esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Analysis
This trend is particularly evident in the Galileo-only IF-PPP solution, where the vertical component showed enhanced accuracy at specific time marks compared with other IF-PPP solutions. The aforementioned outputs of error time series and positioning errors at different time windows clearly demonstrate some fluctuations and degradation. Therefore, a deeper  
Figure 7: The positioning errors in horizontal (**Left**) and vertical (**Right**) direction for different time windows on DOY 71.  
analysis is warranted through the examination of the Cumulative Distribution Function (CDF), as illustrated in Figure 8. It can be seen that 90% of two-dimensional (2D) horizontal errors are less than 30 cm, and 80% of the 2D errors are less than 40 cm of the GPS-only solution using final IGS products and real-time CNES products, respectively. For vertical direction errors of the GPS-only solution, approximately 90% of errors are less than 50 cm and 58 cm using real-time and final products, respectively. The Galileo-only solution demonstrated a notable improvement in performance for final products compared with real-time. Approximately 90% of Galileo-only solution 2D errors are less than 50 cm and 3.5 m using final and real-time products, respectively. Also, 90% of its vertical errors are less than 50 cm and 2 m using final and real-time products, respectively. For the combined GPS/Galileo, 90% of both 2D and vertical errors are less than 35 cm using all solutions, with slightly better performance of CNES products in 2D components. Figure 8: The CDF for the IF-PPP solution using GPS, Galileo, and the combined GPS/Galileo observations on DOY 71.
# GPSMAP 66 sr: Assessment of Its GNSS Observations and Centimeter-Accurate Positioning
## 4 Precise Positioning Results  
Carrier-phase positioning was performed in two different ways. We present results from the absolute positioning technique PPP (Section 4.1) and also from relative baseline positioning with respect to a VRS (Section 4.2).
# Toxins of Prokaryotic Toxin-Antitoxin Systems with Sequence-Specific Endoribonuclease Activity
## 3 Physiological Roles
### Regulation of Translation  
The sequence-specific cleavage by an endoribonuclease would allow differential digestion of unique sets of gene transcripts. In particular, a ribonuclease with a relatively long recognition sequence (e.g., seven-base cutter MazF-hw) would only cleave a minor fraction of the cellular mRNA. For example, the MazF-hw recognition sequence is found only within 7% of coding sequences [49]. Additionally, the RNA-seq of all cellular mRNA has shown that not all of the recognition sequences are digested by corresponding nucleases in vivo [32]. The limited number of actual in vivo cleavages could possibly allow regulation of even fewer sets of genes and/or different sets of genes that share a physiological function.  
The digestion of rRNA, tRNA and tmRNA and/or cleavage of the majority of mRNA has an impact on the global rate of translation. Slowed translation can have multiple consequences. When nutrients are limited, the inhibition of translation would dampen the energetic cost by lowering unwanted anabolic processes. It would also allow for the maintenance of metabolic balance when other processes are inhibited [99]. Simultaneous digestion of mRNA at or near the SD sequence and aSD portions of 16S rRNA may also allow for the preferential translation of specific sets of genes by translating the resulting lmRNA by the stress ribosomes [32].
## 1 Introduction  
### Background  
With the rapid development of information technology, the internet has been ubiquitous in the world. Thanks to the development of optical communication systems (see [1] for more discussions), people can easily communicate with each other and share multimedia messages, including texts, sound, images, videos, etc. Obviously, the internet provides much more impact on human society than any other medium, while at the same time, issues regarding information security have received considerable and critical attention.  
