esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Analysis
### Debris Flow Characteristics
#### 4.1.5 Mobility Ratio  
Figure 10 shows the debris flow mobility ratio observed through the flume experiments and the percent decrease in mobility ratio due to the installation of the berm. Figure 10a,b show the debris flow mobility ratio according to \(\alpha\) and \(C_{V}\), respectively. Figure 10c,d show the percent decrease in the debris flow mobility ratio according to \(\alpha\) and \(C_{V}\), respectively, when the berm was installed. The experimental results confirmed that the debris flow mobility ratio increased as \(\alpha\) or \(C_{V}\) increased (Figure 10a,b). In the case of the straight channel test, the incremental increases in the mobility ratio due to the increase in \(\alpha\) were similar when \(C_{V}\) was less than 0.55, but larger when it was 0.55 or greater. The incremental increases in mobility ratio due to the increase in \(C_{V}\) were similar when \(\alpha\) was 15\({}^{\circ}\) or greater, but considerably smaller when it was less than 15\({}^{\circ}\). In the single-berm channel test, the incremental increases in the mobility ratio due to the increase in \(\alpha\) were similar when \(C_{V}\) was less than 0.55, but larger when it was 0.55 or higher. The incremental increases in mobility ratio due to the increase in \(C_{V}\) were similar when \(\alpha\) was 20\({}^{\circ}\) or greater, but relatively smaller when it was less than 20\({}^{\circ}\). In addition, the debris flow mobility ratio was observed to decrease by 4.6-15.7% due to the installation of the berm on the slope (Figure 10c,d). The average reductions in the debris flow mobility ratio were found to be 9.8%, 9.4%, 9.1%, and 10.2% for a \(C_{V}\) of 0.40-0.55 and \(\alpha\) values of 10\({}^{\circ}\), 15\({}^{\circ}\), 20\({}^{\circ}\), and 25\({}^{\circ}\), respectively, and 13.6%, 10.0%, 8.2%, and 6.7% for an \(\alpha\) of 10\({}^{\circ}\)-25\({}^{\circ}\) and \(C_{V}\) values of 0.40, 0.45, 0.50, and 0.55, respectively. In other words, the installation of the berm was more effective in reducing the debris flow mobility ratio at lower \(C_{V}\) values.
# Biosimilar Medicines: From Development Process to Marketing Authorization by the EMA and the FDA
## 2 Overview of Biosimilar Medicines
### Biosimilars vs. Generics  
There is a common misconception that biosimilar medicines are the same as generic drugs. Both are copies of products already on the market; however, unlike innovative medicines (small-molecule drugs and biologics), which must go through an extensive phase of research, non-clinical and clinical studies and post-authorization evaluations, the development and approval processes of generics and biosimilars follow shortened pathways, based on the data that already exist from their reference medicines [6]. Consequently, they cost less to develop and take less time to reach the market, making them very valuable alternatives for patients, as they can access treatments with the same quality as the original products at a more affordable price.  
Generic medicines are developed through an easily replicable process that uses a series of predictive chemical reactions, and their APIs are small molecules whose characterization, safety and efficacy are well-established [21]. The manufacturing process of generics takes around 2-3 years and the costs involved are relatively low [22]. On the other hand, the development of biosimilar medicines is much more complex since they use large molecules derived from living organisms as the active substance that carry some inherent variability [2]. Therefore, there may be some differences due to the nature of these products and their manufacturing process. These small variations are acceptable as long as they do not affect the safety, efficacy and quality of the biosimilar [22]. The development process of  
Figure 2: Comparison between the development phases of a biosimilar medicine and a reference biological product, together with the number of years required [16].  
biosimilars is longer than that of generics, taking 5-9 years and the associated costs are higher [6; 13].  
Generic medicines must demonstrate through bioequivalence studies, that their behavior in the body is the same as the reference medicine.  
