esa-sceva/satcom-chunk-collection huggingface dataset:
# Antioxidant and Antimicrobial Properties of Selected Phytogenics for Sustainable Poultry Production
## 4 Selected Plant Sources of Antimicrobial and Antioxidant Compounds
### Allium cqa  
_Allium cqa_, commonly known as an onion, is a member of the _Lilliaceae_ family, which is currently grown all over the world and has its roots in central Asia [91]. The onion is an important source of nutritional components, such as proteins, carbohydrates, sugars (arabinose, fructose, galactose, glucose), vitamins, lipids, minerals, and some flavonoids and polyphenols components [92]. Onions are a rich source of vitamin A (\(\beta\)-carotenoid), vitamin B (B1, B2, B3), vitamin E (\(\alpha\)-tocopherol), and vitamin C (ascorbic acid) [93,94], which exhibit antioxidant activities. They are also a good source of diverse dietary flavonoids, phospholipids, and glycolipids, along with organosulfur compounds, such as allicin, allin, diallyl disulfide, S-methyl-L-cysteine S-oxide, propanethial S-oxide, and 3-mercapto-2-methylpentan-1-ol [95]. Furthermore, [PERSON] et al. [96] reported that onions contain various sulfoxides, which include (+)-S-(1-propenyl)-L-cysteine sulfoxide, (+)-S-methyl-L-cysteine sulfoxide, S-propyl-L-cysteine sulfoxide, S-methyl-L-cysteine sulfoxide, and S-propenyl-L-cysteine sulfoxide, which exhibit antibacterial and antioxidant activities. In addition, onion bulbs are said to be a good source of phytonutrients and antioxidants due to the presence of organosulfur compounds, such as cysteine sulfoxides, quercetin, quercetin glucosides, and allicin [97,98].  
Onion bulbs' saponins have been shown to have biological effects, including antifungal and anti-inflammatory properties [97]. These substances stimulate the digestive and immune systems, which in turn enhance growth performance and general health in birds [99]. It has been claimed that onion extracts or powder used in drinking water or livestock feed have both growth-promoting and anti-pathogenic properties [100]. The phytochemicals inions alter the gut flora and immune system and encourage the growth of colonic and mucosal microflora, which function as a barrier to prevent microbes from entering the gastrointestinal tract [101]. [PERSON] et al. [102] found that adding 5 or 7.5 g of onion to broiler diets increasedthe overall feed intake and body weight of the hens without affecting the feed conversion ratio. Supplementing drinking water with up to 1% onion extracts during the starter and grower periods increased the average daily feed without compromising broiler chickens' feed conversion ratio [100]. Muscovy ducks fed diets containing 1% onion meal had an enhanced feed conversion ratio, live body weight, and weight gain [103]. [PERSON] et al. [104] found that Japanese quail hens fed diets with 800 g of dried onion per kg DM had increased body weight.
# Climate Effects on Tallgrass Prairie Responses to Continuous and Rotational Grazing
###### Abstract  
Cattle grazing is an important economic activity in the tallgrass prairie systems in the Great Plains of the United States. Tallgrass prairie may respond differently to grazing management (e.g., high and low grazing intensity) under variable climate conditions. This study investigated the responses of two replicated (rep a and rep b) tallgrass prairie systems to continuous (C) and rotational (R) grazing under different climate conditions over a decade (2008-2017). The enhanced vegetation index (EVI) and gross primary productivity (GPP) were compared between grazing systems (C vs. R), while EVI was compared among paddocks under rotational grazing to show the impacts of time since grazing. The average EVI in rep a was usually higher than that in rep b which could be explained by different land characteristics (e.g., soil types) associated with different landscape positions. Similar to EVI, GPP was usually higher in rep a than rep b. The average growing season EVI and GPP were higher in rotational grazing than continuous grazing in rep b but not in rep a. The average EVI of paddocks in rotational grazing systems only converged in the growing season-long drought year (2011). In other years, EVI values varied from year to year and no paddock consistently outperformed others. The variations in EVI among rotational grazing paddocks in both reps were relatively small, indicating that rotational grazing generated an even grazing pressure on vegetation at annual scale. Overall, climate and inherent pasture conditions were the major drivers of plant productivity. However, the stocking rate in continuous grazing systems were reduced over years because of deteriorating pasture conditions. Thus, the results indirectly indicate that rotational grazing improved grassland productivity and had higher stocking capacity than continuous grazing systems under variable climate conditions. Adaptive grazing management (adjustment in stocking rates and season of use to adapt to changing climatic conditions) instead of a fixed management system might be better for farmers to cooperate with changing climatic conditions.  
