esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Details of the Spectral Measurements and SIF Retrieval Method  
The canopy spectral measurements were made using a customized Ocean Optics QE Pro spectrometer (Ocean Optic, Inc., Dunedin, FL, USA) fitted with a 25\({}^{\circ}\) field-of-view fiber optic that functions in the 645-805 nm spectral range with a full-width-at-half-maximum (FWHM, i.e., spectral resolution (SR)) of 0.31 nm, a spectral sampling interval of 0.155 nm and a peak signal-to-noise ratio (peak SNR) >1000. Canopy radiance measurements were taken by averaging 50 scans with an optimized integration time to weaken the influence of noise, and a dark current correction was applied to each spectral measurement. The canopy spectra were measured at a height of about 130 cm above the canopy for nadir observation. A BaSO\({}_{4}\) calibration panel was used to measure the incoming solar irradiance. For all measurements, a relatively homogeneous view size of about 1 m \(\times\) 1 m was selected. The weather was sunny and stable during all of the above experiments.  
The SIF is a re-emitted signal corresponding to only a small amount of the reflected radiance and about 1% of the total light absorbed. It has two peaks at 685 nm and 740 nm. The SIF signal overlaps with the reflected light and needs to be separated out at the Fraunhofer lines or strong atmospheric absorption bands as, here, the incident irradiance is comparable to the amount of emitted fluorescence [39]. In this study, for the QE Pro spectral measurements, the O\({}_{2}\)-A (761 nm) and O\({}_{2}\)-B  
Figure 2: The photographs of studied canopies at XTS Farm in November (left, winter wheat), Nanbin Farm (middle, cotton) and Sanya Station (right, gold coin grass).  
(688 nm) oxygen absorption bands were used to retrieve the far-red and red SIF, respectively, based on the 3 FLD (3 band Fraunhofer Line Depth) method [40].  
The 3 FLD assumes that the variation in the SIF and in the reflectance are linear over the spectral range used. The single reference channel used in the standard FLD method (sFLD) for the incident irradiance and upwelling radiance is replaced by the weighted average of two channels at the left and right shoulders of the absorption band. [PERSON] et al. [16] assessed the FLD-based SIF retrieval methods for different spectral specifications and showed that the 3 FLD method was more accurate than the standard FLD method and also more robust than the improved FLD method proposed by [PERSON] et al. [41]. Therefore, we adopted the 3 FLD method for SIF\({}_{canopy}\) retrieval in this study. The shoulder wavebands of the 3 FLD method were determined according to [PERSON] et al. [16,42]. In the case of the O\({}_{2}\)-A band, this corresponds to wavelengths of 757.92 nm, 760.72 nm and 768.87 nm for the bands corresponding to the left shoulder of the absorption feature, within the absorption feature and right shoulder of the absorption feature, respectively; for the O\({}_{2}\)-B band, it corresponds to wavelengths of 686.44 nm, 687.09 nm and 688.23 nm, respectively.
# ECG Arrhythmia Classification using High Order Spectrum and 2D Graph Fourier Transform
## 4 Experiments and Analysis
### Transforming the ECG Signal into Bispectrum  
Set the length of every segment as 128 and 50% as the overlapping rate. Figure 5 shows the bispectrum contour plots of the four types (N, S, V, and F beats) of ECG signals (from recordings No. 100, 232, 208 and 213).  
As shown in the Figure 5, due to the symmetrical characteristic of bispectrum, all information can be extracted from one part. Hence, only data in the first quadrant are recorded in this paper. There are no values in the upper triangle of the first quadrant and the lower triangle of the third quadrant of the dual-frequency plane, which corresponds to the undefined term of the bispectrum matrix.  
Bispectrums of different beats have different contour shapes, therefore, the bispectrum can be considered as a feature. However, when bispectrum is used as a feature, matrices which contain much redundant information will be generated. So, bispectrum cannot be directly used as a feature of classification, and further feature extraction is needed. To solve this problem, various methods were proposed, including integral bispectrum [35], slice spectrum [29], Principal Component Analysis (PCA) [10], Independent Component Analysis (ICA) [36] and Kernel Fisher Discriminant Analysis (KFDA) [37].  
Figure 5: Bispectrum contour plots of four segments in ECG records. (**a**) N beat (No. 100); (**b**) S beat (No. 232); (**c**) V beat (No. 208); (**d**) F beat (No. 213).
