esa-sceva/satcom-chunk-collection huggingface dataset:
# Feasibility of Low Latency, Single-Sample Delay Resampling: A New Kriging Based Method
###### Abstract  
Wireless sensor systems often fail to provide measurements with uniform time spacing. Measurements can be delayed or even miss completely. Resampling to uniform intervals is necessary to satisfy the requirements of subsequent signal processing. Common resampling algorithms, based on symmetric finite impulse response (FIR) filters, entail a group delay of 10 s of samples, which is not acceptable regarding the typical interval of wireless sensors of seconds or minutes. The purpose of this paper is to verify the feasibility of single-delay resampling, i.e., the algorithm resamples the data without waiting for future samples. A new method to parametri  
which have a constant group delay of half the filter order, are commonly used. For example, the group delay of audio resampling using a FIR filter with an order 90 is still under 1 ms.  
Resampling data from wireless sensors requires a different design focus. Data typically come in intervals of seconds or even minutes, and group delays of tens of samples are not acceptable. If the wireless sensor system is part of a control application, new samples must be iteratively processed within a guaranteed response time and be converted to the resampled signal with low latency. Such low-latency processing is the basis for a real-time control system. However, other challenges, such as implementation on a real-time operating system, are not addressed in this paper.  
From the perspective of a target time point for calculating the resampled signal, one or multiple future sensor measurements must be known for signal interpolation (Figure 1). However, zero-delay resampling can only be applied to low-frequency signals, extrapolation beyond known past samples leads to stability problems.  
Most existing approaches from the literature provide suitable solutions for the group-delay scenario. In this case, the algorithm is allowed to wait for the required number of future samples, as determined by the half FIR filter order or the half window width of the algorithm. The output is then delayed by the same number of samples. In an extended scenario, offline reconstruction of a signal is possible without any restrictions on processing delays.  
The focus of this paper is on a different scenario, called the single-delay scenario, in which the resampling algorithm is only allowed to use one future sample. This means that as soon as a new sample arrives, the gap between the current and the previous sample should be reconstructed and appended to the resampled signal. Any changes in the input should be immediately reflected in the output.  
This paper tests the feasibility and minimum required oversampling rate (OSR) for single-sample delay reconstruction. Furthermore, it introduces a new method based on Kriging interpolation. Either the resampling algorithm can be implemented as an event processor to update the resampled signal after the arrival of each sensor measurement, or it can be triggered by subsequent signal processing, when the signal processing is ready to receive the next input sample.
# MSSP: A Blockchain Sharding Protocol Based on Multi-Shard Storage
## 5 System Analysis
### Scalability Analysis  
By analyzing the distribution of consensus zones, the bandwidth, storage, and communication complexity of nodes, we can assess the scalability of MSSP. Unlike traditional sharding that divides consensus regions based on shards, in MSSP, consensus regions are composed of nodes that store multiple identical shards. The distribution of consensus zonesis defined as \(m=\{m_{1},m_{2},\ldots,m_{S-1}\}\), where \(m_{1}\) represents the number of consensus zones composed of nodes storing a single shard, and \(m_{i}\) (\(2\leq i\leq S-1\)) represents the number of consensus zones composed of nodes storing \(i\) shards. We define the scalability parameter \(\omega\) as the average complexity of bandwidth, storage, and communication for a single node in the system. From Section 4.2, it can be inferred that a consensus zone consisting of \(m\)-nodes storing \(i\) shards needs to process and validate cross-shard transactions between \(i\) shards. Therefore, its bandwidth, storage, and communication complexity is \(i/S\). Similarly, the scalability parameter for multi-shard storage systems can be expressed as:  
\[\omega^{MSSP}=\frac{1}{S}\cdot\frac{m_{1}}{S}+\frac{2}{S}\cdot\frac{m_{2}}{S}+ \cdots+\frac{S-1}{S}\cdot\frac{m_{S-1}}{S}=\frac{m_{1}+\sum_{2\leq i\leq S-1}m _{i}}{S^{2}} \tag{2}\]  
The scalability parameter for traditional sharding is \(\omega^{traditional}=\frac{1}{5}\), while the non-sharding scalability parameter is \(\omega^{non}=1\).  