Keywords:tallgrass prairie; grazing management; climate; enhanced vegetation index (EVI); gross primary production (GPP) +
Footnote †: journal: Applied Soft Computing
###### Abstract  
Hand motion intentions can be detected by analyzing the surface electromyographic (sEMG) signals obtained from the remaining forearm muscles of trans-radial amputees. This technology sheds new light on myoelectric prosthesis control; however, fewer signals from amputees can be collected in clinical practice. The collected signals can further suffer from quality deterioration due to the muscular atrophy of amputees, which significantly decreases the accuracy of hand motion intention recognition. To overcome these problems, this work proposed a transfer learning strategy combined with a long-exposure-CNN (LECNN) model to improve the amputees' hand motion intention recognition accuracy. Transfer learning can leverage the knowledge acquired from intact-limb subjects to amputees, and LECNN can effectively capture the information in the sEMG signals. Two datasets with 20 intact-limb and 11 amputated-limb subjects from the Ninapro database were used to develop and evaluate the proposed method. The experimental results demonstrated that the proposed transfer learning strategy significantly improved the recognition performance (78.1% \(\pm\) 19.9%, \(p\)-value \(<\) 0.005) compared with the non-transfer case (73.4% \(\pm\) 20.8%). When the source and target data matched well, the after-transfer accuracy could be improved by up to 8.5%. Compared with state-of-the-art methods in two previous studies, the average accuracy was improved by 11.6% (from 67.5% to 78.1%, \(p\)-value \(<\) 0.005) and 12.1% (from 67.0% to 78.1%, \(p\)-value \(<\) 0.005). This result is also among the best from the contrast methods.  
sEMG; transfer learning; motion intention recognition; trans-radial amputation +
Footnote †: journal: _aciences_
# Model Updating of Bridges Using Measured Influence Lines
## 3 Results
### Updated FE Model
#### 3.2.1 Frequency-Based EDMF
Therefore, the assumption of fully restrained bearings at the abutments was considered reasonable, but should be studied in more detail in future research, for example, to model them as springs and change their stiffness gradually. After considering fixed boundary conditions and eliminating the B-Z-3 frequency, the frequency-based EDMF results indicated large \(\alpha_{\text{MG1}}\) ([1.00, 1.50]) and \(\alpha_{\text{MG2}}\) ([0.90, 1.40]) ranges, while \(\alpha_{\text{OTHER}}\) was equal to 1.00. Additionally, some candidates with a high \(\alpha_{\text{MG1}}\) value had a small \(\alpha_{\text{MG2}}\) value, which is not in line with the engineering judgement. Given that MG1 and MG2 are visually in similar conditions, it is expected that \(\alpha_{\text{MG1}}\) and \(\alpha_{\text{MG2}}\) would be comparable and higher than \(\alpha_{\text{OTHER}}\) due to the many prestressing tendons. Those results are similar to the findings from another viaduct [34], where some candidates had a high-low stiffness trend for main girders, despite expectations that they would be comparable. After additional measurement data were added to the EDMF, that trend aligned with the engineering expectations. Figure 19: Frequency-based EDMF results. B-Z-3 frequency is not considered. Experimental values are denoted by red markers, red horizontal lines indicate thresholds, candidates are represented in blue, and models that have been falsified are noted in grey.
