esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Ensemble Framework Based on K-Means and Resampling for Imbalanced Data
## 3 The Proposed Approach EKR
### K-Means Clustering  
Since there are fewer data samples in the positive class and it contains important information, EKR clusters only for the negative class. There are two reasons why we choose k-means as the clustering algorithm. On the one hand, K-means is a relatively low complexity algorithm, and it only needs to specify the parameter \(k\)-value. On the other hand, each sample only belongs to the cluster with the highest similarity after k-means, which can be better combined with the resampling method we proposed.  
The process of K-means is as shown in Figure 1. Figure 1a gives an original dataset for clustering. First, the clustering number k is determined and k initial centroids are randomly selected. We use k = 2 as an example here. In Figure 1b, red and blue forks represent two random clustering centroids. Then the nearest centroid is found for each point, and each point is assigned to the cluster corresponding to the centroid, after that the centroid of each cluster is updated to the average of all points in the cluster, as shown in Figure 1c. Finally, the final cluster centroids and the determined sub-clusters are formed after several iterations as in Figure 1d.  
However, K-means must determine \(k\)-value in advance, different \(k\)-value lead to different final classification results, so \(k\)-value cannot be determined blindly. In this paper, we utilize Average Silhouette Coefficient (ASC) to define \(k\)-value. Silhouette Coefficient combines Cohesion and Separation of clustering to evaluate the effect of clustering. Silhouette Coefficient of point \(i\). is given in the follows equation:  
\[S(i)=\frac{b(i)-a(i)}{max\{a(i),b(i)\}} \tag{1}\]  
where \(a(i)\). denotes the average of Euclidean distance between sample point \(i\). and other points in the same sub-cluster, which is used to describe Cohesion within cluster. \(b(i)\). is the average of Euclidean distance from point \(i\) to all points in the nearest sub-cluster, which quantifies separation between sub-clusters. Silhouette Coefficient is between \(-1\) and \(1\). The larger the value is, the better the clustering effect will be. The average of Silhouette Coefficient of all points is Average Silhouette Coefficient. Hence, we choose the \(k\)-value that maximizes the Average Silhouette Coefficient. For example, we calculate Average Silhouette Coefficient of dataset in Figure 1a, as shown in Figure 2, when k = 3, Average Silhouette Coefficient reaches the maximum, so we choose k = 3 as the number of sub-clusters.  
Figure 1: The process of K-means. (**a**) An original dataset; (**b**) randomly selecting two points as cluster centroids; (**c**) updating cluster centroid; (**d**) determining the final cluster centroids and two sub-clusters after iterations.  
Figure 2: Average Silhouette Coefficient curve.
# A Frequency-Domain Estimation Scheme for Frequency Offset with Large Range in OFDM Systems
## 2 System Description
### Conventional Frequency Offset Estimation Methods
#### 2.2.2 FFO Estimation Method  
[PERSON] and [PERSON] provided an FFO estimation method based on the time-domain repetition structure of the pilot OFDM symbol, which is specially constructed with two repeated halves [4]. Let the subcarriers with even indices transmit the pilot symbols, and the other subcarriers with odd indices transmit zeros. After IFFT, the obtained time-domain pilot OFDM symbol has two similar halves; that is, \(\mathbf{x}\) would be \(\mathbf{x}=(\mathbf{x}(0),\mathbf{x}(1),\ldots,\)\(\mathbf{x}(N/2-1),\mathbf{x}(0),\mathbf{x}(1),\ldots,\mathbf{x}(N/2-1))\). With the corresponding received signal \(\mathbf{y}\), the FFO could be estimated as  
\[\hat{e}_{F}=\frac{1}{\pi}\cdot\operatorname*{angle}(\sum_{n=0}^{N/2-1}\mathbf{y}( n)\mathbf{y}^{*}(N/2+n)) \tag{5}\]  
where the function \(\operatorname*{angle}(x)\) denotes the phase of the complex value \(x\), and \(\operatorname*{angle}(x)\in(-\pi,\pi]\).  