The scalability parameter of multi-shard storage falls between traditional sharding and non-sharding blockchains, which means it strikes a balance in terms of resource utilization and processing efficiency. The main advantage of the multi-shard storage protocol over traditional sharding lies in the handling of cross-shard transactions. In traditional sharding systems, cross-shard transactions require multiple rounds of submission and processing because each sub-transaction must be confirmed on the corresponding shard. In contrast, MSSP utilizes nodes that store the same multiple shards to form a consensus zone, enabling it to process cross-shard transactions within one round without the need for cross-shard communication, thus reducing additional communication and coordination overheads.
## 1 Introduction  
Averting a subsequent natural calamity is certainly relatively impractical. However, due to the rapid progress of simulation science and seismological studies, it is quite possible to mitigate the catastrophic effects post-disaster. If earthquake safety (risk) assessment of buildings is viewed as [PERSON]'s domino theory of cause and effects [1], damage results from a chain of sequential events, metaphorically like a line of dominoes falling over. Where the first piece is the seismic hazard, which plays an important role and is inherently unavoidable, followed by the building vulnerability, then construction characteristics, which lead to seismic risk and damage of buildings and cause loss and injury of residents, respectively. When one of the dominoes falls, it triggers the next one, and the next, but improving or removing a key factor (such as retrofitting buildings) prevent or minimize the impact of the chain reaction of dominoes. As it is not possible to modify the seismic hazard to reduce the risk, emphasis should be placed on the study of vulnerability assessment and reduction as a measure of damage/loss mitigation. In urban areas midst, the construction of new building stock is a substantial number of structures still in service, which were constructed either when national seismic codes were not evolved due to lack of research or they were not strictly enforced by the law. For instance, in Istanbul, Turkey, as a high seismic area, around 90% of buildings are substandard, which can be generalized into other earthquake-prone regions in Turkey [2]. The reliability of this building stock resulting from earthquake-induced collapse currently is uncertain. Nonetheless, it is also not feasible to perform a detailed seismic vulnerability analysis on each building as a solution to the scenario, as it is complicated and expensive to perform. This indicates the necessity of a reliable, rapid, and computationally easy method for seismic vulnerability assessment, commonly known as Rapid Visual Screening (RVS). In RVS methodology, a observational survey of buildings will be performed, and according to the data collected during the visual inspection, a structural score will be calculated without performing any structural calculations to determine the expected damage of a building and whether the building needs detailed assessment [3]. Although this method might save time and resources due to the subjective/qualitative judgments of experts who performed the inspection, the evaluation process is dominated by vagueness and uncertainties, which vagueness can be handled adequately through the fuzzy set theory [4] but do not cover all sort of uncertainties due to its crisp membership functions [5]. In this study, a novel method of rapid visual hazard safety assessment of buildings against earthquake is introduced in which an interval type-2 fuzzy logic system (IT2 FLS) would be used to cover uncertainties. In addition, the scope of this paper is on the reinforced concrete buildings.
## 2 Results and Discussion
### Catalysts' Characterization
#### 2.1.1 X-ray Fluorescence (XRF) and X-ray Diffraction (XRD)
If the sole 200 reflection could be observed in the modified materials, \(d_{100}\) could be calculated by a relationship between the interplanar spacing of the 200 and 100 reflections (Equation (2), see Materials and Methods). Therefore, deconvolution methods [53] \begin{table}
\begin{tabular}{c c c c c c} \hline
**Sample** & **d\({}_{200}\) (nm)** & **d\({}_{100}\) (nm) \({}^{1}\)** & \(a\) (nm) \({}^{2}\)** & **Ce content (wt.\%)** & **Si/Ce Molar Ratio** \\ \hline Si-MCM-41 & 1.94 & 3.88 & 4.48 & - & \(\infty\) \\ Ce-MCM-Cl & 2.00 & 4.00 & 4.61 & 5.89 & 31 \\ Ce-MCM-NO\({}_{3}\) & 2.15 & 4.30 & 4.97 & 5.24 & 36 \\ \hline \end{tabular} \({}^{1}\) Calculated from Equation (2). \({}^{2}\) Calculated from Equation (1).  