_Data hiding_ is an available technique to deal with secure communication so that the secure data is imperceptibly embedded without drawing attention [2]. The multimedia is used as a cover carrier to hide secret data which will be transmitted in the internet. Reversible data hiding (RDH) not only guarantees the safe transmission of data content but also recovers the hidden data as well as the cover images [3,4]. However, most of these RDH algorithms bring permanent distortions to the original carrier during the embeddingprocess, and these distortions are unacceptable in certain applications [5]. In order to achieve information hiding and distortion-free recovery of the original carrier, distortion-free reversible data hiding is considered [4]. This technique enables the receiver to both extract the embedded data correctly and acquire the original carrier without distortion. Generally, RDH is a fragile hiding technology, which is different from digital watermarking. When implementing the RDH method, the distortion that occurs during the transmission of the carrier should be avoided. According to the embedding method, the existing image RDH algorithm can be divided into spatial domain, transform domain, and encryption domain RDH scheme [6]. In this paper, we focus on RDH of spatial domain. Its embedding and extraction frameworks are shown in Figure 1. In the embedding side, the sender embeds secret data into the cover image by a reversible embedding algorithm. In the extraction side, the receiver extracts the secret data embedded in the stego-image by a reversible extraction algorithm and achieves distortion-free recovery image exactly the same as the original image. The performance of RDH algorithm depends on two conflicting factors as trade-offs: the embedding distortion between the cover image and the stego-image and the embedding capacity (EC). For the former factor, PSNR (peak-to-noise-ratio) is widely used (refer to [7] for more discussion). A higher PSNR value means that the stego-image is more similar to the original one. For the latter factor, EC stands for the number of bits which can be embedded into the cover image. Therefore, we favor an RDH algorithm which brings higher EC and lower PSNR, while a trade-off of them is usually considered to fit specific applications [6].
## 3 Interference Mitigation Schemes
### Cognitive Radio Approach  
#### 3.3.1 Fast Dynamic Cognitive Radio  
In [38], the fast dynamic cognitive radio (FDCR) algorithm is implemented in adaptive cognitive enhanced platform (ACEP) to schedule access for a WBASN's node to the idle spectrum. ACEP is designed according to CR-WBASN models and an interference subsystem, an observer subsystem, and a WBASN subsystem. The function of each subsystem is as follows: An interference subsystem can be considered as the existence of WiFi interference in a medical environment. An observer subsystem consists of two observation nodes to monitor the whole system's working status. The coordinator in the WBASN subsystem, which is implemented with FDCR, senses the spectrum and estimates interference behavior patterns.  
In an ACEP system, a MAC algorithm based on the IEEE 802.15.6 standard gives the coordinator the ability to sense the spectrum and estimate interference behavior patterns. FDCR then adjusts the channel access and sensing times based on the spectrum occupancy condition, thereby increasing the stability of the WBASN's access to channels.  
The FDCR algorithm can be divided into two phases: channel sensing with sensing time \(T_{s}\) and channel access with access time \(T_{a}\). If the sensing time is short and the access time is long, the collision rate is low and the channel utilization is therefore good. In the channel sensing phase, the coordinator of a WBASN senses the channel based on the received signal strength indicator (RSSI). The coordinator indicates the spectrum status: idle or busy. In the channel access phase, the coordinator evaluates the channel condition by using the collision rate \(\eta\). The packet collision rate is determined by the average packet collision rate during the last channel access phase. This value is used for comparison to the acceptable limit \(\eta_{threshold}\) to decide the next channel access phase of the WBASN. The WBASN initializes the \(T_{s}\) and \(T_{a}\). If the channel is idle, the coordinator broadcasts the necessary parameters to its nodes, enabling them to access the channel based on access time. To prepare for the next access phase, the overall system collision rate \(\eta\). is calculated and \(T_{s}\) and \(T_{a}\) are updated.
## 3 Nonthermal Methods
### Ionizing Irradiation  
Ionizing irradiation is another method used to eliminate _L. monocytogenes_ from the food chain, in which products are exposed to ionizing radiation, such as gamma rays, high-energy electrons, or X-rays. Irradiation involves exposing food products to gamma radiation to destroy _L. monocytogenes_ and other bacteria that can cause food poisoning or food spoilage. Irradiation has been shown to be more effective when applied to frozen foods, as it allows a higher dose to be delivered before undesirable sensory changes occur. In another study, electron beam irradiation reduced the bacterial load in soft cheeses, but the affected cells recovered during storage. Combining irradiation with other methods, such as the application of nisin, has been shown to be more effective in eliminating _L. monocytogenes_[74; 75].
