esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Characterization of the Materials
#### 3.1.2 Xps  
XPS was employed to investigate the surface elemental composition and chemical states of the XDA-1G resin. XPS spectrum and the deconvoluted high-resolution spectra of C 1s and O 1s are shown in Figure 3a-c, and the corresponding atomic concentrations and binding energies are summarized in Table 1.  
Figure 2: FT-IR spectrum showing characteristic peaks for OH group (\(\sim\)3440 cm\({}^{-1}\)) aromatic C=C stretching (\(\sim\)1600 cm\({}^{-1}\)) and carbonyl (C=O) groups (\(\sim\)1700 cm\({}^{-1}\)), indicating the presence of oxygenated functionalities on the resin’s surface.  
carbon structure, contributing to a hydrophobic surface that facilitates \(\pi\)-\(\pi\) stacking interactions with m-cresol. Minor peaks corresponding to C-O (1.96%) and C=O (4.79%) suggest the presence of oxygen-containing functional groups that may enhance adsorption through hydrogen bonding and dipolar interactions. The \(\pi\)-\(\pi\)* satellite contribution (3.41%) further supports the aromatic nature of the XDA-1G resin [27; 28].  
The O 1s spectrum (Figure 3c) shows three components centered at 531.8 eV, 532.9 eV, and 534.5 eV, attributed to C=O, C-O, and adsorbed H\({}_{2}\)O, respectively. The dominant contribution from C-O groups (71.23%) indicates the abundance of hydroxyl and ether functionalities, while the presence of C=O (18.82%) confirms carbonyl content [29]. These polar functional groups enhance resin-adsorbate interactions, particularly through hydrogen bonding. The peak at 534.5 eV (9.95%) corresponds to physically adsorbed water molecules [30].  
Survey spectra revealed that the XDA-1G resin surface is primarily composed of carbon (91.71 at%), with lesser contributions from oxygen (7.12 at%), silicon (0.69 at%), and chlorine (0.48 at%). The predominance of carbon is consistent with the organic polymer backbone, while the oxygen-containing groups play a crucial role in adsorption performance. These findings confirm that the XDA-1G resin surface is rich in aromatic carbon and oxygen-containing groups, both of which play key roles in facilitating the adsorption of m-cresol through hydrophobic interactions, hydrogen bonding, and \(\pi\)-\(\pi\) stacking [21; 23].
## 4 Empirical Case of a Metropolitan Area of Taiwan  
### Background and Problem Descriptions  
Taiwan's air pollution is severe, especially in the Central and Southern regions. The Environmental Protection Administration [11] compared the pollution situation in various cities and counties in Taiwan in Spring 2018 and Kaohsiung was the most polluted city. See Kaohsiung's location in Figure 1 below. In addition to natural pollution factors, such as its topography and climate, Kaohsiung has many highly polluting heavy industries, factories, and coal-fired power plants. The study used the highly industrialized and commercialized Kaohsiung as an empirical case because it is also a crucial agriculture area and has the largest metropolitan area in the southern Taiwan, with a population of approximately 2.77 million people. This study sought to evaluate air pollution control measures and offer suggestions to improve their effectiveness using a hybrid MCDM model combining DANP with VIKOR.
## 3 Results and Discussion
### Binding Analyses of Test Drugs and Tyrosine Enzyme  
The in vitro and in silico results showed that enoxacin sesquihydrate has more therapeutic potential and is confined in the active binding pocket of the target protein (Figure 5A,B). The docking result of the enoxacin sesquihydrate-mushroom tyrosinase docked complex showed two hydrogen bonds at the His85 and His263 residues. The oxygen atom of enoxacin formed a hydrogen bond against His85, with a bond length of 4.65 A, and His263, with a bond length of 4.17 A. Our docking results showed good correlation with the published research, which strengthens our work and its efficacy [34; 58; 59].  
Figure 4: Molecular docking energy graph of test drugs.  