\end{table} 
Table 1: XRD and XRF data of calcined MCM-41 materials. were applied in the overlapped signal to gain information towards the 20 position of both the 110 and 200 reflections to find \(d_{100}\) and, consequently, the unit cell, \(a\), for the modified materials (see Figure 2). The 110 and 200 reflections were found in 3.86 and 4.42\({}^{\circ}\) for Ce-MCM-Cl and in 3.56 and 4.10\({}^{\circ}\) for Ce-MCM-NO\({}_{3}\), respectively. The values obtained for \(d_{100}\) and \(a\) using the information of the 200 deconvoluted reflections are compiled in Table 1. For Si-MCM-41, the \(d_{100}\) value calculated from the 200 reflection (3.88 nm) was close to the one observed experimentally (3.84 nm), validating our data obtained from Equations (1) and (2). As can be observed in Table 1, the values of \(a\) calculated for the cerium materials confirmed the unit cell expansion mentioned before and this effect was more expressive towards Ce-MCM-NO\({}_{3}\). Differences between both cerium-modified materials could also be observed at the high angle region (Figure S1 in Supplementary Material). While the Ce-MCM-Cl pattern only exhibited the usual broad signal around 25\({}^{\circ}\) common to amorphous silica in the MCM-41 walls, Ce-MCM-NO\({}_{3}\) presented three reflections around 28, 47, and 57\({}^{\circ}\) that suggests the formation of CeO\({}_{2}\) (reflections 110, 220, and 311 according to JCPDS 34-0394, respectively [54]). The surfacing of non-fran  
Figure 1: XRD patterns of the calcined MCM-41 materials at the low angle region. Figure 2: Deconvolution of the overlapped XRD reflections and their 20 positions for Ce-MCM-Cl and Ce-MCM-NO\({}_{3}\) materials. associated with the synthetic procedure [37; 55] and the influence of a particular metal precursor is little discussed in the literature. However, the absence of peaks from segregated CeO\({}_{2}\) in the particles of the Ce-MCM-Cl pattern does not mean there are not any; they might be highly dispersed on the MCM-41 surface. Nonetheless, the XRD analysis demonstrated the clear presence of CeO\({}_{2}\) in one of the materials.
# Cross-Subject Classification of Effectiveness in Performing Cognitive Tasks Using Resting-State EEG
## 2 Materials and Methods
### EEG Recording and Preprocessing  
Resting-state EEG was recorded from 128 channels (Geodesic Sensor Nets Inc., Eugene, OR, USA) with a 0.1 Hz-70 Hz band-pass analog filter, 50 Hz-notch filter, 1000 Hz sampling rate, and online re-reference at the vertex (channel Cz) using Net Station 4.5.1 software (Electrical Geodesics, Inc. (EGI), Eugene, OR, USA). The electrodes were placed using a 10-10 system. Impedance was kept below 50 K\(\Omega\). Further, EEG data were downsampled to 250 Hz. Then, EEG data were re-referenced offline to an average of all 128 channels.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Task Group** & **Task Specification** & **Example** & **Number of Tasks** \\ \hline \multirow{2}{*}{Verbal} & Five-letters anagrams & O E S G O (goose) & 30 \\ \cline{2-4}  & Six-letters anagrams & R L O F E D (folder) & 30 \\ \hline \multirow{4}{*}{Arithmetic} & Addition & 44 + 66 + 12 + 7 & 12 \\ \cline{2-4}  & Subtraction & 6435\(-\)4846 & 12 \\ \cline{2-4}  & Multiplication & 27 \(\times\) 9 + 5 \(\times\) 6 & 24 \\ \cline{2-4}  & Fraction & 7/12–4/10 & 12 \\ \hline Logical & Arithmetic sequences & 93; 82; 41; 30; 15 & 60 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Examples of three types of cognitive tasks.  
