esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Study Area and Data
### Validation Information  
The collection of field measurements in the Arctic was not performed under our project. However, the validation of the DEM extraction methods are often performed by inter-comparison with DEMs extracted by other techniques, for example, airborne stereo photo or lidar [31; 32]. The results of single-pass TDM InSAR DEM validation with ICESat data show that the absolute height errors of the TDM DEM are small, mostly on the order of 1-2 m [33]. A comparison of TDM InSAR DEM for various landcover classes with airborne lidar shows relative accuracy between 1.9 and 5.9 m [34]. A similar relative height error of 2 m was reported [35] for the production of global TDM DEM based on the comparison with laser scanning DEMs, SRTM data and ICESat points. The method of height from shadow was used to measure the great pyramid in Giza by observing the length of the shadow in the 6 th century B.C. [36]. It was used with optical satellite data for ridge height estimation [27; 37] and for building height estimation with validation showing the absolute error within 1.24 m-3.76 m [38]. The problem of building height extraction from VHR and high-resolution SAR images was extensively investigated and several solutions have been proposed [39; 40; 41; 42; 43]. The height retrieval accuracy was evaluated using ground data and lidar digital surface model and was dependent on SAR resolution [40]. For spotlight (1 m resolution) TerraSAR-X images, the mean absolute height errors varied between 1 and  
Figure 1: Map of Study Areas.  
3.4 m, and the standard deviation of height errors were between 1.3 and 5.8 m depending on type of the buildings (primarily, roof shape).
## 3 Results
### Metabolomic Analysis of Pear Fruits  
The clean data from each sample reached 6 GB, and the Q30 score was \(>\)91% in each case (Table S1). A total of 417 VOCs were identified in the S1, S2, S3, and S4 pear fruits with GC-MS (Figure 2a, Table S2). The VOCs were divided into amines (4, 0.96%), aromatics (19, 4.56%), aldehydes (29, 6.95%), alcohols (31, 7.43%), ketones (38, 9.11%), heterocyclic compounds (64, 15.35%), terpenoids (89, 21.34%), esters (94, 22.54%), and others (49, 11.75%). Among them, esters contributed the most (~22.54%) followed by terpenoids (21.34%) and ketones (9.11%), which were the main components in pear fruit VOCs (Figure 2b). Moreover, the curve overlap of the total ion flow was high, suggesting the signal stability is high and the experimental results are reliable (Figure 2c). Next, PCA  
Figure 1: Phenotypic analysis of ‘Panguxiang’ in four growth and development stages. (**a**) The phenotype of ‘Panguxiang’ at the four stages [(23)]. Determination of (**b**) sugar content and (**c**) organic acid content. (**d,e**) The growth rate of sugar and organic acids.  
was performed on the 417 metabolites to clarify the discrepancy in the metabolite profiles among the four stages. The PCA results separated the samples into four distinct periods (Figure 2d). In addition, the R between samples exceeded 0.5, and the R for the same variety exceeded 0.9, implying the results are reliable and reproducible (Figure 2e).  
among the four periods to reveal the accumulation pattern of VOCs during fruit growth and development. Compared to S1, S2 was significantly down-regulated than up-regulated, with significantly more differential VOCs (Figure S3a, Table S3). Starting with S3, large differential VOCs were up-regulated (Figure S3b,c, Tables S4 and S5). Compared with S1, S4 had significantly more differential VOCs than the other groups, and most were up-regulated (Figure S3d, Table S6). Additionally, terpenoids had the highest differential VOC contents in the four stages. Next, we aimed to determine the underlying metabolic pathways of terpenoids.