In ciprofloxacin docking, hydrogen and hydrophobic interactions have been observed at different residue positions. The oxygen atoms in the piperazine ring form a couple of hydrogen bonds with His85 and Asn81; whereas, val283 and Pro284 hydrophobically interacted with appropriate bond distances. In enoxacin sesquihydrate docking, a single hydrogen bond was observed at position Asn260, through an oxygen atom. Moreover, a couple of hydrophobic interactions were reported at His85, His263, His269, Val283 and His244, respectively. Most importantly, enoxacin sesquihydrate also formed a hydrophobic interaction with Cu401, through \(\pi\)-\(\pi\) stacking. In the gemifloxacin docking results, three hydrogen bonds were observed at positions Asn81, His244 and Asn260, through oxygen atoms of the drug. Moreover, Ala323 and Val283 also interacted through hydrophobic interactions. In levofloxacin tyrosinase docking, a couple of hydrogen bonds and hydrophobic interactions have been observed at different residue positions. The oxygen atoms of levofloxacin formed two hydrogen bonds at positions His85 and Asn81, respectively. Moreover, Val283 and Pro284 interacted by forming hydrophobic interactions. In the moxifloxacin and ofloxacin docking results, a couple of hydrogen bonds were reported at positions His85 and Asn81, respectively. Moreover, His244 and Val283 interacted through hydrophobic interactions.  
In the sparfloxacin docking complex, a single hydrogen bond was observed at position Met280, and different hydrophobic interactions were reported at different residue positions, such as Gly281, Asn260, Phe264, His85, His259, His263, Val283 and ser282, respectively. Furthermore, both copper ions interacted hydrophobically with the drug. The comparative results showed that all the drugs bind at binding pocket residues, having slightly different conformational positions inside the binding pocket, and they showed good correlation with the published data, which ensures the credibility of the docking analysis. The 2D conformations, binding poses, and interactions with binding residues of all the drugs are mentioned in Supplementary Figure S10.
## Materials and Methods  
### MOCT Laboratory Hardware  
The MOCT laboratory hardware incorporates the same components as the MOCT space hardware, which includes the modulator, pulse driver, optical amplifier, APD, and TDC. These components are in a modular configuration that allows component performance to be assessed both individually and in groups. The MOCT hardware on a satellite can be designed to fit in a nanosatellite package, while the modular connections on the laboratory hardware, needed to evaluate components individually, would be eliminated to increase efficiency for volume, weight, optical transmission.
# Low-Power 8T SRAM Compute-in-Memory Macro for Edge AI Processors
## 4 Performance Comparison of Proposed Structures
### SRAM-CIM Efficiency Comparison  
Table 2 summarizes the performance metrics calculated for the proposed 8T SRAM structure, focusing exclusively on the core bit-cell design and excluding auxiliary components such as drivers. The values in Table 2 highlight the bit-cell's intrinsic performance, independent of peripheral circuitry, which allows for a direct comparison of efficiency and operational capacity. While the proposed 8T SRAM structure exhibits a slight reduction in memory capacity relative to other conventional SRAM architectures, this limitation is offset by its high operational efficiency and enhanced noise margins. The proposed bit-cell structure is capable of performing a comparable number of operations per second, making it a viable option for applications where noise resilience and power efficiency are prioritized over memory density. This balance between operational stability and efficiency demonstrates the proposed 8T SRAM structure's suitability for low-power, noise-sensitive environments, such as edge computing and AI applications, where robust data integrity and reduced error rates are essential.
## 7 Tree Comparison and Related Problems
### Notations and Signatures  
A tree has a root node and a forest of subtrees, a forest is a (possibly empty) sequence of trees. Problems on trees and forests recursively imply each other, and must be solved jointly. We follow the tradition and simply speak of problems on trees.  
Figure 9: Relationships between our five main RNA problems.  
In the classical formulation, tree nodes carry labels without any meaning. In particular, they have no fixed arity or argument types. This convention is strictly tied to the classical tree edit model, where a node can be inserted or deleted anywhere in a tree. In tree languages over a signature, as we used so far, this would result in a violation of the corresponding operator's arity. Our trees with typed operators and fixed arities require a more elaborate model, and we will look at them in Section 7.5.  
For the moment, we adhere to the classical formulation, where nodes carry arbitrary labels. To accommodate this in our rewriting framework, we will treat all node labels as unary operators, and represent trees like \(a(x,y,z)\), \(a(x)\), and \(a\) as \(a(x{\sim}y{\sim}z)\), \(a(x)\), and \(a(\varepsilon)\), respectively. Forests are built from trees by the associative binary operator \(\sim\), such that \(a{\sim}(b{\sim}c)=a{\sim}b{\sim}c=(a{\sim}b){\sim}c\). Given a set \(F\) of node labels, our satellite signature to represent trees will be  
Satellite signature \(TREE\)  
\[\begin{array}{ccccc}\varepsilon&:&\rightarrow&Tree&\mbox{empty forest}\\ f&:&Tree&\rightarrow&Tree&\mbox{node label, for $f\in F$}\\ \sim&:&Tree\times Tree&\rightarrow&Tree&\mbox{concatenation of subtree forests}\end{array}\]  
Our first core signature is designed to represent arbitrary tree alignments. The core terms representing tree alignments will use operators \(\mathtt{rep}\), \(\mathtt{ins}\), \(\mathtt{del}\), while the operators from \(F\) used in the input signatures now play the role of a leaf label alphabet.  
