esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Implementation and Results  
To show the attack and its mitigation results, the scenario is developed using the NDN simulator (ndnSIM 2.6) [41]. The source code of the scenario can be founded at \"supplementary materials\".  
In this scenario, an NDNtube simulation was developed to analyze adversary node behaviors during the attack. To generate network traffic, the applications were simulated using the real dataset AT&T network topology. The results are analyzed by CHR and hop-counts in _i._ brute-force attack implementation and _ii._ DaD countermeasure.
# Analysis of Turbulence Parameters of Typhoon Morakot along the Southeast Coast of China
## 2 Introduction of Measurement Procedure and Method  
### Observation Site and Measurement Procedure  
#### 2.1.1 Introduction of Typhoon Morakot  
As the eighth tropical storm of 2009, Typhoon Morakot formed over the western Pacific Ocean on 4 August and strengthened into a typhoon the next day. It made landfall around 23:00 on 7 August in Hualien County, Taiwan, with a maximum wind speed of force 13 (40 m/s). At 18:00 on 9 August, this typhoon made landfall in Fujian Province, China, and the maximum wind speed near the center at landfall was force 12 (33 m/s). On the evening of 9 August, the typhoon weakened to a tropical storm. Typhoon Morakot is characterizedby great intensity, lengthy duration of influence, a large affected area, and slow translation speed. The path of Typhoon Morakot is plotted in Figure 1.
# Quantum Coding via Quasi-Cyclic Block Matrix
## 3 Long-Length Coding Constructions Based on Jacket Matrix
\begin{array}{cccc}a_{11}&a_{12}&\ldots&a_{ 1n}\\ a_{21}&a_{22}&\ldots&a_{2n}\\ \vdots&\vdots&\ddots&\vdots\\ a_{m1}&a_{m2}&\ldots&a_{mn}\end{array} \right). \tag{15}\]  
It is easily seen that \(Q\) is essentially an array of cyclic-shift operators. On the other hand, a class of matrix \(J_{n\times n}=(a_{ij})_{n\times n}\) is called a _jacket matrix_, which could lead to a simple encoding algorithm [28; 29], if it satisfies  
\[a_{ij}^{-1}=\left\{ \begin{array}{ll}(a_{ij})/a&\text{if }a_{ij}\
eq 0,\\ 0&a_{ij}=0,\end{array} \right. \tag{16}\]  
i.e., \(J_{n\times n}^{-1}=(a_{ij}^{-1})^{T}/a\), where \(a\) is the normalized constant. It is obvious that Pauli matrices and the Hadamard matrix belong to a jacket matrix. In addition, we also define an operation '\(\otimes\)' between the Jacket matrix \(J\) and the cyclic-shift operator vector \(\vec{K}=(I,K,\ldots,K^{n-1})\) as  
\[J\hat{\otimes}\vec{K}=J\hat{\otimes}(I,K,\ldots,K^{n-1})=(J,JK,\ldots,JK^{n-1}). \tag{17}\]Then, on the basis of the jacket matrix \(J\) and the circulant permutation matrix \(Q\), we design a matrix \(G\) with length \(N=n(n-1)\) constructed by the following circuit  
\[G=J\hat{\otimes}(K\wedge G_{e})=J\hat{\otimes}Q\] \[=J\hat{\otimes}\left( \begin{array}{cccc}K^{a_{11}}&K^{a_{12}}& \ldots&K^{a_{1n}}\\ K^{a_{21}}&K^{a_{22}}&\ldots&K^{a_{2n}}\\ \vdots&\vdots&\ddots&\vdots\\ K^{a_{m1}}&K^{a_{m2}}&\ldots&K^{a_{mn}}\end{array} \right)\] \[=\left( \begin{array}{cccc}JK^{a_{11}}&JK^{a_{12}}&\ldots&JK^{a_{ 1n}}\\ JK^{a_{21}}&JK^{a_{22}}&\ldots&JK^{a_{2n}}\\ \vdots&\vdots&\ddots&\vdots\\ JK^{a_{m1}}&JK^{a_{m2}}&\ldots&JK^{a_{mn}}\end{array} \right), \tag{18}\]  
where, \(G_{e}=(a_{ij})_{mn}\) is the exponent matrix of the basic matrix \(K\) in Equation (13).
