esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Concept of Transfer Function and Frequency Response  
In physical systems, the relationship between an input signal and the system's response can be modeled using a transfer function. Particularly for linear time-invariant systems, the transfer function provides a concise representation of the system's behavior in the frequency domain, allowing for efficient analysis and comparison. The transfer function  
Figure 1: Operating mechanism of the system [48].  
is defined as the ratio of the Laplace transforms of the output signal \(Y(s)\) to the input signal \(X(s)\):  
\[H(s)=\frac{Y(s)}{X(s)} \tag{1}\]  
where \(s\) is the complex frequency variable, defined as \(s=\sigma+j\omega\), with \(\sigma\) representing the real part and \(\omega\) the angular frequency. In this study, the target system is a modular robot composed of interlocking blocks. A sweep signal is used as an input to induce mechanical vibrations throughout the structure, and the resulting acoustic response is captured by a microphone. In this context, the robot and its structure can be viewed as a dynamic system with a transfer function \(H(s)\) that characterizes how it responds to external acoustic stimulation. The relationship between input and output in the Laplace domain is then expressed as Equation (2):  
\[Y(s)=H(s)\cdot X(s). \tag{2}\]  
While the general transfer function \(H(s)\) describes the system's behavior in the complex frequency domain, substituting \(s=j\omega\) allows the analysis to be restricted to the real frequency axis. This yields the frequency response of the system:  
\[H(j\omega)=\frac{Y(j\omega)}{X(j\omega)}. \tag{3}\]  
The function \(H(j\omega)\) describes how the system reacts to acoustic inputs of different frequencies and is a practical expression of the transfer function in real-world measurements. By applying a broadband sweep signal as the input, the full frequency response of the system can be observed.  
In this experiment, the sweep signal \(X(f)\) is applied to the robot, and the response signal \(y(t)\) is recorded. \(t\) represents the time. \(f\) represents the frequency. The output signal is then transformed to the frequency domain using the Fast Fourier Transform (FFT), yielding \(Y(f)\). Given that the input is known and consistent, the observed output \(Y(f)\) reflects the system's transfer function in the frequency domain:  
\[Y(f)=H(f)\cdot X(f). \tag{4}\]  
Any structural changes--such as detachment or failure of a specific component--alter the physical configuration of the system, which in turn changes the transfer function \(H(f)\). Therefore, the frequency-domain characteristics of the output signal also change depending on the condition of the robot. This change in the frequency response serves as the basis for classification. In this study, the frequency-domain features obtained from FFT are used to train machine learning models, which can then distinguish between different fault conditions based on patterns in the transfer function.
# Fretting Fatigue Behavior under Tension-Bending Mixed-Mode Loading
## 2 Problem Formulation
### Dimensional Analysis  
Dimensional analysis [28] is a powerful tool to analyze the relationship between variables in science and engineering. This method is conducive to simplifying the obtainment of the physical phenomena either from calculations or experiments by reducing the number of independent variables in functions. For the problem investigated in this paper, the most important variable stress, \(\sigma_{ij}\), which characterizes fretting damage, can be derived by other variables using general expression \(f\) as follows:  
\[\sigma_{ij}=f(P,F,\theta;E_{1},E_{2},\
u_{1},\
u_{2},\mu;R,t,x,y), \tag{1}\]  
Figure 1: Schematic of cylindrical pad-on-flat tension–bending mixed-mode fretting fatigue model.  