Core signature TreeAl  
alphabet \(F\) - see remark above  
\[\begin{array}{ccccc}\mathtt{rep}&:&F\times F\times Ali&\rightarrow&Ali& \mbox{node label replacement}\\ \mathtt{del}&:&F\times Ali&\rightarrow&Ali&\mbox{node deletion}\\ \mathtt{ins}&:&F\times Ali&\rightarrow&Ali&\mbox{node insertion}\\ \mathtt{mty}&:&\rightarrow&Ali&\mbox{empty tree alignment}\\ \sim&:&Ali\times Ali&\rightarrow&Ali&\mbox{subforest concatenation}\end{array}\]  
The core signature uses an operator \(\sim\) for concatenating forests of sub-alignments.
# Comparative Evaluation of Remote Sensing Platforms for Almond Yield Prediction
## 2 Materials and Methods
### Data Collection and Processing
#### 2.2.3 Vegetation Indices Computation
\begin{table}
\begin{tabular}{c c c} \hline
**Name of Index** & **Equation** & **Reference** \\ \hline Enhanced vegetation index 2 & \(\text{EVI2}=\frac{2S\times(\text{N}-\text{R})}{(\text{N}+2\times\text{R}+1)}\) & [16] \\ Green–red vegetation index & \(\text{GRVI}=\frac{\text{G}-\text{R}}{\text{G}+\text{R}}\) & [42] \\ Normalized difference & \(\text{NDVI}=\frac{\text{N}-\text{R}}{\text{N}+\text{R}}\) & [15] \\ vegetation index & \(\text{SAVI}=\frac{\text{N}-\text{R}}{\text{N}+\text{R}+\text{L}}\times(1+ \text{L})\) & [18] \\ \hline \end{tabular}
\end{table} 
Table 2: Vegetation indices used in almond yield prediction and their respective equations. G: green; L = 0.5; N: near infrared; R: red. Figure 3: Example of the procedure for creating the dataset. The values correspond to the mean NDVI calculated for (**a**) Landsat 8, (**b**) Sentinel-2, and (**c**) PlanetScope (e.g., March 2019), corresponding to almond grower 3.
# Ka-Band Doppler Scatterometry: A Strong Wind Case Study
###### Abstract  
Global joint measurements of sea surface winds and currents are planned using satellite-based Doppler scatterometers operating in the Ka-band to achieve improved spatial resolution and retrieval accuracy. Still, the knowledge of sea surface Ka-band backscatter properties is poor, particularly, at high winds (\(>\)20 m s\({}^{-1}\)). Sea surface radar cross-section in the Ka-band, in contrast to that in the lower frequency Ku-/X-/C-/L-bands, is likely more sensitive to sea spray, small-scale particles typically present at high winds. In this paper, tower-based field data collected by a continuous dual-co-polarized Ka-band radar during a strong offshore wind event (with wind speed reaching 33 m s\({}^{-1}\)) are analyzed. This katabatic wind event (\(\approx\)12 h long) was also recorded by supplementary wave, wind, and current sensors. At the wave fetch of \(\approx\)1 km, the maximum wavelength of observed offshore waves was \(\approx\)10 m. For such extremely young wind-sea conditions, an apparent sea spray generation was observed during wind gusts. Radar measurements were performed at 20\({}^{\circ}\) and 45\({}^{\circ}\) incidence angles, mostly for cross- and up-wind azimuth look geometry. Based on these high wind measurements, the previously developed Ka-band empirical model is tested and compared with other published geophysical model functions. Dual-co-polarized measurements are used to infer resonant Bragg and non-Bragg scattering components and assess the short wind wave spectrum, which shows a clear tendency for saturation at high winds. The presence of sea spray signatures is apparent in the high-frequency tails of radar Doppler spectra, but their overall contribution to the Doppler centroid frequency is weak. Hence, the standard modulation transfer function approach developed for moderate winds is still applicable at high winds for interpreting the wave-induced Doppler velocity and inferring sea surface currents. These results can also be useful for understanding Doppler scatterometry measurements in tropical cyclones.  