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Snow Class**} \\ \hline  & **Constellation ID** & **Tundra** & **Taiga** & **Maritime** & **Ephemeral** & **Prairie** & **Alpine** \\ \hline \multirow{4}{*}{1-day} & (a) & **97.8** & 0.710 & 34.5 & 77.1 & **90.9** & 25.6 \\  & (b) & **98.8** & 0.710 & 29.0 & 72.7 & **91.4** & 32.1 \\  & (c) & **98.7** & 1.83 & 29.6 & 72.9 & **91.4** & 33.8 \\  & (d) & **98.7** & 2.54 & 47.6 & 78.3 & **93.4** & 42.0 \\ \hline \multirow{4}{*}{3-day} & (a) & **100** & 2.00 & 68.9 & **98.0** & **99.0** & 67.4 \\  & (b) & **100** & 2.00 & 74.2 & **98.0** & **99.2** & **81.4** \\  & (c) & **100** & 5.19 & 74.7 & **98.1** & 99.2 & **81.8** \\  & (d) & **100** & 7.14 & **87.3** & **98.4** & **99.3** & **87.0** \\ \hline \multirow{4}{*}{30-day} & (a) & **100** & 19.7 & **95.9** & **98.9** & 99.0 & **98.4** \\  & (b) & **100** & 19.7 & **96.8** & 98.8 & 99.1 & **99.0** \\ \cline{1-1}  & (c) & **100** & 44.5 & **96.9** & 98.9 & 99.1 & **99.0** \\ \cline{1-1}  & (d) & **100** & 49.2 & **97.8** & 99.2 & **99.2** & **99.2** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Effective viewing coverage (in units of percent) of constellations for different snow classes with 1-day, 3-day, and 30-day integration periods. Values greater than 80% are in bold font. be present, optically-thin clouds (e.g., cirrus clouds) may still allow for snow retrieved using LiDAR. Additionally, the geophysical retrievals from each sensor are assumed to be mutually unbiased. As a result, constellation case (a) yields an overly optimistic view of snow-covered terrain. This particular constellation scenario represents a constellation composed of existing sensors. However, there are relatively few studies investigating the conjoined use of these sensors because the observations from each sensor are inherently uncertain and posses their own unique error characteristics. It is difficult to first merge these disparate information sources prior to evaluating their additive value relative to one another. Another factor is then needed to carefully consider each sensor's spatial resolution. Before jointly using the different sensor observations, they must be first integrated into a unified product via data assimilation or some other merging strategy.
# The Use of Underwater Gliders as Acoustic Sensing Platforms
## 4 Application of Gliders to Underwater Acoustics
### Underwater Passive Surveillance
#### 4.2.3 Performance of Target Azimuth Estimation
Since the ship's position will not change to be consistent with the glider's heading, we can tell that the target has a high probability of being reached in the direction near 30\({}^{\circ}\), as can be proven by the ship directions, marked out with dotted lines. For further explanation, the DOA estimated results at each reception location are plotted in Figure 15. The short green arrows indicate the headings of the glider. The glider was set to travel southward, but, due to the influence of ocean currents, it was pushed westward, which led to an  
Figure 14: Estimated DOAs of both sides as a function of time. The yellow line is the heading of the glider, while the dotted line with diamonds is the ship’s direction. The starboard DOAs and the heading have a similar transformation pattern, because the starboard DOAs are not true azimuths of the target but mirror angles of the azimuths. Figure 13: The diagram of ambiguity. Two circles indicate SCHs mounted on the glider. Port- and starboard-side DOAs are symmetrical to each other with respect to the glider’s heading. If the glider changes its heading, the image DOA will also change. However, the DOA formed by the true target remains unchanged. inconsistency in the glider's headings and its travel direction. The red and blue arrows indicate the estimated azimuths of the port side and starboard side of the glider, respectively. The ship is in about a 30\({}^{\circ}\) direction from the glider, and the effects of the scheme are proven: the port-side DOA results point to the area where the ship is drifting, while the starboard-side DOAs distribute randomly in opposite directions relevant to the glider's heading. The results indicate that, even if only two SCHs are used, the estimated DOAs can still distinguish the direction of the ship to a certain extent, which shows the potential of the glider to serve as a platform for underwater passive surveillance. If multiple gliders are used to form a sensor network, they can observe a target from different angles to obtain more accurate information. Considering the low energy consumption, low self-noise, and remote control of the glider, it can be easily redeployed to a new area, bringing about more advantages for long-term underwater network sensing.