where \(E_{1}\), \(\
u_{1}\), \(E_{2}\), and \(\
u_{2}\) are the elastic modulus and Poisson's ratio of the specimen and the pad, respectively. \(R\) is the radius of the cylindrical pad. Among the twelve governing parameters, two of them, namely \(E_{1}\) and \(t\), have been selected as the independent variables, and other variables are dependent variables whose dimensions can be given by:  
\[\begin{array}{l}\left[\sigma_{ij}\right]=\left[E_{2}\right]=\left[E_{1}\right] \\ \left[P\right]=\left[F\right]=\left[E_{1}\right][t]^{2}\\ \left[R\right]=\left[x\right]=\left[y\right]=\left[t\right]\\ \left[\
u_{1}\right]=\left[\
u_{2}\right]=\left[\mu\right]=\left[\theta\right] =1.\end{array} \tag{2}\]  
By applying the \(\Pi\)-theorem to dimensional analysis, a dimensionless stress function expression is obtained:  
\[\frac{\sigma_{ij}}{E_{1}}=\prod(\frac{P}{E_{1}t^{2}},\frac{F}{E_{1}t^{2}}, \theta;\frac{E_{2}}{E_{1}},\
u_{1},\
u_{2},\mu;\frac{R}{t},\frac{x}{t},\frac{ y}{t}). \tag{3}\]  
For the problem studied in this paper, the focus is on the tension-bending mixed-mode loading, thus the parameters \(P\), \(\
u_{1}\), \(\
u_{2}\), \(\mu\), and \(R\) are set to constant. Consequently, the dimensionless stress function can be simplified as follows:  
\[\frac{\sigma_{ij}}{E_{1}}=\prod(\frac{F}{E_{1}t^{2}},\theta;\frac{E_{2}}{E_{1} };\frac{x}{t},\frac{y}{t}). \tag{4}\]  
Based on the above dimensional analysis, the number of governing parameters is reduced, and three important dimensionless variables, \(\frac{F}{E_{1}t^{2}}\), \(\theta\), and \(\frac{E_{2}}{E_{1}}\), are obtained. Defining \(\frac{F}{E_{1}t^{2}}\) as \(\lambda\) and \(\frac{E_{2}}{E_{1}}\) as \(\gamma\), \(\lambda\) is referred to as the dimensional oblique load, \(\gamma\) is the stiffness ratio, where \(\gamma<1\) represents a stiff specimen pressed by a compliant pad, and \(\gamma>1\) signifies that a compliant specimen is pressed by a stiff pad. \(\gamma=1\) means the specimen matches the pad.
# Line of Sight and Image Motion Compensation for Step and Stare Imaging System
## 2 Step and Stare Imaging System
### Imaging Optics Path  
The schematic diagram of the imaging optical path of the system is shown in Figure 3. The beam of the ground target is incident into the detector through M1, M2, M3, and M4 mirrors. The outer gimbal rotates around the pitch axis, and the inner gimbal rotates around the roll axis. Whether the outer or the inner gimbal rotates, it drives the mirror system and detector to rotate synchronously. In the mirror system, the M1 mirror and M2 mirror are rotating mirrors, which respectively compensate for the image motion in roll and pitch directions. M3 and M4 mirrors are fixed mirrors, which mainly realize the internal optical path turning so that the light can be injected into the detector.  
Figure 1: Diagram of compensating mirror and gimbal.  
Figure 2: Working principle and timing diagram; (a) working principle; (b) timing diagram.
# Impact of Arch Dam Cracking on Monitoring Data
## 1 Introduction
### Numerical Background  
Numerical models have proven to be important tools for the study and validation of hypotheses developed during the diagnostic procedure of the different pathologies that affect concrete dams [33; 34; 35]. In particular, the finite element method (FEM) [36], as the most commonly used numerical method, has been widely used to solve contact problems. It is important to note that the different options for modeling structures in simulation packages are not usually equivalent, so different finite element method programs implement different mathematical models to solve cracks or contraction joints. There are several studies on cracked arch dam calculations showing the location of the crack (most of them involving nonlinear finite elements where each element can transition to a cracked state) as a result of deformations and certain formulations [37; 38; 39; 40; 41]. Similarly, many arch dam computational studies incorporate contraction joints between cantilevers, with each cantilever of the dam behaving in a linear elastic way. However, the dam exhibits nonlinear behavior, and therefore the inclusion of a nonlinear component at the interface between the cantilevered faces in contact is recommended [42; 43]. The reason for this is the presence of contraction joints that may open under frictional forces. To avoid these complexities of nonlinear solutions, this study employs an accurate dam-solving methodology that has been previously described and validated [44]. For a better understanding of this method, a summary can be found in Section 2.1.  
