esa-sceva/satcom-chunk-collection huggingface dataset:
# The Non-Linear Impact of Industry 4.0 on Carbon Emissions in China's Logistics Sector
## 3 Results
### Main Findings  
The major findings indicated a significant inverted U-shaped relationship between Industry 4.0 adoption and carbon emissions. Specifically, the results showed that an initial increase in the Industry 4.0 index led to a 10% rise in emissions, followed by a 15% reduction as the index reached higher levels (Table 2). This trend is visually represented in Figure 2, which illustrates the turning point at an index level of 3.5. Additionally, the impacts of the IoT and AI on reducing emissions were found to be more substantial than that of automation technologies, which initially increased emissions due to high energy consumption (Table 3). More specifically, this relationship is portrayed by an inverted U-shaped graph. The initial stage of implementing Industry 4.0 tends to result in carbon emissions rising. Poorly optimized transportation and often not fully assessed demand contribute to CO\({}_{2}\) discharge [16; 17]. Nevertheless, with the deepening of Industry 4.0 technology usage in the logistics processes, the curve flattened out. Upon investigation, it was discovered that once the yield of technological advancement reached a certain point, benefits from Industry 4.0 started to manifest, including improvement in operational efficiency, maximization of the supply chain, and the saving of electricity. These improvements resulted in a substantial fall in carbon dioxide emissions, which suggests that Industry 4.0 can indeed boost sustainable practices in the logistics sector in the long run. This outcome therefore implies that the strategic and staged incorporation of Industry 4.0 technologies into practice is vital in ensuring sustainable long-term achievements (Table 2) (Figure 2). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variable** & **Observations** & **Mean** & **Standard Deviation** & **Minimum** & **Median** & **Maximum** \\ \hline Logistics Carbon & & & & & & \\ Emissions (loc2) & 270 & 2.883 & 0.686 & 0.875 & 2.937 & 4.251 \\ \hline Industry 4.0 Index (ind4) & 270 & 0.239 & 0.141 & 0.087 & 0.195 & 0.717 \\ \hline Production Efficiency & 270 & 3.076 & 0.699 & 1.152 & 3.157 & 4.459 \\ \hline Business Output Scale & 270 & 8.393 & 0.91 & 6.267 & 8.406 & 10.327 \\ \hline Information Integration & 270 & 6.612 & 1.059 & 3.877 & 6.531 & 9.708 \\ \hline Industrial Structure & 270 & 0.362 & 0.062 & 0.15 & 0.376 & 0.464 \\ \hline Fixed Asset Investment & 270 & 7.025 & 0.774 & 4.608 & 7.035 & 8.638 \\ \hline Energy Structure & 270 & 0.523 & 0.109 & 0.037 & 0.542 & 0.685 \\ \hline Degree of Openness & 270 & 17.67 & 1.548 & 13.208 & 17.634 & 20.811 \\ \hline Per Capita GDP & 270 & 10.743 & 0.444 & 9.631 & 10.695 & 11.781 \\ \hline \hline \end{tabular} Explanation: This table summarizes key data variables, showing their distribution and central tendencies. It helps set the stage for further analysis by providing a basic overview of the data.  
\end{table} 
Table 1: Descriptive Statistics of Key Variables (2011–2019).
## 2 The IoV Scenario and Use Cases
### Use Cases
#### 2.2.2 Advanced Driving  
The objective of advanced driving scenarios is providing the vehicles with the ability to interact with each other aiming at safer traveling, collision avoidance, and improvement of traffic efficiency. Differently from vehicle platooning, advanced driving assumes larger inter-vehicle distances and lacks a leader vehicle. All vehicles interact to announce driving maneuvers, such as slowing down, speeding up or lane changing, and exchanging relevant data for cooperative perception. The required reliability of communications is set to 99.999% for operations such as emergency trajectory alignment and 99.99% for most of the rest critical operations. Depending on the scenario, the maximum latency ranges from 3 ms for emergency alignment trajectory, to 10-15 ms for cooperative lane exchange, or 100 ms for the rest of scenarios.
## 5 Modeling of IBC Identification  
This section describes the modeling of IBC identification conducted in the Jupyter Notebook software 7.0.8 [36] using the NumPy library [37] and the KNN algorithm. Jupyter Notebook is an interactive browser-based environment that includes a convenient computer navigator and consists of fields that are filled with text information or Python 3.11.7 code. Jupyter Notebook is used for data analysis. NumPy is an open-source tool designed for array computations. KNN is a classification algorithm based on the assumption that objects located close to each other in the feature space have similar values of the target variable or belong to the same class.  