# Data Privacy in the Internet of Things: A Perspective of Personal Data Store-Based Approaches
## 3 Data Privacy in the Internet of Things
### IoT Data Privacy
In the IoT domain, some inherent characteristics, such as location-aware applications, the omnipresence of data collection technologies, and the high interaction between devices, amplify the availability of this type of data. Profiling methods involve collecting and analyzing data about an individual's activities and behaviors over time and then using this information to group or categorize them based on specific patterns. Frequently, this information is collected without user consent and combined with other personal data to create more refined profiles. It is an important and common method used to personalize the user experience in, for instance, e-commerce. Privacy-violating interaction and presentation refer to sharing personal information in a public environment, which can result in its exposure to an undesirable audience. Numerous IoT applications (e.g., healthcare, infrastructure) require extensive user connectivity, making information available to users through smart things in their environment. However, many interaction and presentation mechanisms are inherently public, raising privacy concerns when exchanging personal information. Lifecycle transitions relate to the disclosure of private information when the ownership of a consumer product changes throughout its lifecycle. This issue is not common with consumer products like smartphones and laptops, which typically remain with the same owner for their entire lifecycle. However, this privacy risk becomes more prominent in the IoT domain, where more everyday objects are connected and can store private data. Inventory attacks involve the unauthorized collection of information about the existence and characteristics of personal things, such as communication speed and response time. Fulfilling the full potential of the IoT, all smart devices will be connected to the Internet, allowing unauthorized parties to exploit this connectivity and compile a list of items linked to a specific target. Such an attack can be helpful as a source of information for profiling users. Linkage relates to unintended exposure of personal information by merging previously separated data sources. By combining different pieces of data, new insights can emerge that the user may not have anticipated. This situation represents a privacy violation, and in an IoT context, the linkage threat is amplified due to its heterogeneous and distributed characteristics. Information leakage. IoT applications can be interconnected through wireless communication, making them susceptible to interception by attackers who may acquire transmitted information. For instance, with the widespread use of smartphones, people are often required to share their identity and location to access apps and services. Consequently, the exposure of this type of information poses a privacy risk.
# Surrogate-Based Robust Design for a Non-Smooth Radiation Source Detection Problem
## 2 Radiation Transport Model and Surrogate Formulations
### Radial Basis Function Surrogate Model  
Due to the presence of the buildings, the model response (7) is non-differentiable with respect to both position and intensity. To apply sensitivity analysis to determine an optimal sensor configuration, smoothness of the model responses must be assured. To address these issues and reduce computational times, we used radial basis functions to provide continuously differentiable approximations of the model responses (7).  
Radial basis function methods provide interpolants to sampled values associated with irregularly positioned points inside the input domain. A radial basis function approximation of the model response \(F_{i}(\mathbf{\theta})\) has the formulation  
\[\tilde{F}_{i}:\mathcal{X}\rightarrow\mathbb{R},\tilde{F}_{i}(\mathbf{\theta})= \sum_{k=1}^{\mathcal{L}}\lambda_{k}\psi(\varepsilon\|\mathbf{\theta}-\mathbf{\theta} _{k}\|_{2}), \tag{10}\]  
where \(\mathbf{\theta}\) denotes a source in the domain \(\mathcal{X}\), \(\psi:\mathbb{R}\rightarrow\mathbb{R}\) is a radial basis function and \(\varepsilon\) is a shape parameter. Possible choices of radial basis functions \(\psi\) include multiquadrics and their inverse formulations, Gaussian functions, and thin plate splines. A more comprehensive list can be found in [14; 24]. We employ Gaussian radial basis functions. The coefficients \(\lambda_{k}\) are computed by requiring that \(\tilde{F}_{i}(\mathbf{\theta}_{k})=F_{i}(\mathbf{\theta}_{k}),k=1,\ldots,\mathcal{L}\), where \(\mathbf{\theta}_{k}\) are selected to cover the entire domain \(\mathcal{X}\) and \(\mathcal{L}\) is the number of interpolation points. We employed the MATLAB radial basis function toolbox based on Cholesky factorization and Tikhonov regularization. We also tried other methods to approximate the model response based on Legendre and Lagrange polynomials and Gaussian process. Our results (not shown here) revealed that the radial basis functions approximation had the best accuracy for our application.