\begin{table}
\begin{tabular}{c c c} \hline
**Device Categories** & **Minimum Measurement Resolution** & **Best Offer Accuracy \({}^{*}\)** \\ \hline Theodolite & & \\ Electronic distance meter & 1 mm & \\ Dumpy level & & \\ \hline Sighting reading pendulum & & \\ Digital level & 0.05 mm [24; 25] & Low: 0.1 mm [24; 25; 26; 27] \\ Optical level & & \\ \hline Remote reading pendulum station & 0.05 mm & Medium: 0.05 mm [17; 28; 29] \\ \hline Optical smart pendulum sensor & & \\ Laser plumbline & 0.0001 mm [30] & High: 0.02 mm [18; 29; 31; 32] \\ Laser tracker & & \\ \hline \end{tabular} \({}^{*}\) In the case of geodetic devices, accuracy refers to readings from a distance of 160 m.  
\end{table}
Table 1: Dam monitoring devices for movement measurement.
# Low-Cost RSAC and Adsorption Characteristics in the Removal of Copper Ions from Wastewater
## 3 Results and Discussion
### Thermodynamic Study of the Adsorption of Copper Ions by RSAC  
As shown in Figure 10, the intercept and the slope of the straight line were calculated by \(\Delta\)S\({}^{0}\) and \(\Delta\)H\({}^{0}\); then, we proceeded to calculate the different temperatures of \(\Delta\)G\({}^{0}\), and Table 12 presents the various thermodynamic parameters obtained. The adsorption free energy \(\Delta\)G\({}^{0}\) of copper ions adsorbed by RSAC particles at different temperatures is negative, and the absolute value of \(\Delta\)G\({}^{0}\) increases gradually with the increase of temperature (Table 12). This indicates that the adsorption of copper ions in solution by RSAC particles is a spontaneous reaction and the spontaneity increases with the increase of temperature [41]. The enthalpy change \(\Delta\)H\({}^{0}\) during the adsorption of copper ions by RSAC particles is positive, which indicates that the adsorption process is a heat absorption reaction and therefore the increase in temperature contributes to the adsorption [42]. As presented in Table 5, the maximum adsorption capacity of copper ions increases with the increasing temperature, which also confirms that the adsorption of copper ions by RSAC particles is a heat absorption reaction.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Initial Concentration (mg/L)**} & \multirow{2}{*}{**Quasi-One Dynamical Model**} & \multicolumn{3}{c}{**Quasi-Secondary Dynamical Model (QSDM)**} \\ \cline{2-7}  & & **q\({}_{\mathbf{e}}\)** & **k\({}_{\mathbf{1}}\)** & **R\({}_{\mathbf{1}}\)\({}^{\mathbf{2}}\)** & **q\({}_{\mathbf{e}}\)** & **k\({}_{\mathbf{2}}\)** & **R\({}_{\mathbf{2}}\)\({}^{\mathbf{2}}\)** \\ \hline
100 & 2.241 & 0.0713 & 0.990 & 2.640 & 0.0955 & 0.993 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Sorption kinetic model fitting parameters.  
Figure 9: Adsorption kinetic curve of copper ions (25 \({}^{\circ}\)C).
