esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Their results showed that the regulation of cell proliferation, bicarbonate transport, Wnt, and IL-17 signaling pathways, and nitrogen metabolism were the most significant pathways associated with their DEGs list. They also constructed a PPI network of the DEGs using Cytoscape software, which was utilized to identify the most critical hub genes (_MYC_, _CXCL1_, _CD44_, _MMP1_, and _CXCL12_). [PERSON] et al. [13] identified 619, 52, and 119 DEGs from the lung tissues, peripheral blood mononuclear cell (PBMC), and skin using ten microarray gene expression data, respectively. The DEGs they identified were used to build a PPI network using the STRING database to find the interacting genes. Their results showed that 12, 2, and 4 functional clusters associated with the three tissues were identified, respectively. In addition, their results revealed that the most significantly enriched pathway in the three tissues was the tumor necrosis factor (TNF) signaling pathway. [PERSON] et al. [14] proposed data integration using microarray, RNASeq, and the integrated data from both microarray and RNASeq. Their integration pipeline obtained 98 DEGs. Thereafter, they ranked the 98 genes using the mutual information-based ranking provided by the minimum redundancy maximum relevance (mRMR) algorithm and selected the first six genes. Their results showed that SVM, RF, and KNN scored an accuracy of 97.6%, 97.4%, and 94.9%, respectively, when using the 98 genes and 96.8%, 87.4%, and 94.1%, respectively, in the case of the reduced six genes. This study aimed to discover new biomarkers or hub genes from DEGs of an integrated analysis of microarray and RNASeq gene expression data, then use these hub genes in a further analysis. The PPI network is constructed using the STRING database and Cytoscape software for visualization. In the PPI network, the interaction can be direct or indirect. In the direct interaction, the interacting proteins are closely bound together in performing certain functions. The indirect interaction is known as functional association. These interactions are predicted using computational methods that use the transfer of knowledge between organisms and the interactions that are collected from other primary databases. Additionally, Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathways and gene ontology (GO) analyses are conducted to construct meaningful information from the DEGs using the Enricher web tool. The top 10 hub genes are obtained from each method of the three methods: maximal clique centrality (MCC), maximum neighborhood component (MCN), and degree method. The intersection of these three methods' top ten hub genes resulted in the eight hub genes (CDK1, KIF11, CCNA2, TOP2A, ASPM, AURKB, CCNB2, and CENPE). Moreover, the survival information for the patients with nodal, oestrogen, and progesterone receptor status is used after splitting them into two groups based on the median expression level of these eight hub genes. Additionally, a correlation analysis of the clinicopathological variables in BC with the eight hub genes is performed.
## 4 Multi-Structural Optimization of BPMSM
### Single Performance Regression Model  
Based on the gradient descent method, a linear regression model is determined for the mathematical expression of each optimized performance. The definition of evaluation coefficient \(R^{2}\) is expressed as follows:  
\[R^{2}=1-\frac{RSS}{TSS}=1-\frac{\sum_{i=1}^{N}\left(y^{(i)}-\hat{y}^{(i)} \right)^{2}}{\sum_{i=1}^{N}\left(y^{(i)}-\overline{y}\right)^{2}} \tag{6}\]  
\[\overline{y}=\frac{1}{m}\sum_{i=1}^{m}y^{(i)} \tag{7}\]  
where \(m\) is the number of runs; \(RSS\) is the sum of the square of all sample data errors, which is \(m\) times the mean square error (_MSE_); \(TSS\) is the difference between the sample data and the mean, which is \(m\) times the variance. Ideally, the predicted value of the sample data is the same as the true value, that is, \(RSS\) is 0 and \(R^{2}\) is 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Factor** & **Level 1** & **Level 2** & **Level 3** & **Level 4** & **Level 5** \\ \hline \(\delta_{0}\) & 3 & 3.25 & 3.5 & 3.75 & 4 \\ \(r\) & 14.5 & 14.75 & 15 & 15.25 & 15.5 \\ \(h\) & 6 & 6.5 & 7 & 7.5 & 8 \\ \(N_{1}\) & 120 & 134 & 148 & 162 & 180 \\ \(N_{2}\) & 360 & 440 & 520 & 600 & 640 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Factor level value.  
Figure 6: The largest back EMF in the structural parameter interval.  
