esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Analysis
### Identification of Fruit Fields  
In order to evaluate the effectiveness of the algorithm proposed in this paper, we employed an industrial camera integrated with this algorithm to perform real-time experiments for cucumber fruit recognition in greenhouse environments.  
The trial was conducted in XiangYou Vegetable Professional Cooperative in Jingyang County, Xianyang City, Shaanxi Province. A light barrier was readied before the experiment to prevent overexposure in the presence of intense light. The camera was configured to move horizontally around 40 cm in the target region according to the focal length of the lens and the target size.  
The industrial camera was linked to the hardware device via USB. HALCON was used to establish the connection, open the camera, and choose the real-time image. A template was then generated in advance, with the target image being the real-time image captured by the camera. Parameters were configured as follows: pyramid level 5, template scale change range of \(\pm\)0.2, angle range of \(\pm\)15\({}^{\circ}\), step sizes of 0.1 and 5\({}^{\circ}\), and a greed factor of 0.7. The target was identified in the real-time image using a green line and enclosed within a red rectangle box. The specific recognition results are shown in Figure 14.  
During the cucumber real-time recognition trials, the color space for gray, BGR24, and image exposure were acquired in real time. The mean recognition time was 38.9 frames per second (FPS), with the lowest matching score recorded as 0.8. The image depicts the real-time segmentation effect and the impact of selecting the maximum outer rectangle box. It is evident that both the gray image and the BGR image successfully identify the fruit target. Additionally, the image showcases the real-time recognition of the cucumber. Although the real-time segmentation effect is somewhat absent in the experiment, the final fruit is effectively identified.  
Overall, the algorithm used in this study has a significant positive effect on the recognition of cucumber fruit and can be applied in practical settings.
## 5 Numerical Example
Table 2 presents the results comparing the impact of the bullwhip effect when applying the control method proposed in this paper versus the Markov jump control method, across different supply chain structures and varying inventory loss rates. The results indicate that the method proposed in this paper outperforms the Markov jump method in suppressing the bullwhip effect. This improvement is particularly evident in application scenarios with high storage loss rates, such as the fresh food industry. Additionally, the proposed method demonstrates superior performance when more intelligence is integrated into the supply chain system. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Supply Chain Structure**} & \multicolumn{2}{c}{**Bullwhip Effect \(Y_{min}\)**} \\ \cline{2-3}  & \(\rho=[0.08\ 0.05\ 0.03\ 0.02]\) & \(\rho=[0.2\ 0.17\ 0.13\ 0.1]\) \\ \hline Three subchains (this paper) & 0.7834 & 0.9682 \\ Three subchains (Markov jump) & 1.0471 & 1.3701 \\ Two subchains (this paper) & 1.4738 & 1.6975 \\ Two subchains (Markov jump) & 1.5364 & 1.9624 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The bullwhip effect in different situations. Figure 7: Supply chain performance indicators under different control strategies: (**a**) cost of inventory spent per month (unit: thousands of dollars); (**b**) average lead time per month (unit: days).
# Monitoring and Forecasting Green Tide in the Yellow Sea Using Satellite Imagery
## 3 Method
### Semi-Automated Green Tide Extraction  
After selecting a suitable threshold range, the RS image was binarized by threshold segmentation, as seen in Figure 4a, where the white value was 1, representing the green tide, and the black value represented the seawater and the land part that has been masked, separating the seawater and the green tide. As there may also be interfering information, such as ships and thick clouds that cannot be separated by thresholding, and manual ROI is always used to select training samples or feature regions [31; 32], so the green tide region was artificially boxed out using an ROI (as seen in Figure 4a, the red polygon boxed area (ROI region)) which was combined with the RS image for further judgment, and finally, the green tide information was obtained with the white value represented by 1 in the ROI region in Figure 4a, and the extraction result of the green tide was shown in Figure 4b. ROI could be used not only to pick out the green tide information but also to remove confounding factors.  
Figure 4: Threshold segmentation separating the seawater and green tide binarized images, (**a**) is the binarized green tide information and (**b**) is the extraction result of green tide, the outer boundary red line is indicated to the distribution of green tide. The data were extracted from Landsat 8 (Date: 23 June 2021; Worldwide Reference System: path 119 and row 35).  
Figure 3: _NDVI_ frequency histogram and second-order Gaussian fit curve (The data in the figure are from Landsat 8 (Date: 23 June 2021; Worldwide Reference System: path 119 and row 35).