# Supply Chain Management in Renewable Energy Projects from a Life Cycle Perspective: A Review
## 3 Results
#### Key Phases of the Renewable Energy Supply Chain
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Energy** & **Raw Material** & **Manufacturing** & **Transport** & **Installation** & **Operation \&** & **End of Life** & **Total CO\({}_{2}\) in** \\
**Source** & **Extraction (kg** & **CO\({}_{2}\)-eq/kWh**) & **(kg CO\({}_{2}\)-eq/kWh)** & **eq/kWh** & **(kg CO\({}_{2}\)-eq/kWh)** & **(kg CO\({}_{2}\)-eq/kWh)** & **(kg CO\({}_{2}\)-eq/kWh)** \\ \hline Solar & 0.018–0.180 & 0.018–0.180 & 0–0.001 & 0–0.001 & 0.002–0.005 & 0.002–0.005 & 0.026–0.190 \\ PV & 0.007–0.056 & 0.007–0.056 & 0–0.001 & 0–0.001 & 0.001–0.004 & 0.001–0.004 & 0.011–0.064 \\ Biomass & 0.130–0.420 & 0.130–0.420 & 0.050–0.100 & 0.050–0.100 & 0.010–0.050 & 0.010–0.050 & 0.230–0.620 \\ Oil & 0.510–1.170 & 0.510–1.170 & 0.050–0.100 & 0.050–0.100 & 0.150–0.220 & 0.100–0.200 & 0.650–1.300 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Life cycle environmental impact of different energy sources (percentage by phase).
## 1 Introduction
Poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) is one of the most widely used polymers in the manufacture of composite films and in the manufacture of optoelectronic devices [28, 29, 30, 31, 32, 33, 34, 35, 36]. Some examples of the application of PEDOT:PSS in composite films with optoelectronic applications are as follows: [PERSON] et al. [37] used spherical nanoparticles of graphene embedded in PEDOT:PSS. [PERSON] et al. [38] used graphene oxide sheets embedded in the polymer. On the other hand, [PERSON] et al. [39] achieved an improvement in the thermoelectric performance of polymer nanocomposites, and [PERSON] et al. [40] also achieved an improvement in the electrical behavior of composite films with PEDOT:PSS and TMD nanosheets due to an improved electron transfer while being used as a hole extraction layer (HEL) [2, 4]. Previous works regarding solar cells have shown that the TMD island morphology allows an efficient hole collection due to a larger contact area between the active layer and the composite layer, a reduced series resistance and an increased Jsc and power conversion efficiency (PCE) compared to PEDOT:PSS [4]. For example, the resulting conductivity and mobility of a PEDOT:PSS/WS\({}_{2}\) composite film, compared to PEDOT:PSS, were found to be 14.3 \(\times\) 10\({}^{-4}\) S cm\({}^{-1}\) (5.7 \(\times\) 10\({}^{-4}\) S cm\({}^{-1}\)) and 1.73 \(\times\) 10\({}^{-5}\) cm\({}^{2}\)/Vs (6.54 \(\times\) 10\({}^{-6}\) cm\({}^{2}\)/Vs), respectively [4]. Additionally, it has been observed that an increase in the TMD concentration of a dye-sensitive solar cell counter electrode composite film (i.e., TiS\({}_{2}\)/PEDOT:PSS) increases the Jsc and PCE, where increases of 12.74 mA/cm\({}^{2}\) (3.91%), 13.81 mA/cm\({}^{2}\) (5.91%) and 15.78 mA/cm\({}^{2}\) (7.04%), have been reported, respectively, for 0 wt%, 5 wt% and 10 wt% [2]. PEDOT:PSS forms a continuous film on either rigid or flexible substrates by various solution processing techniques, including spin casting, slot die coating, spray deposition, inkjet printing and screen printing [28, 29, 30, 31, 32, 33, 34, 35, 36]. PEDOT:PSS films are smooth and have a surface roughness of usually less than 5 nm (deposition technique-dependent) [41], and in the visible light range, PEDOT:PSS films are almost transparent and have a high work function of 5.0-5.2 eV [30]. Therefore, in this work, composite films of CuBi\({}_{2}\)O\({}_{4}\) NPs in PEDOT:PSS were obtained by the spin coating technique, and they were morphologically and optically characterized. As an approximation to determine the effect of PEDOT:PSS on CuBi\({}_{2}\)O\({}_{4}\) NPs, the optical bandgap was calculated using the simple Tauc method, for the NPs and for the amorphous film. Moreover, a device made of glass/ITO/PEDOT:PSS-CuBi\({}_{2}\)O\({}_{4}\)/Ag was constructed, and its transport characteristics were studied from current-voltage (I-V) measurements.
