esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
As the next frontier in wireless communication, the landscape of 6G technologies is characterized by its rapid evolution and increasing complexity, driven by the need to address global challenges such as ubiquitous connectivity, ultra-high data rates, and intelligent applications. Given the significance of 6G in shaping the future of communication and its potential to revolutionize various industries, understanding the technological evolution within this domain is crucial. Traditional topic modeling approaches fall short in adapting to the rapidly changing and highly complex nature of patent-based topic analysis in this field, thereby impeding a comprehensive understanding of the advanced technological evolution in terms of capturing temporal changes and uncovering semantic relationships. This study delves into the exploration of the evolving technologies of 6G in patent data through a novel dynamic topic modeling framework. Specifically, this work harnesses the power of large language models to effectively reduce the noise in patent data pre-processing using a prompt-based summarization technique. Then, we propose an enhanced dynamic topic modeling framework based on BERTopic to capture the time-aware features of evolving topics across periods. Additionally, we conduct comparative analysis in contextual embedding techniques and leverage SBERT pre-trained on patent data to extract the content semantics in domain-specific patent data within this framework. Finally, we apply the weak signal analysis method to identify the emerging topics in 6G technology over the periods, which makes the topic evolution analysis more interpretable than traditional topic modeling methods. The empirical results, which were validated by human experts, show that the proposed method can effectively uncover patterns of technological evolution, thus enabling its potential application to enhance strategic decision-making and stay ahead in the highly competitive and rapidly evolving technological sector.  
technological evolution analysis; dynamic topic modeling; 6G 2025153783  
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## 2 Physical Foundations of X-ray Phase Contrast and Dark-Field Imaging  
Phase contrast and dark-field imaging consider X-ray light interactions with matter from a wave perspective. For extensive comprehension of the physical phenomenon implied in X-ray phase contrast imaging, we refer the reader to [11; 12].  
An X-ray passing through matter interacts with the electronic clouds and nuclei of atoms, more or less strongly depending on the atomic numbers of the atoms. Interactions are described by the refractive index: \(n=1-i\beta-\delta\). \(\delta\) accounts for phase shift (i.e., refraction) and \(\beta\) for attenuation. \(\beta\) is directly related to the linear attenuation coefficient \(\mu=2k\beta\). \(\delta\) and \(\beta\) vary with the sample electronic density and the incident photons energy E (in 1/E for \(\beta\) in the X-ray range of energy and \(1/E^{3}\) for \(\delta\)).
## 4 Descriptions of Different Topologies of WSNs
### Cluster-Based Topology
#### 4.2.4 Advantage and Disadvantage of Cluster-Based Topologies  
Cluster-based routing protocols greatly increase the scalability of a sensor network. The overall energy consumption of the nodes compared with the flat topology protocols is reduced, leading to prolonged network lifetime. The organization of the network into clusters lends itself to efficient data aggregation, which in turn results in better utilization of the channel bandwidth. Cluster-based routing holds good promise for many-to-one and one-to-many communication paradigms that are prevalent in sensor networks. However, non-uniform clustering is the main problem for this topology. Consider the LEACH protocol, there is a fair chance that most of the cluster heads are situated in a same side of the network where as few cluster heads on the other side or even worsen no cluster head in a specific area. Thus, non-uniform clustering happens. Due to non-uniform clustering, the following problems occur:  
* Energy dissipation rate is highly different from one sensor to another sensor, even if they are in the same cluster. Thus energy distribution is not even.
* Total energy dissipation increases due to the long way communication between a cluster member and cluster head.
* Because of very long-way communications, some sensors consume energy rapidly and die. As a result, network lifetime decreases.
* Network connectedness may not be guaranteed.
###### Abstract  
In this paper, a silicon-based radio frequency micro-electromechanical systems (RF MEMS) pattern reconfigurable antenna for a Ka-band application was designed, analyzed, fabricated, and measured. The proposed antenna can steer the beam among three radiating patterns (with main lobe directions of \(-20^{\circ}\), \(0^{\circ}\), and \(+20^{\circ}\) approximately) at 35 GHz by switching RF MEMS operating modes. The antenna has a low profile with a small size of 3.7 mm \(\times\) 4.4 mm \(\times\) 0.4 mm, and consists of one driven patch, four parasitic patches, two assistant patches, and two RF MEMS switches. The active element pattern method integrated with signal flow diagram was employed to analyze the performances of the proposed antenna. Comparing the measured results with analytical and simulated ones, good agreements are obtained.  