Owing to the complexity of biosimilars, demonstration of bioequivalence is not enough to prove their similarity. Comparability studies directly compare a biosimilar to its respective reference and must include a comprehensive evaluation of structural and functional attributes, along with confirming similarity in efficacy and safety [14]. For biosimilars undergoing manufacturing process changes, analytical and functional comparability studies are typically enough to prove similarity. However, sometimes it is necessary to evaluate the impact of certain changes in the production process [23; 24].
# Evaluation of IMERG Precipitation Products in the Southeast Costal Urban Region of China
###### Abstract  
The intensification of extreme precipitation has aggravated urban flood disasters, which makes timely and reliable precipitation information urgently needed. As the high-quality and widely used satellite precipitation products, Integrated Multi-satellite Retrievals for GPM (IMERG), have not been well investigated in coastal urban agglomerations where damages from precipitation-related disasters are more severe. With precipitation measurements from local high-density gauge stations, this study evaluates three IMERG runs (IMERG ER, IMERG LR, and IMERG FR) in the southeast coastal urban region of China. The evaluation shows that the three IMERG products severely overestimate weak precipitation and underestimate heavy precipitation. Among the three runs, the post-corrected IMERG FR does not show a substantial improvement compared to the near-real-time IMERG ER and IMERG LR. The performance of IMERG varies depending on the precipitation pattern and intensity, with the best estimation ability occurring in the coastal urban region in summer and in the northern forests in winter. Due to the year-round urban effect on precipitation variability, IMERG cannot detect precipitation events well in the central high-density urban areas, and has its best detection ability on cultivated lands in summer and forests in winter. Within the urban agglomeration, IMERG shows a poorer performance in areas with higher urbanization levels. Thus, the IMERG products for coastal urban areas need considerable improvements, such as regionalized segmental corrections based on precipitation intensity and the adjustment of short-duration estimates by daily or sub-daily precipitation measurements.  
precipitation evaluation; high-density; gauge; IMERG; coastal urban 2022  
1
# Power Consumption Comparison of GPU Linear Solvers for Cellular Potts Model Simulations
## 1 Introduction
In this work, the structure of the algorithm is modified by varying the linear solver through the use of other three different solvers: LU decomposition, the Gauss-Seidel method, and a custom-made version of the conjugate gradient method (CG). The effects of these choices are measured both in terms of execution time and power saving. By making the Cellular Potts model simulations more energy efficient, one can run more experiments with less power. This is especially important because CPM simulations involve intensive computations. So, improving energy efficiency helps the environment and allows for more detailed studies of cell behavior, leading to better treatments for diseases. Table 1 summarizes the main studies and implementations in the CPM field by highlighting the main contributions of the proposed work, that is, a fair power consumption comparison of GPU linear solvers for the CPM. The rest of the paper is organized as follows: Section 2 provides the mathematical background and principles underlying the Cellular Potts model; Section 3 presents the parallel algorithm including the use of the alternative solvers and shows details about a new custom version of the CG method, and the strategy to parallelize it; Section 4 presents experimental tests highlighting the enhancements in terms of execution time and comparing the energy savings of the proposed solvers. Section 5 addresses the findings about the proposed software by comparing it with existing software. Finally, Section 6 concludes the paper. \begin{table}
\begin{tabular}{c l c c c} \hline \hline \multicolumn{1}{c}{**M**} & **Solver** & \(\mathbf{N_{\tau}}\) **= 3000** & \(\mathbf{N_{\tau}}\) **= 6000** & \(\mathbf{N_{\tau}}\) **= 9000** \\ \hline
150 & [PERSON] & 79.54 & 129.02 & 253.17 \\  & LU & 72.04 & 96.64 & 191.88 \\  & Gauss–Seidel & 65.81 & 80.73 & 140.07 \\  & Custom CG & 49.55 & 64.92 & 112.83 \\ \hline
300 & [PERSON] & 107.79 & 198.84 & 307.56 \\  & [PERSON] & 79.30 & 154.92 & 226.06 \\  & [PERSON]–[PERSON] & 61.34 & 126.92 & 194.02 \\  & Custom CG & 52.22 & 86.92 & 130.31 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Execution time (in seconds) comparison across different GPU solvers. Here, for all simulations \(N=600\) and \(N_{c}=400\); the time iterations \(N_{\tau}\) and number of Metropolis trials \(M\) are varied.
