esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### General Characteristics of De Novo Bacidia rubella Genome
#### 3.1.2 Homosekikaic Acid  
Homosekikaic acid is a major secondary metabolite in _Bacidia gigantensis_ as well as in both _Ramalina internedia_ and _R. peruviana_. However, it has not been found in _B. rubella_. To our knowledge, there was no putative PKS reported as being involved in the biosynthesis of homosekikaic acid, and its biosynthesis has not been characterized using gene expression or heterologous expression experiments. To identify candidate genes involved in the biosynthesis of homosekikaic acid in these three species, we used BLASTp on all predicted BGC sequences from the two _Bacidia_ and _Ramalina_ species. We identified three BGC candidates with BLAST similarity ranging from 53 to 93%, but none of them were a TI-PKS. Instead, we recovered Type 3-PKS homologs from _B. gigantensis_ (BGC 1.2), _R. internedia_ (BGC 6.1) and _R. peruviana_ (BGC 223.1) as potential homosekikaic biosynthetic genes; however, none of _B. rubella_ BGCs showed a high similarity to them. Furthermore, our TI-PKS phylogeny did not reveal any clade with both _Ramalina_ species and _B. gigantensis_ that excluded _B. rubella_. A possible explanation for the observed pattern is that the BGC responsible for synthesizing homosekikaic acid is present in the genome of _B. rubella_, but not expressed, and therefore homosekikaic acid is not produced in detectable amount. This hypothesis requires testing with gene expression experiments and analyses of substance profiles.
## 4 Functional Modules of the Proposed System
### Service Inquiry Posting Module  
An enrolled member with an issued key pair and wallet can start to post service inquiries on the Fabric blockchain. Using the issued key pair, a valid member accesses the peer, uploads his or her service inquiry, and evokes a service posting function, called the service proposal, in the form of service chaincodes. Then, the service proposal is posted on SC. For maximizing the liquidity and circulation of Time Coins, each posted service has a fixed available time slot for waiting for the match up with other qualified services, which fulfilled the conditions defined in each service inquiry.  
Each service inquiry has a lifecycle on the blockchain, and Figure 4 shows the transition diagram of the corresponding state machine of the service inquiry. The states of service inquiries can be divided into five stages: NewPost, Candidate, Designation, Confirmation, and Expiration, which precisely depict the life cycle. Although a service will be terminated sooner or later, the related data are recorded on the blockchain forever. The state information will help the system query for services that can be matched with high probability. Furthermore, the state promotes the system to solve the potential double-spending problem. We will mention the details about the double-spending problem in the next section.
# Future Directions for Electric Propulsion Research
## 4 Challenges for Electric Propulsion Development
### Low-Power Thrusters
#### 4.1.2 Sub-Kilowatt Gridded Ion Thrusters  
Sub-kilowatt gridded ion thrusters appear relatively high on their s-curve in Figure 3, inflecting in the early 1990s. The development of low-power gridded ion thrusters has followed a similar trajectory to sub-kW Hall thrusters in that there was significant advancement several decades ago, and since then, there has been renewed interest in very low-power devices over the last decade. Although much of the early research on GITs focused on \(>\)1 kW devices (in fact, even the very first \"Kaufman-style\" GIT to fly was operated at 1.4 kW [19]), steady development of low-power devices like the Radio-frequency Ion Thruster (RIT) series originally developed by the University of Giessen [20] and JAXA's Kiku ion thrusters [21] led to early inflection of the s-curve. Since then, there has been steady development of low-power GITs, like the 350 W microwave discharge gridded ion engines on JAXA's Hayabusa mission, which in total accumulated over 25,000 h of operation in flight [22]. More recent very low-power GITs to be developed are the Busek BIT series [23], Astrium's \(\upmu\)NRIT [24], and UCLA's Miniature Xenon Ion (MiXI) thruster [25], shown in Figure 6.  
Despite the recent development of these new systems, the majority of very low-power GITs remain less mature than higher power versions due to several major obstacles. Foremost is that ion thrusters require extensive power electronics to support the numerous electrodes involved in these designs. Naturally, then, significant development of more compact and efficient power processing systems is required to make sub-kW GITs practical. Second, the current that can be extracted from a gridded ion thruster is fundamentally space-charge-limited, such that for a given grid design, the beam current scales with the grid area of the device. Unlike in other technologies such as Hall thrusters where the current density can theoretically be increased for smaller thrusters to compensate for a lesser volume of plasma, this space-charge limitation constrains sub-kW GITs to low beam currents. Finally, gridded ion thrusters are often incidentally or intentionally \"source-limited\" rather than space-charge-limited [27], meaning that the ion flow into the grids is too small to realize the theoretical space-charge limit. Just as with Hall thrusters, for smaller GITs where wall losses may be exacerbated due to the small ratio of the volume-to-surface area, this source limiting may be more severe [28].  