# Fog Computing: Enabling the Management and Orchestration of Smart City Applications in 5G Networks
## 2 Related Work
### Standardization Activities
#### 2.1.1 ETSI oneM2M  
In January 2009, the ETSI M2M technical committee was established with the aim to develop an end-to-end high level architecture for M2M. ETSI oneM2M aims to establish for M2M what 3 GPP realized for mobile networks. The final release has been available since 2016 [9], and the correspondent architecture is shown in Figure 1. The architecture consists of two distinct domains: the field domain and the infrastructure domain. The Application Entity (AE) is responsible for implementing an M2M application service logic in the application layer. Each execution instance is identified with a unique AE Identifier (AE-ID). Examples of the AEs include an instance of a remote blood sugar monitoring application, a power metering application or an emergency response application. The Common Services Entity (CSE) represents an instantiation of a set of common service functions of the M2M environments. These service functions are exposed to other entities through the AE - CSE (Mca) and CSE - CSE (Mcc) reference points. Each CSE is identified with a unique CSE-ID. Examples of service functions offered by CSEs include: data management, device management and M2M service subscription management. Finally, the Network Service Entity (NSE) provides services from the underlying network to the CSEs. Examples of such services include location services and device triggering [9]. The reference point CSE - NSE (Mcn) is used for accessing underlying NSEs. The proposed fog computing framework follows the guidelines of ETSI OneM2M regarding device management and security standards by including the correspondent components in our framework. Nevertheless, the definition of MANO functionalities and components in the ETSI OneM2M reference architecture are still in an early stage.
# Evaluating MPTCP Congestion Control Algorithms: Implications for Streaming in Open Internet
## 2 Materials and Methods
### Quality Measures
#### 2.1.1 Path Delay  
Path Delay, often referred to as end-to-end delay, measures the time taken by a data packet to traverse from the source (sender) to the destination (receiver) over a path. It is a critical metric for gauging the efficiency and responsiveness of a network, especially for real-time applications where a low delay is paramount.  
Several factors contribute to Path Delay, including:  
* **Propagation Delay**: The time taken by a packet to cover the distance between the sender and receiver. This is primarily influenced by the physical distance and the medium, e.g., fiber, copper, or wireless, the packet traverses.
* **Transmission Delay**: The time taken to place the bits onto the link. This depends on the packet size and link bandwidth.
* **Processing Delay**: The time the routers take to process header and control information.
* **Queuing Delay**: The time the packet spends in a buffer before it is relayed on the outgoing link.  
Given these components, the total delay \(T_{i}\) on a specific path \(i\) can be mathematically expressed by  
\[T_{i}=\tau_{i}+\theta_{i} \tag{1}\]  
* \(T_{i}\)--total delay on path \(i\).  
* \(\tau_{i}\)--Smoothed Round-Trip Time (SRTT) for path \(i\). It represents the low-pass filtered time taken by a packet traveling from the sender to the receiver and acknowledgment backward.
* \(\theta_{i}\)--waiting time for processing the data stream on path \(i\). This delay component is influenced by the scheduler algorithm, which determines the order in which the packets are directed to the interfaces.
# APT Attack Detection Scheme Based on CK Sketch and DNS Traffic
## 4 Experiments
### Comparison with Previous Work  
In the scheme proposed by [PERSON], the author used eight DNS traffic features combined with deep learning to judge APT attacks. However, these eight features lack the consideration of APT attack access time and TLL value in DNS traffic packets, resulting in the failure to accurately detect APT attacks with time rules. Therefore, on the basis of some of the above eight features, this paper adds access time period, packet length, TTL, and other features, so as to obtain a more accurate detection effect. In the part of data preprocessing, the author mainly uses the means of domain name ranking and access times to screen the initial data, and this method has a certain effect. However, the amount of data cannot be reduced effectively, which leads to the training time of the model being too long and the decrease in detection efficiency. Therefore, this paper introduces the CK sketch structure to compress and count the DNS traffic while guaranteeing the accuracy, and significantly reduce the time for model training. However, due to data privacy, the above authors' datasets were not available. In the comparative experiment, the dataset in Section 4.1 was used to compare the accuracy and model training time of this paper and Yan Guanghua with the same amount of data. The experimental results are shown in Table 5. We find that the introduction of new features in this paper can improve the accuracy of APT attack detection to some extent. More importantly, the use of the CK sketch structure in this paper greatly reduces the time of model training, because the CK sketch structure uses statistics and compresses the data used for model training.  
