esa-sceva/satcom-chunk-collection huggingface dataset:
# Segmentation of Preretinal Space in Optical Coherence Tomography Images Using Deep Neural Networks
## 4 Methods
### Preretinal Space Segmentation
#### 4.1.3 Attention UNet  
An extension of the UNet architecture is the Attention UNet proposed by [72]. It introduces attention gates to highlight any significant features that are passed through the skip connection. Its advantage is maintaining a simple design while decreasing model sensitivity to the background regions.  
The general design of this network is similar to the baseline UNet, with five double \(3\times 3\) px convolution blocks in the encoder and decoder paths. The attention module is applied to each encoding result before they are concatenated to the decoder blocks. The function of this grid-based gating mechanism is to minimize the influence of irrelevant or noisy features. The PyTorch implementation of the Attention UNet network utilized in this experiment was obtained from [73].
## 3 Results
### Test 1  
The first test was an indoor laboratory test whose goal was to confirm that the system works correctly as a precondition for outdoor tests. The distance between the transmitter and receiver was around 3 m. The Raspberry Pi 3 and pMDLL2450 data link modules were connected to the laboratory power supplies (5 and 12 V).  
* Channel bandwidth--8 MHz;
* Channel Frequency--76--2477 MHz;
* Tx/Rx power--15 dbm;
* Wireless Distance--100 m;
* MIMO--on;
* Tx/Rx rate--Auto.  
The system worked as assumed, with an average data rate of around 2.8 MB/s. Notably, the antenna position did not significantly affect the data rate, but this does not mean that this situation will hold true for greater distances.
# Smart Antenna for Application in UAVs
###### Abstract  
In the present paper, a smart planar electrically steerable passive array radiator (ESPAR) antenna was developed and tested at the frequency of 1.33 GHz with the main goal to control the main radiation lobe direction, ensuring precise communication between the antenna that is implemented in an unmanned aerial vehicle (UAV) and the base station. A control system was also developed and integrated into the communication system: an antenna coupled to the control system. The control system consists of an Arduino, a digital potentiometer, and an improved algorithm that allows defining the radiation-lobe direction as a function of the UAV flight needs. The ESPAR antenna was tested in an anechoic chamber with the control system coupled to it so that all previously established requirements were validated.  
P-ESPAR antenna; smart antenna; control system; Arduino; UAV communication system 0.000.
## 4 Joint Optimization of Altitude and Energy Beamforming for Multiple EHDs  
In this section, we devise an efficient algorithm in the joint design of UAV altitude and energy beamforming by solving the problem \(\mathbf{P}_{2}\) in a multiple-EHD scenario (i.e., \(U\geq 2\)) based on the dual problem. Next, we propose an efficient low-complexity algorithm that yields near-optimal altitude and energy beamforming at the UAV. Finally, we further explore a suboptimal design by developing weighted-sum energy beamforming in closed form, where the computational complexity is considerably reduced by not using an SDP solver.
## 2 Materials and Methods
### Simulation Inputs
#### 2.2.1 Sea State Spectrum  
A Sofar Spotter buoy deployed near the MBARI-WEC logged data every hour. An example of a spectrum measured by the Spotter buoy on 16 September 2022 at 19:31:18 PST with a significant wave height (\(H_{\mathrm{s}}\)) of 1.6 m and peak period (\(T_{p}\)) of 5.7 s is shown in Figure 4. Fifty-six such observations were selected; a grid showing the significant wave height and peak period of these selected conditions is shown in Figure 5.  
Figure 4: Example spectrum used for the simulation depicting ocean spectra for the date 16 September 2022 at 19:31:18 PST, obtained via Sofar Spotter buoy data.  
In the statistical analysis section of the paper, the significant wave height and peak period of each observation serve as the sea state condition for a set of simulations. Each sea state was simulated using multiple phase realizations to account for non-linearities present in the model. Five simulations, each lasting five minutes for a total of 25 min of simulation, proved adequate to yield consistent statistical outcomes.