Figure 6: The Miniature Xenon Ion (MiXI) gridded ion thruster; reproduced from [26] courtesy of the [PERSON] research group, UCLA.
## 4 Discussion
For example, at a medium LAI (LAI = 3), when Cab increased from low levels (25-30 \(\mathrm{\SIUnitSymbolMicro g}\) cm\({}^{-2}\)) to high levels (95-100 \(\mathrm{\SIUnitSymbolMicro g}\) cm\({}^{-2}\)), the indices increase by approximately 50% of their whole range of variation (Figure 8b,c), and hence did not show a strong correlation with LAI in the model-simulated data (\(\tau_{k}\) = 0.38). On the other hand, BNDVI (similarly to GNDVI) clearly saturated with LAI (Figure 7a,c), while GARI was more linear with LAI (Figure 7b). The slope of the GARI-LAI relationship, however, depended on Cab (Figure 8b). The slope varied from 0.94 to 0.19 when Cab increased from low (25-30 \(\mathrm{\SIUnitSymbolMicro g}\) cm\({}^{-2}\)) to high levels (95-100 \(\mathrm{\SIUnitSymbolMicro g}\) cm\({}^{-2}\)). SR displayed only slight saturation with LAI, regardless of MTA and the chlorophyll content. This index was largely insensitive to Cab (Figure 8h) and showed similar slopes (approximately 0.15) when plotted against LAI for MTA < 60\({}^{\circ}\). Unfortunately, MTA created varying offsets in the LAI-SR relationship (Figure 7h). As a result, SR showed only an average performance, with \(\tau_{k}\) = 0.41 and 0.53 in the field-measured and model-simulated datasets, respectively. Nevertheless, it could be the index of choice for mapping areas with limited variations in structure, e.g., covered by the same crop species. Indeed, together with MSAVI, SR was among the indices independent of Cab and producing the most linear relationships with LAI (Figure 8). For reasons unknown to us, MSAVI and SAVI were the worst performers with field-measured data (Table 4) and hence cannot be recommended based on this study. LAI and Cab affect canopy reflectance in a similar manner [51] in visible and near-infrared spectral regions, explaining the better performance of VIs in LAI estimation under high Cab. Although the relationships between VIs and LAIs may be tight for a limited set of species under a controlled environment, MTA, as well as other structural parameters, causes scatter in these relationships at larger scales and thus reduces the LAI retrieval capacity of the VIs. This may make the design of a universal optimal spectral index for all crops and growth conditions impossible [52]. LAI can still be rapidly and reliably estimated using VIs in breeding projects with limited within-sample structural variation in which early vigor is of interest. LAI estimation can be used to select the populations with the greatest leaf area as the most vigorous ones, as early vigor gives an advantage over weeds [53; 54]. VI-based LAI estimation could also be potentially used in optimizing crop production and the development of best crop management practices, such as the timing of application of water, fertilizers, and pesticides [55; 56; 57].
# Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives
## 2 Historical Review
### Characteristics of Research Output
#### 2.1.2 Historical Trend  
While [PERSON] [33] first observed the SUHI phenomenon in 1972, SUHI research was not a highly active field between 1972 and 2005, with less than five studies published per year (Figure 1). SUHI publications started to grow exponentially in 2005. The number of studies from 2006 to 2010 was 40% higher than the total number of publications from 1972 to 2005 (74 vs. 53). The growth rate has continued to increase, with more than 75 articles published in recent years. This increase can be attributed, on one hand, to the increasing interest in this area associated with rapid urbanization [17] and global warming [10]. On the other hand, advancements of remote sensing techniques and computing power have dramatically enhanced the quantity and quality of remote sensing data, and, therefore, contributed to increased SUHI research [28]. In particular, the freely-available Landsat series data since 2008 and MODIS data have largely reduced the research costs in recent years.