There are three main metrics for calculating the distance between objects: Euclidean distance, Manhattan distance, or other distance metric.  
After removing unnecessary data from \"all_measurements_by_freq_filtered dataset\" from [21], we obtained modeling Table 1. The table only left the data of the first experiment and a frequency of 12.5 MHz. The table contains data, from 616 measurements made on 30 subjects, and several columns:  
* subject_id--unique subject identifiers.
* distance--distance between the transmission and reception points.
* rx_gain_50--receiver gain using a 50 \(\Omega\) load resistance.
* rx_gain_1M--receiver gain using a 1 m\(\Omega\) load resistance.  
This table was loaded into NumPy (Figure 8) and divided into a measurement matrix and a subject vector. Figure 9 shows the measurement matrix, which has a size of 616 rows and three columns: distance, rx_gain_50, and rx_gain_1M. Figure 10 shows the subject vector containing the subject identifiers from the subject_id column.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Subject\_id** & **Distance** & **Rx\_gain\_50** & **Rx\_gain\_1M** \\ \hline  
\(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) \\ \hline \end{tabular}
\end{table}
Table 1: Modeling table (out of 616 rows).  
```
defdist_one_loop(M_random): dist=np.zeros(((M_random.shape[0],M.shape[0])) for i,m_random in enumerate(M_random): m_len=M-m_random dist[i,:]=np.sqrt((m_len**2).sum(axis=1)) returndist dist=dist_one_loop(M_random) print(dist)
```  
Then, we formed an array of class labels for a given sample of vectors. The subject vector was used to obtain the labels.  
Ten modeling attempts were conducted, with 10 random values selected in each. As a modeling result, 100 out of 100 values were correctly identified; therefore, the achieved identification accuracy was 99.9%. The modeling was carried out on cleaned and labeled data, so this is an ideal case. In a practical case, the identification accuracy can be as low as 98.5%, as indicated in [20].  
It is also necessary to mention that, based on regression analysis, the channel amplification is influenced by the characteristics of the human body when using a load resistance of 50 \(\Omega\) (rx_gain_50) (Figure 11). In contrast, when a load resistance of 1 m \(\Omega\) is employed, the characteristics of the human body have a negligible effect on channel amplification (Figure 12).  
Figure 11: Dependence of channel amplification rx_gain_50 on weight.
# Application of Three Metaheuristic Techniques in Simulation of Concrete Slump
###### Abstract  
Slump is a workability-related characteristic of concrete mixture. This paper investigates the efficiency of a novel optimizer, namely ant lion optimization (ALO), for fine-tuning of a neural network (NN) in the field of concrete slump prediction. Two well-known optimization techniques, biogeography-based optimization (BBO) and grasshopper optimization algorithm (GOA), are also considered as benchmark models to be compared with ALO. Considering seven slump effective factors, namely cement, slag, water, fly ash, superplasticizer (SP), fine aggregate (FA), and coarse aggregate (CA), the mentioned algorithms are synthesized with a neural network to determine the best-fitted neural parameters. The most appropriate complexity of each ensemble is also found by a population-based sensitivity analysis. The findings revealed that the proposed ALO-NN model acquires a good approximation of concrete slump, regarding the calculated root mean square error (RMSE = 3.7788) and mean absolute error (MAE = 3.0286). It also outperformed both BBO-NN (RMSE = 4.1859 and MAE = 3.3465) and GOA-NN (RMSE = 4.9553 and MAE = 3.8576) ensembles.  
Keywords:concrete; slump; neural computing; ant lion optimizer +
Footnote †: journal: Applied
# Review of Land Surface Albedo: Variance Characteristics, Climate Effect and Management Strategy
## 3 Variance Characteristics of Surface Albedo for Essential Land Types and Scales  
Surface albedo generally varies by land cover type for natural (e.g., wildwood) and artificial surfaces (e.g., buildings) [70; 71] and is also sensitive to various factors besides atmospheric [72; 73] and cloud [74] conditions, such as soil-vegetation [66; 67; 75; 76], snow [77; 78; 79] BRDF characteristics, topography [80], diurnal asymmetry [81] and spatial resolution [82]. Some studies also focus on surface albedo across mixed land covers, special objects and hotspot regions.