# Dissimilarity Space Based Multi-Source Cross-Project Defect Prediction
## 4 Experiments
### Analysis of Results
#### 4.3.3 Different Dissimilarity Metric Method  
In this part, we compare the effects of several different dissimilarity metric methods on the experimental results and verify which measurement is efficient in CPDP.  
Figure 4 is the box plot of performance indicators, which compare the values of several different dissimilarity metric methods on F-measure, AUC, and cost-effectiveness. Euclidean distance, Manhattan distance, and correlation coefficient are chosen as the measurement of dissimilarity.  
Figure 4: Box plot of different dissimilarity metric methods. (**a**) is the situation of each method on F-measure; (**b**) is the situation of each method on AUC; (**c**) is the situation of each method on cost-effectiveness. Box plots represent maximum, upper quartile, median, lower quartile and minimum values from top to bottom. In addition, circles represent outliers.  
The experimental results show that the prediction model using Manhattan distance and Euclidean distance as the measurements of dissimilarity are poor. The value of median, quartile, maximum, and minimum on F-measure and AUC are lower than the arc-cosine kernel. These values on cost-effectiveness are higher than the arc-cosine kernel. During the course of the experiment, we found that the average values of the arc-cosine kernel method on F-measure and AUC are still higher than those three methods, and cost-effectiveness index of arc-cosine kernel method is lower than other methods. In addition, it can be seen from the box plot that when the correlation coefficient is used as the measuring method, the value of median, quartile, maximum, and minimum on F-measure, AUC, and cost-effectiveness are still inferior to the arc-cosine kernel, but superior to the Manhattan and Euclidean distances.  
The reasons for these results are as follows. When the relationship between samples is measured by Euclidean distance and Manhattan distance, if the sample and the prototype data are highly correlated, the attribute value of the sample in the dissimilarity space is lower. So that the samples with the same distribution to the target set receive less attention in the process of modeling. Correlation coefficient as the measurement is opposite to the above two methods. This method makes the same distribution samples get higher attention when building the classifier. The arc-cosine kernel function is superior to other methods because the kernel function can better represent the intrinsic relationship between samples, and more attention can be paid to the data that is highly correlated with the target sample in the process of modeling. So it can be concluded that using the arc-cosine kernel function as the measuring method of dissimilarity between samples works better.
# LiDAR-Based Dense Pedestrian Detection and Tracking
## 2 Methodology
### Fine Segmentation of Dense Pedestrians  
After RBNN clustering, the objects in the scene are segmented into different groups. However, adjacent pedestrians might be assigned the same group if their point clouds are really close or even connected. As a result, the dense pedestrians are often undersegmented. As the foundation, the RBNN clustering is used as a preliminary result, which allows dense pedestrian point clouds to be divided into the same cluster. The preliminary clustering needs to be further refined to separate the adjacent pedestrians.  
To obtain the fine segmentation of dense pedestrians, the result of RBNN clustering is projected onto the horizontal plane. Point cloud density is highly correlated with the height of pedestrians within the LiDAR illumination range. To a person, whose point cloud may spread in large area, the points from the body will focus on the center. Thus the point cloud from the torso will be denser than that of the adjacent area. Kernel density estimation can be used to estimate the density distribution of pedestrian point clouds on the horizontal projection surface, and the local maximum of the density distribution can be used as the clustering center of a single pedestrian. Then, the undersegmented pedestrian point clouds can be finely segmented according to the center of the pedestrian.  