# IoT Services for Monitoring Food Supply Chains
## 3 Methodology and Implementation  
In recent years, the adoption of LoRaWAN technology has surged within the realm of cold chain monitoring applications. This is primarily due to its extensive coverage capabilities, low energy consumption, and remarkable scalability, which make it an ideal solution for tracking temperature-sensitive goods throughout the supply chain. By leveraging LoRaWAN technology, businesses can enhance their cold chain operations, ensuring that products remain within the required temperature thresholds and preventing spoilage, thereby guaranteeing the safety and efficacy of perishable products.  
A cold chain monitoring system that utilises LoRaWAN technology generally comprises several key components, including a network of LoRaWAN sensors, LoRaWAN gateways, a cloud server, and a user-friendly application. To ensure effective monitoring, high-quality devices, such as LoRaWAN temperature and humidity sensors and LoRaWAN trackers, can be strategically deployed in various critical locations, including storage warehouses, refrigerated transport vehicles, and distribution centres. The role of the LoRaWAN gateway is crucial, as it gathers environmental data from the temperature and humidity monitoring devices within the cold chain. This collected information is then transmitted to a cloud-based platform for thorough processing and analysis. As a result, businesses gain the capability to maintain comprehensive oversight of both the temperature conditions and the geographic location of their cold chain assets, ensuring that the integrity of their products is preserved throughout the entire supply chain.  
LoRaWAN is a freely available network protocol that is governed by the LoRa Alliance [21]. It is specifically designed to cater to devices requiring stringent energy resource management. The devices that utilise this network functionality are equipped with chips that employ the LoRa modulation technique. This particular technology prioritises long-range transmission capabilities over high-speed data transfer, making it ideal for implementing wireless sensor networks (WSN) where there is a need to transmit small data packets at consistent intervals. When compared to other wireless communication solutions for the Internet of Things (IoT) based on the 802.15.4 standard, LoRa modulation offers the advantage of requiring less complex transceiver devices [22]. This is because the system does not depend on an exact and costly reference clock signal source, allowing for a more cost-effective solution in environments where energy conservation and distance are of utmost importance.
# Development and Evaluation of Light-Weight Active Noise Cancellation Earphones
###### Abstract  
This paper presents the development of active noise control (ANC) for light-weight earphones, and proposes using music or natural sound to estimate the critical secondary path model instead of extra random noise. Three types of light-weight ANC earphones including in-ear, earbud, and clip phones are developed. Real-time experiments are conducted to evaluate their performance using the built-in microphone inside KEMAR's ear and to compare with commercially-available ANC headphones and earphones. Experimental results show that the developed light-weight ANC earphones achieve higher noise reduction than the commercial ANC headphones and earphones, and the in-ear ANC earphone has the best noise reduction performance.  
active noise control; light-weight earphone; natural sound; secondary path model +
Footnote †: journal: Applied Sciences
# Multi-Classifier Feature Fusion-Based Road Detection for Connected Autonomous Vehicles
## 3 Problem Statement
### Solution  
In this TLFC-RD method, better classification is achieved even in the changing environments by extracting the optimal features from the images. For example, LBP is used in the feature extraction to extract the local texture of the image, even under heavy lighting conditions. Therefore, a variety of features such as spatial pixels, color features, texton and LBP features are used to extract the optimal features. Further, the deep learning classifier used in the TLFC achieves an effective classification of roads into drivable areas and non-drivable areas. Next, the classified information is transmitted between the connected vehicles to gain information about the environment.
## 5 Open Issues and Challenges
### QoS and Reliability  
WBASNs have specific QoS requirements for each type of sensor or application [1; 7]. Specifically, the QoS constraint relies on bit error rate (BER) or the priority of a transmitted signal. In a scenario with interference in a hospital or a long queue in a public place, WBASNs with a high QoS constraint  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Interference** & **Robustness to** & **Lossy Channel** & **Self-Learning** & **Effectiveness** & **Cost** \\
**Mitigation Approach** & **Mobility** & **Support** & **Ability** & **Effectiveness** & **Cost** \\ \hline Power control & & Yes & No & Yes & Medium & High \\ approach & & & & & \\ MAC approach & Yes & No & Yes & High & High \\ Cognitive radio & & Yes & Yes & Yes & High & Low \\ approach & & & & & \\ UWB approach & & Yes & Yes & No & High & Medium \\ Signal processing & & No & Yes & No & Medium & High \\ approach & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the five interference mitigation approaches.  
should have a high priority to access the channel because they may carry vital data about a chronic illness such as a heart disease.  