# Ensemble Classifiers for Network Intrusion Detection Using a Novel Network Attack Dataset +
## 2 Background
### Machine Learning
#### 2.2.1 Hyperparameter Optimization  
Model parameters are the set of configuration variables that are internal to a model and can be learned from the historical training data. The value of these parameters is estimated from the input data. Model hyperparameters are the set of configuration variables external to the model. They are the properties that govern the entire training process and cannot be directly learned from the input data. A model's parameters specify how input data are transformed into the desired output, while the hyperparameters define the structure of the model.  
Hyperparameter optimization (also known as hyperparameter tuning) is the process of finding the optimal hyperparameters for a learning algorithm in ML. A set of different measures for a single ML model can be used to generalize different data patterns. This set is known as the hyperparameters set, which should be highly optimized so that the ML model can solve the assigned problem as optimally as possible. The optimization process locates the hyperparameters tuple and produces a model that minimizes the predefined loss function for the given data. The objective function takes the hyperparameters tuple and returns the associated loss [17]. The generalization performance is often estimated using cross-validation [18]. Hyperparameter optimization techniques typically use one of several optimization algorithms: grid search, random search, or Bayesian optimization.
# Intelligent Environment-Adaptive GNSS/INS Integrated Positioning with Factor Graph Optimization
###### Abstract  
Global navigation satellite systems (GNSSs) applied to intelligent transport systems in urban areas suffer from multipath and non-line-of-sight (NLOS) effects due to the signal reflections from high-rise buildings, which seriously degrade the accuracy and reliability of vehicles in real-time applications. Accordingly, the integration between GNSS and inertial navigation systems (INSs) could be utilized to improve positioning performance. However, the fixed GNSS solution uncertainty of the conventional integration method cannot determine the fluctuating GNSS reliability in fast-changing urban environments. This weakness becomes solvable using a deep learning model for sensing the ambient environment intelligently, and it can be further mitigated using factor graph optimization (FGO), which is capable of generating robust solutions based on historical data. This paper mainly develops the adaptive GNSS/INS loosely coupled system on FGO, along with the fixed-gain Kalman filter (KF) and adaptive KF (AKF) being taken as comparisons. The adaptation is aided by a convolutional neural network (CNN), and the feasibility is verified using data from different grades of receivers. Compared with the integration using fixed-gain KF, the proposed adaptive FGO (AFGO) maintains the 100% positioning availability and reduces the overall 2D positioning error by up to 70% in the aspects of both root mean square error (RMSE) and standard deviation (STD).  
GNSS; urban canyon; deep learning; multipath effect; factor graph optimization (FGO)
###### Abstract  
Accurate evapotranspiration migration (ET) estimation is crucial for sustainable water management in the diverse and water-scarce Mediterranean region. This study compares three prominent models (Simulator of Terrestrial Ecohydrological Processes and Systems (STEPS), Soil-Canopy-Observation of Photosynthesis and Energy fluxes (SCOPE), and Two-Source Energy Balance (TSEB)) with established global datasets (Moderate Resolution Imaging Spectroradiometer 8-day global terrestrial product (MOD16A2), Global Land Evaporation Amsterdam Model (GLEAM), and TerraClimate) at multiple spatial and temporal scales and validates model outcomes with eddy covariance based ground measurements. Insufficient ground-based observations limit comprehensive model validation in the eastern Mediterranean part (Turkey and Balkans). The results reveal significant discrepancies among models and datasets, highlighting the challenges of capturing ET variability in this complex region. Differences are attributed to variations in ecosystem type, energy balance calculations, and water availability constraints. Ground validation shows that STEPS performs well in some French and Italian forests and crops sites but struggles with seasonal ET patterns in some locations. SCOPE mostly overestimates ET due to detailed radiation flux calculations and lacks accurate water limitation representation. TSEB faces challenges in capturing ET variations across different ecosystems at a coarser 10 km resolution. No single model and global dataset accurately represent ET across the entire region. Model performance varies by region and ecosystem. As GLEAM and TSEB excel in semi-arid Savannahs, STEPS and SCOPE are better in grasslands, croplands, and forests in few locations (5 out of 18 sites) which indicates these models need calibration for other locations and ecosystem types. Thus, a region-specific model calibration and validation, sensitive to extremely humid and arid conditions can improve ET estimation across the diverse Mediterranean region.  