# Cascade Network with Deformable Composite Backbone for Formula Detection in Scanned Document Images
## 4 Experimental Results
### Evaluation Metrics
#### 4.2.6 Detection Accuracy  
We report results on the Marmot dataset using the metrics of detection accuracy. As explained in [11], we classify the prediction into correct and partially correct based on the IoU value:  
1. Correct: the predicted bounding box is considered correct when the IoU score between the predicted formula region and the ground truth is equal to or greater than 0.5.
2. Partial: when the IoU score between the inferred and the ground truth formula region is in the interval (0; 0.5), the detection is categorized as partial.
## 3 Methodology
### Data Analyses Methods  
Initially, a descriptive analysis of the collected data was carried out (Table 1). The questionnaire included several items which affect public transport use during the pandemic. Many of these items measured a single underlying factor. Exploratory factor analysis is one  
Figure 1: COVID-19 timeline in Pakistan.  
such technique which can be used to extract those underlying factors. Hence, exploratory factor analysis (EFA) was conducted on the items affecting public transport use during the COVID-19 pandemic. The underlying factors were extracted using the maximum likelihood method. Orthogonal Varimax rotation was employed to obtain interpretable underlying factors. For further statistical analyses, factor scores were computed for each participant using regression approach to represent its relative standing on the underlying factors. Three different regression models were then developed to model the intentions of the users to use public transport during the pandemic, to follow safety precautions and to use public transport while having the COVID-19 symptoms (Figure 2). SEDs and the underlying factors were entered as the explanatory variables in all three models. \(p\) values of 0.05 were used to indicate statistically significant variables which imply 95% confidence level. The statistical software package SPSS v. 20 was used for the statistical analyses. The flow of the study is described in Figure 3.  
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{1}{c}{**Characteristics**} & \multicolumn{1}{c}{**Distribution (\%)**} \\ \hline Gender & Male (65.5), female (34.5) \\ Age (years) & Under 20 (11.9), 20–30 (48.3), 31–40 (30.8), 41–50 (7.5), above \\  & 50 (1.5) \\ Marital status & Single (56.7), married (43.3) \\  & High school (13.3), higher secondary school (14.8), bachelor \\ Education & (45.5), master and above (26.5) \\  & Students (22.8), civil employees (27.3), private employees \\ Profession & (35.2), others (14.8) \\ Household income (PKR) & Under 30,000 (14.7), 30,000–60,000 (33.3), 61,000–100,000 (31.5), more than 60,000 (20.5) \\ Car ownership & Yes (37.6), No (62.4) \\ Motorcycle ownership & Yes (61.8), No (38.2) \\ Trip frequency & 1–2 days a week (11.8), 3–4 days a week (18.7), 5–6 days a week (43.2), almost every day a week (26.3) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Scio-economic and -demographic characteristics (SEDs) of the respondents.  
Figure 2: Description of the models developed in this study.
Water_**2015**, \(7\), 175-187; doi:10.3390/w7010175  
_Water_  
**ISSN 2073-4441**  
www.mdpi.com/journal/water  
Genetic Basis for Geosmin Production by the Water Bloom-Forming Cyanobacterium, _Anabaena vicrainica_  
**[PERSON] 1, [PERSON] 2, [PERSON] 3, [PERSON] 4 and [PERSON] 3,* 1**  
Key Laboratory of Plant Germplasm Enhancement and Speciality Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China; E-Mail: [EMAIL_ADDRESS]  
Resources and Environment College, Hunan Agricultural University, Changsha 410128, China; E-Mail: [EMAIL_ADDRESS]  
Key Laboratory of Algal Biology, Institute of Hydobiology, Chinese Academy of Sciences, Wuhan 430072, China; E-Mail: [EMAIL_ADDRESS]  
Henan University Minsheng College, Kaifeng 475001, China; E-Mail: [EMAIL_ADDRESS]  
Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86-027-687-800-67; Fax: +86-027-687-801-23.  