## 2 Biochemistry and Genetic Regulation of Aroma and Flavor Compounds in _C. sativa_ L.
### Practical Implications for Breeding  
The associations and exclusivity of aroma and flavor-related traits in _C. sativa_ L. have important implications for breeding programs. Understanding the genetic and metabolic mechanisms underlying these relationships allows breeders to make informed decisions when selecting parent varieties.  
Breeding _C. sativa_ L. cultivars with an elevated terpene content while maintaining balanced cannabinoid levels necessitates a nuanced understanding of their shared biosynthetic pathways and potential metabolic interactions. Marker-assisted selection, which identifies genetic markers associated with desired traits, is a valuable tool in this process [72; 73; 74]. As an example, by targeting markers linked to high TPS activity or efficient cannabinoid biosynthesis, breeders can optimize multiple traits simultaneously.  
Managing trait exclusivity is important for achieving consistent variety profiles. For medical purposes, varieties with high cannabidiol (CBD) and specific terpene profiles are often chosen for their therapeutic potential, whereas recreational markets may favor high tetrahydroccannabinol (THC) levels combined with unique aromatic qualities. Recognizing the balance between these traits helps ensure that breeding programs meet their intended market objectives.  
Genomic and metabolomic technologies, such as clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9, can clarify cannabis trait relationships and fine-tune metabolic pathways [72; 75; 76]. Such technologies could enable the co-expression of previously exclusive traits. Further exploration will enhance the understanding of cannabis phytochemistry and drive innovations in breeding and cultivation.  
Maintaining robust agricultural properties--such as optimized nutrient and water use, temperature tolerance, pest resistance, and shortened growth cycles--remains critical even as breeders reconfigure cannabinoid and aroma profiles [77]. While modifications to flavor traits and the cannabinoid content can enhance the product quality, there is increasing evidence that these traits are intertwined with plant stress responses and the overall cultivation performance [78]. For instance, certain secondary metabolites may play roles in mediating plant defense mechanisms, potentially influencing resilience to environmental stresses. However, current breeding programs increasingly emphasize decoupling these traits to sustain or even improve core agronomic characteristics independently of aromatic and cannabinoid profiles [73; 77]. This separation is essential to ensure that an enhanced flavor or potency does not come at the cost of reduced cultivation robustness, thereby enabling growers to meet both market demands and production sustainability targets.
# Co-Efficient Vector Based Differential Distributed Quasi-Orthogonal Space Time Frequency Coding
## 3 Co-Efficient Vector Differential Procedure  
### Co-Efficient Vector Generator  
Our proposed co-efficient vector differential procedure is explained in this section. The design typically comprises three sub-systems, including the co-efficient vector generator, the mapper, and the feedback sub-system as illustrated in Figure 2. Given \(\Gamma m\) input bits, the source node constructed a length \(2^{\Gamma m}\) co-efficient vector set given by \(\mathbf{v}=\left\{[v_{1,1},\ldots,v_{\Gamma,1}]^{T},[v_{1,2},\ldots,v_{\Gamma,2}]^{T},\ldots,[v_{1,2^{2m}},\ldots,v_{\Gamma,2^{2m}}]^{T}\right\}\). The co-efficient vector set \(\mathbf{v}\) was made up of \(2^{2m}\) unit-length distinct vectors \([v_{1,d},\ldots,v_{\Gamma,d}]^{T},d\in\left\{1,2,\ldots,2^{2m}\right\}\); \(m\) was the spectral efficiency. Then, a pseudo-random one-to-one mapping scheme \(P(.)\) was defined for any \(m=log_{2}M\)\(M\)-PSK signal constellation such that the \(\Gamma m\) input bits were mapped onto \(\mathbf{v}\). Note that \(M\) was the constellation size. The \(\Gamma m\) input bits arriving at the encoder in the \(\left(k+1\right)_{th}\) block were used to select the corresponding \(\Gamma\times 1\) vector from the co-efficient vector set \(\mathbf{v}\). In other words, the co-efficient vector selected from \(\mathbf{v}\) was exclusively dependent on the input bits generated and the pseudo-random one-to-one mapping scheme \(P(.)\). Given any random set of input bits, the selected co-efficient vector could be written as \(\overline{v}_{k+1}=[v_{1},\ldots,v_{\Gamma}]^{T}\). The elements of \(\overline{v}_{k+1}\), given by \(\left\{v_{1},\ldots,v_{\Gamma}\right\}\), represented the valid transmitted information symbols that had to be recovered at the destination without CSI.  