## 4 Experiments and Results
### Discussion  
The experiment also compared the performance of the traditional interpolation method: bilinear interpolation at a resolution of 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\), as shown in Figure 17. The comparative analysis demonstrates that bilinear interpolation is markedly inferior to our proposed method, whether evaluated as a single moment or an average moment. Specifically, bilinear interpolation shows higher error magnitudes and a more concentrated distribution of error points. It is worth noting that bilinear interpolation performs particularly poorly in mountainous areas or at the edges of land and sea. We attribute this to the fact that bilinear interpolation only considers the correlation of neighboring points during the calculation process without adequately accounting for the impact of topography on temperature variations. This results in a smoothing effect on the reconstructed data. Comparing the SR data in Figures 9 and 10 reveals that the temperature field reconstructed by bilinear interpolation is overly smooth, leading to the loss of information. Furthermore, a comparison between Figure 17c,d demonstrates that both the error magnitudes and the density of error points are reduced at an average moment compared to those at a single moment. This observation further corroborates the earlier hypothesis that minor random errors inherent in individual moments tend to cancel each other out when averaged over time, ultimately leading to improved overall performance.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & _RMSE_ & _MAE_ & _MAPE_ & _Corr_ & _PSNR_ & _SSIM_ \\ \hline CNN & 0.4075 & 0.2946 & 0.1064 & 0.9873 & 53.0385 & 0.9584 \\
2-Residual Blocks & 0.2916 & 0.1874 & 0.0840 & 0.9985 & 58.5250 & 0.9641 \\
4-Residual Blocks & 0.2774 & 0.1837 & 0.0824 & 0.9991 & 58.6461 & 0.9635 \\
8-Residual Blocks & 0.2897 & 0.1711 & 0.0625 & 0.9997 & 58.8929 & 0.9721 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Error estimations of the model under different frameworks.  
Figure 16: CNN training results at 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) resolution.  
The overview of the performances of traditional methods related to the super-resolution reconstruction of temperature data is shown in Table 3, as well as the performance of ResNet-Subpixel. Comparison results show that our method achieves the best performance on all indicators. Notably, ResNet-Subpixel has shown a significant improvement in \(RMSE\) in this SR task. Compared with traditional logistic regression or interpolation methods, our approach has enhanced RSME by approximately 49.67%. Additionally, the high PSNR values are noteworthy. According to the PSNR benchmark [49], values exceeding 40 dB indicate high-fidelity reconstruction. Our method achieves a PSNR of 58.89 dB, which can be regarded as a high-quality super-resolution reconstruction.
## 1 Introduction
### The Digital Divide in Taiwan  
Despite considerable success in the field of information technology, Taiwan still faces a digital divide. In the dissemination of ICT, it is inevitable that the timing associated with the introduction of new technologies will influence the proficiency with which users practice ICT [8]. This issue is most noticeable as it pertains to age, geographical location, ICT education, and social status [8, 9, 10]. Studies have demonstrated that the uneven distribution of information resources can be attributed to the distance between urban and rural areas [11, 12]. For example, computer ownership and internet usage rates are far higher in northern Taiwan (which is more metropolitan) than in other regions. Internet access rates also vary according to the degree of urbanization. Among individuals 51-64 years of age, residents of Taipei City present the highest internet usage (68 per cent), while residents of Changhua County, Yunlin County, Chiayi County, Tainan County/City, Kaohsiung County and Pingtung County (the rural areas of Taiwan) have the lowest internet usage (18 per cent) [13]. Aside from age and urban-rural issues; In 2010, immigrant bridges accounted for 1.95% of the total population of Taiwan; however, in remote areas, as many as 34% of the students in elementary schools have immigrant mothers [14]. Thus, immigrant bridges have become a new focus of ICT courses aimed at bridging the digital divide.  
To address the rural-urban digital divide, the government of Taiwan introduced a \"Create Digital Opportunity for Rural Areas\" project which ran between 2005 and 2011. The official \"National Information and Communications Initiative Committee\", educational institutions, and local governments cooperated to establish \"Digital Opportunity Centers\" in 300 rural communities. These centers offered a number of courses, including information literacy, computer-based training, web design, computermaintenance, computer programming, e-commerce, and website backend management [15]. These courses emphasized the uniformity of the content, and quantitative indicators, such as the number of participants or internet hardware and equipment [3]. These courses were not designed to appeal to different students and the ICT skills were not based on rural community development.  
The core issues in bridging the digital divide between urban and rural areas in Taiwan are as follows: (1) Identifying the requirements of rural residents with regard to the development of ICT skills; (2) Determining the means of using digital applications to make rural life more convenient and further the development of communities; and (3) Establishing suitable methods for the instruction of elderly individuals in rural areas.
