esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of K-Band Power Amplifier with 180-Degree Phase-Shift Function Using Low-Power CMOS Process
## 3 Simulation and Measurement Results
The cause of the discrepancy of the phase differences of simulated and measured results is expected to be the difference in parasitic capacitance components in the cross-over region of the metal lines circled in Figure 4.  
Figure 12 shows the measured output spectrum and constellation for 64-quadrature amplitude modulation (QAM) cyclic prefix (CP)--orthogonal frequency division multiplexing (OFDM) signal with 100 MHz channel bandwidth under error vector magnitude (EVM) \(-\)25 dBc condition. When the EVM of \(-\)25 dBc was satisfied at 25.0 GHz, the measured output powers were 8.39 dBm and 8.53 dBm for the 0\({}^{\circ}\)- and 180\({}^{\circ}\)-modes, respectively. Table 1 shows a comparison with the previous CMOS PAs for 5G applications. The core size of the power amplifier of this study excludes the areas of pads and decoupling capacitors. The PAE was measured somewhat lower due to the power loss in the power transistor due to the low \(g_{m}\) as the power amplifier was fabricated in the slow corner of the LP process. Due to the low \(g_{m}\) of the LP process, the size of the power transistor increased, and the increased transistor required relatively high driving power. Therefore, when \(g_{m}\) was low, power consumption in the driver stages as well as the power stage increased. For this reason, the PAE of the power amplifier designed using a low \(g_{m}\) power transistor deteriorated. However, although the designed power amplifier was fabricated under the slow corner condition of the LP process with a low \(g_{m}\), as shown in Figure 1, it can be seen that the designed power amplifier had reasonable output power and gain characteristics  
Figure 11: Simulation and measurement results: phases and phase difference of the 0\({}^{\circ}\)- and 180\({}^{\circ}\)-modes. Figure 12: Measurement results of linearity: (**a**) output spectrum and (**b**) constellation for 25 GHz 64-QAM CP-OFDM modulated signal with 100 MHz channel bandwidth under EVM \(-\)25 dBc condition. compared to those of previous works. In particular, thanks to the four stages structure, the designed power amplifier had the highest gain. Even though the LP CMOS process was used, the linear output power using the modulation signal was similar to that of previous studies. In addition, even though the -shift function was included, the compact size was secured thanks to the application of the proposed structure.
# Nanomaterials in Photocatalysis: An In-Depth Analysis of Their Role in Enhancing Indoor Air Quality
## 14 Cost and Economic Feasibility  
The integration of nanomaterials, particularly TiO\({}_{2}\)-based catalysts, into photocatalytic air purification technologies offers significant potential to enhance indoor air quality. These materials are capable of degrading a wide range of pollutants, including volatile organic compounds (VOCs), bacteria, and particulate matter.  
While the environmental and health benefits of improving indoor air quality are clear, the widespread adoption of photocatalytic air purification technologies faces several barriers, primarily cost and economic feasibility. For any technological advancement to transition from a research concept to a real-world application, a thorough economic evaluation is essential. This section explores the key factors influencing the cost of implementing nanomaterials in photocatalytic oxidation processes and provides a comprehensive analysis of their economic feasibility. However, it will not delve deeper into the detailed aspects of economic balance, such as return on investment (ROI), market dynamics, or cost-benefit optimization, as these topics require specialized expertise and would likely warrant a full paper dedicated to this subject [282]. Instead, the focus here is on the primary cost-related challenges and the general feasibility of incorporating nanomaterials into real-world applications.
# A Review of Lithium-Ion Battery Fault Diagnostic Algorithms: Current Progress and Future Challenges
## 3 The Role of BMS in Fault Diagnosis  
One of the main functions of the BMS is to minimize the risks associated with the operation of a lithium-ion battery pack to protect both the battery and the users. Hazardous conditions are mostly caused by faults, and the safety functions of the BMS should minimize the likelihood of occurrence and the severity of these faults. Sensors, contactors, and insulation are common features added to the battery system to ensure its safety [13]. There are also operational limits for voltage, current, and temperature, which are monitored with sensors connected to the cells [41]. However, as the hardware and software implementation of the BMS becomes increasingly complex, battery faults can be more complicated, and these safety measures are often not adequate [42; 43]. Fault diagnostic algorithms are, hence, a requirement for BMS. These algorithms serve the purpose of detecting faults early and providing appropriate and immediate control actions for the battery and the users [8]. Figure 2 illustrates the mechanism of fault diagnosis in the BMS.  
