esa-sceva/satcom-chunk-collection huggingface dataset:
# An Exploratory Bibliometric Analysis of the Birth and Emergence of Industry 5.0
## 2 Background
### Industry 4.0  
The concept of Industry 4.0 was introduced at the 2011 Hannover Fair [22; 23]. Industry 4.0 generally refers to the fourth industrial revolution in manufacturing. The consulting firm McKinsey & Company highlights that \"Industry 4.0 is more than just a flashy catchphrase. A confluence of trends and technologies promises to reshape the way things are made\" [24] (p. 1). These trends and technologies include robotics, artificial intelligence, virtual reality, blockchain, big data analytics and the Internet of Things [25]. According to proponents of Industry 4.0, these new technologies can enable the transformation and automation of manufacturing processes and increase operational efficiency and organizational performance [24; 25; 26].  
In the decade since its introduction, the Industry 4.0 concept has attracted considerable attention both in academia and in the business world. The literature on Industry 4.0 has grown exponentially [15; 17; 27; 28] and many studies have shown that Industry 4.0 has become a popular and influential management trend across a wide range of industries and in many parts of the world [23; 29; 30; 31; 32; 33; 34; 35]. While Industry 4.0 started in manufacturing, it has become a much broader and universal approach, applied and adapted to a wide range of contexts such as healthcare (Healthcare 4.0), auditing (Auditing 4.0), and tourism/hospitality (Tourism 4.0) [23; 29; 36].  
Despite its widespread popularity, the Industry 4.0 concept is not without critics. Different authors have already started to criticize and question different aspects of Industry 4.0. For example, some critics have suggested that the ideas behind Industry 4.0 are not new and that similar approaches have been circulating in academic research for quite some time [28; 37]. Others have criticized the technocratic nature of the concept and its strong emphasis on technology and automation see, e.g., [38; 39; 40].  
Therefore, it is not surprising that a potential successor to Industry 4.0 has appeared in social media discourse and academic journals. As early as in 2014, [PERSON] et al. [41] observed that the label Industry 4.0 is \"reminiscence of software versioning.\" As [PERSON] [23] noted, using a version number in the label implies that the last word is not said, and it invites others to propose new versions (e.g., 5.0 or 6.0). For example, [PERSON] and [PERSON] [2] suggest that Industry 5.0 could be a \"prelude to the sixth industrial revolution.\"
## 3 Results and Discussion
### Non-Linear Dynamic Analysis Application  
In the non-linear dynamic analysis, after applying the accelerogram corresponding to the 2011 Lorca earthquake, significant displacements were generated, as shown in Figure 7. In the X-axis (Figure 7A), a displacement of 2.55 cm was recorded, exceeding the results obtained in the previous non-linear dynamic analysis. Similarly, in the \(Y\)-axis (Figure 7B), a notable deformation of 5.22 cm was observed, being a considerably high value.  
Figure 5: (**A**) Axis X. Viscous damping ratio 5%. (**B**) Axis X. Viscous damping ratio 25% (high ductility).  
Figure 6: (**A**) Axis Y. Viscous damping ratio 5%. (**B**) Axis Y. Viscous damping ratio 25% (high ductility).  
The Lorca earthquake, characterised by its highest accelerations in the north-south direction, represents the so-called strong direction of the event, with striking results. In this direction, a displacement exceeding 5 cm is observed, highlighting the magnitude of the phenomenon.  
Regarding the stresses, when analysing the Y direction, which corresponds to the strong direction of the earthquake, as illustrated in Figure 8, significant loads and tensions generated by the seismic event can be appreciated.  
The basal pillars record flexural moment values exceeding 771 kN\(\cdot\)m. Regarding the shear stress in the Z direction, it is depicted in Figure 9, illustrating the non-linear dynamic analysis of the discretised FEM during the Lorca earthquake in the north-south direction, focusing on the shear moment in Z.  
Figure 8: Non-linear dynamic analysis during the Lorca Earthquake in North-South Direction: Flexure in Y.  
Figure 9: Non-linear dynamic analysis during the Lorca Earthquake in North-South Direction: Shear in Z.  