## 2 Description of Compressor and Computational Considerations
### Computational Considerations
Usually, a gird independent study can give the proper mesh number of simulations, and we adopted the grid convergence index (GCI) method [38]. Three sets of grids were used in our verification, and their numbers were N1 = 10 million, N2 = 6.4 million, and N3 = 3 million. The corresponding discharge total pressures were \(\Phi_{1}\) = 6,823,850 Pa, \(\Phi_{2}\)= 6,824,503 Pa, \(\Phi_{3}\) = 6,830,380 Pa, respectively. The results of the grid convergence study based on the GCI method are shown in Table 4. The GCI\({}^{21}\) and GCI\({}^{32}\) were less than 1%, indicating that the solutions were within the asymptotic range of convergence, and the exact solution was approximately obtained. \begin{table}
\begin{tabular}{c c c} \hline
**Item** & **Symbol** & **Value** \\ \hline Grid refinement ratio between & \(r^{21}\) & 1.16 \\ grids 1 and 2 & & \\ \hline Grid refinement ratio between & \(r^{32}\) & 1.28 \\ grids 2 and 3 & & \\ \hline The order of convergence & \(p\) & 7.8064 \\ \hline The extrapolated values & \(\Phi_{ext}^{21}\) & 6,823,552 (Pa) \\ between indicators 1 and 2 & \(\Phi_{ext}^{32}\) & 6,823,553 (Pa) \\ \hline Approximate relative error & \(e_{a}^{21}\) & 0.0096\% \\ between indicators 1 and 2 & \(e_{a}^{32}\) & 0.0861\% \\ \hline Extrapolated relative error & \(e_{ext}^{21}\) & 0.00463\% \\ between indicators 1 and 2 & \(e_{ext}^{32}\) & 0.01393\% \\ \hline \end{tabular}
\end{table} 
Table 4: Grid convergence study using discharge total pressure as an indicator. Figure 3: Meshes display of (**a**) the radial inlet, (**b**) the first stage impeller, and (**c**) the return channel. Notably, a gird independent study for the whole compressor was verified with experimental data in previous research [39]. Thus, in subsequent calculations, the 6.4 \(\times\) 10\({}^{6}\) elements were employed with minimum orthogonality of 23.45\({}^{\circ}\). The number of elements for the four simulation domains was as follows. The radial inlet contained 3.2 \(\times\) 10\({}^{6}\) elements, the first stage impeller had 0.8 \(\times\) 10\({}^{6}\) elements, the vaneless diffuser contained 0.3 \(\times\) 10\({}^{6}\) elements, and finally, the return channel contained 2.1 \(\times\) 10\({}^{6}\) elements. Meanwhile, the Y plus was less than 1, the max aspect ratio was less than 1000, and the max expansion ratio was less than 3 in the simulation domains. Thus, the meshes satisfied the requirement of the SST turbulence model and guaranteed good resolution of the viscous sub-layer.
# Electronic Properties of Electron-Deficient Zn(II) Porphyrins for HBr Splitting
## 3 Results and Discussion
### Computational Results
Increased electron density is indicated by a green surface, depleted electron density (i.e., a “hole”) is indicated by the blue surface. BTD acceptor. A larger electron density is seen in correspondence of the excitation at 2.22 eV, whereas the EDDMs associated with the Soret band do not show substantial involvement of the BTD linker but remain mainly confined within the tetrapyrrolic ring, probably due to the inductive effect caused by the perfluorinated rings. The electron transfer rate constant from the excited dye to the semiconductor can be conveniently described within the framework of the semiclassical Marcus Model, according to [33]:  
\[\mathrm{k_{et}}=\frac{2\pi}{\hbar}\mathrm{H_{RP}^{2}}(4\pi\mathrm{\alpha kT})^ {-1/2}\exp{(-\frac{\left(\lambda+\Delta G^{0}\right)^{2}}{4\mathrm{k}\mathrm{T }})} \tag{7}\]  
\[\mathrm{H_{RP}}=\langle\mathrm{\psi_{R}}|\mathrm{H}|\mathrm{\psi_{P}}\rangle \tag{8}\]  
where \(\mathrm{H_{RP}}\) is the electronic matrix element determined by the wavefunctions of the reactant and the product of the electron transfer reaction where the electron can reside either in the excited molecular sensitizer in the R = reactant state or in the semiconductor in the P = product state; \(\Delta G^{0}\) is the free energy for electron injection and \(\lambda\) is the reorganization energy associated with the electron transfer. In the most favorable thermodynamic case, where \(\Delta G^{0}\) = \(-\lambda\), the electron transfer rate will be controlled by the electronic coupling \(\mathrm{H_{RP}}\). From the computational results, we can conclude that probably neither porphyrin has optimal electron/hole spatial distribution of the excited state to perform efficient injection into the n-type semiconductor, since, as a result of the excitation, the shift of electron density on the linker arm, which connects the sensitizer to the semiconductor surface, is comparatively small with respect to that remaining on the porphyrin ring, which is relatively remote from the SnO\({}_{2}\) surface. With this limitation present, the calculations also suggest that photoinduced charge injection by **1** should be more efficient than in the case of **2**, since, when population of the lowest singlet states is achieved, an excess of electron density is created on the BTD pendant in relative proximity to the semiconductor surface. The n-type sensitization capability of these dyes will be, thus, experimentally assessed in the forthcoming section.