## 1 Introduction
[PERSON] et al. (2024) utilized a quartz crystal microbalance and HS-SPME-GC-MS/ECD for analysis of substrates such as polystyrene, cardboard, steel, and glass exposed to trinitrotoluene (TNT), triacetone tri peroxide (TATP), cocaine, and hexamethylene triperoxide diamine (HMTD). They observed that volatiles were retained the most by cardboard followed by polystyrene and glass [22]. Additionally, a multitude of previous studies have analyzed the elemental composition and structure of substrates using scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDX) which can play a role in the sorption process for different analytes [23; 24; 25; 26]. Despite these advancements, the effectiveness of GC-MS for explosive residue detection also relies on the storage conditions, sampling techniques, and extraction parameters employed prior to the instrumental analysis [27]. Therefore, there is an imperative need for evaluating sampling techniques and establishing the most optimized GC-MS methods for detection of VOCs associated with explosives. One of the most frequently used sampling techniques involves swabing of the substrate to collect explosive residues, particularly if the substrate is large and cannot be moved [19; 27]. The key parameters involved in the swabbing method are the type of substrate, extraction solvent, and extraction time needed to obtain maximum recovery of VOCs. Previous studies have investigated a range of these parameters, and the results have differed depending upon the nature of the substrate and the analyte of interest [28; 29; 30; 31; 32; 33; 34]. Various extraction methods such as liquid injection, headspace analysis, and different solid phase microextraction (SPME) techniques such as total vaporization SPME (TV-SPME) and headspace SPME (HS-SPME) are used for VOCs associated with explosives. While each extraction method has its merits, its suitability for use depends upon numerous factors such as the sample matrix, volatility of the analytes, sample concentration, need for extraction solvent and possibility of field sampling to name a few. Currently, there are no established GC-MS methods for analysis of substrates exposed to volatiles emitting from C4. Substrates like cotton, wood, cardboard, metal, and glass have unique surface properties that enable them to bind or retain other substances. The research described in this paper focuses on the sampling and comparison of extraction techniques coupled with GC-MS for the analysis of substrates exposed to this explosive. Given the widespread presence of substrates such as cotton, glass, sheet metal, cardboard, and wood, these substrates are of interest for this study.
###### Abstract  
To analyze the hyperspectral reflectance characteristics of rice canopies under changes in diffuse radiation fraction, experiments using different cover materials were performed in Nanjing, China, during 2016 and 2017. Each year, two treatments with different reduction ratios of diffuse radiation fraction but with similar shading rates were set in the field experiment: In T1, total solar radiation shading rate was 14.10%, and diffuse radiation fraction was 31.09%; in T2, total solar radiation shading rate was 14.42%, and diffuse radiation fraction was 39.98%, respectively. A non-shading treatment was included as a control (CK). Canopy hyperspectral reflectance, soil and plant analyzer development (SPAD), and leaf area index (LAI) were measured under shading treatments on different days after heading. The red-edge parameters (position, \(\lambda_{0}\); maximum amplitude, \(D\lambda\); area, \(\alpha_{0}\); width, \(\sigma\)) were calculated, as well as the area, depth, and width of three absorption bands. The location of the first absorption band appeared in the range of 553-788 nm, and the second and third absorption bands appeared in the range of 874-1257 nm. The results show that the shading treatment had a significant effect on the rice canopy's hyperspectral reflectance. Compared with CK, the canopy reflectance of T1 (the diffuse radiation fraction was 31.09%) and T2 (the diffuse radiation fraction was 39.98%) decreased in the visible light range (350-760 nm) and increased in the near-infrared range (800-1350 nm), while the red-edge parameters (\(\lambda_{0}\), \(D\lambda\), \(\alpha_{0}\)), SPAD, and LAI increased. On the other hand, under shading treatment, the increase in diffuse radiation fraction also had a significant impact on the hyperspectral spectra of the rice canopy, especially at 14 days after heading. Compared with T1, the green peak (550 nm) of T2 reduced by 16.12%, and the average reflectance at 800-900 nm increased by 10%. Based on correlation analysis, it was found that these hyperspectral reflectance characteristics were mainly due to the increase in SPAD (2.31%) and LAI (7.62%), which also led to the increase in \(D\lambda\) (8.70%) and \(\alpha_{0}\) (13.89%). Then, the second and third absorption features of T2 were significantly different from that of T1, which suggests that the change in diffuse radiation fraction could affect the process of water vapor absorption by rice.  