silicon-based; RF MEMS pattern reconfigurable antenna; Ka-band; RF MEMS switch; active element pattern method +
Footnote †: journal: International Journal of Electronics and Electronics and Electronics Letters  
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Footnote 1: email: [EMAIL_ADDRESS]
# Regulated Assembly of LPS, Its Structural Alterations and Cellular Response to LPS Defects
## 8 Regulated Structural Alterations in LPS
### Modifications in the Heptose Region of the LPS Inner Core  
The inner core of LPS exhibits the limited structural diversity, since it plays a crucial role in maintaining the OM stability. However, some non-stoichiometric substitutions have been demonstrated in this region of LPS. These include the incorporation or loss of phosphate residues and the addition of P-EtN, GlcN, or GlcUA. Among these, the phosphorylation of the HepI residue by WaaP is critical for the OM permeability and also provides the attachment sites for the other substituents and ensures the completion of core synthesis [11; 24; 55]. Accordingly, in _E. coli_ K-12, phosphorylated HepI serves as an acceptor for P-EtN, which requires the EptC phosphoethanolamine transferase [55]. Furthermore, the HepIII residue, whose incorporation requires prior phosphorylation of HepI by WaaP, can be modified by GlcN (in _E. coli_ R1 and R3 isolates) or by GlcUA (in _Salmonella_ and _E. coli_ B, K-12, R2 and R4 core types). Importantly, the modification of the HepIII residue is always accompanied by loss of phosphate residue at the HepII, thereby maintaining a net negative charge [55; 74]. Structural studies revealed that GlcUA is attached to O-7 of the side-chain Hep, with the phosphate residue found at position O-4 of the HepII being absent [55] (Figure 2). WaaH shares 20% amino acid sequence similarity with WabO of _Klebsiella pneumoniae_. However, WabO in _K. pneumoniae_ is responsible for the galacturonic acid (GalA) incorporation [75]. In _E. coli_ K-12, _waaH_ and \(eptC\) genes, whose products mediate transfer of GlcUA and P-EtN modifications of the Hep I and Hep III residues respectively, are positively regulated by the PhoB/R TCS [55]. However, the P-EtN-modification of HepI can also occur at the basal level without a requirement for induction of either PhoB/R or BasS/R systems. The EptC-dependent modification of HepI is important for the permeability function, as \(\Delta\)_eptC_ mutants exhibit sensitivity to exposure to sub-lethal Zn\({}^{2+}\) concentration or the presence of SDS [55].
# Carrier Dynamics in InGaN/GaN on the Basis of Different In Concentrations
## 3 Results and Discussions
### PL Results  
Figure 4 shows the temperature-dependent PL spectra of the samples. In SI27 (Figure 4a), the temperature-dependent PL peak energy exhibited no S-shape behavior. The S-shape behavior has been attributed to the carrier dynamics associated with carrier localization in potential minimums [23; 24; 25; 38; 39]. As shown in Figure 4b, two PL spectral peaks were identified below 150 K. The low-energy peak corresponded to localized states. The high-energy peak was attributed to free-carrier states, corresponding to the background InGaN compound on which clusters were distributed. However, the background InGaN compound also consisted of potential fluctuations of  
\begin{table}
\begin{tabular}{c c} \hline
**InGaN** & **Eg (eV)** \\ \hline In\({}_{0.27}\)Ga\({}_{0.73}\)N & 2.389 \\ In\({}_{0.48}\)Ga\({}_{0.52}\)N & 1.747 \\ In\({}_{0.19}\)Ga\({}_{0.81}\)N & 2.667 \\ In\({}_{0.47}\)Ga\({}_{0.53}\)N & 1.775 \\ In\({}_{0.2}\)Ga\({}_{0.8}\)N & 2.631 \\ In\({}_{0.15}\)Ga\({}_{0.85}\)N & 2.813 \\ \hline \end{tabular}
\end{table}
Table 1: Relationship between In concentration of In\({}_{\rm x}\)Ga\({}_{1-x}\)N and band gap.  