# A Study on Design Requirement Development and Satisfaction for Future Virtual World Systems
## 2 Trend Analysis  
Studying the trends of virtual-world-related technologies can provide evidence for future virtual world system architecture design. To expand the scope covering all related techniques and obtain some rudimentary knowledge of their trends along with the change of time, we conducted a search on Statista (Statista is an online portal providing more than one million statistics collected from more than 22,500 trusted partners, such as Experian Simmons and Euromonitor. It maintains sources transparency and adherence to academic standards: [[https://www.statista.com/sources/l/](https://www.statista.com/sources/l/)]([https://www.statista.com/sources/l/](https://www.statista.com/sources/l/))) with the keyword \"Virtual World\", which returned 821 results, including statistics, forecasts, and studies (the search result can be downloaded from [[https://sunniel.github.io/VirtualNet/post/trend-analysis/Statista.xlsx](https://sunniel.github.io/VirtualNet/post/trend-analysis/Statista.xlsx)]([https://sunniel.github.io/VirtualNet/post/trend-analysis/Statista.xlsx](https://sunniel.github.io/VirtualNet/post/trend-analysis/Statista.xlsx))). The search results were then examined by relevance and filtered by the year range from 2015 to 2035. Here, we manually identify the relevance by inspecting the titles of results. For example, \"Estimated virtual reality (VR)/augmented reality (AR) headset unit shipments by type\" is relevant to virtual world system design, while \"Top earners in virtual worlds of Warcraft\" is not. This reduced the results to 538. We, then, tagged them with relevant topics and showed them in a bar chart in Figure 1. With the obtained knowledge, we further looked into each scope and investigated the trends of related technologies, including mobile computing, social networks, virtual/augmented reality, virtual world applications, internet of things (IoT), wearable devices, game intelligence, multisensory multimedia, and computer graphics. The trends were analyzed along with five directions: mobility, diversity, interconnectivity, immersion, and intelligence. Instead of exploring the details of specific technologies, the trends are mainly studied from the perspective of revenue, investment, population, and consumption (which are the representative form of market/industrial evidence on Statista, but may not be all available in each direction) of them to show industrial, market, and user acceptance and adoption, as virtual world applications have to be built on top of them. They are elaborated in the subsections below, followed by the implications for future virtual world design.
## 4 Nutritional and Bioactive Potential of Fresh Fruit and Vegetable Waste
### Vegetables
#### 4.2.3 Potato  
Potato is the fourth main crop consumed worldwide and it is an important constituent in the human diet [148].  
Potato peels represent about 10% of the total potato waste and are an inexpensive source of valuable bioactive compounds, such as secondary metabolites and cell wall materials, which can be used to functionalize foods or replace synthetic additives with natural ingredients [179; 180]. Studies have shown that potato peels contain glycoalkaloids, such as \(\alpha\)-solanine and \(\alpha\)-chaconin, polysaccharides, the phenolic compounds quercetin and rutin, and ferulic, gallic, p-coumaric, caffe, and chlorogenic acids [180; 181], which are important precursors for steroid hormones and natural antioxidants, respectively [149]. Acylated anthocyanins were identified in the peels of red and purple potato varieties, with pelargonidin, penoidin, and malvidin being the most prominent aglycones [150].
# Estimating Forest Carbon Stock Using Enhanced ResNet and Sentinel-2 Imagery
## 4 Results
### Error Analysis
#### 4.3.3 Case Study of Extreme Prediction Error  
To further assess model reliability in challenging ecological contexts, we analyzed individual test cases with large prediction deviations (>30 Mg/ha). One representative case involved a high-elevation plot at 2350 m, where the observed carbon stock was 128.1 Mg/ha. Traditional models significantly underestimated this value, with predictions from RF, GBDT, and ViT all falling below 80 Mg/ha. In contrast, the Enhanced ResNet predicted 124.6 Mg/ha, closely matching the ground truth.  