# Best Frame Selection to Enhance Training Step Efficiency in Video-Based Human Action Recognition
## 5 Conclusions  
This research proposed a method for selecting the keyframes and suitable regions in a video to increase the speed and accuracy of HAR systems. The proposed method achieves its goals by removing unnecessary data from the video and creating a HAR-compatible input. The proposed hierarchical method identifies the moving areas in a video using the gradient operator, edge extraction and the difference of the gradients between frames, and extracts the frame sequence with more relevant motion information.  
The best compatible edge detection method for the proposed method was found using simulations. The threshold value for keyframe selection was found by analyzing its effect on the system accuracy. After this step, a region that includes the most relevant motion information, based on the built motion energy map of the selected frames, is found. The selected frames are then cropped using the founded region, and the final video is used as an input to the HAR system. A high speed, being applicable to all actions and an appropriate approximation in selecting the action area are the main advantages of the proposed method. Finally, the proposed method was combined with several new HAR methods, and it was verified that, by adding its pre-processing to the HAR input, the system accuracy was improved, and its training time decreased.  
An interesting research area in HAR systems is the extension of the pre-processing method to remove unnecessary parts from input videos. The unnecessary parts can be defined as unnecessary frames or unnecessary objects in the video scene. Future work can focus on human semantic analysis [36] or human parsing methods [37] to increase the efficiency of the pre-processing method of HAR systems, mainly in their training process.  
Conceptualization, funding acquisition and supervision by J.M.R.S.T.; investigation, data collection, formal analysis and writing--original draft preparation by [PERSON] and [PERSON]; writing--review and editing by [PERSON], [PERSON] and J.M.R.S.T. All authors have read and agreed to the published version of the manuscript.  
This article is a result of the project Safe Cities--\"Invacao para Construir Cidades Seguras\", with reference POCI-01-0247-FEDER-041435, cofunded by the European Regional Development Fund (ERDF), through the Operational Programme for Competitiveness and Internationalization (COMPETE 2020), under the PORTUGAL 2020 Partnership Agreement. The second author would like to thank \"Fundacao para a Ciencia e Tecnologia (FCT)\", in Portugal, for his PhD grant with reference 2021.08660.BD.  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **HMDB51** & **UCF101** \\ \hline Two-Stream I3D [30] & 0.88 & 0.94 \\ \hline Motion Guided Network [31] & 0.94 & 0.92 \\ \hline Spatiotemporal network [32] & 0.92 & 0.93 \\ \hline Correlation net [33] & 0.94 & 1.02 \\ \hline \hline \end{tabular}
\end{table}
Table 6: \(Relative_{Runtime}\) after adding the proposed method to the HAR systems under study.
# Research on Intelligent Perception Algorithm for Sensitive Information
###### Abstract
Language model pre-training combined with word vector training has grown to be the most popular technique available. Later, Google made improvements to the RNN model's slow parallel computation issue [9] and introduced the Transformer model [10], an encoder and decoder model made up entirely of attention mechanisms that addressed the RNN model's inability to be calculated in parallel. [PERSON] et al. [11] then built the Bert model by extracting the encoder from the Transformer model, which produced the best test scores on 11 natural language processing tasks. In 2019, Baidu enhanced the Bert model's masking technique and offered the ERNIE model with entity machine masks and phrase-level masks [12], and its trial results on five Chinese NLP tasks produced the best results. Baidu proposed the ERNIE 2.0 model [13] in 2020, which is based on the ERNIE model and continuously learns multi-tasks while performing pre-trained models on created tasks to produce the best results on 16 tasks. However, the word embedding of the result shows that the information about significant entities in it has not been properly applied. To solve these problems, we propose a sensitive information perception model based on ERNIE and knowledge graph embedding (KG-ERNIE), which can use a pre-trained text encoder to encode the text, then the convolutional learning of intermediate vectors and the extraction of semantic information to perform intelligent perception of sensitive information on the text. The TSIIP algorithm, which uses word-level, statement-level, and text-level text detection, is proposed. The following are the significant contributions of the work in this paper:(1) Based on THUNews and Chinese Wikipedia, a Chinese sensitive information dataset, JWBD, and sensitive vocabulary are built, and the proposed KG-ERNIE model and the TSIIP algorithm are trained and tested using these datasets. (2) The KG-ERNIE sensitive information detection model is proposed, which employs a knowledge graph-based entity embedding technique and an ERNIE-based pre-training model to encode input text and extract semantic information and features. A convolutional neural network-based model (CNN) is then used to recognize and categorize the encoded information. (3) The TSIIP algorithm, which perceives sensitive words at the word level and sensitive statements at the statement level to determine the final evaluation score of the text, is created and applied to the proposed KG-ERNIE model to solve the problems with low machine recognition accuracy of text-sensitive information and only taking into account single-level semantics. The rest of the paper is organized as follows: Section 2 presents related work. Section 3 describes, in detail, the KG-ERNIE sensitive information detection model and the TSIIP algorithm proposed in this paper. Section 4 presents the experiments and analysis related to this work. Section 5 summarizes our work and provides a future outlook.