The larger the \(R^{2}\) is, the better the BPMSM fitting performance is. The evaluation coefficient (\(R^{2}\)) of the second-order and third-order regression models are determined individually. The \(R^{2}\) with the second-order regression model is 0.99 for all optimized performances. Based on the third-order regression model, the evaluation coefficient (\(R^{2}\)) is 0.99, 0.99, 1, 1, 1, and 1 for \(k_{a}\), \(k_{z}\), \(P_{Fe}\), \(P_{Cu}\), \(k\), and \(T\), respectively. Therefore, the third-order regression model is utilized for subsequent structural parameter optimization.  
To evaluate the accuracy of the formed third-order regression model, another 10 sets of data points are taken as testing samples. The percentage of the difference between the computed result based on the proposed virtual prototype and the fitting results based on the third-order regression model, is taken as the rationality evaluation index of the test point selection, which is defined as follows:  
\[\Delta=(y_{\text{iro}}-y_{\text{iso}})/y_{\text{iso}}\times 100\% \tag{8}\]  
where \(y_{\text{iso}}\) is the optimized performance calculated using the virtual prototype and \(y_{\text{iro}}\) is the optimized performance calculated using the regression model.  
As shown in Figure 7, the absolute value of error percentage \(\Delta\) is less than 5%, indicating that the constructed third-order regression model fits well with the simulation results. The regression model is fitted based on the sample points selected by the orthogonal test, indicating the feasibility of the orthogonal test.
## 1 Introduction  
The collision of the Indian and Eurasian plates can be observed physically from the development of the Himalayas and subsequent formation of the foreland basin [1]. The tectonic history of the processes occurring in the past, during the mountain building and erosion, are likely stored by the detrital sediments of the foreland basin. The reconstruction of the paleo-tectonic processes of the fold-thrust belts and foreland basins can be determined by the detailed study of the detrital sediments in the foreland [1; 2; 3]. The detrital sediments can be helpful in finding the origin of the sedimentary rocks, rise and fall of sea level, and history of the orogen and tectonic basins [4; 5; 6; 7]. Modern techniques (i.e., U-Pb geochronology) have been extensively used to investigate the sedimentary records of foreland basins [8; 9; 10; 11; 12; 13; 14; 15; 16; 17], in order to confine their provenance and find out the maximum ages of deposition. Geochronology of detrital zircons is a very powerful tool in finding the absolute record of provenance of sediments. This provenance can be further used to construct the paleogeography of blocks in geological time [18].  
The Himalayan foreland basin sediments have attracted enormous attention, for constraining the timing of the India-Eurasia collision and the exhumation history [1; 19; 20]. This collision was followed by the evolution of the mountain belts, due to the propagation of the thrust sheets. Sedimentary geochronology has been performed to find out the source of the Chitarwatta Formation and eventually the exhumation history of the Himalayan blocks and its foreland. Structurally, our study area is located in the eastern Sulaiman fold-thrust belt, which occupies a position along the western margin of the Indian plate (Figure 1A,B).  
Figure 1: (**A**) Simplified map showing regional tectonic features. The blue rectangle shows the location of the Sulaiman fold–thrust belt (SFB), and the red rectangle shows the Hazara–Kashmir syntaxal bend (after [11]). (**B**) The simplified geological map of SFB, showing the location of the studied section (after [21]). (**C**) Geological map of the study area, showing major geological units [22]. The solid black line represents the location of the Mughalkot section.  
The Sulaiman fold-thrust belt consists of Permian to Eocene sediments, which are overlain by the Oligocene to Miocene molasses of the Himalaya (Figure 1B,C). The area has been studied in detail with respect to geochemistry, petrography, structure and tectonics, paleontology, and sedimentology [23; 24; 25]. However, in terms of modern detrital geochronology, the data are very limited [26]. Therefore, in this study, we adopted the modern U-Pb geochronology to study the Chitarwatta Formation, to provide an insight to the exhumation of the Higher Himalayan slab.