The shears in the basal pillars reach approximately 392 kN. Based on the results presented, the shear stresses in the basal columns and the bending moments in the central columns are key factors influencing the seismic response of structures during high-intensity earthquakes. The shear and longitudinal reinforcement is imposed by the basal columns. Reinforced concrete structures are expected to suffer severe damage, although this will depend on the type of stress and location in the structure. Bending moments in the central columns can contribute to facilitating a failure of global stability by buckling in the structure. In addition, the P-delta effect can have an adverse effect by facilitating the formation of plastic hinges [39].  
Regarding the flexural moment in the Z direction, it is depicted in Figure 10, showing the non-linear dynamic analysis of the discretised FEM during the Lorca earthquake in the north-south direction, focusing on the flexure in Z.  
The flexural moments in the central pillars reach approximately 1322 kN.m. This notably high value is significant for a pillar within a building of this height.
## 1 Introduction
Although existing methods have proposed multi-scale architectures [18; 19] and adaptive topology learning independently [20], their integration remains inadequate. We decompose joint feature extraction into three sub-tasks: multi-scale human modeling, adaptive joint connectivity learning, and spatiotemporal feature extraction. Building on this decomposition, we introduce a single-scale mixed adaptive graph convolution module and achieve multi-scale modeling through the interconnection of different scales. This design enhances the network's ability to capture diverse joint coupling relationships under various motion patterns, thereby significantly improving its modeling capabilities. To summarize, the primary contributions of this study are as follows:  
1. We introduce joint torques as dynamic features and integrate them with kinematic information to predict future motion. Additionally, we analyze the respective roles of kinematic and dynamic features in enhancing motion prediction accuracy. 2. We propose a Multiscale Mixed-Graph Neural Network (MS-MGNN) which effectively extracts both kinematic and dynamic joint information to facilitate more accurate motion prediction. We demonstrate that our proposed model outperforms existing methods in predicting multiple motions of the Human3.6 dataset, achieving a reduction in prediction error by 9.1%, 12.2%, 10.9%, and 7.1% at 160 ms, 320 ms, 400 ms, and 560 ms, respectively.
## 2 Material and Methods
### Expression Analysis of ADGRG3 in AML  
The gene expression of ADGRG3 and its interacting genes in AML was analyzed using the publicly available GEO dataset GSE216738 [68; 69] accessed on 5 th December 2023, which includes normalized log2-transformed counts per million (CPM) for 506 AML samples. This dataset encompasses patients with varying ages, disease subtypes, and clinical stages, reflecting the heterogeneous nature of AML. The dataset specifically highlights the inflammatory state in the cancer microenvironment, making it highly relevant to the study of cancer and inflammation. A total of 29 genes were selected, including ADGRG3, genes from its protein-protein interaction (PPI) network, and key cytokines or inflammation-related genes such as IL8, IL-10, CSF3R, TNF, and CXCL8. The expression values of these genes were extracted from the dataset, and missing or non-numeric values were imputed with zero. To visualize the expression trends, the pheatmap package in R was used to generate a clustered heatmap, employing a custom color scale where white, red, and blue represented neutral (0), upregulated, and downregulated expressions, respectively. Hierarchical clustering was applied to explore co-expression patterns, and the heatmap displayed aggregated expression data across all 506 samples. This approach enabled a comprehensive analysis of the relationships between ADGRG3, its network, and inflammatory pathways in AML.
# Fuzzy-Based Control System for Solar-Powered Bulk Service Queueing Model with Vacation
## 4 Results and Discussion
### Fuzzy Numerical Results  
The arrival rate (\(\chi\)) and service rates (a, b, c) are considered trapezoidal fuzzy numbers (TFNs), and the corresponding fuzzified parameters are chosen as \(\chi=[3,3.15,3.56,4.78]\), \(a=[6,6.15,6.65,7.78]\), \(b=[10,12,26,10.6,11]\), and \(c=[7,7.25,7.75,8]\), respectively.  