This section may be divided by subheadings. It should provide a concise and precise description of the experimental results, their interpretation, and the experimental conclusions that can be drawn.
# Nationwide Flood Monitoring for Disaster Risk Reduction Using Multiple Satellite Data
## 3 Results  
Overall, this study confirmed the validity of the proposed method because the in situ hydrograph in Figure 3 and the high-resolution SAR data in Figure 4 show that the flood propagation of 2015 was in good agreement with the observation data. As a result, this method can be an effective tool with the capability of performing change detection for nationwide flood extent extraction directly related to risk changes, strongly depending on the temporal and spatial dynamics of hazard such as a maximum flood extent. The results of the flood dynamics are summarized as follows.
# Predictive Modelling for Blasting-Induced Vibrations from Open-Pit Excavations
## 6 Discussion  
The ANN model showed the best agreement with measured PPVs among eight prediction methods, including the globally used ISEE and USBM models. It would be due to using the most influential factors, which has the ability to reproduce and model the non-linear connections between input and output parameters, and to deal with noise. As shown in Figure 7, the seven influential factors have similar strengths of relation. It indicates that using these seven factors is more effective than using the only four factors which are included in the conventional empirical formula to predict PPVs. The complex connections between PPV and influential factors could be found in the comparison between the MLRA and MnLRA. When we developed these two models, they showed a statistical significance; however, the MLRA had a low R (0.495) while the MnLRA had a high R (0.927). The MAE from the MLRA showed about twice that of the MnLRA. These two models differ in their use of linear and non-linear relationships to explain PPV from influencing factors. Because of this difference, the MnLRA showed better predictive performance than the MLRA. It means that the relationships between the influential factors and PPV are non-linear. The ability to deal with noise could be verified by the prediction results about the biggest measured PPV, 4.58 cm/s, which is over 17 times the average measured PPVs, 0.26 cm/s. The prediction results from the ANN, EF-1, EF-2, EF-3, ISEE model, USBM model, MLRA, and MnLRA were 4.04, 8.7, 6.3, 2.72, 5.43, 2.25, 0.81 and 3.45 cm/s, respectively. The prediction results from the ANN model showed the closest to the measured PPV. It implies that the ANN has an excellent ability to deal with noise.  
EF-2 showed the worst performances at MAE and RMSE and it would be due to its applicability. EF-2 is suitable for road construction sites because it was developed using only blasting datasets from road construction sites. These results mean that applying the conventional representative formula for a preliminary blasting design from road constructions has a limitation in applying it to other open-pit blastings. Therefore, a new alternative prediction equation is required. EF-3 which was developed using datasets from 50 diverse open-pit blasting construction sites would be suitable as the alternative prediction equation since it has the same form as EF-2, and it showed better predictive performances than EF-2.  
The proposed model has been applied only to open-pit blasting construction sites. Future studies of PPV prediction models such as ANN model and EF-3 will include blasting records from underground caverns, tunnels, and mines as well to ensure the prediction models be generally applicable to any region and type of blasting.  
Figure 7: Sensitivity analysis of the influential factors.