The coordinate of the point cloud set \(P\) in the XOY plane is \(P_{xoy}=p_{1},\ldots,p_{n}\), and the point cloud density at position \(p\) can be obtained using kernel density estimation, as shown in Equation (1).  
Figure 1: Dense pedestrian detection and tracking flow.  
\[f(p)=\frac{1}{n}\sum_{i=1}^{n}K(p-p_{i},\tau) \tag{1}\]  
where \(n\) is the point number of the point cloud set, \(\tau\) is the window width, which is related to the width of the pedestrian, \(p_{i}\) is the position of the \(i\)th point of the set, and \(K(\bullet)\) is the Gaussian core function, as shown in the Equation (2).  
\[K(p-p_{i},\tau) = \frac{1}{2\tau\tau\Sigma^{1/2}}\exp\{-\frac{(p-p_{i})^{T}\Sigma^{ -1}(p-p_{i})}{2\tau^{2}}\} \tag{2}\]  
where \(\Sigma\) is the covariance matrix of Gaussian distribution and \(T\) means matrix transpose. As the 3D coordinates are uncorrelated, this paper uses an identity matrix as the covariance matrix. Combining Equations (1) and (2), and then ignoring the constant coefficient, we find  
\[f(p)=\frac{1}{n}\sum_{i=1}^{n}\exp\{-\frac{(p-p_{i})^{T}(p-p_{i})}{2\tau^{2}}\} \tag{3}\]  
Equation (3) can be used to describe the density distribution of the point cloud on the horizontal plane. Local extreme values will be generated somewhere in the pedestrian's torso, and the local maximum point of the density distribution can be used as the cluster center of a single pedestrian.  
After obtaining the cluster centers, the distances from each point to cluster centers on the horizontal projection plane can be used to assign it to the nearest cluster center. The fine segmentation is to divide confused points into isolated clusters.
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Analyte** & **Validated Levels L1, L2, L3 (ng L-1)** & **WL\({}_{\star}\) Trueness (RSD\({}_{\star}\)) (c3) \({}^{\star}\)** & **WL\({}_{\star}\) Trueness (RSD\({}_{\star}\)) (c3) \({}^{\star}\)** & **LOD \({}^{\star}\)** & **LOD \({}^{\star}\)** & **LOD \({}^{\star}\)** & **Recovery \% (RSD\%, \(n=3\)) at** \\  & **L2, L3 (ng L-1)** & **L1** & **L2** & **L3** & **L1** & **L2** & **L3** & **(ng L-1)** & **(ng L-1)** & **20/40 ng L-1** & **20/400 ng L-1** \\ \hline
**Salicyanillas and substituted phenols** & & & & & & & & & & \\ BITH & 20, 80, 200 & 112 (5.6) & 112 (4.7) & 104 (2.7) & 114 (7.2) & 106 (4.8) & 101 (3.8) & 1.0 & 5.0 & 98 (10.8) & 84 (3.7) \\ CLOS & 5, 50, 200 & 105 (4.8) & 104 (2.0) & 101 (1.0) & 105 (7.1) & 101 (3.7) & 99 (3.2) & 0.5 & 2.0 & 103 (3.6) & 76 (3.5) \\ NICLOS & 5, 50, 150 & 107 (10.3) & 106 (3.7) & 96 (2.6) & 114 (9.5) & 105 (7.2) & 96 (6.9) & 0.125 & 1.0 & 94 (7.0) & 100 (5.4) \\ NITROX & 20, 80, 200 & 107 (19.5) & 107 (17.3) & 91 (4.6) & 96 (19.4) & 104 (12.4) & 96 (8.7) & 2.5 & 10.0 & 105 (4.6) & 56 (4.7) \\ OXY & 20, 80, 200 & 113 (6.7) & 108 (7.4) & 101 (2.4) & 