For \(0\leq\alpha\leq 1\), the lower bounds and upper bounds corresponding to \(\chi\), \(a\), b, c are obtained as follows:  
\([(x_{1})_{a}^{LB},(x_{1})_{a}^{UB}]=[3+0.25\alpha,4.5-0.25\alpha]\), \([(x_{2})_{a}^{LB},(x_{2})_{a}^{UB}]=[6+0.25\alpha,7-0.5\alpha]\), \([(x_{3})_{a}^{LB},(x_{3})_{a}^{UB}]=[10+0.25\alpha,11-0.5\alpha]\),\([(x_{4})_{a}^{LB},(x_{4})_{a}^{LB}]=[7+0.25\alpha,8-0.5\alpha]\), respectively. The fuzzified parameters \((\chi,a,b,c)\) and fuzzified metrics \((EL_{s},EW_{s},TH)\) are reported in Tables 4 and 5, respectively.  
As the \(\alpha\)-level increases from 0 to 1, the performance metrics (service rate, cost measures) gradually stabilize, reflecting reduced uncertainty in system behavior. For instance, the waiting time and cost values decrease significantly at lower \(\alpha\)-cuts, indicating wider uncertainty, but converge at higher \(\alpha\)-cuts where confidence is stronger. This trend demonstrates how fuzzy control effectively narrows the decision space and improves reliability in system performance and cost estimation.  
We performed a sensitivity analysis by introducing \(\pm 10\)-\(20\%\) perturbations to fuzzified arrival/service rates, showing minimal degradation in key metrics, confirming robustness. Currently, rules are static but can be updated through heuristic tuning; real-time adaptation via reinforcement learning is a planned extension. The architecture is modular and scalable to multi-server or embedded environments with minor adjustments.
# Applications of Pulsed Light Decontamination Technology in Food Processing: An Overview
## 6 Effect on Food Quality Characteristics
### Effects on Enzymes  
PL has also been studied for its effects on enzyme activity. The PL processing has a great potential to inactivate the food quality degrading enzymes. Broad spectrum PL may be useful to inactivate enzymes like polyphenol oxidase (PPO) of vegetables, which is related to enzymatic browning in these. Thus, PL treatment could be a solution to enzymatic browning in cut fruits like apples. Studies by [PERSON] et al. [30] showed that by exposing 2-5 flashes of full spectrum PL treatment of 3 J/cm\({}^{2}\), potato slice browning could possibly be inhibited. On the contrary, [PERSON] et al. [15] reported PL treatment of mushroom induced some PPO activity leading to enzymatic browning due. [PERSON] et al. [30] showed that PL could also inactivate alkaline phosphatase found in various natural food products and milk. About 94% reduction in enzymatic activity of alkaline phosphatase was seen in PL treated milk [101]. In relation to pectin degrading enzymes, there was no significant effect on enzymes like pectin methyl esterase (PME) and polygalactouornase (PG) by PL treatment of tomatoes at 8 J/cm\({}^{2}\)[115]. Moreover, 20 days storage of both treated and untreated samples showed higher enzyme activity in the former and there was an approximately 40% reduction in the both enzymes' activity after 20 days. Contrastingly, a study has been done by [PERSON] et al. [153] shows that PG can be degraded by 90%after applying 128 J/cm\({}^{2}\) on enzyme solution samples (0.5 mg/mL, 20 mL) through the photochemical process. Also, lipase enzyme, which is responsible for hydrolysis of lipids, was reduced to 10% by intense PL treatment [154].
## 5 Results and Discussion
### Influence and Analysis of Frequency on Scribing Depth  
According to the corresponding data of laser power and scribing depth under different laser frequencies, the laser scribing depth under unit power is calculated; that is, the scribing depth/the laser power under different frequencies. The scribing depth per unit power corresponding to different frequencies is calculated, as shown in Figure 13.  
According to the trend line in Figure 11, the linear coefficient is 4.41 \(\times\) 10\({}^{-4}\). The laser frequency has little effect on the scribing depth. This result is also consistent with the theoretical model of laser scribing. It should be noted that the heat accumulation of the previous pulse cannot be completely eliminated with the increase of frequency, which will  
Figure 14: Relationship between laser frequency density and scribing depth.  
Figure 13: Accuracy of mathematical equations. (**a**) Accuracy of mathematical Equation (13); (**b**) Accuracy of mathematical Equation (14).  
increase the material temperature of the current laser scribing. When the laser parameters remain unchanged, the same laser influence will remove more materials.  
The laser frequency will also affect the scribing speed and accuracy based on the laser overlap rate. If the pulse overlap rate is not selected properly, it will lead to the phenomenon of \"sawtooth\" at the edge of the maskant layer, and the width of the scribing line cannot be guaranteed.  