Figure 3: XRD results of four samples.  
shallow distributions. The merging of PL peaks above 150 K was attributed to carrier liquidation among the localized states and free-carrier states. The decreasing trend of high-energy peak was mainly due to the band-gap shrinkage of phonon effect [40]. As shown in Figure 4c, the PL peaks showed broader bandwidth than the SI27 sample, indicating an evident In concentration fluctuation. As shown in Figure 4d, the PL peak position corresponded to low In concentration (~15%). The poor optical quality of PIN15 was due to the low emission energies existing between 450 and 500 nm.
# Multi-Head Self-Attention-Based Fully Convolutional Network for RUL Prediction of Turbofan Engines
## 3 The Proposed Neural Network
### Architecture of MSA-FCN
#### 3.2.3 Multilayer Perceptron Module (MLPM)  
MLPM processes all degradation representations using a Multi-Layer Perceptron (MLP) [28; 30] and outputs the corresponding RUL value. Firstly, MLPM utilizes a flattening layer to convert the degradation representations \(D_{i}\) from a two-dimensional vector to a one-dimensional vector \(\hat{E}_{i}\). Then, MLPM employs the MLP with two fully connected layers to process \(\hat{E}_{i}\), integrating relevant representations. Finally, MLPM processes the integrated representations \(E_{i}\) through a regression layer and outputs the predicted RUL value. The above computational steps are represented by Equations (13)-(16).  
\[\hat{E}_{i}=\text{Flatten}(D_{i}), \tag{13}\]  
\[\overline{E}_{i}=\text{ReLU}\big{(}W_{1}\hat{E}_{i}+b_{1}\big{)}, \tag{14}\]  
\[E_{i}=\text{ReLU}\big{(}W_{2}\overline{E}_{i}+b_{2}\big{)}, \tag{15}\]  
\[y_{i}^{pre,tra}=W_{r}E_{i}+b_{r}, \tag{16}\]  
where \(W_{1}\), \(b_{1}\), \(\overline{E}_{i}\), \(W_{2}\), \(b_{2}\), ReLU, and Flatten represent the weights of the first layer of the MLP, the bias corresponding to \(W_{1}\), the mapping of the first layer of the MLP, the weights of the second layer of the MLP, the bias corresponding to \(W_{2}\), the activation function, and the flattening layer, respectively. \(W_{r}\), \(b_{r}\), and \(y_{i}^{pre,tra}\) represent the weights of the regression layer, the bias corresponding to \(W_{r}\), and the predicted RUL value, respectively.
## 3 Results  
The MAE loss out of training process reached 0.000826 after 100 epochs, while for each layer, the average radiation was predicted every 0.54 s with a V100 GPU. Since the pixel value was normalized during training, the pixel-wised MAE of grey value was \(\frac{0.000826}{2}\times 2^{16}=27.07\) in 16-bit image. For the training job, the mean grey value through the training data set was 320.07, so the average of pixel-wised prediction error was \(\frac{27.07}{320.07}\times 100\%=8.46\%\). Since the MAE loss was calculated through the calibrated OT image of whole building platform with a resolution of 2125 \(\times\) 2125 pixels, the prediction error was influenced by the part amount and orientation on the building platform.  
Considering the network performance of a different depth, the comparison result is shown in Figure 6. All networks were trained for 100 epochs and tested by the test set. The high depth benefits the network performance and leads to low MAE loss. The network with a depth of 11 shows the lowest MAE in both the training and test set. Since the training and test dataset were not relevant and there was only one part on the building platform in the test dataset, the test loss can be lower than the training loss due to the average of MAE over the whole building platform. The result showed that a high depth of UNet structure provides more tunable parameters under lower resolutions due to the downscaling and upscaling behavior, which was beneficial to the radiation prediction.  
Figure 5: (**a**) Test job; (**b**) front view of the test job; (**c**) left view of the test job.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Part** & **Overhang Angle** & **Layer Amount of Strut Section** &
\begin{tabular}{c} **Layer Amount of Printed** \\ **Overhang Section** \\ \end{tabular} \\ \hline
1 & 75\({}^{\circ}\) & 257 & 21 \\
2 & 60\({}^{\circ}\) & 157 & 242 \\
3 & 65\({}^{\circ}\) & 202 & 197 \\
4 & 45\({}^{\circ}\) & 154 & 511 \\
5 & 80\({}^{\circ}\) & 340 & 3 \\
6 & 45\({}^{\circ}\) & 160 & 495 \\
7 & 70\({}^{\circ}\) & 207 & 53 \\
8 & 50\({}^{\circ}\) & 239 & 426 \\
9 & 85\({}^{\circ}\) & 341 & 17 \\
10 & 50\({}^{\circ}\) & 234 & 431 \\
11 & 70\({}^{\circ}\) & 279 & 48 \\
12 & 55\({}^{\circ}\) & 307 & 358 \\
13 & 90\({}^{\circ}\) & 366 & 1 \\
14 & 55\({}^{\circ}\) & 304 & 361 \\
15 & 75\({}^{\circ}\) & 310 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Layer height of printed parts.  