Keywords:diffuse radiation fraction; rice; hyperspectral reflectance +
Footnote †: journal:
# Robustness of Artificial Neural Networks Based on Weight Alterations Used for Prediction Purposes
## 2 Background and Related Research
The authors of [13] introduced E2 CNNs, a new design methodology to improve robustness against memory errors in embedded systems. This work proposes a heuristic method to automate the design of a voter-based ensemble architecture. This design methodology increases the error robustness of CNNs by using ensemble architectures. The authors of [14] studied the sensitivity of weight perturbation in neural networks and its impact on model performance. They further designed a new theory-driven loss function for training generalization and robust neural networks against weight perturbations: bounded weight perturbations. Moreover, the authors of [15] extended the definition of robustness to any type of input for which some alterations can be defined. They proposed the ROBY tool, which accepts different types of data, and some alterations can be performed on these data providing the ability to classify the input data correctly. The authors of [16] present a new scheme for robust DNNs called coded DNN. This alters the internal structure of DNNs by adding redundant neurons and edges to increase reliability--a new middle layer is added. The authors of [17] proposed an approach that is complementary to other forms of defense and replaces the weights of individual neurons with robust analogs derived from the use of Fourier analytic tools. Additionally, the authors of [18] propose a new method called robustness-aware filter pruning (RFP) and utilize this filter pruning method to increase the robustness against adversarial attacks. In the proposed method, the filters that are involved in non-robust features are pruned. Lastly, the authors of [19] designed a novel neuron that uses \(\mathsf{L}\mathsf{o}\) distance as its basic operation, known as an \(\mathsf{L}\mathsf{o}\)-dist neuron. They show that the \(\mathsf{L}\mathsf{o}\)-dist neuron has a natural 1-Lipschitz function with respect to the \(\mathsf{L}\mathsf{o}\) norm, and the neural networks constructed with this neuron (\(\mathsf{L}\mathsf{o}\)-dist Nets) have the same property [19]. This directly provides a theoretical guarantee of the certified robustness based on the margin of the prediction outputs. In the following sections, we present a new robustness method to maintain high levels of robustness in ANNs with minimum hardware overheads.
# Compilation of a City-Level & Four-Digit Industry Code MRIO Table Based on Firm-Level Data
## 3 Materials and Construction Methodology
### Correction of Sample Selection Bias  
The newly merged dataset shows that the matching ratio between the financial data and product data at the enterprise level is 49%. This means that for nearly 51% of enterprises, we cannot observe their imported intermediate input data directly. Sample selection bias will occur if we do not consider the import data of these enterprises, which results in the final estimation coefficient bias. Therefore, we refer to the two-stage method by [PERSON] [33] to correct the problem of sample selection bias. This paper applies the Probit model to estimate the probability of the whole sample's usage of imported intermediate inputs and then calculates the Inverse Mills Ratio \(\lambda_{ij}\).  
We have a model for estimation purposes:  
\[\mathbf{y}_{ij}^{k}=\beta_{1}X_{ijk}+\beta_{2}W_{ij}+\beta_{3}Z_{j}+\epsilon_ {1i} \tag{2}\]  
Among them, \(\mathbf{y}_{ij}^{k}\) is the output value of product \(k\) in enterprise \(i\) of sector \(j\). \(X_{ijk}\) is the total value of intermediate inputs of the product \(k\) in enterprise \(i\), sector \(j\). \(W_{ij}\) is the control variables at the enterprise level of the enterprise \(i\) in sector \(j\). \(Z_{j}\) is the control variable at the macro-level.  
