esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 AI's Benefits for Business and Peculiarities for SMEs
### Technological Determinant  
Among the technological factors that can limit or enable AI adoption in organizations, several have been extensively studied. These include comparative advantage, compatibility,  
Figure 1: Illustrative TOE–DOI Dimensions, factors, and attributes for AI adoption in SMEs.  
complexity, testability, traceability, ease of use, perceived usefulness, intensity of information, and uncertainty [62; 68]. In the context of SMEs, the most relevant technological factors identified are compatibility, complexity, and competence.  
**Compatibility** refers to the degree to which AI solutions align with existing systems (hardware, software, technological frameworks, and digital resources) and how well they integrate with current production processes [59; 62]. The positive impact of compatibility on the adoption of AI in SMEs has been supported in various studies [6]. However, it is important to emphasize that we are talking about the perceived compatibility of AI within a specific organizational context. When perceived compatibility is low, businesses are faced with two difficult choices: adapting the AI system or reengineering their internal processes. Both scenarios involve high costs and time-consuming transitions [11; 69].  
**Complexity tolerance--**the degree to which an organization perceives itself ready to handle the technological sophistication of AI--is one of the most critical barriers to adoption. Research has shown that higher perceived complexity negatively impacts AI adoption decisions [70; 71]. As a result, it is essential for SMEs to develop strategies that strengthen internal confidence and reduce fear or resistance associated with AI complexity [62]. If managers believe that the adoption of AI will require excessive effort or disruption, their support for such initiatives is likely to diminish [59].  
**Competence** is defined here as the degree to which the existing technological resources of an SME are adequately prepared to support the adoption of AI [59; 72]. It is considered a fundamental determinant of successful AI integration, particularly in the SME sector [6]. Competence encompasses three critical dimensions: tangible resources, intangible resources, and data. Tangible resources refer to infrastructure components such as servers, middleware, storage, hardware, and networking [73]. Intangible resources include absorptive capacity--measured through prior technological experience [74], internal knowledge [75], and the presence of innovation strategies or digital transformation roadmaps [76]. Both types of resource play a vital role in enabling and maintaining AI integration.  
Finally, data competence forms the third pillar of technological preparedness. Data directly influences the effectiveness of AI and the level of trust in its outputs. The main challenges for SMEs often stem from inadequate data availability, poor data quality, and insufficient protection measures [77]. As emphasized by [78], robust data governance and management practices must become a strategic priority for SMEs looking to adopt AI technologies effectively.
# A Super-Resolution Reconstruction Driven Helmet Detection Workflow
## 3 Method
### Training  
The entire method is implemented on the workstation equipped with two NVIDIA RTX3090 GPU and Intel i9 CPU. All coding work is based on Python 3.7 and PyTorch 1.8. The SR module and detection module are first trained separately from scratch. Then the two modules are finetuned together, alternately. Specifically, in each iteration, one module will be frozen while the other one is updating, then vice versa. The initial learning rate of the SR and detection are 0.0001 and 0.01, respectively. The Adam optimizer is applied with the momentum 0.9 and the batch size is 2.
## 3 Results and Discussion
### Temperature Sensor  
Temperature is an essential parameter, an important indicator for health monitoring of the human body. Various types of stretchable temperature sensors have been developed; two types are conductive composites (by thermal expansion differences) and resistive conductors on stretchable elastomer substrates (by resistance changes on conductors following the temperature) [56; 57; 58; 59]. Conductive composites use various nanomaterials, including carbon nanotubes [60; 61], graphene [62; 63], conducting polymers [64; 65], and metal nanowires [58; 66; 67]. However, this type generally has a narrow temperature range, low accuracy, and slow response. [66] We used a wavy Au-SEBS electrode as a resistive temperature sensor. In Figure 5c, the Au-SEBS temperature sensor showed its temperature sensitivity (\(\sim\)0.6 \(\Omega\)/\({}^{\circ}\)C). It was comparable with other nanomaterial-based temperature sensors [56; 66], specifically with copper or silver nanowires. Figure 5d showed the correlation of the controlled temperature value we set on the hot plate machine and real-time temperature value fluctuation measured by an infrared thermal imaging camera. Basically, the heating stage we used could not keep constant temperature conditions, so real temperature conditions were continuously fluctuated. However, the Au-SEBS temperature sensor correctly followed the real temperature fluctuation trend measured by a thermal imaging camera, so it was shown that it can be worked as a reliable temperature sensor.