Academic Editor: [PERSON]  
_Received: 15 October 2014 / Accepted: 23 December 2014 / Published: 31 December 2014_  
Geosmin is a common, musty-smelling sesquiterpene, principally produced by cyanobacteria. _Anabaena vicrainica_ (Schhorb.) Watanabe, a water bloom-forming cyanobacterium, is the geosmin producer responsible for odor problems in Dianchi and Erhai lakes in China. In this study, the geosmin synthase gene (_geo_) of _A. vicrainica_ and its flanking regions were identified and cloned by polymerase chain reaction (PCR) and genome walking. The _geo_ gene was found to be located in a transcription unit with two cyclic nucleotide-binding protein genes (_cnb_). The two _cnb_ genes were highly similar and were predicted members of the cyclic adenosine monophosphate (cAMP) receptor protein/fumarate nitrate reductase regulator (Crp-Fnr) family. Phylogenetic and evolutionary analyses implied that the evolution of the geosmin genes involved a horizontal gene transfer process in cyanobacteria. These genes showed a close relationship to 2-methylisoborneol genes in origin and evolution.  
geosmin; synthesis gene; cyanobacteria; _Anabaena vicrainica_; cyclic nucleotide-binding protein
## 3 Results and Discussion
### Validation of MW-SPM and HPLC-FD Methods  
#### 3.3.1 Linear Range and Sensitivity  
Calibration curves were built under the predetermined refined optimal conditions for both MW-SPM and HPLC-FD (Figure 11), and the least-squares approach was adopted to analyze the data using linear regression. The graphs were linear and the lines' correlation coefficients were excellent in the ranges of 14.52-200 ug/well and 0.12-3.2 ug/mL, for MW-SPM and HPLC-FD, respectively. The calibration parameters for both MW-SPM and HPLC-FD are given in Table 3.  
The limits of detection (LOD) and limits of quantitation (LOQ) were calculated according to the International Council for Harmonization (ICH) guidelines [48]. The LOD values achieved by MW-SPM for DUV and SEL were 4.4 and 3.17 ug/well, respectively. The LOD values achieved by HPLC-FD for DUV and SEL were 0.03 and 0.05 ug/mL, respectively. Table 3 provides a summary of the MW-SPM and HPLC-FD calibration and validation parameters.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Parameter**}} & \multicolumn{2}{c}{**Value**} \\ \cline{2-3}  & **DUV** & **SEL** \\ \hline Retention time (min) & 3.25 & 4.20 \\ Retention factor (k\({}^{\prime}\)) & 1.196 & 1.857 \\ Separation factor (\(\alpha\)) & 1.55 & 1.55 \\ Resolution factor (Rs) & 2.64 & 2.64 \\ Peak asymmetry factor & 1 & 1 \\ Number of theoretical plates (N) per meter & 8002 & 8939 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Chromatographic parameters for DUV and SEL by the suggested HPLC-FD method.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Parameter**}} & \multicolumn{2}{c}{**Value for MW-SPM**} & \multicolumn{2}{c}{**Value for HPLC-FD**} \\ \cline{2-5}  & **DUV** & **SEL** & **DUV** & **SEL** \\ \hline Linear range 1  & 14.52–200 & 10.46–200 & 0.12–3.2 & 0.15–3.2 \\ Intercept & 0.0018 & 0.0158 & 0.1751 & 0.1384 \\ Standard deviation of intercept & \(7.11\times 10^{-3}\) & \(9.73\times 10^{-3}\) & 0.0834 & 0.1056 \\ Slope & 0.0049 & 0.0093 & 0.9173 & 0.6968 \\ Standard deviation of slope & \(5.466\times 10^{-6}\) & \(4.389\times 10^{-5}\) & 0.0075 & 0.0094 \\ Correlation coefficient & 0.9997 & 0.9996 & 0.9998 & 0.9997 \\ LOD 1  & 4.4 & 3.17 & 0.03 & 0.05 \\ LOQ 2  & 14.52 & 10.46 & 0.12 & 0.15 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Calibration parameters for the determination of DUV and SEL by the proposed MW-SPM and HPLC-FD.