## 3 Analytical Solution for the Inviscid Model with Constant Source Term  
### Analytical Solution  
For the inviscid model where \(\
u=0\), the free interface model (3)-(6) can be written  
\[\partial_{t}U(t,Z)+S(t,Z)=0\quad\text{ for all }Z\in(b(t),h), \tag{12}\]  
with the following boundary condition for all \(t>0\),  
\[U(t,b(t))=0, \tag{13}\]  
and with the initial condition (5) and the static equilibrium condition (6). Moreover, if the source term is chosen constant and uniform as in Equation (10), i.e.,  
\[S(t,Z)=S:=g\cos\theta(\tan\delta-\tan\theta)\geq 0, \tag{14}\]  
with \(\mu_{s}=\tan\delta\), \(\theta\leq\delta\), then we can infer an analytical solution. Specifically, the solution to Equations (5), (12) and (13) is given by  
\[U(t,Z)=\max\Bigl{(}U^{0}(Z)-St,0\Bigr{)}\quad\text{ for all }Z\in[0,h]. \tag{15}\]  
Equation (15) shows that the velocity profile (at all times when a flowing layer exists) has the same shape as the initial velocity profile: it is just shifted towards decreasing velocities at constant speed \(S\) and clipped below the value \(0\). Note that the velocity profile only depends on \(g\), \(\theta\), \(\delta\) through \(S\) in (14). If \(S=0\), the solution is steady, whereas if \(S>0\), the velocity decreases with time until the flow stops. Furthermore, the interface position \(b(t)\) results from the following implicit equation:  
\[U^{0}(b(t))=St. \tag{16}\]  
This equation has a unique solution in \([b^{0},h]\) for all times \(t\leq t^{\text{stop}}\), where \(t^{\text{stop}}\) is the time when the whole mass stops, defined by  
\[t^{\text{stop}}=\frac{U^{0}(h)}{S}=\frac{U^{0}(h)}{g\cos\theta(\tan\delta-\tan \theta)}. \tag{17}\]  
The complete stopping of the flow occurs at \(t=t^{\text{stop}}\). For all times \(t>t^{\text{stop}}\), the velocity \(U\) can be extended by setting \(U(t,Z)=0\) for all \(Z\in[0,h]\), and \(b(t)=h\).
# ChurnKB: A Generative AI-Enriched Knowledge Base for Customer Churn Feature Engineering
## 2 Background  
### Customer Journey, Cognitive Status, and Behaviours  
The customer journey encompasses the entire experience a customer has with a business, including interactions at various touchpoints (i.e., points of interaction) such as purchasing and post-purchase services [1]. Optimizing these touchpoints is essential in enhancing customer experience. Customer service, as a key component of the journey, involves communication with customers, follow-ups after sales, and gathering qualitative data on customer sentiments. Combining these data with quantitative factors provides valuable insights into customer feedback and trends [1].  
The customer journey significantly influences customer cognitive status, satisfaction, and overall churn risk [1,21]. Meanwhile, customer behaviour refers to the activities and decision-making processes customers undergo when purchasing and consuming products [22; 23; 24]. Various factors, including personal attributes (e.g., gender, age, education, and income), psychological influences (e.g., motivation, perception, and attitudes), social elements (e.g., family and reference groups), and cultural aspects (e.g., social class), shape customer cognitive status and behaviour [25]. Emotional responses such as pleasure, fear, anger, and sadness further impact the experiences and purchasing decisions of customers, ultimately influencing their likelihood of staying with or leaving a business [26].
