensive, considering that the current detailed kinetic mechanism of methane combustion, GRI-Mech 3.0 [35], contains 53 species and 325 elementary reactions. Therefore, the simplified 18-step and 53-step methane combustion models based on GRI-Mech 3.0 were adopted [36], and these models will be verified in subsequent studies to select the best methane combustion reaction mechanism suitable for simulation analysis.
## 1 Introduction
Some authors found adult-like balance performance in children at the end of the first decade of life (7-10 years old [34] and 11-13 years old [35]), indicating equal or even better balance performances of children compared to adolescents. Other studies [36], however, reported better balance performances of adolescents compared to children. Hence, the true extent of age-related differences in balance performance in youth remains debatable. Several studies assessed balance in children by using the dual-task model. The results of these studies showed that concurrent performance of cognitive and motor tasks affect motor skills of children in the 7-9-year-old age groups [22; 27; 37]. Other studies also showed that 4-6-year-old children had conflicts in combining visual and sensory information to control posture but 7-10-year-old children had no difficulty in processing visual and sensory information for balance control [11; 38]. [PERSON] et al. also showed that attentional constraints have a crucial role in cognitive-motor interference [10]. The results of the former study showed that a shift occurred in eight-year-old children and they could prioritize their attentional resources, even though they have not reached maturity at the age of eleven. Although previous studies found a relationship between postural control in the bipedal standing position, age, and involvement in a cognitive task, [PERSON] et al. (2015) suggested that there is only feeble evidence for a trend towards enlarged DT costs in children [39]. Given the confounding results and the necessity for further studies in this field using a larger sample size, the study aimed to assess the effect of dual tasks on balance during manipulation of visual information, the base of support, and cognitive dual tasks in 7-12-year-old children and adults. The results could be used in many training fields, including physical education and sports for young adults. Therefore, the aim of this study was to answer these questions: Does the balance of children become similar to adults by the age of 12? Which sensory system has the greatest impact on the balance of children and adults? It was assumed that balance control improves with advancing age and postural sway (PS) increases when manipulating task demands at all ages in both genders.