Figure 1: Timeline of trial presentation with an example of an arithmetic task. * represents the fixation image.  
Epochs with low signal-to-noise ratio (SNR) were rejected using minimum and maximum peak-to-peak amplitudes.  
Electrooculogram (EOG) artifacts were removed using independent component analysis (ICA) and the corresponding EOG channels after high-pass filtering with a cutoff frequency of 1 Hz (ICA is sensitive to low-frequency drifts), then a band-pass filter of 3-25 Hz was applied. Thirty-six EEG channels with high muscle artifacts exceeding an absolute voltage threshold of 140 \(\upmu\)V and EOG channels were discarded from further analysis (the excluded channels were: E8, E14, E17, E21, E25, E43, E44, E48, E49, E56, E57, E63, E64, E65, E68, E69, E73, E74, E81, E82, E88, E89, E90, E94, E95, E99, E100, E107, E113, E114, E119, E120, E125, E126, E127, and E128).  
Continuous recordings of resting-state EEG were segmented into 2 s epochs without overlapping. PSD values for each epoch were estimated using the multitaper method, which calculates spectral density for orthogonal tapers, then averages the values together for each channel. Individual PSD values were averaged over each of seven frequency bands: 01 (theta1): 4-6 Hz, 02 (theta2): 6-8 Hz, \(\upalpha\)1 (alpha1): 8-10 Hz, \(\upalpha\)2 (alpha2): 10-12 Hz, \(\upbeta\)1 (beta1): 12-16 Hz, \(\upbeta\)2 (beta2): 16-20 Hz, and \(\upbeta\)3 (beta3): 20-24 Hz. Relative power equal to the power in each frequency band divided by the total power were used as features for classification and the permutation cluster test. The feature space was \(\mathrm{X}\in\mathrm{R}^{\mathrm{N}\times\mathrm{K}}\), where K is the number of frequency bands and \(n\) is the number of EEG channels.
# Automated Visual Identification of Foliage Chlorosis in Lettuce Grown in Aquaponic Systems
## 1 Introduction
Several portable meters, such as SPAD (soil plant analysis development), are developed based on this method [13]. The spectral instruments are fast and fairly accurate but very expensive. Hyperspectral imaging and spectral remote sensing also use the spectral reflection principle [14]. Again, hyperspectral instruments are costly and require specific environmental conditions for proper sampling. With the development of technology, some researchers applied computer vision techniques to detect the quality of plants and leaf yellowing based on their nutritional status. Computer vision is a low-cost and non-destructive approach, but it requires a large amount of data for training and achieving the desired performance of the model [15]. Considering the aforementioned challenges, this paper proposes a methodology based on an image processing technique to identify chlorosis in lettuce crops grown in an aquononics facility based on their foliage color. To be more certain, the estimation of chlorophyll content or nutrient deficiency is out of the scope of this study. The focus of the study is to determine the plant quality by extracting the foliage and its red (R), green (G), and blue (B) channel values using HSV space segmentation, where HSV stands for hue, saturation, and value [16]. The foliage color detection model is then developed using mean values of the R, G, and B channels and a color distance model. The color distance model calculates the foliage color difference from the threshold values. Numerous color distance models are available for this purpose, such as the Euclidean and color approximation distances (CIE76, CIE94, CIEDE2000, etc.). In this study, the Euclidean distance (ED) model is used,as it is the simplest method of finding the distance between two colors within an RGB color space [4]. Moreover, it works well when a single color is to be compared to a single color, and the need is to simply know whether a distance is greater or smaller, which is the case with the proposed model in this study. The model is built in a Jupyter notebook and saved in a local directory. An ontology model, 'AquaONT', developed by authors in previous work, is integrated with the proposed model through a cloud-based application built on Streamlit, which is an open-source app framework for machine learning and data science [17]. The ontology model provides information on causes and abiotic stresses responsible for leaf chlorosis in lettuce crops. The remainder of the paper is structured as follows: Section 2 will present the related work; Section 3 will explain the methodology used to develop the system; Section 4 will present the results and discussion along with model significance; and finally, Section 5 will discuss the conclusions and future work.