## Appendix B  
Equation (25) can be simplified as follows:  
\[z^{3}-z\text{\textcdot}2\text{cos}\varphi+az+b=0, \tag{8}\]where  
\[a=1+K_{cf}K_{pvm}\sqrt{\frac{C_{f}}{L_{f}}}\text{sin}\varphi-K_{p}K_{pvm}F(R_{o} \text{cos}\varphi+L_{o}\omega_{res}\text{sin}\varphi-R_{o})\]  
\[b=-K_{cf}K_{pvm}\sqrt{\frac{C_{f}}{L_{f}}}\text{sin}\varphi+K_{p}K_{pvm}F(R_{o} -R_{o}\text{cos}\varphi+L_{o}\omega_{res}\text{sin}\varphi).\]  
In order to easily identify the quantity of the unstable poles, \(w\)-transform \(z=(1+w)/(1+w)\) is introduced. Therefore, Equation (B1) can be transformed as follows:  
\[a_{3}w^{3}+a_{2}w^{2}+a_{1}w+a_{0}=0,\] (B2)  
where  
\[\left\{\begin{array}{l}a_{3}=1+2\text{cos}\varphi+a-b\\ a_{2}=3+2\text{cos}\varphi-a+3b\\ a_{1}=3-2\text{cos}\varphi-a-3b\\ a_{0}=1-2\text{cos}\varphi+a+b\end{array}\right..\]  
It can be obtained that \(a_{0}>0\). Thus according to Routh's Method, the following requirement must be satisfied to maintain the system's stability:  
\[\left\{\begin{array}{l}a_{3}>0\\ a_{2}>0\\ \frac{a_{2}a_{1}-a_{3}a_{0}}{a_{2}}>0\\ a_{0}>0\end{array}\right..\] (B3)  
From Equation (B3), the filter capacitor current feedback gain \(K_{cf}\) must meet the following requirement:  
\[K_{cf\text{min1}}<K_{cf}<K_{cf\text{max1}}\text{ and }K_{cf\text{min2}}<K_{cf}<K_{cf \text{max2}},\] (B4)  
where  
\[K_{cf\text{min1}}=\frac{-1-\text{cos}\varphi+K_{pvm}L_{o}\omega_{res}F\text{ sin}\varphi}{K_{pvm}\sqrt{\frac{C_{f}}{L_{f}}}\text{sin}\varphi}\]  
\[K_{cf\text{max1}}=\frac{1+\text{cos}\varphi-K_{p}K_{pvm}F(R_{o}\text{cos} \varphi-R_{o}-2L_{o}\omega_{res}\text{sin}\varphi)}{2K_{pvm}\sqrt{\frac{C_{f} }{L_{f}}}\text{sin}\varphi}\]  
\[K_{cf\text{min2}}=-\frac{1-2\text{cos}\varphi}{2}-K_{p}K_{pvm}F(R_{o}-R_{o} \text{cos}\varphi+L_{o}\omega_{res}\text{sin}\varphi)+\sqrt{\frac{(1-2\text{ cos}\varphi)^{2}}{4}+K_{p}K_{pvm}(2R_{o}F\text{cos}\varphi-2R_{o}F)}\]  
\[K_{pvm}\sqrt{\frac{C_{f}}{L_{f}}}\text{sin}\varphi\]  
\[K_{cf\text{max2}}=-\frac{1-2\text{cos}\varphi}{2}-K_{p}K_{pvm}F(R_{o}-R_{o} \text{cos}\varphi+L_{o}\omega_{res}\text{sin}\varphi)-\sqrt{\frac{(1-2\text{ cos}\varphi)^{2}}{4}+K_{p}K_{pvm}(2R_{o}F\text{cos}\varphi-2R_{o}F)}\]
## 4 Simulation Verification
### Change of Droop Coefficients  
DC microgrids can coordinate the output of resources by adjusting the droop coefficient, which means that the system may operate under different droop coefficients due to different requirements [29]. Therefore, the stable operation of the system under different droop coefficients must be ensured. Figure 9 shows the simulation result of the proposed control strategy under different droop coefficients. It can be seen that the system remained stable whether the droop coefficient was switched to 0.3 or 0.7. Thus, it is evident that the control strategy can ensure the system stability under different droop coefficients.  
Figure 8: Simulation results of a DC microgrid system with the proposed control strategy.
# Offloaded Computation for QoS Routing in Wireless Sensor Networks
## 4 Proposed Methodology
### Architectural Overview  
While cloud computing is widely adopted, its physical distance from WSN devices can introduce unacceptable latency for time-sensitive QoS applications. The drawbacks of relying solely on cloud infrastructures are emphasized in [15], where data centers struggle to meet real-time data processing demands. To address these limitations, edge and fog computing paradigms have emerged, providing localized computation to reduce latency and alleviate central processing bottlenecks.  
Among these, multi-access edge computing (MEC) offers particular advantages by enabling proximity-aware task delegation and high-performance processing. MEC architectures for WSNs, as highlighted in [55], have demonstrated improved support for distributed QoS routing. Furthermore, MEC reduces the energy and computational burden on sensors [14], although optimal offloading remains a complex optimization problem [56; 57; 58].  
Figure 2: An example of QoS-aware paths from sources to a BR.