# Seismic AVOA Inversion for Weak Anisotropy Parameters and Fracture Density in a Monoclinic Medium
## 3 Numerical Analysis
In order to demonstrate the advantage of the proposed algorithm in fracture density estimation, we made use of the same synthetic data as used in the Figure 4a and estimated anisotropy through elliptical inversion of interval velocities. The resulting NMO velocity varied ellipsoidally with the azimuth. The anisotropy intensity was defined by the axial ratio. To compare with the true value of fracture density, the anisotropy intensity, multiplied by a factor, is shown in Figure 7. We can see that the accuracy of the proposed inversion, in relative terms, is greater than that of elliptical inversion. Figure 6: Inversion results using synthetic data (Figure 4a), computed with the fracture radius at 10 m and fracture width at 1 mm. (**a**) Inversion results with the forward operator that the fracture radius was 0.25 m, (**b**) Inversion results with the forward operator that the fracture width was 5 mm.
# Unsupervised Learning for Concept Detection in Medical Images: A Comparative Analysis
## 2 Methods
### Bags of Visual Words  
For each data set, images were converted to grayscale without resizing, and visual keypoint descriptors were subsequently extracted. We employed two keypoint extraction algorithms separately:Scale Invariant Feature Transform (SIFT) [17], and Oriented FAST and Rotated BRIEF (ORB) [18]. While both algorithms obtain scale and rotation invariant descriptors, ORB is known to be faster and require less computational resources. The keypoints were extracted and their respective descriptors computed using OpenCV [19] (version 3.3.0, OpenCV team, 2017). Each image yielded a variable number of descriptors of fixed size (128-dimensional for SIFT, 32-dimensional for ORB).  
In the event that the algorithm did not retrieve any keypoints for an image, the algorithm's parameters were adjusted to loosen the edge detection criteria. In SIFT, the contrast threshold (contrastThreshold) was changed from 0.04 to 0.01; the edge threshold (edgeThreshold) was increased from 10 to 20; and the sigma of the Gaussian on the first octave (sigma) was modified from 1.6 to 1.4. With ORB, the FAST keypoint detection threshold (fastThreshold) was changed from 20 to 10. Nonetheless, such cases of images yielding no keypoints were residual in the data set (less than 0.05% of all images), and occurred mostly in images with little to no potential for visual understanding.  
All procedures described henceforth were the same for both ORB and SIFT keypoint descriptors. From the training set, 3000 files were randomly chosen and their respective keypoint descriptors collected to serve as template keypoints. A visual vocabulary (codebook) of size \(k=512\) was then obtained by performing k-means clustering on all template keypoint descriptors and retrieving the centroids of each cluster, yielding a list of 512 keypoint descriptors \(\mathcal{V}=\{V_{i}\}\).  
Once a visual vocabulary was available, we constructed an image's BoW by determining the closest visual vocabulary point and incrementing the corresponding position in the BoW for each image keypoint descriptor. In other words, for an image's BoW \(B=\{o_{i}\}\), for each image keypoint descriptor \(d_{j}\), \(o_{i}\) was incremented when the smallest Euclidean distance from \(d_{j}\) to all other visual vocabulary points in \(V\) was the distance to \(V_{i}\). Finally, each BoW was normalized so that all elements lay between 0 and 1. The idea was to picture the bag of visual words as a histogram of visual descriptor occurrences to be used as a global image descriptor [20].