Reliability is considered in terms of packet delay and probability of packet loss. The end-to-end delay of the received data depends on the WBASN application; for example, the delays for an ECG signal and a video signal must be below 250 ms and 20 ms, respectively [7]. Furthermore, in the case of interference among WBASNs, the convergence time in which WBASNs return to normal operation or stable status affects packet delay. The shorter the convergence time, the more effective the interference mitigation scheme; therefore, WBASN quickly returns to the stable operation leading to low end-to-end delay. In addition, the probability of packet loss determines the extent to which the packet drop rate affects the reliability of a WBASN in terms of BER or packet error rate.
## 4 Numerical Simulation
### Effect of Magnetic Force on the Output Power  
Figure 4 shows the effect of magnetic force on the output power of the HEH. The first and second resonant frequencies are induced by magnetic and piezoelectric oscillators, respectively. It is the typical characteristic of the multi-frequency VEH. It can be seen that both of the two resonant frequencies increase with the decreasing of \(d_{0}\), which is the same as the tendency of the difference  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline PZT length\(\times\)width\(\times\)thickness (mm\({}^{3}\)) & 50 \(\times\) 20 \(\times\) 0.2 \\ PZT density (kg/m\({}^{3}\)) & 7500 \\ [PERSON]’s modulus of PZT layer (GPa) & 60.6 \\ Substrate length\(\times\)width\(\times\)thickness (mm\({}^{3}\)) & 50 \(\times\) 20 \(\times\) 0.24 \\ Substrate density (kg/m\({}^{3}\)) & 8920 \\ Young’s modulus of substrate (GPa) & 90 \\ Magnet A radius\(\times\)thickness (mm\({}^{2}\)) & 10 \(\times\) 10 \\ Magnet B outer radius\(\times\)inner radius\(\times\)thickness (mm\({}^{3}\)) & 9 \(\times\) 4 \(\times\) 7 \\ Magnet density (kg/m\({}^{3}\)) & 7800 \\ Residual magnetic flux density (T) & 1.25 \\ Spring stiffness (N/m) & 220 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Geometric and physical properties of the hybrid energy harvester (HEH).  
Figure 3: Electromagnetic coupling coefficient _versus_\(z_{2}\).  
of the two resonant frequencies. Therefore, we can tune the resonant frequencies of the system by changing the initial separation distance \(d_{0}\). However, when \(d_{0}\) is small enough and the repulsive force is large enough, the two resonances will merge into one resonance. In this situation, the magnetic interaction acts as a rigid connection between two oscillators, so that two oscillators have the same resonant frequency around the \(\omega_{1}\). With the decrease of \(d_{0}\), the magnitude of the first peak power gradually increases, while the one of the second peak constantly decreases. When the second peak finally disappears, the first peak reaches the maximum value. Although the magnetic force is large enough when \(d_{0}\) = 10.0 mm, there is still no obvious hardening or softening response, which is the typical phenomenon in magnetic coupling nonlinear VEH [20]. The main reason is that the difference of displacements between two oscillators (\(u_{1}\)-\(u_{2}\)) is very small under the small-amplitude excitation, so that the magnetic nonlinearity is invisible, which can be referred in Equation (9).
# Image-Based Crack Detection Using Total Variation Strain DVC Regularization
## Appendix A Strain Field Computation Problem  
The displacement of a body can be described by the combination of two major components: the rigid displacement (global) and the non-rigid displacement (local), i.e., the deformation. Rigid displacement refers to a motion that involves translation and rotation but does not involve any changes in shape or size. Conversely, deformation entails changes in both shape and size due to alterations in the relative positions of image features. The strain is a measure that characterizes the deformation of the material. In continuum mechanics, the local strain is computed as the local variation of the displacement field (i.e., displacement gradient) computed by using DVC.  