109 (9.6) & 103 (8.6) & 101 (4.1) & 1.5 & 5.0 & 93 (7.7) & 104 (5.6) \\ RAFOX & 5, 50, 200 & 105 (8.7) & 101 (3.0) & 99 (1.8) & 102 (10.3) & 102 (4.3) & 101 (2.5) & 0.3 & 2.0 & 97 (5.8) & 86 (4.8) \\
**Tentachyprovimissing** & & & & & & & & & & & \\ MOR & 5, 50, 200 & 101 (1.8) & 98 (1.4) & 95 (1.8) & 100 (2.3) & 97 (1.9) & 98 (2.8) & 0.3 & 1.0 & 100 (4.0) & 100 (2.5) \\
**Imadinizades** & & & & & & & & & & & \\ LEV & 5, 50, 200 & 102 (1.5) & 100 (1.4) & 100 (0.7) & 102 (2.1) & 100 (1.1) & 101 (1.7) & 0.125 & 0.5 & 89 (5.7) & 96 (1.9) \\
**Organinosphates** & & & & & & & & & & & \\ COUMA & 10, 50, 150 & 83 (9.3) & 93 (2.9) & 104 (3.8) & 88 (8.3) & 95 (5.8) & 106 (4.7) & 1.0 & 5.0 & 84 (6.0) & 99 (3.6) \\ COUMA-O & 5, 50, 200 & 95 (3.7) & 89 (3.9) & 98 (1.6) & 96 (6.6) & 92 (3.4) & 99 (3.2) & 0.25 & 1.0 & 93 (5.6) & 102 (2.5) \\ HALOX & 20, 80, 200 & 94 (11.7) & 94 (3.6) & 100 (2.0) & 90 (11.8) & 94 (5.3) & 102 (3.1) & 1.0 & 5.0 & 83 (0.8) & 99 (0.6) \\
**Amino-acetonitrile derivatives** & & & & & & & & & & & \\ MONE & 10, 50, 150 & 103 (5.1) & 96 (4.3) & 93 (3.2) & 104 (12.1) & 97 (6.0) & 94 (5.2) & 0.5 & 5.0 & 90 (6.9) & 96 (3.0) \\ MONE-SO2 & 5, 50, 150 & 94 (8.1) & 91 (6.2) & 93 (3.2) & 98 (8.9) & 94 (4.6) & 98 (5.3) & 0.2 & 1.0 & 92 (2.6) & 102 (1.7) \\
**Miscellaneous** & & & & & & & & & & & \\ CLOR & 80, 300, 800 & 95 (12.8) & 97 (5.8) & 95 (4.9) & 96 (14.9) & 95 (10.0) & 94 (8.4) & 10 & 40.0 & 101 (11.6) & 110 (3.6) \\ \hline \hline \end{tabular}  
* \({}^{\star}\)WL \(\star\) Within-lababoratory repeatability while RSD\({}_{\star}\) = Relative standard deviation under repeatability conditions, \({}^{\star}\) WRL= Within-laboratory reproducibility, while RSD\({}_{\star}\) = Relative standard deviation under reproducibility conditions \({}^{\star}\) LOD = Limit of Detection based on 5N = 5, \({}^{\star}\) LOD = Limit of Quantitation based on 5N = 10, L1, L2 and L3, refer to each of the three levels at which the validation was performed.  
\end{table} 
Table 3: _Cont._
# Evaluation of Forward Models for GNSS Radio Occultation Data Processing and Assimilation
### FM Algorithm
In the experiments conducted in this study, a point-to-point comparison is made horizontally. It should be noted that we ignore the tangent point drift (that is the practical RO profiles are slanting rather than being vertical to the ground, which may result in the refractivity gradient bias [27]), since the interval of each level is less than 10 km [28]. This paper is organized as follows. The principles of the four algorithms in the forward model are described in Section 1. In Section 2, the progress of experiments 1 and 2 are introduced, together with data selection and low-pass filter strategies. Results are presented in Section 3. Finally, we discuss and present the discussion and conclusion in Section 4.