To correct the bias resulting from the sample selection problem, we set the following binary discrete selection model:  
\[IMP_{ij}=\beta_{4}S_{ij}+\epsilon_{2i} \tag{3}\]  
where \(IMP_{ij}\) is a binary variable representing whether the enterprise uses imported intermediate inputs. \(S_{ij}\) is the firm characteristics variables of the enterprise \(i\) in sector \(j\)  
If  
\[E(\epsilon_{1i})=0\text{ and }E(\epsilon_{2i})=0\text{, }\epsilon_{1}\text{ and }\epsilon_{2}\text{ are positively correlated; we have the following:}\]  
\[E(\epsilon_{1i}\big{|}IMP_{ij}\geq 0)=E(\epsilon_{1i}\big{|}\epsilon_{2i}\geq- \beta_{4}S_{ij}) \tag{4}\]  
\[E(\mathbf{y}_{ij}^{k}\big{|}X_{ijk},\ IMP_{ij}\geq 0)=\beta_{1}X_{ijk}+E( \epsilon_{1i}\big{|}\epsilon_{2i}\geq-\beta_{4}S_{ij}) \tag{5}\]  
\[E(\mathbf{y}_{ij}^{k}\big{|}X_{ijk},\ IMP_{ij}\geq 0)=\beta_{1}X_{ijk}+\beta_{5} \lambda_{ij} \tag{6}\]  
We then obtain the revised model:  
\[y_{ij}^{k}=\beta_{1}X_{ijk}+\beta_{2}W_{ij}+\beta_{3}Z_{j}+\beta_{5}\lambda_{ ij}+\mu \tag{7}\]  
A correction as described above during the estimation of the consumption coefficient can address the sample selection bias to some extent.
# Quantum-Secure Coherent Optical Networking for Advanced Infrastructures in Industry 4.0
## 7 Discussion
### Constraints and Potential Trade-Offs
As organizations move beyond the laboratory phase, pilot projects focused on narrower network segments, like inter-data center links, offer a gradual pathway toward broader production rollout, helping validate interoperability and refining installation procedures before large-scale adoption. Although industry prototypes exist for MHCD and certain QKD hardware solutions, real-world testing under variable traffic loads and fault conditions remains limited. Consequently, the transition from controlled lab demonstrations to fully operational networks will require incremental rollout strategies, vendor collaboration for firmware and hardware refinements, and rigorous field trials to validate interoperability with existing MACsect-enabled infrastructures.
# Rotavirus A Genome Segments Show Distinct Segregation and Codon Usage Patterns
## 4 Discussion  
While segment exchange between different RVA genotypes is common, reassortment is not a random process [90; 91; 92; 93; 94]. However, the limits of segment exchange, whether ecological or mechanical, are poorly understood. Some segment combinations work well together, whereas others are incompatible [95]. Numerous factors potentially could affect whether segments from different RVA genotypes are able to reassort, including protein interactions, RNA-RNA and RNA-protein interactions, and the need to maintain host range and ensure RNA packaging efficiency.  
The goal of this study was to better predict potential (or unlikely) genome constellations that may emerge in nature and enhance our understanding of why some segments co-segregate, and some do not. To this end, we compared the evolutionary histories as well as some of the selective pressures acting on the 11 RVA segments, first by performing phylogenetic analyses on a large collection of complete RVA genomes, and then by  
Figure 10: Box plots of CAI and RCDI values for each ORF from strains isolated from specific hosts. RCDI and CAI values were derived by sampling 7 complete genomes (77 segment genomes) for each host, isolated from either avian (red), cow (green), human (blue), or pig (purple) hosts. These RVA genomes were then separately compared to cow, chicken, human or pig codon usage charts. ** if \(p<0.01\), *** if \(p<0.001\), *** if \(p<0.0001\), and as if \(p>0.05\).  
assessing the selective pressures acting on synonymous sites by segment and host type. We estimated the diversity levels of each RVA segment over the last \(\sim\)60 years and linked decreases in diversity to the introduction of RVA vaccines between 2006 and 2008.  
The segments that share highly similar tree space may share important interactions that rely on a higher percentage of the sequence (e.g., selective pressure at synonymous sites), while the segments that inhabit distinct tree space are likely more flexible on a variety of genetic backgrounds. RVA's RNA segments are under different evolutionary pressures, which is clear from the distinct evolutionary histories of the segments (Figure 2), and the significant difference in nucleotide composition between the segments, despite coming from the same genome (Figures 7 and 9). The zoonotic potential for rotavirus makes understanding restrictions on genetic exchange important, as outcrossing events can result in novel strains which may cause more severe disease or be better able to evade a vaccine or immune response. Both codon usage and reassortment potential can be important factors in the viral host range, and both are constrained by RNA interactions, translational selection, and mutational bias. Understanding the relative constraints of the RVA genome can help better assess the risk of zoonotic outbreaks and emerging strains.