## 1 Introduction  
In recent years, biopolymers as environmental friendly materials have attracted much attention, due to their potential applications as novel optical and electronic components. For example, organic thin-film transistors (OTFTs) have been prepared with biopolymers because these devices can be fabricated with large-scale manufacturing techniques, such as screen printing, spin-coating, gravure printing, and others. Also, as biopolymers are contained in abundant resources, the fabricated devices are generally of low-cost [1; 2; 3; 4; 5]. These fascinating characteristics of OTFTs make them potential candidates for moveable memory devices [6], active matrix displays [7], radio frequency identification tags [8], and biochemical sensors [9].  
The selection of biopolymer is essential to fabricate a BioTFT memory device with good performance, as various biopolymer dielectrics have significant effects on the electric properties of OTFT memory devices [10]. [PERSON] et al. have first reported the preparation of DNA-cetyltrimethylammonium (CTMA) complexes, which were soluble in organic solvents, and proven to effectively reduce counter sodium ions [11]. After that, many researchers utilized various cationic surfactants to prepare electronic devices, including OTFTs, organic light emitting diodes (OLEDs), and even solar cells [12; 13; 14; 15; 16; 17; 18]. In our previous work [19; 20; 21], we have improved the performance of BioTFT memory devices through applying biopolymerDNA-octadecyltrimethylammonium (OTMA), and DNA-lauroylcholine (Lau) complex as the gate dielectric. The ON/OFF current ratio of DNA-OTMA-based BiOTFT devices has been improved significantly. The device exhibited a large memory window during recording of the transfer characteristic, and also, a stable memory performance based on switching response.  
Ribonucleic acid (RNA), one of the famous biopolymers, is a ribonucleic acid containing genetic instruction on the sequence of the hydroxyl groups, which could be applied as a good alternative to DNA. Purified RNA could be soluble in polar solvents such as water, and the resulting films are also water-sensitive, and have insufficient mechanical strength for applications in the photonics and optoelectronics fields [22; 23]. So, there are still many works need to be done on the modification of RNA-based complex to improve the electric performance.  
In this work, we utilized RNA-CTMA complex to fabricate the BioTFT memory device. The RNA-CTMA complex film was obtained via an ion exchange reaction, and the structure evolution of the film has been studied by the optical analysis, including UV spectra and circular dichroism (CD) spectra. Furthermore, the thermal stability, surface roughness, and microstructure of the RNA complex have been investigated. Finally, the OTFT devices were fabricated and characterized, which exhibit a large memory window and stable device performance.
# WAMs Based Eigenvalue Space Model for High Impedance Fault Detection
## 1 Introduction
### Eigenvalue Space  
As previously mentioned, one of the advantages of the State Space representation is that it offers inherent access to the eigenvalues of a system. In the context of this work, the eigenvalues represent the frequencies of oscillation of a system, which can be represented as complex numbers. These eigenvalues evolve overtime and thus provide an estimation of the system's operating condition. In this work, the impedance of the transmission line is referred to as the system, and the eigenvalues are the frequencies of oscillation of the powerline. This work takes advantage of the unique nature of the eigenvalues of a system. One can think of eigenvalues as the finger-prints of the system, a unique identifier arising from a combination of resistance, capacitance, and inductance seen across the system. The temporal characteristics of the eigenvalues are accounted for in this work, as they are estimated dynamically, and their drift over time is used to make decisions regarding the status of the system. Eigenvalues are mapped into a space this works refers to as the Eigenvalue Space, which is based on the Cartesian Coordinate System. This is done to avoid complications related to the handling of complex numbers.
## 4 Results and Analysis
### Trading Results for Interval II  
Figure 12 shows the trading pattern for Interval II using different trading methods. For Interval II, the customers traded the majority of their demand from the market due to a relatively higher excess generation of the prosumers compared to the customers' demand (Figure 12a). The market operator cleared only a small portion of the customers' demand at the RTP for Interval II. Contrarily, the prosumers traded a small portion of their PV energy with the customers (Figure 12b), but the majority of the PV energy of the prosumers was cleared by the market operator as an FIT. This led to a lower welfare of the prosumers for Interval II when compared to the customers, as is evident from Figure 12c. The same trend was observed for the GT approach, i.e., due to a small value of the \(\alpha_{j}\), the welfare of the market participants was lower as compared to the ZI strategy.  