According to (5), the estimation range is \((-1,1]\). It could be further analyzed that the estimation range of (5) is mainly limited by the timing interval between the two repetitions. Therefore, increasing the number of repetitions in one pilot OFDM symbol by a factor of two (i.e., reducing the timing interval of two adjacent repetitions by a factor of two) would double the estimation range of the frequency offset [4,6].
# [PERSON]'s Law and Perceptual Features for Face Image Quality Assessment
## 4 Results  
### Evaluation Protocol  
To evaluate the performance of Benford's law-inspired and perceptual features for FIQA, the generic face image quality assessment 20k database (GFIQA-20k) [56] was utilized, which is currently the largest publicly available one in the literature. The images of this database were sampled from the Yahoo Flickr creative commons 100 million dataset (YFCC100M) [57] and were evaluated by freelancers using the standard five-point absolute category rating [58] scale (bad, poor, fair, good, excellent). On the whole, this database contains 20,000 quality-annotated face images with a \(512\times 512\) resolution. Further, the quality ratings are in the range \([0,1]\) where a higher rating indicates better visual quality. The distribution of quality ratings is shown in Figure 4. Further, Figure 5 depicts several face images from the GFIQA-20k [56] database with the corresponding quality values.  
Figure 4: Measured empirical distribution of GFIQA-20k’s [56] quality ratings.  
Machine learning-based methods were evaluated on the GFIQA-20k [56] database as follows. First, the database is randomly divided into training (appx. 80% of images) and test (appx. 20% of images) sets. Second, a machine learning-based FIQA algorithm is trained on the training set. Third, the Pearson's linear correlation coefficient (PLCC), Spearman's rank order correlation coefficient (SROCC), and Kendall's rank order correlation coefficient (KROCC) are computed between the quality labels of the test set and the predicted quality labels. This process is repeated 100 times. Finally, the median PLCC, SROCC, and KROCC values are reported.
# Potential of Sentinel-1 Radar Data for the Assessment of Soil and Cereal Cover Parameters
###### Abstract  
The main objective of this study is to analyze the potential use of Sentinel-1 (S1) radar data for the estimation of soil characteristics (roughness and water content) and cereal vegetation parameters (leaf area index (LAI), and vegetation height (H)) in agricultural areas. Simultaneously to several radar acquisitions made between 2015 and 2017, using S1 sensors over the Kairouan Plain (Tunisia, North Africa), ground measurements of soil roughness, soil water content, LAI and H were recorded. The NDVI (normalized difference vegetation index) index computed from Landsat optical images revealed a strong correlation with in situ measurements of LAI. The sensitivity of the S1 measurements to variations in soil moisture, which has been reported in several scientific publications, is confirmed in this study. This sensitivity decreases with increasing vegetation cover growth (NDVI), and is stronger in the VV (vertical) polarization than in the VH cross-polarization. The results also reveal a similar increase in the dynamic range of radar signals observed in the VV and VH polarizations as a function of soil roughness. The sensitivity of S1 measurements to vegetation parameters (LAI and H) in the VV polarization is also determined, showing that the radar signal strength decreases when the vegetation parameters increase. No vegetation parameter sensitivity is observed in the VH polarization, probably as a consequence of volume scattering effects.  
S 2017 17 2617 10 November 2017; Published: 14 November 2017
# ODGNet: Robotic Grasp Detection Network Based on Omni-Dimensional Dynamic Convolution
## 4 Experimental Validation
### Experimental Results and Analysis
#### 4.4.1 Comparison Experiments on Cornell Dataset
the ODGNet algorithm introduces ODConv to enhance the feature extraction ability of the grasping region, and, at the same time, introduces the attention mechanism to construct the feature enhancement fusion module of the grasping region, which strengthens the feature learning ability of the graspable region. The last object is a banana; comparing the grasping quality maps of the two algorithms, the GR-ConvNet algorithm predicts a significantly wider distribution and lower scores of grasping regions, with poor grasping results. Considering that the algorithm does not use multi-scale features, it is not capable of learning enough grasping features for objects of different scales. In order to evaluate the robustness of ODGNet more rigorously, the Jaccard index was modified. The experiments were conducted at Jaccard index values of 0.25, 0.30, 0.35, and 0.40, respectively, and the corresponding detection results are shown in Table 2. From Table 2, it can be seen that the detection accuracy of both algorithms decreases with the increase in the Jaccard index, but the accuracy of ODGNet decreases more slowly, and the detection accuracy of ODGNet is above 91% both with the IW subset and OW subset. This proves that the proposed method is more robust. Figure 9: Comparison of grasp detection results on the Cornell dataset.