# The Identification and Assessment of Rail Corrugation Based on Computer Vision
## 4 Estimation of Rail Corrugation Period  
The corrugation is a type of wave-like rail surface defect that appears as a periodic texture pattern in the rail surface image. In general, the wavelength of corrugation is used to further analysis corrugation and mathematically, the wavelength is proportional to the period. Hence, it is meaningful to estimate the period of the corrugation. However, the corrugation rail image obtained from the field includes not only the corrugation area but also other regions (such as edge rust and normal surface area), as shown in Figure 8a. Therefore, the corrugation interval is first localized and then the corrugation period is estimated based on the combination of one-dimensional fast Fourier transform [39] and normalized distance matching function (FFT-NDMF) in the corrugation interval.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Precision (\%)** & **Recall (\%)** & **Accuracy (\%)** \\ \hline Gabor + SVM & 91.80 & 93.33 & 92.50 \\ AET & 97.00 & 96.50 & 96.80 \\ ME + SVM & 98.97 & 97.00 & 98.00 \\ The proposed & 99.67 & 98.34 & 99.00 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The comparison of detection performance between the proposed method and the baselines.
###### Abstract  
Thermoelectric power generation is a promising candidate for automobile energy harvesting technologies because it is eco-friendly and durable owing to direct power conversion from automobile waste heat. Because Bi-Te systems are well-known thermoelectric materials, research on \(\bf(Bi_{2})_{m}(Bi_{2}Te_{3})_{n}\) homologous series can aid the development of efficient thermoelectric materials. However, to the best of our knowledge, \(\bf(Bi_{2})_{m}(Bi_{2}Te_{3})_{n}\) has been studied through experimental synthesis and measurements only. Therefore, we performed density functional theory calculations of nine members of \(\bf(Bi_{2})_{m}(Bi_{2}Te_{3})_{n}\) to investigate their structure, phase stability, and electronic band structures. From our calculations, although the total energies of all nine phases are slightly higher than their convex hulls, they can be metastable owing to their very small energy differences. The electric transport types of \(\bf(Bi_{2})_{m}(Bi_{2}Te_{3})_{n}\) do not change regardless of the exchange-correlation functionals, which cause tiny changes in the atomic structures, phase stabilities, and band structures. Additionally, only two phases \(\bf(Bi_{2}Te_{3})\), BiTe) became semimetallic or semiconducting depending on whether spin-orbit interactions were included in our calculations, and the electric transport types of the other phases were unchanged. As a result, it is expected that \(\bf Bi_{2}Te_{3}\), \(\bf Bi_{2}Te_{3}\), and BiTe are candidates for thermoelectric materials for automobile energy harvesting technologies because they are semiconducting.  
energy harvesting; automobile; \(\bf(Bi_{2})_{m}(Bi_{2}Te_{3})_{n}\) homologous series; mixing energy; phase stability; electronic band structures; electric transport types; density functional theory calculations
## 3 Results  
Three clinicians completed the MWA clinical experiment. One of the doctors completed a puncture and adjustment of biological tissues using the observation from an ultrasound scanner. Another doctor operated an ultrasound scanner to measure the distance between the ablation antenna and thermometric antenna, and assisted the first doctor in completing the ablation antenna and thermometric antenna. The last doctor operated on MWA ablation equipment. He set the selected power and timing, and implemented heating of the ablation antenna. Before the experiment, biological tissue was placed flat on the operating table, and an ultrasound probe was used to move vertically downward on the tissue surface for observation. Then, the ablation antenna and thermometric antenna were pierced in parallel from the horizontal plane of tissue, respectively. The distance between two antennas was observed and measured using an ultrasound scanner. To attain a useful temperature and to protect thermometric antenna, two antennas were kept at a 2 mm distance. Finally, 50 W power and 60 s heating time were set. After heating was over, another 60 s was recorded as a cooling period.  