radar; ocean; backscatter; Ka-band; field measurements; spray; droplets; normalized radar cross-section; Doppler spectra; scatterometry; saturation +
Footnote †: journal: Remote Sensing
# Kiwifruit Detection Method in Orchard via an Improved Light-Weight YOLOv4
## 3 Kiwifruit Recognition Network  
### YOLOv4 Network Model  
A sketch of the YOLOv4 network structure is shown in Figure 4. The network model is mainly divided into three parts: the benchmark network Backbone, Neck network, and Head output [25], which are used for classification regression and prediction through three effective feature layers. Yolo Head output: this part contains a 3 \(\times\) 3 convolution and a 1 \(\times\) 1 convolution, which are used for the feature set and channel number adjustment, respectively, to complete the output of target prediction results. Neck network: The SPP [26] module and PANet [27] module are used to fuse the feature information of different size feature maps to further improve the diversity and robustness of the features. SPP pools the last feature layer of the backbone network, which can greatly increase the perceptual field and separate the most significant contextual features. PANet (Path Aggregation Network) is a bottom-up feature pyramid added to the FPN (traditional feature pyramid), which achieves iterative feature extraction with strong semantic features and strong localization features. Backbone network CSPDraknet53: The CSPnet structure connects a small amount of processing directly to the end with a large residual edge, which enhances the learning ability of CNN. The Mish activation function is also introduced in the CSPnet structure, and Mish has the properties of no upper bound, fast convergence, and smooth nonmonotonicity, which helps to stabilize the network gradient flow, avoid gradient saturation, and improve the generalization ability of the model. The Mish function [28] is as Equation:  
\[\text{Mish}\ =\ \text{x}\ \times\tan\text{h}(\ln(1+\text{e}^{\text{x}})) \tag{1}\]  
where, x is the input value and \(\tan\text{h}()\) is the hyperbolic tangent function.
## 5 Before and after Travel-Mode Choice Model Analysis
### Travel-Mode Choice Model Analysis before the Opening of Wanda Shopping Plaza  
In the first survey before the opening of Wanda Shopping Plaza, 304 persons' 304 valid trip and travel-mode samples were collected. Using this data, travel-mode choice models were estimated. The travel-mode choice set in the survey consists of six travel modes: shopping shuttle bus, walking, bike, motorcycle/moped, car, public transit (including bus and metro). The data input of the model includes travel-mode selection, mode availability, demographic characteristics, and other characteristics (travel time and cost of each mode). The demographic characteristics consist of age, gender, Shanghai city permanent residency, education, working status, relocatee status, years living locally, household size, apartment size, and driver's license. Table 2 shows the mixed logit models for travel-mode selection. Among all the estimated models, we finally chose the mixed logit model with a log-normal distribution of time value because we only have hundreds of samples and it is not sufficient to estimate significant travel-time and cost coefficients. With likelihood ratio tests, we find this model is the best model among all other estimated models, including but not limited to the multinomial logit model and the mixed logit model with time value as a value. As shown in Table 2, there are two groups of alternative-specific constants (ASCs) for maintenance and subsistence travel, respectively, in the model. Subsistence travel is that which has the purpose of going to school or work. Maintenance travel is that which has the purpose of going shopping for life necessaries, going to the hospital, or picking up others. Among all demographic characteristics, only the male dummy variable (1 for male and 0 for others) and the retired dummy variable (1 for retired people and 0 for others) are significant. Among all travel-mode correlations, the correlation of \"motorcycle or moped\" and \"bicycle\" has the most significant result. This may be related to the fact that they have a similar riding style, so the correlation is reasonable and is kept in the model. In Table 2, the model before Wanda opened has a time value distributed log-normally. This model is acceptable since a log-normal distribution for the time value is sensible. It is reasonable because the time value in China is 0.15-0.57 CNY/min (i.e., 1.44-5.44 USD/h) which is the shaded area in the distribution figure above. The time-value log-normal distribution's median is 3.94 USD/h, and variance is 0.20. This model is only using the most frequent trips, so the median time value of 3.94 USD/h is closer to the upper bound of 5.44 USD/h. Thus, it is acceptable. \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Before the Opening of Wanda:** & & & & & & & **After the Opening of Wanda:** & & & \\
**Mixed Logit Model for Maintenance and Subsistence Travel** & & & & & & & **Multimomial Logit Model** & & & \\ \hline
**Description** & **Detailed Description** & **Coeff. Est. ** & **Robust [PERSON]** & \(p\)**-Value** & **Description** & **Coeff. Est.