In the battery system, the BMS plays a significant role in fault diagnosis because it houses all diagnostic subsystems and algorithms. It monitors the battery system through sensors and state estimation, with the use of modeling or data analysis to detect any abnormalities during the battery system operation [13]. Since there are many internal and external faults, it is difficult to carry out this task efficiently. Various fault diagnostic methods need to work in tandem to correctly detect and isolate a specific fault, to administer the correct control action. However, the fault diagnostic algorithms in the BMS have limited computing space and time. Because of the large number of cells in the battery system in some applications, these fault diagnostic algorithms need to have low computational effort, while maintaining accuracy and reliability [44]. In recent years, there has been extensive effort in the research and development of efficient fault diagnostic approaches for Li-ion battery, which will be discussed in the following section.  
Figure 2: A schematic of fault diagnosis in the battery management system (BMS).
# Integrity Monitoring of GNSS Constellations with Only LEO-PNT Satellites +
## 2 Exploring the Options
### Option 1: LEO-PNT Supports ARAIM  
This is the basic option, in which the LEO-PNT constellation provides additional dual-frequency L-Band ranging measurements with a committed minimum performanceand without a bespoke integrity payload. This is of interest for ARAIM users [1], who benefit greatly from the additional geometrical redundancy provided by LEO satellites.
# A Review of Deep Learning Applications for Railway Safety
## 7 Station Safety  
### Accident Prevention  
A railway station is dynamic and complex due to the presence of many people, including passengers and station staff, and trains that stop and depart quickly. Therefore, it is necessary to prevent and deal with various safety accidents in stations. [PERSON] et al. [10] proposed a model that quickly identifies three safety incidents (fall, slip, and trip). It used diverse images of platforms, escalators, and tunnels captured by CCTV in the station. The CNN-based deep learning model classified input images into two classes (fall and not fall), and it achieved a high accuracy of 82.20% and an AUC value of 82.33%.
# Comparative Study of Energy Efficient Routing Techniques in Wireless Sensor Networks
## 3 The Proposed Taxonomy
### Application Type
#### 3.1.1 Event-Driven  
In event-driven protocols, a sensor node may send data only after the detection of a significant event over its sensing region, as shown in Figure 2. Those protocols are used in several domains thanks to their several advantages. In fact, they allow immediate detection of different triggered events. In addition, they permit to decrease the amount of communication and to avoid the unnecessary use of energy and computation resources of nodes. However, also, event-driven protocols present some limitations. Indeed, due to the fact that events are triggered randomly, the workloads will be unbalanced, and certain  
Figure 1: A taxonomy of routing protocols in WSNs.  
nodes will be activated more than the others. This will lead to the discharge and the death of some nodes, which can cause several problems, such as isolated areas.  
Many of WSNs' protocols are event-driven and can be classified into sink-centric or node-centric protocols.
# Research on Task-Oriented Computation Offloading Decision in Space-Air-Ground Integrated Network
###### Abstract  
In Space-Air-Ground Integrated Networks (SAGIN), computation offloading technology is a new way to improve the processing efficiency of node tasks and improve the limitation of computing storage resources. To solve the problem of large delay and energy consumption cost of task computation offloading, which caused by the complex and variable network offloading environment and a large amount of offloading tasks, a computation offloading decision scheme based on Markov and Deep Q Networks (DQN) is proposed. First, we select the optimal offloading network based on the characteristics of the movement of the task offloading process in the network. Then, the task offloading process is transformed into a Markov state transition process to build a model of the computational offloading decision process. Finally, the delay and energy consumption weights are introduced into the DQN algorithm to update the computation offloading decision process, and the optimal offloading decision under the low cost is achieved according to the task attributes. The simulation results show that compared with the traditional Lyapunov-based offloading decision scheme and the classical Q-learning algorithm, the delay and energy consumption are respectively reduced by 68.33% and 11.21%, under equal weights when the offloading task volume exceeds 500 Mbit. Moreover, compared with offloading to edge nodes or backbone nodes of the network alone, the proposed mixed offloading model can satisfy more than 100 task requests with low energy consumption and low delay. It can be seen that the computation offloading decision proposed in this paper can effectively reduce the delay and energy consumption during the task computation offloading in the Space-Air-Ground Integrated Network environment, and can select the optimal offloading sites to execute the tasks according to the characteristics of the task itself.  