The comparison of results shows test losses in both networks, and the standard deviation of test errors in Figure 7. Pix2 Pix and U-Net show a similar performance in MAE, while the Pix2 Pix algorithm shows a higher standard deviation of test error.  
The test results of the presented U-Net based framework and Pix2 Pix structure are shown in Figure 8. To visualize the radiation, the grey value was mapped using a look-up table (LUT) of 'thermal' in Fiji [25]. High radiation on layer 300, due to the critical overhang structure on the top of the hole, was predicted by both algorithms.  
Figure 6: Network performance with different depths.  
Figure 7: Comparison of the performances of Pix2 Pix and U-Net.
## 4 Results and Discussion
### Parametric Analysis
#### 4.3.3 Effect of Solar Field Area  
The area and the number of solar collectors play an important role in determining the optimal configuration of the capture solar system. The collector surface has a decisive effect on the efficiency and feasibility of SACS. The simulation was carried out to establish the influence of this parameter on the overall performance of SACS under study, based on the collector's tilt angle 20\({}^{\circ}\). The area of each collector was 2.5 m\({}^{2}\), the water flow rate was 40 L/h per m\({}^{2}\), the solar tank volume was 30 L/m\({}^{2}\)  
Figure 10: Variation of the solar fraction with the flow of water circulating through the collector.  
Figure 9: Solar coverage variation with the solar collector tilt.  
the lower and upper solar tank temperatures were T\({}_{\text{lower}}\) = 75 \({}^{\circ}\)C, T\({}_{\text{upper}}\) = 90 \({}^{\circ}\)C. Figure 11 depicts the variation of the solar fraction with the number of collectors installed. The evaluation involves changing the number of a collectors from 4 to 24 (10 m\({}^{2}\) to 60 m\({}^{2}\)) by a step of 2 (2 m\({}^{2}\)). It is clear that an increase in the collector surface area tends to enhance solar coverage due to the proportion between the captured energy from the ETC field and solar fraction, according to simulation results displayed in Figure 11. It is predicted that the solar coverage stays constant, especially at the higher solar surface field (\(>\)55 m\({}^{2}\)). As an example, an evacuated tube collector operating in Baghdad, inclination angle 30 degrees, presents a solar coverage of about 88.1% for 22 collectors (55 m\({}^{2}\)) and 88.3% for 24 collectors (60 m\({}^{2}\)). The stability in solar fraction SF (Solar Fraction), which was also achieved in published works [PERSON] and Assilzadeh [42; 43], indicates that the system achieves its optimum level, and any additional increase in the surface field leads to overproduction of thermal energy, which can cause technological problems and significantly increase the initial investment. Therefore, with equal investment costs, the best design will be the one that offers the greatest coverage.
## 2 Materials and Methods  
In this work, we base experimentation on cluster-based routing protocols because nodes are organized in groups to improve transmissions' power efficiency. During the network formation phase in this type of protocol, before the nodes send a joint packet, each of the cluster members performs a network selection process, which determines its performance characteristics until the next stage of the cluster formation. This process can be carried out based on the sensors' characteristics and capabilities if a heterogeneous network is being used or randomly assigned to a homogeneous network. It can even be the network administrator who determines the membership of each node to each specific network.
# Chronotype, Risk and Time Preferences, and Financial Behaviour
## Literature Review
### Conceptual Model and Hypotheses Development
#### 2.2.2 The Mediating Role of Time Preferences (H2)  
To provide a richer explanation for morning chronotype's influence on delinquent credit card payments, we use theories of time perspective (i.e., future-oriented or present-biased). An individual's conception of time helps them to organize each life event in a methodical and anticipated rhythm [65]. According to the neurological explanation, decisions that are expected to result in a future award in the region of the rational prefrontal cortex, whereas decisions that are expected to result in a present award occur in the emotional limbic region in the brain [66]. Present-oriented individuals appear to seek immediate gratification at the cost of future utility. In contrast, future-oriented individuals are more likely to delay gratification in order to achieve long-run goals [67].  