## 2 Methods  
### COP Estimation for Thermoacoustic Devices  
In the context of the efficiency of transferring acoustic energy from the speaker to the environment, the \(COP\) and acoustic power are related [17; 18] in two transformations. Firstly,  
Figure 1: Structure diagram of a thermoacoustic refrigerator and selected five parameters (\(D,X_{s},\Delta T_{m},B,L_{s}\)). an electrodynamic driver converts the electrical power (voltage times current) to acoustic power. Secondly, the acoustic power (pressure times velocity) is converted to a gradient temperature \(\Delta T_{m}\) and heat flow \(Q_{c}\). [PERSON] (2016) contributed foundational knowledge and methodologies for analyzing thermoacoustic systems, including the introduction and application of the \(COP\) to assess the efficiency of these refrigerators, and [PERSON] built upon [PERSON]'s principles, expanding the analysis of the \(COP\) in thermoacoustic devices by investigating the effects of various design parameters and operational conditions on system performance. The performance of a thermoacoustic system depends strongly on the characteristics and properties of the porous medium (referred to in the literature as the stack) used to exchange heat with the working fluid; see Figure 1.  
For the development of a thermoacoustic refrigerator, the basic theory and equations for linear standing-wave thermoacoustic systems have been extensively developed by [PERSON] (2017, 2018). The term \"regenerator\" or stack is used when the pore size of the porous element is small compared to the depth of thermal penetration. The stack is contained inside the resonator tube; see Figure 1. The thermal penetration depth refers to the layer around the regenerator where the thermoacoustic phenomenon occurs. The thermal \(\delta_{x}\) and viscous \(\delta_{v}\) penetration depths are defined as follows:  
\[\delta_{x}=\sqrt{\frac{2K}{\rho c_{p}\omega}} \tag{1}\]  
and  
\[\delta_{v}=\sqrt{\frac{2\mu}{\rho\omega}} \tag{2}\]  
where the following apply:  
* \(\omega=2\pi f_{0}\) is the angular frequency of the sound wave; \(f_{0}\) is the design frequency;
* \(\mu,\rho,\,K,\) and \(c_{p}\) are the viscosity, density, thermal conductivity, and isobaric specific heat of the gas, respectively. The performance of the thermoacoustic regenerator depends on many parameters. [PERSON] and [PERSON] (1973) were the first to apply the similarity principle to thermoacoustic equations (continuity, motion, and heat transfer) written in their dimensionless form. The dimensionless cooling power obtained from the similitude \(Q_{cn}=Q_{c}/P_{m}aA\) and the acoustic power \(W_{n}=W/P_{m}aA\) (where \(P_{m}\) is the mean pressure, \(a\) is the sound velocity, and \(A\) is the cross-sectional area of the stack proposed by (1973)) are mentioned by [PERSON] et al.
## 6 Tephritid Sexual Chemoecology: Real-World Applications and Challenges
### Semichemical-Based Tools to Enhance the Sterile Insect Technique  
The identification and functional characterisation of tephritid semiochemicals may be beneficial for SIT applications and can also favour the integration of SIT and MAT to maximise the efficacy of pest management campaigns.  
In SIT programs, sterilised males are released in the field to mate with wild females and induce sterility and hence no viable egg production [515]. Typically, sterile males are release as immature adults, which have to survive until sexual maturation, localise females and achieve copulation for SIT programs to be successful [516]. Thus, survival, dispersal capacity and mating competitiveness, for which male signalling is essential [329; 517], are key factors that have to be fulfilled. Mating-enhancing semicomechicals have been widely described in several tephritid species and include both plant-derived compounds and synthetic chemicals, such as ME, Cue-Lure, RK, ginger root oil (GRO), citrus and guava fruit volatiles, and manuka oil (see [518; 519] for reviews, and [458]).  
SIT campaigns are more effective when pest population density is reduced before the release of sterile males. MAT applications, as well as inundative releases of biological control agents, have the potential to reduce the density of feral population size and hence to precede the implementation of the SIT. In _Bactrocera_ species, the pre-release exposure of males to plant-derived semiochemicals and synthetic lures has been shown to reduce their subsequent response to attractants used in MAT [35; 520]. Recently, the use of a diet containing RK fed to immature sterile _B. tryoni_ prior to release resulted in increased subsequent survival and reduced response to MAT [521]. These results are particularly promising for the implementation of SIT-MAT simultaneous used in the field, to both increase control effectiveness and reduce operational costs.