# Air-Water Bubbly Flow by Multiple Vents on a Hydrofoil in a Steady Free-Stream
###### Abstract  
Flow features, due to air injection through multiple vents on the surface of a hydrofoil inclined at an angle with respect to the free-stream in a cavitation tunnel, are presented here. The hydrofoil, with a chord length, \(c\), is oriented at the angle of inclination, \(\alpha=3.5^{\circ}\). The Froude number, \(Fn\), based on the free-stream velocity, \(V_{\infty}\), and air injection vent diameter, \(d_{hy}\) is 30.30, 50.51 and 70.71. Air is injected through multiple vents on the hydrofoil at the non-dimensional air injection coefficient, \(C_{q}\sim 16-8917\). The air bubble packing per unit area is quantified using spatial density, \(S_{D}\), at various combinations of \(Fn,C_{q}\) based on a high-speed video from the side view. The time-averaged spatial density, \(<S_{D}>\), is observed to increase in a logarithmic manner with an increase in the air injection rate, \(Q\), at various Froude numbers. There is an increase in the mean spatial density of the bubbles with the increase in \(C_{q}\) at all \(Fn\). A power-law relation is shown to exist between the time-averaged spatial density, \(<S_{D}>\), and the non-dimensional flow variables, Reynolds number, \(Re_{air}\), \(Fn\) and \(C_{q}\) based on a regression analysis. By tracking individual finite volume bubbles flowing with the free-stream, the bubble dimensions in pixels are quantified using quantities such as the deformation rate, \(\epsilon\), and standardization, \(\epsilon_{S}\), from the side-view videos. It is observed that \(\epsilon\) and \(\epsilon_{S}\) change with time, even as they become advected with the free-stream. Through high-speed imaging from the top view, we characterize the bubbly flow features' time-averaged thickness, \(t\), at various combinations of \(Fn,C_{q}\) at \(\alpha=3.5^{\circ}\). We obtain a power-law relation between the non-dimensional time-averaged jet thickness, \(t/c\), and the non-dimensional flow parameters such as, \(Re_{air}\), \(Fn,C_{q}\) and the non-dimensional streamwise distance, \(x/x_{ref}\), based on a regression analysis, where \(x_{ref}\) is a reference distance. The results are relevant to engineering applications where the air-water bubbly flow in a free-stream is important.  
bubbly flows; two-phase flows; high-speed imaging; hydrofoil +
Footnote †: journal: Applied Sciences
# Noise Modeling to Build Training Sets for Robust Speech Enhancement
## 1 Introduction
### Contributions
The experimental results demonstrate that the SE model trained on our synthesized training set presents a robust SE effect on a real noisy speech, while the SE model trained on the real training set and the synthesized noisy speech presented a similar performance. Besides, the NM-GAN-prepared training sets better train the DNN-based SE models than the \"limited\" training sets, demonstrating our method's feasibility and reliability. Further details are presented in the comparative experiments carried out in Section 4.