The strain field is defined by the 3D Lagrangian strain tensor. The 3D Lagrangian strain tensor is defined by [50]  
\[\epsilon(\mathbf{u})=\frac{1}{2}[\mathbf{B}-\mathbf{I}] \tag{10}\]  
where \(\mathbf{B}=\mathbf{F}^{T}\mathbf{F}\) is the left Cauchy-Green deformation tensor, \(\mathbf{F}\) is the deformation gradient, and it is defined as \(\mathbf{F}=\mathbf{I}+\
abla\mathbf{u}\), \(\
abla\mathbf{u}\) is defined as  
\[\
abla\mathbf{u}=\begin{bmatrix}\frac{\partial u_{1}}{\partial\mathbf{x}_{1}} &\frac{\partial u_{1}}{\partial\mathbf{x}_{2}}&\frac{\partial u_{1}}{\partial \mathbf{x}_{3}}\\ \frac{\partial u_{2}}{\partial\mathbf{x}_{1}}&\frac{\partial u_{2}}{\partial \mathbf{x}_{2}}&\frac{\partial u_{2}}{\partial\mathbf{x}_{3}}\\ \frac{\partial u_{1}}{\partial\mathbf{x}_{1}}&\frac{\partial u_{2}}{\partial \mathbf{x}_{3}}&\frac{\partial u_{1}}{\partial\mathbf{x}_{3}}\end{bmatrix} \tag{11}\]If there is no deformation, \(\mathbf{B}^{-1}=\mathbf{I}\) and \(\epsilon=0\). \(\mathbf{X}\) is the position vector of a material point in the initial configuration.  
As mentioned above, because of its derivatives, the strain field tends to be noisy. To avoid a high noise level, the displacement field is typically smoothed before being differentiated. Many smoothing techniques exist in the literature: finite-elements-based methods [22; 48], Geers' method [26], and local filters [18]. Such techniques have the downside of reducing the locality of the solution to favor the noise reduction. The reduction of the spatial resolution, besides not being realistic, might also involve a loss of information, (see Section 4.2).  
The proposed method aims to reduce the noise of the strain field without affecting the locality of the solution.
## 3 Results  
### ERA5 Horizontal Wind Speed Assessment  
Figure 2a,b show the absolute deviations of horizontal wind speed at various heights. In Figure 2a, for January, February, and March, most absolute deviations of horizontal wind speed at 50 m are between \(-5\) m/s and \(5\) m/s, with some blue outliers. This may be related to several dust storms and sandstorms during the observation period, with specific times of large deviations shown in different colors in Figure S1. The median absolute deviation at 100 m is near 0 m/s. In Figure S1(a1,b1), the horizontal wind speed deviations are  
small for most of the time series, but significant deviations are noted from 10 January to 18 January, coinciding with a sandstorm event. At 200 m, the median absolute deviation is below 0 m/s, indicating an underestimation of horizontal wind speed by ERA5, with the maximum underestimation reaching 8 m/s. This phenomenon is evident in Figure 3c and is likely due to local circulation influenced by the Yinshan Mountains northwest of the Baotou observation site [42]. At 500 m, ERA5 overestimates horizontal wind speed in winter, as shown by the higher median absolute deviation above 0 m/s and evident blue outliers in Figure 2. At 1000 m, most deviations are above 0 m/s with some outliers reaching 10 m/s while limited observations at 1500 m result in fewer deviation samples, as shown in Figure S5(f1), where deviations within 20 m/s are minor during observed periods. In Figure 2b, for July and August, most absolute deviations at 50 m are between \(-\)5 m/s and 5 m/s, with some blue outliers exceeding 5 m/s, due to strong convective weather events such as thunderstorms and heavy rain on 17 July, 25 July, and 18 August, causing low wind speed estimates by ERA5. These phenomena are evident in box plots at all heights in Figure 2b. At 200 m, 500 m, and 1000 m, northwest wind speeds observed are higher than ERA5 estimates, influenced by both strong convective weather and Foehn winds, where northwest winds accelerate over the Yinshan Mountains. Figure 2c,d show many blue outliers below 200 m, indicating the overestimation of horizontal wind speeds by ERA5. Overall, summer's convective weather causes lower horizontal wind speed estimates by ERA5, with leeward topography also contributing to the underestimation. Winter predictions are better, but high-level deviations still occur.  
Figure 2: Absolute and relative deviations of horizontal wind speed at various heights. (**a**) Absolute deviations of horizontal wind speed (ERA5 reanalysis data minus Doppler wind lidar observations) for January, February, and March; (**b**) absolute deviations for July and August; (**c**) relative deviations (ERA5 reanalysis data minus Doppler wind lidar observations, divided by Doppler wind lidar observations) for January, February, and March; (**d**) relative deviations for July and August. Red circles and blue dots indicate the distribution of deviation data, with deeper colors indicating denser areas. Blue dots represent outliers, and the light green box plot shows the interquartile range (75 th percentile, median, and 25 th percentile).