The formula of laser scribing line accuracy, speed and frequency is shown in Equation (17). The overlap rate and residual height of ablation width are shown in Figure 15:  
\[\sqrt{1-(1-\Delta)^{2}}=\
icefrac{{v}}{{df}} \tag{16}\]  
\(\Delta\) is the residual height of ablation width;  
\(v\) is the laser scribing speed;  
\(d\) is the laser spot diameter;  
\(f\) is the laser frequency.
# REISCH: Incorporating Lightweight and Reliable Algorithms into Healthcare Applications of WSNs
## 4 The Proposed Data Collection Scheme
### Network Model  
Figure 4 shows the network model in which our proposed REISCH scheme is based:  
1. Sensor (\(SN\)): This entity collects raw data related to a specific patient. It sends these data to the \(CH\).
2. Cluster Head (\(CH\)): This entity aggregates data from the sensors that followed it. Then, it sends these data to the \(LS\).
3. Local Server (\(LS\)): This entity receives data from all \(CH\)s in each round and stores it in EMR's repository. These data are subsequently sent periodically to the Central Server (\(CS\)).  
4. Central Server (\(CS\)): This entity is a gateway accessing remote servers such as the Attributes Server (\(AS\)) and the Data Server (\(DS\)). It receives data from the \(LS\) and sends data to the \(DS\) after being authenticated by the \(AS\). Security procedures in \(AS\) and \(DS\) are left for future directions.  
Among the WSNs, the low-energy adaptive clustering hierarchy (LEACH) protocol is used. LEACH uses clustering architecture to improve the WSN lifetime. More details about this protocol are available in [70]. Each group of \(SN\)s collects raw data for a specific patient [71]. These \(SN\)s sign data before sending them to \(CH\)s. Each \(CH\) aggregates data and signatures from his followers. Then, each \(CH\) uses homomorphic property with all data and signatures without verifying the signatures to reduce energy consumption on the \(CH\) and send them to the \(LS\). As the \(LS\) has unlimited resources, it verifies and validates collected data from \(SN\)s. The \(LS\) sends data stored on the EMR's repository to the central repository to allow HC users (patients and providers) to access them by sending authentication/authorization requests to the \(CS\), \(AS\), and \(DS\). This paper focuses on performance and security issues in \(SN\)s, \(CH\)s, \(LS\), and \(CS\). Security issues for datasets and transferred data in \(CS\), \(AS\), and \(DS\) are left for future works.
# A Survey of the Applications of Text Mining for the Food Domain
## 4 Data Sources in the Food Domain
### Media and Social Data  
In recent years, media and social data have played an increasingly pivotal role in the information ecosystem of the food domain. This type of data primarily comes from public news outlets, informational releases by industry organizations, and data exchanges among consumers across various social platforms [68]. Online social platforms actively generate real-time textual data, enabling the analysis of behaviors, dietary patterns, and health monitoring [1; 68; 69].  
Media data not only monitor and report significant occurrences, policy trajectories, and industrial advancements in the food domain, but also provide timely insights into public concerns, highlighting hotspots and focal points. They serve as an indispensable source for surveilling food-related public opinions, supporting research, and facilitating decision-making processes [6]. Systematic collation and content analysis of media reports allow the identification of evolving trends and issues, enabling the anticipation of public sentiment trajectories and empowering relevant authorities to respond swiftly and accurately. Concurrently, media data provide valuable insights into the industry. Social data, on the other hand, present an abundance of fragmented, user-generated content with expansive coverage and a profusion of information. For example, by analyzing the linguistic patterns of food-related conversations on social media and their correlations with demographic characteristics, researchers have uncovered intricate associations between food-related discourse and geographical, as well as community attributes [70].  
The comprehensive use of data from various social platforms enables access to a diverse cross-section of individuals, capturing genuine user sentiments. This equips enterprises with insights to enhance user experience and product optimization and enablesregulatory bodies to align themselves with the voice of consumers [4; 29]. In comparison to media data, social data encompass more subjective and emotionally infused content. However, the unstructured nature of such data increases the complexity of analysis and data mining. For instance, [PERSON] et al. [71] used geographically tagged food-related tweets on Twitter to analyze food environments and chronic health outcomes across distinct U.S. census regions. By harnessing data from social media to analyze the uneven distribution of sustenance and resources, as well as the motivations, attitudes, beliefs, and emotions expressed by individuals in their online discourse concerning food, researchers discuss the instrumental role of social media in ameliorating disparities in food accessibility and health [72; 73].  