Figure 12: Throughput comparison.
## 5 Conclusions  
In this study, using dual FBB had a higher classification accuracy than using dFBB in classifying the AD patient group and control group. In the case of dual FBB, the classification accuracy was highest when RF was used among the ML classification models. The RF achieved accuracy of 78.21% (F1 score: 78.06%, AUC: 0.8724) in the composite SUVR and accuracy of 78.52% (F1 score: 78.54%, AUC: 0.8456) in the regional SUVR. It was confirmed that the frontal and temporal lobes are important areas in the early and delayed amyloid regions. The ratio of the number of features selected was 32.64% for frontal lobe and 24.39% for temporal lobe in eFBB and 33.45% for temporal lobe and 29.09% for frontal lobe in dFBB.  
Although ML helps in the quantitative analysis of dual amyloid PET by reducing subjectivity and ambiguity, this study still has limitations. In future studies, it will be necessary to improve the classification accuracy for use in a clinical environment by using multimodal data, including dual imaging data.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app12157355/s1](https://www.mdpi.com/article/10.3390/app12157355/s1)]([https://www.mdpi.com/article/10.3390/app12157355/s1](https://www.mdpi.com/article/10.3390/app12157355/s1)), Figure S1: Correlation between early-phase \({}^{18}\)F FBB PET SUVR and \({}^{18}\)FDG PET SUVR.  
Conceptualization, H.-S. and D.-Y.K.; methodology, H.-J.S., H.Y. and S.K.; software, H.-J.S. and H.Y.; validation, H.-J.S. and H.Y.; formal analysis, H.-J.S., H.Y., D.-Y.K. and S.K.; investigation, H.-J.S. and H.Y.; resources, D.-Y.K. and S.K.; data curation, H.-J.S.; writing--original draft preparation, H.-J.S. and H.Y.; writing--review and editing, S.K. and D.-Y.K.; visualization, H.-J.S. and H.Y.; supervision, S.K. and D.-Y.K.; project administration, S.K. and D.-Y.K.; funding acquisition, S.K. and D.-Y.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Research Foundation (NRF) of Korea, funded by the Ministry of Science, ICT & Future Planning (NRF-2018 R1A2B2008178).  
This study was performed in accordance with the ethical standards laid down in the Helsinki Declaration of 1964 and its later amendments or comparable ethical standards. The research protocol was reviewed and approved by the Institutional Review Committee of Dong-A University Hospital.  
Informed Consent Statement: Informed consent was obtained from all individual participants included in this prospective study.  
The data used for this study are available upon request from the corresponding author.  
The authors declare no conflict of interest.
## 2 Materials and Methods  
### Materials  
The materials used in the study to fabricate cement mortar composite were ordinary Portland cement (OPC), natural aggregate (NA), recycled expanded glass aggregate (EGA), superplasticizer (SP), and nano titanium dioxide (nTiO\({}_{2}\)). OPC from Boral Australia Co. and in accordance with AS3972 was used as a binder and Sikament NN was used as a superplasticizer (SP) in the mix, which meets all requirements as per AS1478.1 for high range water reducing admixture. EGA with a particle size of 0.25-4 mm from EGT Co. is shown in Figure 1. The specifications of EGA are compliant with EN and DIN standards. Figure 2 shows a SEM image of the utilized EGA in this study. Table 1 demonstrates the physical, mechanical, and thermal properties of the EGA.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Property**} & \multicolumn{4}{c}{**Grain Size**} \\ \cline{2-5}  & **0.25–0.5** & **0.5–1** & **1–2** & **2–4** \\ \hline Loose bulk density (kg/m\({}^{3}\)) & 300 & 250 & 220 & 190 \\ Particle density (kg/m\({}^{3}\)) & 540 & 450 & 350 & 310 \\ Compressive strength (MPa) & \(\geq\)2.9 & \(\geq\)2.6 & \(\geq\)2.4 & \(\geq\)2.2 \\ Thermal conductivity (W/mK) & 0.07 & 0.07 & 0.07 & 0.07 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical, mechanical, and thermal properties of the EGA (Expanded Glass Technologies).  
Figure 1: Expanded glass aggregate (EGA) with a different grain size.  
Crushed gravel with the maximum size of 4.0 mm and density of 2800 kg/m\({}^{3}\) was used as NA. The NA was subjected to the particle size distribution test to precisely replicate the distribution of NA for the replacement of EGA by volume in the cement mortar. The size distribution testing was completed in accordance with AS1012 and the results are found in Figure 3.  