Field records indicated that this plot was located in a dense, mature coniferous-broadleaf mixed stand with a multi-layer canopy structure. Such complex spatial and spectral patterns are typically difficult to capture using hand-crafted features or shallow models. The near-accurate estimation by the Enhanced ResNet highlights its strength in leveraging multiscale spatial cues and adaptive spectral attention to model forest heterogeneity. This case underscores the practical advantage of the proposed method in ecologically complex and high-biomass regions--conditions where accurate carbon accounting is often most critical.
# Satellite SAR Data-based Sea Ice Classification: An Overview
###### Abstract  
A review of the main approaches developed for sea ice classification using satellite imagery is presented. Satellite data are the main and very often only information source for sea ice classification and charting in the remote arctic regions. The main techniques used for ice classification and ice charting in several national ice services are considered. Advantages and disadvantages of various SAR data-based methods for ice classification are analyzed. It is shown that an increase of SAR technical abilities contributes to the enhancement of sea ice classification reliability. The possible further development of satellite data-based methods for ice classification is discussed.  
sea ice cover; satellite remote sensing; SAR data processing; sea ice classification 13 March 2019; 28 March 2019; 31 March 2019
# High Reliable Uplink Transmission Methods in GEO-LEO Heterogeneous Satellite Network
## 5 Simulation Results
### Outage Probability With Ordered Channel Gain  
In this subsection, we provide simulation results under the condition of Assumption 1. Figure 6 illustrates the results in an FHS environment with \(\alpha=0.2\). It can be clearly noticed that NOMA can provide a huge performance gain compared with cooperative OMA when we consider channel ordering. This is because \(|\rho_{2,l}|^{2}<|\rho_{1,l}|^{2}\) is assumed in channel ordering circumstances. In this case, \(|h_{2}|^{2}<|h_{1}|^{2}\) always holds when calculating the SINR. Then, the interference can be relatively relieved while the diversity order of \(U_{1}\) and \(U_{2}\) increases. The result can also be confirmed from Remarks 1 and 2. In real uplink transmission scenarios, the channel ordering exists since the signal of a beam-edge user often experiences significant path loss. Further, we can find that the asymptotic outage  
Figure 4: Outage probability without channel ordering in AS when \(\alpha=0.2\).  
Figure 5: Outage probability without channel ordering in AS when \(\alpha=0.4\).  
probability approximates the exact outage probability curves well, which validates the correctness of Corollaries 1 and 2.  
By setting a relatively high achievable rate for \(U_{1}\) and \(U_{2}\), we derive the outage probability in Figure 7, where \(R_{1}=0.7\) bps and \(R_{2}=0.4\) bps. The power factor for \(U_{2}\) is assumed to be \(\alpha=0.4\). It is noteworthy that the performance of \(U_{1}\) has degraded since the system provides less power for \(U_{1}\). Another reason is that we set a higher achievable rate for \(U_{1}\). This will cause outage probability performance degradation. Again, NOMA outperforms cooperative OMA when the channel is ordered.