Space-Air-Ground Integrated Network; computation offloading; Markov decision process; DQN +
Footnote †: journal:  
10 May 2021  
Footnote †: journal:  
**Publisher's Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.**
# ViT-Cap: A Novel Vision Transformer-Based Capsule Network Model for Finger Vein Recognition
## 3 Materials and Methods
### Motivation
#### 3.1.1 Overview of Vision Transformer  
Recently, the amazing effects of transformer models in natural language tasks have attracted the attention of computer vision. Researchers have proposed many transformer-based vision models, such as ViT [14], DETR [30], DeiT [31], GLiT [32], etc. Vision transformer is a modified NLP transformer for image classification without any convolutional layers. The image is split into patches, which are then flattened and put into a lower-dimensional embedding space. An extra token is added to each vector to denote its relative location in the image, and another learnable token is added to the entire sequence of vectors to denote the class. The sequence of vectors is fed to a standard transformer encoder, which has been modified with an extra fully connected layer at the end for classification.  
For vision transformers, the attention mechanism provides key advantages that CNNs do not have. The transformer can capture the long-range relationship and has dynamic adaptive modelling capability. Moreover, the transformer's self-attention mechanism has a built-in attention map, which provides a new way for models to make decisions. CNNs are not highly interpretable and can only provide a rough visualization. Transformer tokens provide more detailed attention images than CNN's limited receptive field, and the self-attention mechanism clearly simulates the interactions between each area in the image. However, vision transformers need a very large dataset to surpass CNNs. The performance of ViT can only achieve the performance of SOTA when it is trained on Google's private image dataset JFT-300M. This problem is particularly serious in the field of finger vein recognition due to the small data set of finger veins. As with the ViT model, CNNs perform worse when data is scarce.
# NPP-VIIRS DNB Daily Data in Natural Disaster Assessment: Evidence from Selected Case Studies
## 3 Results
### Storms
The average DNB radiance and PNL value for the composite image increased as the P\({}_{\text{nopower}}\) decreased. A linear regression analysis showed that the correlation coefficient (R\({}^{2}\)) between PNL and P\({}_{\text{nopower}}\) was 0.94 (Figure 6). \begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Pure Storms**} & \multicolumn{4}{c}{**Pue Storms**} \\ \hline Date & 22 August & 23 August & 24 August & 25 August & 30 August & 1 September & 18 September & 27 September & 28 September & 29 September & 7 October & 8 October \\ \hline Date & & & & & & & & & & & \\ Date & 29 & 28 & 27 & 23 & 21 & 19 & 2 & 7 & 8 & 9 & 17 & 18 \\ Date & & & & & & & & & & & \\ Closed & & & & & & & & & & & \\ Coverage & 15.2 & 8.1 & 11.4 & 11.1 & 19.9 & 19.7 & 20.3 & 41.2 & 71.8 & 48.2 & 70.8 & 34.4 \\ \hline \end{tabular}
\end{table} 
Table 6: The dates and cloud coverages of pre- and post-storm DNB images of Hurricane Maria. Figure 6: The correlation between PNL and the percent of customers without power (P\({}_{\text{nopower}}\)) in Puerto Rico. The second storm event we assessed was Cyclone Hudhud in Visakhapatnam, India. The dates of the pre-storm DNB images are listed in Table 8. Because the cloud cover was always high during that period, the 8-day MODIS reflectance data were affected by the cloud and did not reflect the true surface reflectance. Therefore, we removed the lunar contaminated DNB images (3 and 8 October). Figure 7 shows the pre- and post-storm averaging composite DNB images and the PNL image. Using