Moreover, the mercury intrusion porosimetry (MIP) test was undertaken to measure the porosity as well as pore size distribution of the EGA. The MIP test results of the EGA are revealed in Figure 4.  
Nanoparticles titanium dioxide (nTiO\({}_{2}\)) purchased in the powder form from US Research Nanomaterials, Inc. Table 2 demonstrates the properties of the nTiO\({}_{2}\) as indicated by the manufacturer.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Properties** & **Value** \\ \hline Purity & 99.98\% \\ Average Particles Size & 30 (nm) \\ Specific surface area & 50 (m\({}^{2}\)/g) \\ Bulk Density & 0.42 (g/cm\({}^{3}\)) \\ True Density & 3.9 g/cm\({}^{3}\)) \\ PH & 5.5–6.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The properties of the nano-nTiO\({}_{2}\) (US Research Nanomaterials, Inc.).  
Figure 3: Size distribution of the natural aggregate and EGA.  
Figure 2: SEM pictures of the utilized EGA.
## 2 Nonlinear Switch Characterization
### CPW Test Fixture  
The test-fixture used is supported on an Alumina substrate (\(\epsilon_{r}=9.6,\tan\delta=0.0004\), stable up to 67 GHz), and consists of a CPW transmission line with strip width \(w=0.1\,\mathrm{mm}\), gap \(g=0.05\,\mathrm{mm}\), and length \(L_{Line}=1.175\,\mathrm{mm}\) from the GSG probe contact plane to the device reference plane, wherein the diode under test is mounted with its cathode connected to the CPW strip and anode connected to ground as is shown in Figure 2. This setup is representative of a PIN diode switch on a reconfigurable antenna when modelled as an internal port where reflection coefficient is of interest.  
In order to proceed with the extraction of the diode model, a broadband Short-Open-Load-Thru (SOLT) calibration is first performed, followed by input power calibration and harmonics phase reference calibration as is required for the measurement of X parameters [42]. The diode is biased using an external DC-power supply connected to a built-in biasing-tee of the network analyzer. Subsequently, the diode S parameters are measured, retaining only the reflection coefficient.  
As the SOLT calibration reference plane is defined on the GSG probe-CPW line contact, a de-embedding procedure is applied to remove the contribution of the CPW line from the measured diode S parameters and retrieve the actual diode reflection coefficient. A model for the CPW-_even_ mode accounting for dispersion, obtained with the aid of accurate 2.5D electromagnetic simulation using Keysight Momentum, is used for that purpose.  
Considering the diode mounting and that the X-parameters measurement system used in this work requires the calibration and registering of both ports, some comments on the validity of the measurements are deemed convenient.  
Although in the case of S parameters, it is clear that two- and one-port S-parameter measurements would yield the same reflection coefficient, this may not be evident in the case of X-parameters, as in general, the nonlinear functions involved depend on the incoming signals from all the ports/harmonics combinations. However, as in the test fixture used for diode characterization both ports are physically uncoupled, the waves injected at port 2 do not affect the measurement at port 1, hence, inter-port wave dependence vanishes. This is expressed by the reduced one port X-parameters  
\[B_{1k}=X_{1m}^{(FB)}p^{m}+\sum_{n\
eq 1}X_{1m,1n}^{(S)}A_{1n}P^{m-n}+\sum_{n \
eq 1}X_{1m,1n}^{(T)}A_{1n}^{*}P^{m+n}, \tag{2}\]  
which can be posed in matrix form, allowing the integration of the results into further circuital formulations such as harmonic balance calculations.  
\[B_{1}=PX_{1}^{(FB)}+PX_{1}^{(S)}(P^{\prime})^{-1}A_{1}+PX_{1}^{(T)}P^{\prime}A_ {1}^{*}, \tag{3}\]  
where \(B_{1}=[B_{11}\cdots B_{1N}]^{T}\), \(P=diag(P^{m})\), \(P^{\prime}=diag(P^{m+1})\), \(X_{1}^{(FB)}=[X_{11}^{(FB)}\cdots X_{1N}^{(FB)}]^{T}\), \(A_{1}=[A_{12}\cdots A_{1N}]^{T}\), and \(X_{1}^{(S)},X_{1}^{(T)}\) are matrices whose elements are of the form \(X_{1m,1n}^{(\cdot)}\).