## 3 Results and Discussion
### IFT Measurement
The process described above makes some portion of the surface available for the CA reaction with carbonate. According to the surface complexation mechanism, CA leaches positively charged Ca\({}^{2+}\) ions and thus increases the negative charge of the surface (Figure 9c). Excessive negative charge of the surface, in turn, causes spontaneous desorption of negatively charged stearate ions because of negative-negative repulsion (Figure 9d). The lack or absence of adsorbed long-chained stearate ions makes the surface more hydrophilic. Plain AO solution is less active than plain CTAB in wettability alteration at both temperatures. AO is an amphoteric surfactant, which means that both negative and positive charges are present in its hydrophilic head. Therefore, electrostatic attraction between carboxylic groups of stearic acid and the headgroup of the surfactant is less than for cationic surfactants. However, the logP value of AO is high (4.03) and the interplay between the high affinity to hydrophobic chains of surfactant and the \"lack\" of positive charges determines the intermediate performance of AO, which is between CTAB and CPB. The effectiveness of the mixture of AO and CA is higher than the effectiveness of plain AO at both temperatures. This phenomenon is the opposite of the one observed in the case of cationic surfactants. It may be suggested that the presence of both charges decreases the electrostatic screening between CA and surfactant. Therefore, synergism exists under any studied condition. The possible mechanism of the synergism is the same as for cationic surfactants. AO was selected for the following experiments because of this synergism. Another reason to select AO was the absence of halogen-containing compounds, which can negatively affect oil refining.
## 2 Solution of Foundation Pit Seepage Field  
### Establishment and Solution of Theoretical Analysis Model  
Under the condition that the thickness of the flow cross-section is not particularly large, the groundwater on both sides of the foundation pit flows to the foundation pit due to the pumping effect inside the foundation pit, and the vertical velocity of the area below the waterproof curtain is negligible compared with the horizontal direction. Therefore, in some studies, the hydraulic head in the vertical direction from the bottom of the waterproof curtain to the top of the aquiclude is regarded as a constant [24; 27]. With the further increase in the thickness of the aquifer, as shown in Figure 1, the groundwater flowing into the foundation pit is more from the aquifer below the foundation pit. At this time, the seepage in the vertical direction of the area below the waterproof curtain cannot be ignored. In addition, when the thickness of the flow cross-section is much larger than the depth of the waterproof curtain inserted into the aquifer, the range of pumping action inside the foundation pit is limited, and the groundwater seepage velocity at the top of the aquiclude being nearly zero. In the numerical simulation results of this kind of foundation pit, the horizontal velocity at the top of the aquiclude is approximately 0.08% of the horizontal velocity at the bottom of the waterproof curtain [11]. It can be inferred that if the thickness of the aquifer is deep enough, the groundwater velocity at the top of the aquiclude will approach zero. A strip foundation pit was selected as the research object, and it is assumed that the stratum where the foundation pit is located is a homogeneous medium and the seepage is isotropic. For the foundation pit whose length is much larger than the width, the seepage field parallel to the width can be approximately simplified as plane seepage. Given the symmetry of the foundation pit, analysis can be conducted on only half of the structure. In order to facilitate the solution of the equation, the intersection of the water level in the pit and the waterproof curtain is taken as the coordinate origin, and the coordinate system shown in Figure 2 is established. Figure 1: Seepage in foundation pit with suspended waterproof curtain. Where \(h_{n}\) is the water level in the pit, \(h_{w}\) is the water level outside the curtain, \(L\) is the water entry depth of a waterproof curtain in the pit, \(M\) is the thickness of the flow cross-section, which is equal to \(h_{n}-L\); \(b\) is half of the width of the foundation pit, \(d\) is the width of the waterproof curtain, \(R\) is the influence radius of precipitation, and \(H_{0}\) is the water level at the influence radius. The above parameter units are m.  
According to the geological conditions of the stratum where the foundation pit is located, and the seepage characteristics of the groundwater, the governing equation and boundary conditions of OABCD in the regular rectangular seepage area inside the foundation pit are listed.