## 4 Simulation Tests  
A variety of scenarios are generated to verify the effectiveness of the proposed coordinated control framework. The proposed hierarchical frequency control scheme is simulated in Matlab/Simulink. To validate the superiority of the proposed control strategy, the system contingency condition and wind fluctuations have been taken into consideration. The simulation has been first utilized in single-area control system, and then extended to a two-area interconnected system. Specifically, the frequency stability of a single-area control system under system contingency is illustrated in Case 1. Case 2 illustrates the frequency performance of single control area under the condition of wind fluctuations. Case 3 demonstrates the effectiveness of the proposed allocation strategy compared with the widely accepted allocation strategy. In Case 4, the improved control performance is depicted in a two-area interconnected power system. The single-area system under consideration is a per-unit system with \(\mathrm{Sbase}=1000\) MVA. The structure of single-area system is shown as Figure 3. The parameters of the two-area system are shown in Table 1. The parameters of inverter ACs are shown in Table 2. The total capacity of the aggregated ACs in Area 1 is 0.04 pu, and the capacity of individual ACs is respectively [0.0064, 0.006, 0.0056, 0.0052, 0.0048, 0.0044, 0.004, 0.0036] MWh. The AGC participation factors of ACs and generators \(\alpha_{AC}=0.1\) and \(\alpha_{G}=0.9\). The gain parameter of finite-time consensus control \((\chi,\phi,\kappa)\) is set to be 0.2, 0.33, and 0.6, respectively. Note that all the simulation cases are conducted under the condition that the baseline power of units and ACs in the real-time schedule are neglected and the focus of numerous cases is the regulation of power in the frequency control.  
Figure 3: The structure of a single-area system.
# Signal Design and Compatibility Assessment for LEO Navigation Augmentation System +
## 3 Signal Design Scheme  
As analyzed in the previous section, the key requirement for LEO navigation augmentation signals is to support the dual-frequency signal broadcasting, high-precision measurement, and high data rate. This section designs the LEO navigation augmentation signal scheme from the frequency selection, modulation type, and signal component multiplexing method.
# Understanding How Image Quality Affects Transformer Neural Networks
## 2 Background  
The transformer is a type of neural network architecture introduced by [PERSON] et al. [42] in 2017. It was a breakthrough in the field of natural language processing (NLP) and machine translation. Unlike previous sequence-to-sequence models, the transformer model does not rely on recurrent neural networks (RNNs) or CNNs for sequential data processing. Instead, it is based entirely on attention mechanisms. The key components of the transformer architecture are the followings:  
1. Self-attention mechanism: it allows the model to weigh the importance of different words in a sequence when predicting a particular word. It can focus on different parts of the input sequence for each word in the output sequence. This is particularly useful for tasks involving long-range dependencies in the input data.
2. Multi-head attention: To capture different aspects of the input sequence, the self-attention mechanism is extended to multiple heads, allowing the model to jointly attend to information from different representation subspaces.
3. Positional encoding: Since the transformer does not inherently understand the order of the input sequence, positional encodings are added to the input embeddings to give the model information about the positions of the words in the sequence.  
4. Feed-forward neural networks: After the attention layers, the model uses feed-forward neural networks to process the information from the attention layer and produce the final output.
5. Encoder and decoder stacks: The transformer model consists of an encoder stack and a decoder stack. The encoder processes the input sequence, while the decoder generates the output sequence. Both the encoder and decoder contain multiple layers of the components mentioned above.  
The absence of sequential processing (like in RNNs) and the ability to attend to different parts of the input sequence simultaneously (through self-attention) make transformers highly parallelizable and efficient for training on large datasets. Transformers have become the foundation for many state-of-the-art models in NLP, including BERT [43] (bidirectional encoder representations from transformers), GPT [44] (generative pre-trained transformer), and T5 [45] (text-to-text transfer transformer), among others. They have also found applications in various other domains beyond NLP due to their flexibility and effectiveness in capturing complex patterns in data.  
Transformers can be applied to various computer vision tasks, including image classification. While CNNs have traditionally been the go-to architecture for image-related tasks, recent research has shown that transformers can also achieve competitive results [46].
## 6 Simulation Results
### Experimental Results and Analysis
KL divergence measures the difference between the current policy and the previous policy, where MURPPO ensures training stability by limiting these changes. In the early stages of training, all UAVs exhibit significant fluctuations in KL divergence, indicating substantial policy updates consistent with the exploration process. As training progresses, KL divergence gradually decreases and stabilizes, suggesting policy convergence for all three UAVs. This validates the effectiveness of the MURPPO algorithm in balancing exploration and exploitation in a complex, multi-UAV reinforcement learning environment. Figure 8: KL divergence trend of red UAVs over 1000 training episodes: (**a**) KL divergence of UAV 1; (**b**) KL divergence of UAV 2; (**c**) KL divergence of UAV 3. Figure 7: The comparison of localization time ratio per episode among the proposed algorithm and two baseline methods.