To test the reliability of the numerical model, it was compared with thermal law obtained from MWA clinical experiment. In the numerical model, starting from the central axis of the ablation antenna, a series of thermometric points were established at a 2 mm distance (z = 10 mm-z = 22 mm). All thermometric points were consistent with the clinical operation method. They were first heated at 50 W microwave power for the 60 s and kept a 60 s cooling period. Figure 2 records the temperature changes in all thermometric points in 120 s. The red dotted line represents MWA temperature. It can be seen that the maximum temperature obtained by MWA is almost the same as the maximum temperature obtained by the numerical model at 8 mm and 10 mm. So, it can be judged that the actual thermometric position of the thermometric antenna is approximately within this interval. During the heating process, the surface water evaporation process of the isolated tissue will be strengthened, causing the boundary conditions to change, which may result in the highest temperature of MWA to be reduced within a small range. From these conclusions, the temperature distribution of the numerical model has similarities with the MWA experiment. A numerical model has reliability under ideal conditions.
# Experimental and Numerical Study of Orifice Coefficient of Cargo Tank Design of LNG Vessels
## 4 Numerical Simulation
### Governing Equations  
The fluid flow through the crack was used to simulate the flow of single-phase fluid in a pipeline and crack section. A CFD model was constructed using the volume of fluid (VOF) method for the leakage simulation. The VOF model was used in a RANS CFD analysis and included pressure boundary conditions on the fluid flow [18]. The governing equations for the fluid flow use the following mass and momentum conservation equations, expressed in Equations (2) and (3).  
\[\frac{\partial\rho}{\partial t}+\frac{\partial}{\partial x_{i}}(\rho u_{i})\ =\ 0 \tag{2}\]  
\[\frac{\partial}{\partial t}(\rho u_{i})+\frac{\partial}{\partial x_{j}}(\rho u _{i}u_{j})\ =\ -\frac{\partial p}{\partial x_{i}}+\mu\frac{\partial^{2}u_{i}}{ \partial x_{j}\partial x_{j}}+b_{i} \tag{3}\]  
where \(b_{i}\) is the external body force; \(p\) is pressure; and \(u_{i}\) and \(x_{i}\) are the Cartesian velocity and coordinate tensors, respectively. Only one momentum conservation equation is used for both fluids. The homogenous CFD approach uses the dynamic viscosity (\(\mu\)) and density (\(\rho\)) of the fluid mixture calculated using the volume fraction (\(r_{k}\)) with the constraint \(\sum r_{k}=1\), as expressed in Equations (4) and (5).  
\[\rho\ =\ \sum\rho_{k}r_{k} \tag{4}\]  
\[\mu\ =\ \sum\mu_{k}r_{k} \tag{5}\]  
The standard k-e turbulence model was used in the CFD model to reflect the turbulence of the fluid flowing through the crack specimen.
# Dynamic Response of a Heavy-Haul Railway Tunnel's Bottom Structures in Hard Rock
## 6 Conclusions  
In this study, to examine the coupling effect of the static rock pressure and dynamic heavy-haul train load, a train-tunnel-surrounding rock numerical model was established. Then, the dynamic distribution and axle-load influence of the hard-rock tunnel were obtained. We drew the following conclusions:  
1. Like soft-rock tunnels, the bottom structure below the rail and side drain are the two main vulnerable positions of the hard-rock tunnel. The maximum principal stress caused by the static rock pressure was 1.27 MPa, which emerged in the lower part of the side drain. The maximum principal stress amplitude caused by the dynamic train load was 129.3 kPa, which emerged right below the rail.
2. Under the 30 t train load and surrounding rock pressure coupling, the maximum principal stress amplitudes of the tunnel structure and base rock were 129.3 kPa and 43.0 kPa, respectively, which were obviously greater than that of soft rock [27; 31; 36; 49].
3. In hard-rock tunnels, the probability of basement disease under the rail may be higher than that in soft-rock tunnels due to its greater dynamic response and thinner structure as compared to a soft-rock tunnel.
4. When the axle load increased by 1 t, the dynamic amplitude of the structure's maximum and minimum principal stresses increased by about 4.14 kPa and 7.50 kPa, and the base rock's maximum and minimum principal stresses increased by about 1.33 kPa and 3.61 kPa.