the Mann-Whitney U-test, the difference between the pre-and post-earthquake DNB images was statistically significant at \(p<0.001\). A visual observation of the images showed that the scope and intensity of light reduced substantially after the cyclone. A pixel-by-pixel comparison showed that 695.3 km\({}^{2}\) of the area became darker, 11.8 km\({}^{2}\) became brighter, and 3.9 km\({}^{2}\) remained unchanged after the cyclone. The average radiance of the pre- and post-storm DNB were 7.8 nW\(\cdot\)cm\({}^{-2}\cdot\)sr\({}^{-1}\) and 2.4 nW\(\cdot\)cm\({}^{-2}\cdot\)sr\({}^{-1}\), respectively. The PNL for the whole study area was 30.8%. To test whether the linear regression model derived from the example of Hurricane Maria (Figure 6) could be used to estimate the power outage in other cases, we applied the model to Cyclone Hudhud. The P\({}_{\rm nopower}\) after Cyclone Hudhud was estimated to be 77.6%, which was in good agreement with the reported 20% or so power supply restoration work that was completed (that is, 80% of the power supply was not restored) by 16 October [77].
# The State of Ethereum Smart Contracts Security: Vulnerabilities, Countermeasures, and Tool Support
## 4 Smart-Contracts Vulnerabilities and Countermeasures
### Vulnerabilities and Countermeasures
#### 4.2.6 Randomness Generation  
Generating random numbers is a problem for many programming languages. The pseudo-randomness generation process leverages certain secret seeds to achieve a level of randomness. However, seeds cannot be privately stored on the chain in a smart contract in Solidity because everything on the blockchain is visible to its participants. The current practice is using block-relate information such as block.timestamp or block.hash. If historical blocks' timestamp or hash is used, attackers can use the same random number-generation process to obtain the same result because historical blocks never change; if using the future blocks, the process may be susceptible to malicious miners who can intentionally choose transactions and their execution orders.  
_Countermeasures_. Block-related information should not be used as an entropy source for randomness. The source should be outside of the blockchain environment. Other proposals are a crowd commit to the random number and reveal process (commit-reveal [80]) or RANDAO [81].
## 2 Materials and Methods
### Methods
#### 2.3.1 GPP Estimation with the VPM Model and the GPP Mean Calculation  
The VPM model is a GPP estimation model driven by the RS indexes, such as the EVI and land surface water index (LSWI), which was developed by [PERSON] [36] and is based on conceptual partitioning of vegetation canopies into nonphotosynthetic vegetation (NPV) and photosynthetically active vegetation (PAV) or chlorophyll (CHL). The EVI and LSWI were calculated by the MOD09A1 bands that include blue (459-479 nm), red (620-670 nm), near-infrared (841-875 nm) and shortwave infrared (SWIR, 1628-1652 nm). The calculation formulas of the EVI and LSWI and the calculation procedures of the model are referred to in [23]. Two indexes were combined with other datasets as input parameters of the VPM model to calculate the GPP time series data (Figure 3.). The formula is as follows.  
\[GPP=\varepsilon_{0}\times W_{scalar}\times P_{scalar}\times T_{scalar}\times FAP \AR_{PAV}\times PAR \tag{1}\]  
where \(GPP\) is the cultivated land \(GPP\) (Kg-C/m\({}^{2}\)), \(\varepsilon_{0}\) is the maximum light use efficiency, and \(T_{scalar}\), \(W_{scalar}\), \(P_{scalar}\) are the scalars for the effects of temperature, water, and leaf phenology on light use efficiency of vegetation, respectively. \(PAR\) (MJ/m\({}^{2}\)) is the photosynthetically active radiation in a period, \( FAP\AR_{PAV}\) represents the fraction of \(PAR\) absorbed by the vegetation canopy.  