Figure 11: Convergence of the Stackelberg game for one of the samples of market. (**a**) Prosumers’ power. (**b**) Customers’ savings.  
Figure 12: Comparison of P2P trading results for Interval II (excess PV generation of prosumers was higher compared to the demand of the customers) for different trading strategies. (**a**) Customers’ trading pattern for Interval I. (**b**) Prosumers’ trading pattern for Interval I. (**c**) Welfare metrics for Interval I.
## 6 Simulation Result and Analysis
### Convergence Analysis of DRL-QoS-RA Method  
Figure 3 shows the changing of reward through a training process when the terminals' data transmission frequency is five per hour, representing the convergence effect of the methods. The horizontal axis represents the number of transmission requests, while the vertical axis indicates the value of the reward. The shaded area is drawn according to the standard deviation of \(r_{t}\) to show its fluctuation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm Parameters** & **Value** \\ \hline Replay start size & 2000 \\ Replay memory & 20,000 \\ Batchsize & 32 \\ Target network update step & 50 \\ Discount factor & 0.99 \\ Initial exploration rate & 1.0 \\ Final exploration rate & 0.01 \\ Exploration rate decay & \(5\times 10^{-4}\) \\ Learning rate & 0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 2: DRL-QoS-RA algorithm parameters.  
The replay start size of DRL-QoS-RA is set to 2000. The agent uses random decision mechanisms for the environment exploration and experience gathering when the number of requests is below 2000. As a result, the reward is not significantly improved in this stage. Then, the value of the reward shows significant growth, indicating the improved performance of the algorithm. After the number of requests reaches 8000, the fluctuation of \(r_{t}\) is reduced, implying the convergence of DRL-QoS-RA.  
Figure 4 shows each methods' performance on the reward \(r_{t}\). Similar to Figure 3, the horizontal axis represents the number of transmission requests, while the vertical axis denotes reward \(r_{t}\), which is drawn by averaging steps of 100.  
First of all, the performance of the three methods based on DRL, namely DRL-QoS-RA, DRL-RA, and DRL-EERA, is similar to that of the random mechanism before the training process of DRL. The reward of the three methods increases with the number of iterative steps.  
On the training process, when the request number is between 2000 and 6000, DRL-RA and DRL-EERA take about 4500 training times to achieve convergence, while DRL-QoS-RA only needs 2000 training times. Such an improvement on convergence efficiency can be mainly attributed to the following two points: (1) Compared with DRL-EERA and DRL_RA, DRL-QoS-RA formulates the condensed feature tensor rather than the intuitive location-based zero-padding mechanism, which is used by DRL-EERA and DRL_RA. As a result, the deep network can perceive the critical information more easily and reduce  
Figure 4: The simulation process of each methods’ training process.  
Figure 3: Convergence process of DRL-QoS-RA method.  
the number of parameters that the deep network needs to train. (2) The state space of DRL-QoS-RA contains more contextual information, such as the amount of data to be transmitted, channel quality, and QoS requirement.  
When the request number is more than 6000, all four methods obtain convergence. Compared with the GA method, the reward after convergence of DRL-RA is lower by 14.3%, and the DRL-EERA method achieves similar performance, while the reward of the proposed DRL-QoS-RA method is 11% higher than that of GA.
# Hypoxia Modulates Regenerative Potential of Fetal Stem Cells
## 3 Results  
Effects of Hypoxia on Proliferation Potential, Surface Marker Expression, and Stemness Gene Expression of Fetal Cells  
Despite no significant differences in cell morphology between NPCs and SDSCs during expansion (Figure 2A), RT-qPCR data showed a discrepancy in stemness gene expressions of these two human fetal cells and their response to hypoxia (Figure 2B). In brief, fetal NPCs exhibited higher expression of _MYC_, _NANOG_, and _SOX2_ than fetal SDSCs whereas fetal SDSCs displayed higher expression of _KLF4_, _NES_, and _NOV_ than fetal NPCs. Interestingly, hypoxia decreased _MYC_, _BMI1_, and _NES_ and increased _NANOG_ and _SOX2_ in both fetal cell types.  