## 2 Literature Review
### MFA Based on Graphical Passwords  
It has been proven that humans can remember pictures better than text, and, consequently, graphical password schemes are a better alternative to text-based schemes [27]. [PERSON] [7] proposed a mechanism which presents images at a time interval in which the user must click on a predefined space within the image and type a password to complete the authentication; however, security can be compromised if the defined click area is too large. [PERSON] [28] proposed a mechanism that requires the user to choose three images and memorize them in an initial registration phase; then, in the authentication phase, the user must choose the correct images that he or she considered during the registration process in a specific order. The downside of the mechanism is that when long periods of time elapse between authentications, users tend to forget the selected images. [PERSON] [29] proposed a mechanism in which the user receives an image on his or her screen, and on a second device the user receives information on where to click, the number of clicks, and in what order. The user follows the instructions on the image to complete authentication. This mechanism requires a second device, which makes implementation expensive and compromises user experience and usability.  
All three of the reviewed mechanisms, based on graphical passwords, present some strengths; the main strength being that they provide a much larger password space compared to simple text-based passwords. The mechanisms also overcome many different security threats, such as key-loggers, screen capture, shoulder-surfing, and weak passwords. Furthermore, except for the latter one, the mechanisms entail low implementation costs.
# Latest Advances in the Global Navigation Satellite System--Reflectometry (GNSS-R) Field
## 6 New Science Investigations
### Phytoplankton  
In [144] GNSS-R was demonstrated to be an effective method for detecting and monitoring phytoplankton levels on the ocean surface for the first time. The authors connected a massive dust storm which formed over the Sahara Desert in June 2020 to a phytoplankton bloom event near the coast of Florida. The presence of phytoplankton on the ocean surface forms a film that affects the surface tension of the ocean, causing a local decrease in ocean surface roughness. CYGNSS, designed to detect changes in ocean surface roughness over tropical areas, provided valuable coverage in space and time for this application. An increase in ocean surface tension was reflected in CYGNSS data as an increase in reflectivity as the surface became smoother. In [144], the authors demonstrated the ability of GNSS-R measurements to provide an effective means of mapping areas covered with phytoplankton for the first time. Although ocean color products can detect algal blooms by mapping the levels of chlorophyll, their effectiveness is limited by cloud cover, which covers roughly 70% of the Earth's surface on any given day. The authors established GNSS-R as a highly reliable remote sensing tool for detecting and monitoring the presence of phytoplankton blooms across the ocean surface. In subsequent studies, independent teams investigated the detection of red tide [145] and green algae [146] on the ocean surface. The authors in [145] developed a method to estimate the density of red tide using TDS-1 data. The method involved associating GNSS-R observations with the density of red tide on the sea surface (i.e., the area covered by algae within a TDS-1 pixel). Landsat-8 near-infrared data and TDS-1 GNSS-R data were used to build and test the proposed method for a red tide outbreak in the sea off the Tsingtao coast of China. The retrieved red tide density had a root mean square error of 2.84%, and the correlation coefficient was 0.73. The same authors published a companion study in [146] where they modeled green algal blooms using GEO-R, a process which involved analyzing the reflected signals of the geostationary Earth orbit satellites collected by shipborne receivers. The developed model was verified using experimental data collected in Qingdao, Jiaozhou bay, and the inversion accuracy of the green algae density model was found to be better than 4%.