## 2 Materials and Methods
### Machine Learning Classifiers
#### 2.3.6 AdaBoost  
Adaptive boosting is an ensemble algorithm that constructs a strong classifier via the linear combination of weak classifiers. The AdaBoost algorithm begins with a base learner, mostly a decision tree, and weighting all the training samples equally. At every iteration, the weights of all the instances are updated, and the weights of the correctly predicted samples are decreased while the weights of incorrectly predicted samples are increased [37]. Therefore, wrongly predicted samples have higher weights in subsequent training rounds, which ensures the algorithm pays more attention to these samples. The final prediction is obtained through the weighted sum of the predictions from the various base models [40]. Assuming \(D=\{(x_{1},y_{1}),\ldots,(x_{i},y_{i}),\ldots,(x_{n},y_{n})\}\) represents the training set, where \(y_{i}\) represents the class label of sample \(x_{i}\), and \(y_{i}=-1,+1\), the weights are assigned to every example in \(D\) at each iteration [55]. Given \(t=\{1,\ldots,T\}\) number of iterations and \(h_{t}\) (x) weak classifiers that are trained using the base learner \(L\), then the sample weight \(D_{1}\) and weight update \(D_{t+1}\) are computed using:  
\[D_{1}(i)=\frac{1}{n},\ i=1,2,\ldots,n \tag{9}\]  
\[D_{t+1}(i)=\frac{D_{t}(i)}{Z_{t}}exp(-\alpha_{t}y_{i}h_{t}(x_{i})),\ i=1,2, \ldots,n \tag{10}\]  
where \(Z_{t}\) represents a normalization parameter and \(\alpha_{t}\) is the weight of the classifier \(h_{t}(x)\). The classifier weight \(\alpha_{t}\) measures the importance of \(h_{t}(x)\) when calculating the final ensemble prediction. Meanwhile, the samples that are wrongly predicted in \(h_{t}(x)\) are assigned higher weights in the \(t+1\) training round [56; 57]. The goal of the weak classifiers is to minimize the error rate \(\epsilon_{t}\):  
\[\epsilon_{t}=P[h_{t}(x_{i})\
eq y_{i}]=\sum_{i=1}^{n}D_{i}(i)I[h_{t}(x_{i}) \
eq y_{i}] \tag{11}\]  
Finally, after the given number of training rounds, the ensemble classifier is achieved using:  
\[H(x)=sign(\sum_{t=1}^{T}\alpha_{t}h_{t}(x)) \tag{12}\]
# Conductive Textiles for Signal Sensing and Technical Applications
## 6 Challenges and Future Outlook  
During the past two decades, conductive textiles have been a promising segment of textile engineering and have drawn significant research interest. Though outstanding progress has been achieved in this segment through the relentless effort of researchers, there are still some challenges that hinder the commercialization of conductive textiles on a large scale. Most of the textile materials are not conductive, and it is a daunting challenge to make them conductive by incorporating conductive materials within them. There are several challenges to conductive textiles, including connectivity, textiles and conductive materials and their fabrication, and wear of the materials [350]. Besides these challenges, the designer faces some challenges during the prosperity of conductive textiles, such as mechanical environment, washability of the textiles, power supplies in the circuit, commercialization, and cost of the conductive materials [5]. Durability is one of the major concerns for conductive textiles because textiles are prone to wear abrasion, bending of the coated materials, and stretching [351]. The mechanical characteristics of textiles sensors resist being flexible and permeable to air and water [249]. The mass production of conductive textiles is challenging due to the self-energy generation form and the high number of elements [288]. The textile sensor is one of the widespread areas of conductive textiles in which the conventional concept of attaching the solid-state electrical components with textile materials has been turned into flexible e-textiles capable of sensing various phenomena. Until now, one of the major challenges in this regard is maintaining the efficiency of these textile sensors compared to the solid components [352]. The challenge with textile connectors is maintaining the connectivity eminetly through the complete textile surface during the assembling stage of developing wearable e-textiles [353]. The textile composite electrode has drawn eminent attention to converting conventional batteries and supercapacitors into flexible energy storage devices. Some challenges are still present to pave the way for the commercialized production of these flexible electrodes. These possess relatively higher resistance, reduced mechanical strength of yarn-like electrodes, and lower energy density than rigid-state batteries and supercapacitors [354]. Higher resistance is mainly due to textiles' porous structures and lower intrinsic conductive nature. Textile yarn-like electrodes seem more vulnerable to mechanical stress, resulting in more yarn breakage during the fabrication process than conventional yarn [355]. This indicates a challenge to maintaining the high speed of industrial machinery during the fabrication process of these electrodes. Future researchers should focus on these challenges of conductive textiles to pave the way for large-scale commercial production of these materials.