## 3 Results and Discussion
### Electrochemical Characterization of TYR-Biosen for CC Sensing
These results suggest an interesting new line of research to explore the electrochemical response of the (bio)sensor to enzymatic reaction products, such as hydrogen peroxide and NADH or the use of [Fe(CN)6]\({}^{3-/4-}\) as a mediator in many other enzymatic reactions. Figure 8: CVs in 50 mM PBS (pH 7.4), (**a**) on SPCE-GRAFT-Diazo-TYR (gray dashed line) and in presence of 1 mM Fe(CN)6\({}^{3-}\) on SPCE-GRAFT-Diazo-TYR (blue line), on activated SPCE BARE (black line) and SPCE-GRAFT (red line). (**b**) 200 uM CC on unmodified SPCE BARE (red line) and 1 mM Fe(CN)6\({}^{3-}\) as mediator on SPCE-GRAFT-Diazo-TYR, in absence (blue line) and in presence of 200 uM CC (black line). Scan rate: 100 mVs\({}^{-1}\). The analytical parameters obtained from the SPCE-GRAFT-GLU-DA and SPCE-GRAFT-Diazo-TYR surfaces for CC sensing were compared to those of other (bio)sensor devices based on carbon electrodes reported in the literature (see Table 1). It is noteworthy that both (bio)sensors exhibit remarkable sensitivity, even when compared to devices employing differential pulse voltammetry (DPV) or square wave voltammetry (SWV), which are techniques that are recognized for their higher sensitivity than CV. A comparison of the (bio)sensors' performance with other sensors utilizing CV reveals a similar LOD or lower, thereby demonstrating its enhanced performance. Moreover, the DA-sensor developed in this study exhibits a broader dynamic range, spanning three orders of magnitude in concentration that none reaches. This expanded linear range of the DA-sensor and the impressive sensitivity of the TYR-biosensor, coupled with the short manufacturing time required for fabrication and the low-priced reagents used, highlights the potential of both (bio)sensors described here for practical applications in the detection of CC without the need for complex nanostructure modifications. In this sense, it should be noted that the DA-sensor is more stable and cheaper as it does not involve enzymes. However, the high specificity of the TYR-biosensor could bear great interest when working with complex samples, thus a better performance is expected when faced with real cases. Figure 9: (**a**) Electrochemical detection of CC via TYR enzyme and [Fe(CN)\({}_{6}\)]\({}^{3-/4-}\) mediator reactions. (**b**) Sensitivity (slope) comparison of CC calibration on catalytic SPCE-GRAFT-GLU-DA (black dots) (data extracted from Figure 7) and SPCE-GRAFT-Diazo-TYR surfaces (red dots).
# Design of Infinite Impulse Response Filters Based on Multi-Objective Particle Swarm Optimization
## 2 Methodologies and System Description
### Butterworth Filter  
The Butterworth filter, known for its unique design and performance characteristics, has a maximally flat frequency response in the passband, meaning there are no ripples. Its crucial feature is achieving a smooth response across the entire passband by eliminating ripples, ensuring signal stability and predictability. The characteristics of this filter can be primarily divided into the following three aspects.  
1. Maximal Flatness  
The frequency response of a Butterworth filter in the passband is flat, with no ripples. This means all frequencies passing through have the same gain, ensuring stable signal transmission.  
Transition Band Performance  
Its transition band is smooth but less steep than Chebyshev or elliptical filters. This ensures the Butterworth filter provides gradual and smooth control in the transition band, preventing sudden changes to the signal.  