Currently, media and social data are extremely time-sensitive, so there is an urgent need to develop standardized tools and intelligent analytical models, while at the same time digging deeper into the intrinsic value of textual data. This has become an important development direction for contemporary research.
## 3 Field Trialed System
### Local Private Network
The office bubble has a stand-alone 4G access  
Figure 1: Topology of the private network bubble system with three interconnected bubbles. Figure 2: Setup phase of ad hoc private network bubble. point in a van. This 4G access point uses an EPC in the van to authenticate the end users. The van EPC is also used for a 4G access point next to the field that needs to be fertilized with the tractor. There is a separate stand-alone 4G access point outside the smart barn. This access point uses its own 4G EPC for authentication. With this kind of setup, bubbles can also be used independently. In addition to the setup shown in Figure 1, air sensors are connected to the internet via LoRaWAN [42, 43] and soil sensors in the fields are connected with LoRaWAN and with ISM bands and 3G [44]. This technology provides a long range and a good battery life. Moreover, the tractor can use a commercial 4G network with a higher priority subscriber identity module (SIM), denoted as QC128. A satellite connection is also available as a backup communication system [45]. The geostationary (GEO) satellite provides sufficient throughput but cannot support remote control of the tractor due to its large latency. Nevertheless, GEO satellites can be efficiently used to share situational awareness data for farms. Although commercial mobile network infrastructure is widespread, there are still gaps in rural and sparsely populated areas. Another aspect is that it might be impossible to deploy terrestrial infrastructure. Satellite systems can provide a fast deployment solution. Once the antenna is deployed, and line-of-sight to the satellite is in place, transmission is possible. See Table 3 for more specific information about the trial access points.
## 3 DASH Interferometer Mount
### Low Heat Conduction Behavior  
In contrast to existing support structures in the literature, the flexible structure, cover, and base were divided into different parts; therefore, different structural materials could be used. Because the flexible structure must be in direct contact with the BS, it must be made of metal with a low expansion coefficient, whereas the cover and base can be made of TC4 with a low thermal conductivity. Figure 12 shows the difference between the existing structure and the flexible structure. Additionally, the flexible structures were connected to the cover and the base through the wings, which reduced the contact area.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Frequency Range (Hz)** & **Acceleration Power Spectral Density (g\({}^{2}\cdot\)Hz\({}^{-1}\))** \\ \hline
20–190 & 0.00335 \\
190–500 & 0.015 \\
500–750 & 0.01125 \\
750–2000 & 0.0005 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Space-borne random vibration test conditions of assembly.  
Figure 11: Deformation of the interferometer assembly.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Direction**} & \multicolumn{2}{c}{**Natural Frequencies (Hz)**} \\ \cline{2-3}  & **Structural Frame** & **DASH Interferometer** \\ \hline \(X\) & 1066 & 634 \\ \(Y\) & 1187 & 686 \\ \(Z\) & 2672 & 939 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Natural frequencies of FEM.  
These approaches increased the thermal resistance to heat conduction, thereby reducing heat conduction and minimizing the impact of ambient temperature.  
Therefore, without considering thermal radiation, the thermal resistance of the DASH interferometer can be calculated as follows [25]:  
\[\frac{1}{Ak}=\frac{1}{Ah}+\frac{l}{A\lambda}, \tag{17}\]  
where \(k\) is the heat transfer coefficient, \(A\) is the contact area, \(\frac{1}{Ak}\) is the thermal resistance, \(h\) is the convective heat transfer coefficient, \(\frac{1}{Ah}\) is the heat convective resistance, \(\lambda\) is the thermal conductivity, and \(\frac{l}{A\lambda}\) is the thermal conductivity resistance.  
Assuming that the thermal convection can be ignored in the space environment, Equation (12) indicates that by selecting TC4, a material with a lower thermal conductivity, the thermal conductivity can be reduced by nearly 50%, the contact area can be reduced by 75%, and the thermal resistance of the assembly can be increased 7.5 times.