5. When considering the rock pressure, the dynamic response of the tunnel bottom structure and base rock were all greater than that without considering the rock pressure. The maximum principal stress amplitudes of the tunnel structure and base rock increased by 28.4% and 56.4%, respectively.  
Conceptualization, C.L.; methodology, C.L.; formal analysis, C.L. and Q.T.; writing--original draft preparation, C.L.; writing--review and editing, C.L., Q.T., B.W., Q.W., and Y.Y.; supervision, B.W.; funding acquisition, Q.T. and B.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science and Technology Research Project of Jiangxi Provincial Department of Education (GJJ204611), Science and Technology Project of Jiangxi Transportation Department (2022H0031), National Science of China (52168055), Natural Science Foundation of Jiangxi Province (20212 ACB204001) and \"Double Thousand Plan\" Innovation Leading Talent Project of Jiangxi Province (jxsq2020101001), Startup funding for doctoral research of East China University of Technology (DHBK2021010).  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflict of interest.
## 4 Simulation and Discussion
### Reconstruction Performance versus the Sparsity Level
#### 4.3.3 Performance of FRSVssMP and SAMP versus Variable Step Sizes  
Additionally, to illustrate the influence of variable step sizes on reconstruction performance, FRSVssMP were compared with SAMP in different variable step sizes \(S\), i.e., \(5\) and \(10\). Figure 7a,b demonstrate success rates versus variable step sizes for synthetic and real signals. The horizontal axis represents the different sparsity level, and the vertical axis shows the corresponding success rates (in percentages).  
We can observe that, as sparsity levels become larger, the success rates decrease. For a small \(K\), there is no significant difference among different variable step sizes in terms of reconstruction success rates. However, for a large \(K\), as the variable step size become larger, the success rates decrease obviously. The reason for this lies in that a small variable step size approximates the actual sparsity level much more precisely. It can be concluded that, for both types of sparse signals, FRSVssMP achieves better success rates compared to SAMP with the same step size (\(5\) or \(10\)).
## 3 Results
### Longitudinal Regions of Strong Earthquake Occurrences and Geomagnetic Pole Locations  
In Figure 8, we show the aspatial distributions of 14 epicenters of (M \(\geq\) 7.5) earthquakes (from Table 2) that occurred around the globe in 2013 and 2015 (circles); color symbols with digits in the Northern and Southern Hemispheres show the locations of the magnetic poles for the epochs of 1975-2015 for three options: 1--geomagnetic poles, 79.5\({}^{\circ}\)N, 71.4\({}^{\circ}\)W and 79.5\({}^{\circ}\)S, 108.6\({}^{\circ}\)E; 2--magnetic dipoles 80.3\({}^{\circ}\)N, 114.9\({}^{\circ}\)W and 64.9\({}^{\circ}\)S, 138.4\({}^{\circ}\)E; 3--corrected geomagnetic poles 81.46\({}^{\circ}\)N, 82.33\({}^{\circ}\)W and 74.18\({}^{\circ}\)S, 126.19\({}^{\circ}\)E.  
It can be seen from Figure 8 that the longitudinal regions where M \(\geq\) 7.5 earthquakes occurred in 2013 and 2015 (America and East Asia) are closely confined to the longitudes of the locations of the magnetic poles. Earthquake epicenters on the American continent are centered around the longitudes of the locations of the magnetic poles in the Northern Hemisphere. In contrast, earthquake epicenters in East Asia are centered around longitudes of the locations of the magnetic poles in the Southern Hemisphere (color quadrants, circles, and triangles).  
To confirm once more the reality that the longitudinal regions of the occurrence of strong earthquakes are confined to the longitudes of the magnetic pole locations, we present in Figure 9 the histogram of the distribution of the number of (M \(\geq\) 7.5) earthquakes that occurred around the globe in 1973-2017 (212 events) dependent on the longitude. Here, blue quadrants, red circles, and purple triangles mark the longitudes of the magnetic poles' locations in the Northern and Southern Hemispheres, respectively, as discussed above. It can be seen in Figure 9 that, around the globe, the longitudinal regions of the strongest earthquake occurrences are centered around the longitudes of the magnetic poles' locations.  