Relative EdU incorporation data (Figure 2C) and SSEA4 expression (Figure 2D) indicated that SDSCs exhibited higher proliferation capacity than NP cells, both of which increased in median intensity after hypoxia treatment. Flow cytometry data also showed that SDSCs exhibited higher expression of CD73 (Figure 2E) despite lower expression of CD90 (Figure 2F), CD105 (Figure 2G), and CD146 (Figure 2H) in median intensity compared to NPCs; interestingly, hypoxia increased median intensity of the above CD markers of both fetal cells to some degree compared to their normoxic counterparts.  
Figure 2: Evaluation of stemness gene expression, proliferation potential, and surface marker expression in fetal cells following either normoxia (N) or hypoxia (L) incubation. (**A**) Fetal NPC and SDSC cell morphology during expansion. (**B**) RT-qPCR assessed the expression of stemness genes (_MYC_, _KLF4_, _BMI1_, _POU5F1_, _NES_, _NOV_, _NANOG_, and _SOX2_) in both fetal cell types after hypoxia treatment, normalized against _GAPDH_ levels as an internal control. Data (_n_ = 4) are represented in bar charts. Different letters indicate a statistically significant difference compared to the groups within the same gene type (_p_ < 0.05). Flow cytometry assessed the positive percentage and median intensity of relative EdU incorporation (**C**) and expression of MSC surface markers SSEA4 (**D**), CD73 (**E**), CD90 (**F**), CD105 (**G**), and CD146 (**H**).
## 4 Methodology
### Test Time Augmentation  
Test time augmentation (TTA) has been widely used to improve the accuracy of computer vision models in various domains, including object detection. By applying transformations such as rotations, flips, and changes in brightness and contrast to test images, TTA can effectively increase the diversity of the test data, leading to more robust and accurate models. This is particularly important in the context of detecting helmets, where variations in lighting conditions, camera angles, and helmet types can greatly affect the accuracy of the model. TTA can help mitigate these issues by providing the model more diverse test data, allowing it to better generalize to unseen data. Several studies have demonstrated the effectiveness of TTA in improving the accuracy of helmet detection models, suggesting that it is a valuable technique for this task. However, it should be noted that TTA can increase the computational cost of testing, and careful consideration should be given to the trade-off between accuracy and computational efficiency.
## 3 Finite Element Method (FEM)
### Pile Spacing  
Figures 9 and 10 show the relationship between the soil compaction coefficient and the pile spacing. However, due to possible coupling between the pile diameter and the pile spacing, in order to investigate the sensitivity of the influence of the pile spacing on the compaction coefficient to the pile diameter, three groups of supplementary numerical analyses (\(D\) = 0.5, 0.6, and 0.7 m) were carried out. It can be found from Figures 9 and 10 that the variation in the pile diameter \(D\) has no effect on the variation rule of the soil compaction coefficient with the pile spacing.  
The parameter \(n\) was defined as the ratio of the pile spacing to the pile diameter, and the soil compaction coefficients at the centroid and the edge midpoint were defined as \(\eta_{\rm cc}\) and \(\eta_{\rm cm}\), respectively. Based on the SP\(-\)PSC pile group tests and numerical analyses, themechanical model of the change in the soil compaction coefficient at the centroid and the edge midpoint with the change in the pile spacing was obtained as Equations (5) and (6).  
\[\eta_{\rm cc}=0.74+\frac{4.41}{1+e^{\frac{n+1.33}{2.86}}}\;(n\geq 1) \tag{5}\]  
\[\eta_{\rm cm}=0.74+\frac{69}{1+e^{\frac{n+1.65}{2.86}}}\;(n\geq 1) \tag{6}\]  
Based on theoretical analysis, when the pile spacing \(n\) tends to 1, the soil compaction coefficients at the centroid and the edge midpoint should equal to 1. Furthermore, when the pile spacing \(n\) tends to infinity, based on [PERSON]'s principle, the soil compaction coefficients at the centroid and the edge midpoint should equal 0.74, which is the compaction coefficient of undisturbed soil. According to the presented mechanical models of the soil compaction coefficient, it can be found that the soil compaction coefficients at the centroid and the edge midpoint are 1 and 0.74, corresponding to \(n=0\) and \(n\sim\infty\), respectively.