# Stability Study of Erythrhitol as Phase Change Material for Medium Temperature Thermal Applications
## 1 Introduction
In addition, it has a high latent heat of 340 J/g, almost equivalent to that of the ice to liquid water phase change. [PERSON] et al. [14] studied the thermophysical properties of erythroid using differential scanning calorimetry (DSC), thermogravimetry-differential thermal analysis (TG-DTA) and a heat storage tube. [PERSON] et al. [11] carried out an experimental characterization of erythroid by measuring their physical properties such as specific heat and thermal conductivity, among others. The suitability of erythroid as PCM was examined by [PERSON] et al. [15] according to its thermodynamic, kinetic and chemical properties. [PERSON] et al. [17] found that the crystal growth speed of some SA is highly dependent on temperature, whereas [PERSON] et al. [18] claimed that the solid erythroid obtained from the melt has different degrees of crystallization depending on the used cooling rate. Some eutectic mixtures have also been studied, such as erythroid-xylitol or erythroid-sorbitol [19], and erythritol-based composites with the addition of expanded graphite and carbon nanotubes were also evaluated by [PERSON] et al. [20]. However, all these studies addressed thermal cycling tests rather than erythroid thermal stability at constant temperature, which would allow a degradation rate to be determined under conditions closer to real operation. On the other hand, [PERSON] et al. [16] investigated the heat release performance of a direct-contact heat exchanger using erythritol as PCM along with a heat-transfer oil. The suitability and appropriate parameters for charging and discharging erythroid to power a LiBr/H\({}_{2}\)O absorption cooling system were assessed by [PERSON] et al. [21]. [PERSON] et al. [22] studied the thermal and flow behaviors in a trans-heat container filled with erythroid as part of a waste heat transportation system. Nevertheless, the conclusions made by these authors are ambiguous about the performance of erythritol as PCM. Therefore, this study aims to investigate the effect of thermal treatment on the erythroid-tol thermal properties at constant temperatures above its melting point. The experiments are also conducted under an inert atmosphere and an antioxidant is added to the samples. Degradation rates at different temperatures and conditions (under air/inert atmosphere and with/without antioxidant) are calculated. In addition, degradation kinetics with respect to its latent heat are evaluated. A mechanism of oxidation of erythroid under air is proposed, as well as the possible underlying mechanism on the stability enhancement of erythritol by antioxidant. The reaction pathway of thermal dehydration of erythritol and the likely relationship between dehydration and oxidation products with the sample browning are suggested
## 4 Discussion
### The Influence of Slim Straight Electrode Insertion Depth on NRT and PSA
#### 4.2.2 Electrode Insertion Depth and PSA  
No correlation was found between the depth of electrode insertion and PSA at 65 dB word discrimination. Similar conclusions were found by [PERSON] et al. [20], who made sure to control other variants that might influence speech performance such as the duration of deafness, sound-processing strategy, potential for central impairment, and age at implantation.  
The study by [PERSON] et al. [21] found monosyllabic word comprehension scores significantly correlated with electrode insertion depth one year after implantation. Other potentially disturbing factors that can affect the functioning of the CI were not controlled in their study. [PERSON] et al. [20] argued that there is a possibility that a reduction in variability in subjects who all used the same implant system (Nucleus 22) and speech-processing strategy (SPEAK) increased the relative saliency of the insertion-metric's effect. Other studies have found that the site of cochlear stimulation can affect speech perception [22; 23; 24].
# Wake-Up Receiver-Based Routing for Clustered Multihop Wireless Sensor Networks
## 1 Introduction
To improve the communication in time and energy aspects, we have integrated a multicast wake-up, which allows a simultaneous wake-up of a specific group of nodes. Therefore, the Fog Node can use a high data transmission power for directly reaching the destination node as well as the nodes acting as relays. We implemented the proposed method in a real scenario using the parameters of the WuRx developed at the Leipzig University of Applied Sciences. Evaluations show that our proposed approach has drastically optimised the energy consumption and has reduced the communication delay. The rest of this paper is organised as follows. Section 2 describes the background. Section 3 presents the related work. Section 4 explains our proposed approach and Section 4 presents the measurement evaluation. Finally, Section 5 concludes this paper and points out future work.