## 3 Results
### Annual Ecosystem Respiration
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Heterotrophic** & **Autotrophic** & **Total Soil** & **Water Table** & **Soil** \\
**Scheme** & **Respiration** & **Respiration** & **Respiration** & **Depth** & **Temperature** \\ \cline{2-6}  & \multicolumn{4}{c}{**Mg CO\({}_{2}\) ha\({}^{-1}\) yr\({}^{-1}\).**} & **cm** & \({}^{\circ}\)C \\ \hline Logged peat swamp forest 1 & 43.0 \(\pm\) 5.4 & 10.9 \(\pm\) 4.6 & 53.9 \(\pm\) 5.3 & 39.9 \(\pm\) 10.5 & 27 \(\pm\) 0.2 \\ Logged peat swamp forest 2 & 39.6 \(\pm\) 5.1 & 6.1 \(\pm\) 4.2 & 45.6 \(\pm\) 4.9 & 34.0 \(\pm\) 9.9 & 26.8 \(\pm\) 0.2 \\ Logged peat swamp forest 3 & 39.4 \(\pm\) 4.3 & 11.6 \(\pm\) 3.4 & 51.0 \(\pm\) 5.4 & 45.1 \(\pm\) 10.6 & 27.3 \(\pm\) 0.3 \\ Logged peat swamp forest & 40.7 \(\pm\) 1.2 \({}^{\rm a}\) & 9.5 \(\pm\) 1.7 \({}^{\rm a}\) & 50.2 \(\pm\) 2.4 \({}^{\rm a}\) & 39.7 \(\pm\) 20.3 \({}^{\rm a}\) & 27 \(\pm\) 0.5 \({}^{\rm a}\) \\ \hline Early seal 1 & 26.0 \(\pm\) 2.5 & 14.9 \(\pm\) 4.1 & 39.6 \(\pm\) 4.3 & 59.8 \(\pm\) 13 & 30.5 \(\pm\) 0.5 \\ Early seal 2 & 31.3 \(\pm\) 4.0 & 6.1 \(\pm\) 1.2 & 37.5 \(\pm\) 4.2 & 40.4 \(\pm\) 12.5 & 29.1 \(\pm\) 0.3 \\ Early seal 3 & 34.6 \(\pm\) 2.0 & 10.7 \(\pm\) 5.8 & 45.3 \(\pm\) 5.8 & 50.7 \(\pm\) 9.9 & 28.9 \(\pm\) 0.3 \\ Early seal mean (\(n\) = 3) & 30.7 \(\pm\) 2.5 \({}^{\rm b}\) & 10.6 \(\pm\) 2.5 \({}^{\rm a}\) & 40.8 \(\pm\) 2.3 \({}^{\rm b}\) & 50.3 \(\pm\) 23.5 \({}^{\rm a}\) & 29.5 \(\pm\) 0.8 \({}^{\rm b}\) \\ \hline Oil palm estate 1 & 42.2 \(\pm\) 7.0 & 8.6 \(\pm\) 5.4 & 49.3 \(\pm\) 4.3 & 88.4 \(\pm\) 10.3 & 31.5 \(\pm\) 0.3 \\ Oil palm estate 2 & 38.5 \(\pm\) 3.3 & 5.9 \(\pm\) 3.2 \({}^{\rm a}\) & 44.4 \(\pm\) 3.5 & 74.4 \(\pm\) 9.1 & 30 \(\pm\) 0.3 \\ Oil palm estate 3 & 35.4 \(\pm\) 3.6 & 13.3 \(\pm\) 6.0 & 48.7 \(\pm\) 4.9 & 72.2 \(\pm\) 9.3 & 30 \(\pm\) 0.4 \\ Oil palm estate mean (\(n\) = 3) & 38.7 \(\pm\) 2.0 \({}^{\rm a}\) & 9.3 \(\pm\) 2.2 \({}^{\rm a}\) & 47.5 \(\pm\) 1.6 \({}^{\rm a}\) & 78.3 \(\pm\) 19.1 \({}^{\rm b}\) & 30.5 \(\pm\) 0.8 \({}^{\rm b}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._  
Figure 3: Heterotrophic respiration trend from August 2014 to December 2015 in PSF (black circle), LPSF (square hollow), ES (cross), and OP (hollow circle). Dry months are represented by data from August, September, and October (5 months). Wet months are represented by data from February to July and November to December (7 months). Error bars show \(\pm\) standard error (SE) of the heterotrophic respiration data.