Flexible Order Design  
The Butterworth filter can be designed to any order. Higher orders lead to steeper transitions between the passband and stopband but can result in increased phase distortion.  
Various mathematical formulas, such as the transfer function and frequency response, can be used to design and analyze a Butterworth filter, for example, the Transfer Function of Equation (7) [15].  
\[H(s)=\frac{G}{\sqrt{1+\left(\frac{s}{\omega_{c}}\right)^{2N}}} \tag{7}\]  
\(s\) is a complex frequency variable (i.e., Laplacian variable), \(s=j\omega\) is the angular frequency. \(\omega_{c}\) is the cutoff frequency. \(N\) is the order of the filter. \(G\) is the gain, usually set to 1, so that when \(\omega=\omega_{c}\), the value of \(|H(j\omega)|\) is \(1/\sqrt{2}\).
influencing flavor also after cooking. However, there are practically no studies on the comparison of volatile compound profiles of frozen, fresh and cooked _Brassica_ vegetables. In the case of the analyzed plants, the specific interest focused on bioactive molecules is particularly high and they have been extensively studied recently, mainly due to the presence of isothiances. These are the products of enzymatic hydrolysis of glucosinolates and their beneficial influence on human health has been widely examined [3; 4; 5; 6; 7; 8].  
A wide array of analytical techniques have been used to analyze volatile compounds in plants, including fruit and vegetables. _Brassica_ volatiles have been collected using simultaneous distillation-extraction (SDE) [9; 10], sorbent (Tenax) trapping [11], solvent extraction [12], as well as solid phase microextraction (SPME) [13; 14]. The latter is the most widely applied solvent-free isolation method for plant materials and food flavors, thanks to its sensitivity, preconcentration abilities, ruggedness, potential automation and ease of use [15; 16].  
The aim of the present study was to evaluate differences in the profiles of volatile compounds in fresh and cooked _Brassica_ vegetables. Thanks to the results obtained for 15 different cultivars of kohlrabi, broccoli, caulfilower and Brussels sprouts, generalized conclusions may be drawn in terms of changes in volatile compounds in the main representatives of _Brassica_ vegetables. The volatile compounds were analyzed using solid phase microextraction and comprehensive two-dimensional gas chromatography with time of flight mass spectrometry (SPME-GC\(\times\)GC-ToFMS).
## 3 Database of Water Content and Hyperspectral Information
### Analysis of Measured Water Content
#### 3.2.2 Results of Measured Water Contents  
The water content measured on the surface and inside is shown in Figure 8a. The surface water content was measured to be smaller than the inside water content ratio (883 cases, 88.3% of the total). This is because the location is the Republic of Korea and the date is July to August 2023, which corresponds to the summer and rainy seasons. The soil on the surface dries quickly due to solar heat, and the water content inside the ground is relatively greater. Accordingly, a lot of data (719 cases, 71.9% of the total) were distributed when the surface water content was less than 2%, and in this case, the inside water content was distributed with a large error range.  
The ratio (\(w_{s}/w_{in}\)) of the surface and the inside water content is shown in Figure 8b. The closer the \(w_{s}/w_{in}\) is to 1, the closer the surface and inside water content values are same. Overall, the \(w_{s}/w_{in}\) was within the range of 0.29-58.4, and when targeting a surface water content of 4% or more, it was within the range of 0.56-2.0, except for seven points. In other words, it can be said that the surface water content of 4% or more represents the inside water content. There are 164 data with a surface moisture content of 4% or more, with an average of 1.18 times, standard deviation of 0.322, and coefficient of variation of 27%. This is relatively good compared to the average of 0.346 times, standard deviation of 0.346, and coefficient of variation of 100% of all data (including data below 4%).
## 3 Results  
### Improvement of Cropland Productivity Level after HSF Construction  
Among the 109 project regions of the general survey in Henan Province, there is no region with reduced productivity. Increased cropland productivity is from 77 to 552 kg/mu, while average cropland productivity increases by 145.0 kg/mu after HSF construction (Table 3, Figure 5). Among them are 86 project regions with a level of productivity increase of more than 100 kg/mu, which is 78.9% of all project regions with an average productivity increase of 160.7 kg/mu. Among these 86 project regions, there are 75 HSF construction projects (82.4% of all HSF construction projects) and 11 MLPF transformation projects (61.1% of all MLPF transformation projects). There are 23 project regions with a level of productivity increase from 50 to 100 kg/mu, accounting for 21.1% of all project regions, and the average productivity increase is 86.2 kg/mu.  