# Review on the Role of GNSS Meteorology in Monitoring Water Vapor for Atmospheric Physics
## 4 Spatio-Temporal Analysis
### Antarctic Region  
[PERSON] et al. [103] studied the GNSS IWV over 9 sites in Antarctic during two years (2003-2005). The diurnal variation was remarkable in summer, but not in winter. IWV was higher in summer than in winter. Moreover, it was found that the presence of strong winds can cause a decrease in monthly IWV.
# Influence of Rice Variety and Freezing on Flour Properties
## 3 Results and Discussion
### Effect of Rice Freezing on the Grinding Process
On the other hand, freezing the Arborio rice variety allowed obtaining whole grinding products with higher amounts of particles smaller than 160 \(\upmu\)m and lower amounts of particles with size in the 160 to 400 \(\upmu\)m range, compared to the corresponding raw rice sample (Table 2). For the Basmati variety, the flour yield significantly increased from 50.9% to 56.5% (\(p<0.05\)), and the fineness module significantly decreased from 2.11 to 1.53 (\(p<0.05\)). Our observation regarding the decrease of the particle size when grinding the frozen samples compared to the raw sample agrees with the findings of [PERSON] and [PERSON] [2]. The results from Table 2 revealed the different grinding behavior of the two varieties after freezing. Moreover, it can be observed that the flour yield significantly increased (\(p<0.05\)) with different percentages when subjecting the rice grains to freezing treatment prior to grinding: the flour yield increased by 20% and 11% for Basmati and Arborio flours, respectively. On the other hand, a similar decrease of about 28% of the fineness module was registered for both flours.
# Combinatorial Generation Algorithms for Some Lattice Paths Using the Method Based on AND/OR Trees +
## 3 Results
### Combinatorial Generation Algorithms for North-East Lattice Paths
#### 3.1.3 Ranking and Unranking Algorithms  
Applying the general approach described in [17], we develop algorithms for ranking (Algorithm 1) and unranking (Algorithm 2) the variants of the AND/OR tree for \(L_{n}^{m}\). In these algorithms, \(()\) denotes an empty sequence and the function \(\texttt{concat}(a,b)\) denotes merging sequences \(a\) and \(b\).  
```
1 Rank_L(\(v=(v_{1},v_{2},\ldots)\), \(n\), \(m\))
2 begin
3 if\(n=0\) or \(m=0\)then\(r:=0\)
4 else
5 if\(v_{1}=1\)then\(r:=\texttt{Rank\_L}((v_{2},\ldots)\), \(n\), \(m-1\))
6 else\(r:=L_{n}^{m-1}+\texttt{Rank\_L}((v_{2},\ldots)\), \(n-1\), \(m\))
7 end if
8 return\(r\)
9
10 end for
```  
**Algorithm 1**An algorithm for ranking a variant of the AND/OR tree for \(L_{n}^{m}\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Lattice Path** & **Variant of AND/OR Tree** & **Rank** \\ \hline \(E,E,N,N,N\) & \((1,1,1)\) & 0 \\ \(E,E,N,E,N,N\) & \((1,1,2,1)\) & 1 \\ \(E,N,E,E,N,N\) & \((1,1,2,2,1)\) & 2 \\ \(N,E,E,E,N,N\) & \((1,1,2,2,2)\) & 3 \\ \(E,E,N,N,E,N\)  
The developed algorithms have the following computational complexity:  
* Algorithm 1 has at most \(m\) recursive calls where \(v_{1}=1\) (each such recursive call requires one assignment) and has at most \(n\) recursive calls where \(v_{1}=2\) (each such recursive call requires calculations of \(L_{n}^{m-1}\)). Applying Equation (1), the calculation of \(L_{n}^{m}\) has linear time complexity \(O(m)\) for \(m<n\) and \(O(n)\) for \(m>n\). Hence, Algorithm 1 has polynomial time complexity \(O(nm)\) for \(m<n\) and \(O(m+n^{2})\) for \(m>n\);
* Algorithm 2 has at most \((n+m)\) recursive calls where each such recursive call requires calculations of \(L_{n}^{m-1}\). Hence, Algorithm 2 has polynomial time complexity \(O(m(n+m))\) for \(m<n\) and \(O(n(n+m))\) for \(m>n\).  
Table 1 presents an example of ranking the combinatorial set of all North-East lattice paths beginning at \((0,0)\) and ending at \((3,3)\).