## 5 Results and Analytical Discussion
### Algorithm Performance Comparison  
5.1.1 Classification Performance Metrics  
A hierarchy of performance efficacy was revealed through the comparison of several categorization methods. With an accuracy of 99.99%, RF classifier (RFC) showed superior performance than other techniques in the classification of DDoS and other attack types. The CNN model attained the second-highest accuracy with 99.80%; SVM followed with 99.26%. With KNN (K = 7) at 99.10%, KNN (K = 5) at 99.09%, and KNN (K = 3) at 99.14%, the KNN variations showed equivalent accuracy levels. LSTM networks obtained an accuracy of 99.25%.  
The analysis of the confusion matrix revealed that RFC maintained consistent performance across all DDoS attack classes (DDoS_HTTP, DDoS_ICMP, DDoS_TCP, and DDoS_UDP), with minimal misclassifications. This insight is especially important considering the multi-class classification challenge, including 15 different kinds of attacks and consistent traffic patterns.  
Figure 1 compares the performance metrics of all evaluated models, demonstrating the considerable overall accuracy advantage of RFC and CNN. With the 99.80% highest precision, RFC proved remarkable capacity to prevent false positives across all DDoS variants and other attack types. CNN showed 99.00% precision, same as KNN variants. The LSTM attained 97.00%, while the SVM had a precision of 99.00%. These metrics evaluate the capacity of each model to accurately identify DDoS attack without incorrectly classifying legitimate traffic.  
RFC once again outperformed other models in terms of recall, achieving a remarkable 99.90% accuracy rate in identifying all DDoS attacks. CNN and LSTM both scored 99.00%, KNN (K = 3) scored 99.00%, and KNN = (K = 5, 7) scored 98%. With even lower recall at 97.00%, SVM found some undetectable attacks.  
The F1-score followed similar trends with RFC scoring 99.90%, followed by CNN and KNN = (K = 3, 5, 7) all at 99.00%. With 98.00%, LSTM and SVM showed rather lower F1-scores. More details, including heatmaps and full confusion matrix visualizations, are provided in Appendix A.
# Comparing Sentinel-2 and Landsat 8 for Burn Severity Mapping in Western North America
## 2 Materials and Methods
### Calculating Burn Severity: Paired Scene Approach  
We mapped burn severity for all 26 fires (Table 1) using the well-established paired scene approach, in which a single pre- and post-fire image is manually selected [11; 23]. For all fires that were mapped by the Monitoring Trends in Burn Severity (MTBS) program (n = 23), we selected the pre- and post-fire Landsat 8 imagery from the acquisition dates reported by MTBS. With the three remaining fires that were not mapped by MTBS (Table 1, Kenow, Mammoth, and Chippean), we selected pre- and post-fire imagery that minimized cloud and snow cover within the fire perimeter, while also attempting to match the imagery acquisition dates between years to be as similar as possible to minimize seasonality differences that can affect phenology and sun angle (see Table S1 for scene dates used in the paired scene analysis). Because MTBS map products are distributed with only two of the burn severity indices (i.e., dNBR and RdNBR), we performed the calculation of all burn severity indices using the corresponding pre- and post-fire image for each fire from the Landsat 8 Surface Reflectance Tier 1 dataset in GEE.  
For the Sentinel imagery used in the paired scene burn severity mapping approach, we attempted to select the scene closest in acquisition timing to the corresponding Landsat scene used for each fire. This generally resulted in Sentinel and Landsat pre- and post-fire scenes acquired within approximately two weeks of each other, however, the presence of cloud or snow contamination covering the fire footprint required the use of some Sentinel scenes that were acquired further apart. Finding temporally matched imagery was more challenging for earlier dates prior to the launch of the Sentinel-2B satellite in March of 2017, thus requiring the use of eight pre-fire scenes that were acquired during a different year than their Landsat counterparts (Table S1).  
To assess the effect of atmospheric correction applied to Sentinel imagery, we used both Level-1C Top of Atmosphere Reflectance (TOA) and Level-2A Bottom of Atmosphere Reflectance (BOA) data products. Acquiring the latter required post-processing the Level-1C imagery using the Sen2 Cor atmospheric correction algorithm [68], as the European Space Agency did not begin global distribution of Level-2A imagery until late in 2018. We used the R package sen2r v1.5.0 to download all Sentinel-2 Level-1C imagery and post-process it to Level-2A [69]. We then uploaded the Sentinel TOA and BOA scenes to GEE, where we calculated burn severity maps using both sets of imagery separately for each fire following the paired scene approach described above.