The improvement level of cropland productivity in eight project regions of the sample survey is from 120 to 263 kg/mu, and the maximum productivity reaches 907 kg/mu (Project A). The productivity of six HSF project regions increased from less than 750 kg/mu to more than 850 kg/mu, and the increase was from 120 to 185 kg/mu. The two MLPF transformation project regions have a higher increase in cropland productivity than the six HSF project regions, which have increased by 261 and 263 kg/mu, respectively. However, although the transformation of middle-low productive farmland has achieved remarkable results, the cropland productivity of MLPF is still lower than that of cropland in HSF (Table 4).  
Figure 5: The improvement of cropland productivity level in the HSF construction project regions and the MLPF transformation project regions.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Productivity** \\ **Improvement** \\ **Mg/mu** \\ \end{tabular} } & \multicolumn{4}{c}{\begin{tabular}{c} **High-Standard Farmland** \\ **Construction Projects** \\ **2011** \\ \end{tabular} } & \multicolumn{4}{c}{\begin{tabular}{c} **Middle–Low Productive Farmland** \\ **Construction Projects** \\ **2012** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Propor-** \\ **Productivity** \\ **Increase (kg/mu)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Average** \\ **Productivity** \\ **Increase (kg/mu)** \\ \end{tabular} } \\ \hline \(<\)0 & 0 & 0 & 0 & 0 & 0 & 0\% & 0 & 0 & 0 & 0 & 0 & 0\% & 0 & 0 & 0 \\ \(0\)-50 & 0 & 0 & 0 & 0 & 0\% & 0 & 0 & 0 & 0 & 0\% & 0 & 0 & 0 \\ \(50\)-100 & 1 & 5 & 3 & 7 & 16 & 17.6\% & 2 & 1 & 4 & 0 & 7 & 38.9\% & 23 & 21.1 & 86.2 \\ \(>\)100 & 0 & 7 & 10 & 58 & 75 & 82.4\% & 1 & 3 & 7 & 0 & 11 & 61.1\% & 86 & 78.9 & 160.7 \\ Total & 1 & 12 & 13 & 65 & 91 & 100\% & 3 & 4 & 11 & 0 & 18 & 100\% & 109 & 100 & 145.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistics of the improvement level of cropland productivity in the HSF construction project regions and the MLPF transformation project regions.
# Dependency Analysis based Approach for Virtual Machine Placement in Software-Defined Data Center
## 8 Conclusions  
In this work, we introduce a VM placement strategy to address the scalability concerns in SDDC. Efficient VM placement can reduce the data rate in the underlying physical network and improve the performance of the data center. In this context, we formulate the optimization problem of reducing the data rate in the physical network and propose a traffic-dependency-based VM placement algorithm. To minimize the data traffic, the traffic dependencies between the VMs is analyzed by PCA, and highly dependent VMs are grouped and placed within the same PM by the gravity-based clustering. Through extensive numerical analyses and emulations, we demonstrated the effectiveness of the proposed SP strategy in various data center network configurations with different traffic patterns. In addition, we built an SDDC testbed environment using OpenStack cloud platform and ONOS SDN controller, and the efficiency of the proposed placement strategy was experimentally verified on the testbed. The performance evaluation results indicate that the proposed VM placement strategy significantly reduces the traffic volume in physical network and improves the data center network performance.  
**Author Contributions:** Conceptualization, J.N.; Methodology, T.H.; Software, J.N. and J.B.; Supervision, H.L.; Writing--original draft, J.N.; and Writing--review and editing, H.L.  
**Funding:** This work was supported by Institute of Information and Communications Technology Planning and Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2017-0-00421, Cyber Security Defense Cycle Mechanism for New Security Threats).  
**Conflicts of Interest:** The authors declare no conflicts of interest.  
Figure 13: Average latency between the VMs of each group.