To obtain the spatial distribution of the level of CLP during 2000-2018, the GPP mean of the period was calculated by the following formula:  
\[\overline{GPP}=\frac{1}{n}\sum_{i=1}^{n}GPP \tag{2}\]  
where \(\overline{GPP}\) is the mean during 2000-2018, and \(n\) is the number of years.  
Figure 3: Data Source Diagram of VPM Model Parameters.
## 3 Field Testing  
Field testing was conducted to validate the vibration-based monitoring method. The conventional deflection-based method as well as the proposed vibration-based method were applied to assess LTE at different joints. A comparative analysis was employed to validate the accuracy of the vibration-based method.
## 5 Challenges and Open Research Directions
### Tooling Maturity and Ecosystem Development  
Holochain's ecosystem is still in a nascent stage and suffers from immature tooling and limited community support compared to more established DLTs like IOTA and Hyperledger. These mature platforms provide robust SDKs, middleware, testing tools, and extensive documentation, which ease development and deployment in IoT environments. Holochain's relative lack of such resources slows development, increases onboarding difficulty, and complicates maintenance. Enhancing tooling, documentation, and community engagement is vital for wider adoption and competitiveness.  
Future research directions include the following:  
* Development of comprehensive SDKs, middleware, and standardized protocols to simplify integration with IoT systems and improve interoperability with other DLTs.
* Creation of advanced developer tools and resources, including debugging, testing, simulation environments, and detailed documentation.
* Fostering an active and supportive developer community by encouraging reusable components, plugin architectures, and feedback-driven iterative improvements in tooling and deployment practices.
## 3 Inherent Optical Properties (IOPs)
### Relationship between Effective Radius and Mode Radius  
The particle size distribution may also be characterized by an effective radius  
\[r_{\rm eff}=\frac{\int_{r_{\rm min}}^{r_{\rm max}}n(r)r^{3}dr}{\int_{r_{\rm min }}^{r_{\rm max}}n(r)r^{2}dr} \tag{70}\]  
and an effective variance  
\[v_{\rm eff}=\frac{\int_{r_{\rm min}}^{r_{\rm max}}(r-r_{\rm eff})^{2}n(r)r^{2 }dr}{r_{\rm eff}^{2}\int_{r_{\rm min}}^{r_{\rm max}}n(r)r^{2}dr} \tag{71}\]  
where \(r_{\rm eff}^{2}\) is included in the denominator of Equation (71) to make \(v_{\rm eff}\) dimensionless [35]. The effective radius, \(r_{\rm eff}\), can be used to describe the IOPs in an approximate manner as will be discussed below for cloud as well as snow/ice materials. For a single mode, the lognormal size distribution is given by [see Equation (67)]  
\[n(r)=\frac{dN(r)}{dr}=\frac{N}{\sqrt{2\pi}\sigma}\frac{1}{r}\exp\Bigg{[}- \left(\frac{\ln r-\ln r_{n}}{\sqrt{2}\sigma}\right)^{2}\Bigg{]}\]  
where \(r_{n}\) is the mode radius, \(n(r)\) is the number density or PSD in units of [m\({}^{-3}\cdot\) m\({}^{-1}\)] and \(N=\int_{0}^{\infty}n(r)dr\) [m\({}^{-3}\)] is the total number of particles per unit volume since  
\[\int_{0}^{\infty}\frac{dr}{\sqrt{2\pi}\sigma}\frac{1}{r}\exp\Bigg{[}-\left( \frac{\ln r-\ln r_{n}}{\sqrt{2}\sigma}\right)^{2}\Bigg{]}=1. \tag{72}\]  
With the change of variable \(x=\frac{\ln(r/r_{n})}{\sqrt{2}\sigma}\), Equation (72) becomes  
\[\frac{1}{\sqrt{\pi}}\int_{-\infty}^{+\infty}\exp(-x^{2})dx=1 \tag{73}\]  
and it can be shown that [35]:  
\[r_{\rm eff}=r_{\rm n}\exp[2.5\sigma^{2}], \tag{74}\]\[v_{\rm eff}=\exp\left[\sigma^{2}\right]-1 \tag{75}\]  
(see [3] for details).