## 2 Materials and Methods
### Extract Preparation
#### 2.3.3 Estimation of Cannabinoid Content  
Chromotographic analysis of cannabinoids was conducted following a modified procedure by [PERSON] et al. [22]. The commercial product \"Ultra Extract,\" containing 20% CBD hemp oil (Kropla CBD, [PERSON], Lublin, Poland), obtained through supercritical CO\({}_{2}\) extraction, served as a reference for the qualitative comparison of the experimental extract. Standard mixtures of cannabinoids, including cannabidiol (CBD), cannabidiolic acid (CBDA), \(\Delta\)9-tetrahydrodcannabinol (\(\Delta\)9-THC), cannabigerol (CBG), cannabichromene (CBC), cannabidivarin (CBDV), cannabinoid (CBN), cannabigerolic acid (CBGA), and tetrahydrodcannabivarin (THCV), were appropriately diluted in acetonitrile for eight points within the concentration range of 0.05 to 100 mL\({}^{-1}\) (0.05, 0.50, 4.17, 8.33, 16.70, 25.00, 50.00, and 100.00 mg\(\cdot\)mL\({}^{-1}\)). These standard solutions were stored away from light at a temperature of 20 \({}^{\circ}\)C. The reference sample was diluted with a methanol and water mixture to a concentration of 0.025 mg/mL, based on the CBD content declared by the manufacturer, while the experimental extract sample remained undiluted. Subsequently, using a syringe filter with a diameter of 0.45 \(\upmu\)m, extract samples were filtered into glass vials. The separation and quantification of cannabinoids were performed using a Thermo-Scientific UltiMate 3000 HPLC system.  
HPLC analysis conditions:  
* RP C18 Nex-Leaf CBX Potency 150 \(\times\) 4.6 mm, 2.7 \(\upmu\)m column with Nex-Leaf CBX 5 \(\times\) 4.6 mm, 2.7 \(\upmu\)m precolumn.
* Gradient elution: water + 0.085% phosphoric acid (A), acetonitrile + 0.085% phosphoric acid (B); 70% B until 3 min, 85% B until 7 min, 95% B from 7.01 min to 8.00 min, then 70% B until 10 min.
* Mobile phase flow rate 1.0 mL/min.
* Chromatographic column temperature 35 \({}^{\circ}\)C.  
* Injection volume 5 \(\mu\)L.
* Detection at \(\lambda\) = 220 nm
* Analysis time 8 min.  
A qualitative analysis was performed, considering the retention times of the individual peaks of the standards. Quantitative analysis involved determining the areas of the identified cannabinoids' individual peaks and correlating these values with the established equations of the calibration curves for each standard.
# Improving IoT Data Security and Integrity Using Lightweight Blockchain Dynamic Table
## 2 IoT Architecture and Security Challenges  
The IoT deployments contain heterogeneous uniquely identifiable, low power consuming, and limited computational power devices with embedded sensors interconnected through a network [2]. The device is designed to provide remote services for IoT users. Transmitting data through a network and hops is required to implement an encryption mechanism for data confidentiality. Moreover, storing data on the device expose the data to privacy violations and make the devices susceptible to attacks that can modify the stored data and affect the system's integrity.  
The second security challenge when using IoT and cloud technology is the security of communication and the authentication of the network parties [14]. The device must be identified and authenticated before being used for the services and transmitting the data to the server. However, the heterogeneous architecture and environment of the IoT network required a powerful authentication and authorization system to tackle this diversity.  
With the increasing number of IoT devices and the passage of time, as well as equipment ranging from small embedded processor chips to huge high-end servers, many security challenges at various architectural levels of embedded IoT devices must be addressed [15]. The security threats/issues relating to the IoT device deployment architecture are classified as follows: [16]:  
1. Issues with low-level security;
2. Issues of security at the intermediate level;
3. Issues of high-level security.  
Solving these security challenges can be easy without the resource-constrained and power consumption of the IoT devices [17]. Therefore, each instruction and algorithm should be implemented within these limitations. This leads to an extra challenge, which is the power consumption of the attack protection. The attacker can exhaust the IoT resource by flooding the network with redundant forged requests. The most prominent and widely used identification and authentication mechanism for IoT device security is Open Authorization (OAuth) [18]. End users and IoT devices get tokens using OAuth, an open standard communication mechanism. Tokens are saved in a database or on a server. End users make use of the system's resources. Tokens are used to authenticate end users in the system. There are four actors in the open standard protocol: the data and resource owner are responsible for creating verified resources and granting users access to the server, respectively; the Open Authentication Server (OAS) generates tokens for safe communication with valid clients/users or other entities [19]; the database or resource server offers authenticated resources/data.