Figure 8: Spatial distribution of 14 epicenters for M \(\geq\) 7.5 earthquakes that occurred around the globe in 2013 and 2015 (circles); color symbols with digits indicate the location of magnetic poles in the Northern and Southern Hemispheres for three options: 1—geomagnetic poles, 2—magnetic dipoles, and 3—corrected geomagnetic poles.
# Mutual Information, the Linear Prediction Model, and CELP Voice Codes
## 5 Cascaded Signal Processing  
Figure 5 shows a cascade of \(N\) signal processing operations with the estimator blocks at the output of each stage as studied by [PERSON] [19]. He used the conditional mean at each stage and the corresponding conditional mean squared errors to obtain a representation of the distortion contributed by each stage. We analyze the cascade connection in terms of information theoretic quantities, such as mutual information, differential entropy, and entropy rate power. Similar to [PERSON], we consider systems that have no hidden connections between stages other than those explicitly shown. Therefore, we conclude directly from the data processing inequality [16] that  
\[I(X;Y_{1})\geq...\geq I(X;Y_{N-1})\geq I(X;Y_{N})\geq I(X;\widehat{X}). \tag{7}\]  
Since \(I(X;Y_{n})=h(X)-h(X|Y_{n})\), it follows from Equation (7) that for non-negative \(h(\cdot)\),  
\[h(X|Y_{1})\leq...\leq h(X|Y_{N-1})\leq h(X|Y_{N})\leq h(X). \tag{8}\]  
Figure 4: Synthesis chain.  
For the optimal estimators at each stage, the basic data processing inequality also yields \(I(X;Y_{n})\geq I(X;\widehat{X}_{n})\), and thus \(h(X|Y_{n})\leq h(X|\widehat{X}_{n})\).  
These are the fundamental results, that additional processing cannot increase the mutual information.  
Now we notice that the series of inequalities in Equation (8) along with the entropy power expression in Equation (6) gives us the series of inequalities in terms of entropy power at each stage in the cascaded signal processing operations:  
\[Q_{X|Y_{1}}\leq...\leq Q_{X|Y_{N-1}}\leq Q_{X|Y_{N}}\leq Q_{X}. \tag{9}\]  
We can also write that  
\[Q_{X|Y_{n}}\leq Q_{X|\widehat{X}_{n}}. \tag{10}\]  
In the context of Equation (9), the notation \(Q_{X|Y_{n}}\) denotes the minimum variance when reconstructing an approximation to \(X\) given the sequence at the output of stage \(n\) in the chain [8].
# An Energy-Efficient Routing Protocol for Reliable Data Transmission in Wireless Body Area Networks
## 3 System Model
### Energy Consumption Model  
The energy consumption of nodes mainly comes from data collection, data transmission, data receiving and idle interception. Among them, the energy consumption of data transmission accounts for the main part. Because this paper mainly studies the communication between nodes, only the energy consumption of sending and receiving is considered, while the energy consumption of data collection and idle listening is neglected.  
In this paper, the proposed energy consumption model is used in [32]. In this model, the energy consumption for transmitting and receiving \(k\) bit data is expressed as follows:  
\[E_{rx}(k)=E_{elce}\times k \tag{1}\]  
\[E_{tx}(k,d)=E_{elce}\times k+E_{amp}\times k\times d^{2} \tag{2}\]  
where \(E_{rx}(k)\) denotes the energy consumed by receiving \(k\) bit data, \(E_{tx}(k,d)\) denotes the energy consumed by sending \(k\) bit data to the node at distance. \(E_{elce}\) and \(E_{amp}\) represent the energy consumed by the circuit when the node sends or receives data and the energy consumed by the power amplifier when sending data, respectively.  
If the initial energy of node \(i\) is \(E_{i}^{initial}\), the residual energy is \(E_{i}^{res}\), and the energy consumed is \(E_{i}^{com}\), then the quantitative relationship shows that:  
\[E_{i}^{con}=E_{rx}+E_{tx} \tag{3}\]  
\[E_{i}^{res}=E_{i}^{initial}-E_{i}^{con} \tag{4}\]