Prior studies have reported a relationship between circadian rhythms and time perspectives. In particular, morning-types are positively associated with future time preference [22; 23; 65] and the processing channel is self-control [65], as more self-control results in reasonable decisions to delay gratification and to concentrate on achieving future goals. On the other hand, previous research has also suggested a link between time preference and credit card debt. [PERSON] and [PERSON] [24] reported that present-discounting individuals have higher amount of revolving credit balances, as compared with future-oriented individuals. Their results are also robust when changing the method of calculating the time perspective and the sample selection criteria. Therefore, to examine whether time discounting explains the association between morning-types and delinquent credit card payments, we propose:  
**Hypothesis 2**.: _Time preference mediates the relationship between morning-types and delinquent credit card payments._
## 3 Characteristics of OS Graphs
(The numbers in parentheses are the valences of the vertices). The possible SSs of P\({}_{1}\)(2) can be either SS = 4,5 or SS = 3,5. Let us discuss each of these cases separately. **Case 1. **\(SS(P_{1}(2))\) = 4,5. In this case (see Figure 5d), P\({}_{1}\)(2) is connected to P\({}_{5}\)(4) and P\({}_{6}\)(5). Then, P\({}_{5}\)(4) is connected to P\({}_{2}\)(3), P\({}_{3}\)(3), and P\({}_{6}\)(5). Now, P\({}_{2}\)(3) and P\({}_{3}\)(3) have two connections (to P\({}_{5}\)(4) and P\({}_{6}\)(5)) instead of three, and the only available connection is to a vertex having V = 3. But such connections will yield that the SS of these vertices (circled in the figure) will be SS = 3,4,5, so that these two vertices will be equal, in contradiction to axiom e.  
**Case 2. **\(SS(P_{1}(2))\) = 3,5. In this case (see Figure 5e), P\({}_{1}\)(2) is connected to P\({}_{2}\)(3) and P\({}_{6}\)(5). Then, P\({}_{5}\)(4) is connected to P\({}_{2}\)(3), P\({}_{3}\)(3), P\({}_{4}\)(3), and P\({}_{6}\)(5). (As noted earlier, all the vertices have to be connected to P\({}_{6}\)(5).) As a result, all the vertices are fully connected in accordance with their V, except the vertices having V = 3. P\({}_{3}\)(3) and P\({}_{4}\)(3) are circled in the figure. They each have only two connections instead of three. Trying to form a connection between these vertices will result in making them equal (they will both have SS = 3,4,5), in contradiction to axiom e.  
The analysis of these two cases shows that it is impossible to design an OS satisfying the conditions of the first row of Table 7.  
Let us examine now the second row in which m\({}_{2}\) = 2, m\({}_{3}\) = 1, m\({}_{4}\) = 2, and m\({}_{5}\) = 1. Let us denote the vertices of the possible OS by P\({}_{1}\)(2), P\({}_{2}\)(2), P\({}_{3}\)(3), P\({}_{4}\)(4), P\({}_{5}\)(4), and P\({}_{6}\)(5)--see Figure 5f. The possible SSs of P\({}_{1}\)(2) and P\({}_{2}\)(2) are, respectively, SS = 3,5, and SS = 4,5 since, \begin{table}
\begin{tabular}{c c c} \hline
**V = 2** & **V = 3** & **V = 4** \\ \hline
1 & 3 & 1 \\ \hline
2 & 1 & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The possible multiplicities of the V = 2,3,4.  
as was indicated previously, they cannot be connected to each other. Hence, P1(2) has to be connected to P3(3) and P6(5), and P2(2) has to be connected to P4(4) and P6(5). At this stage, P5(4) has only two connections (to P6(5) and to P4(4)), and it cannot be connected to P1(2) and P2(2), which do not have any more available connections. Therefore, only P3(3) is available for connection. Hence, P5(4) and P4(4) (circled in the figure) each have only three connections instead of four. Hence, it is impossible to design an OS satisfying the conditions of the fourth row. \(\square\)  
**Remark 1**. : _We described two OSs of order 7 in Section 2--O1(7) and O2(7)--satisfying the axioms of apriorics. There may be other OSs of order 7. The enumeration of all the OSs of order 7 and of higher orders is still an open problem in apriorics._