evapotranspiration modeling; mediterranean; energy balance; water balance; eddy covariance; eco-hydroology +
Footnote †: journal: Applied Sciences  
1
## 5 Case Study and Analysis
### The Detailed DM Steps
#### 5.2.2 Determine the Criteria Weights Using GBWM Method  
In this section, the weight of each DM calculated in the process of Section 5.2.1 is utilized to determine the criteria weights. For convenience, we aggregated each DM's weights associated with the criterion \(C_{\tau}(\tau=1,2,\ldots,n)\) into the comprehensive Equation (30). \[ \begin{array}{l}\omega_{\kappa}=\frac{1}{n}{\sum_{\tau=1}^{n} \omega_{\kappa}^{\tau},\;\kappa=1,2,\ldots,\eta.}\end{array}  \tag{30}\]  
Then, we can gain three DMs' comprehensive weights \(\omega_{1}=0.3364\), \(\omega_{2}=0.3343\), \(\omega_{3}=0.3294\). **Step 1:** Determine a collection of criteria \(C_{\tau}(\tau=1,2,\ldots,6)\) for decision making, and the detailed information can be introduced in Table 2.  
**Step 2:** Identify the best criterion  
**Step 4:** Utilize the scale numbers between 1 to 9 to determine the preference of all the criteria over \(C_{W}\), simplified as the OTW vector \(\overline{A}_{W}=(\overline{a}_{1W},\overline{a}_{2W},\ldots,\overline{a}_{nW})^ {T}\), which is listed in Table 4.  
**Step 5:** Obtain the optimal weights \(\omega^{*}=\left(\omega_{1}^{*},\omega_{2}^{*},\ldots,\omega_{n}^{*}\right)\), the model in Equation (21) is presented as follows:  
\[\text{Min}(0.3364\varepsilon_{1}+0.3343\varepsilon_{2}+0.3294\varepsilon_{3})\]  
\[St.\]  
\[\left|\frac{\omega_{1}}{\omega_{2}}-2\right|\leq\varepsilon_{1},\ \left|\frac{\omega_{1}}{\omega_{2}}-3\right|\leq \varepsilon_{2},\ \left|\frac{\omega_{1}}{\omega_{2}}-2\right|\leq \varepsilon_{3},\ \left|\frac{\omega_{1}}{\omega_{3}}-4\right|\leq\varepsilon_{1},\ \left|\frac{\omega_{1}}{\omega_{3}}-5\right|\leq \varepsilon_{2},\ \left|\frac{\omega_{1}}{\omega_{3}}-3\right|\leq\varepsilon_{3},\]  
\[\left|\frac{\omega_{6}}{\omega_{4}}-6\right|\leq\varepsilon_{1},\ \left|\frac{\omega_{6}}{\omega_{4}}-5\right|\leq \varepsilon_{2},\ \left|\frac{\omega_{6}}{\omega_{4}}-6\right|\leq\varepsilon_{3}\]  
\[\omega_{1}+\omega_{2}+\omega_{3}+\omega_{4}+\omega_{5}+\omega_{6}=1\]  
\[\omega_{1},\omega_{2},\omega_{3},\omega_{4},\omega_{5},\omega_{6}\geq 0.\]  
Thus, the optimal weights can be derived as:  
\[\omega_{1}^{*}=0.2601,\ \omega_{2}^{*}=0.2059,\ \omega_{3}^{*}=0.1115,\ \omega_{4}^{*}=0.0325,\ \omega_{5}^{*}=0.1950,\ \omega_{6}^{*}=0.1950\text{ and}\ \varepsilon_{1}^{*}=1.6667,\ \varepsilon_{2}^{*}= \varepsilon_{3}^{*}=2.6667.\]  
Note, that the above model was disposed by LINGO or MATLAB software.
# Towards Automatic License Plate Detection
## 5 Conclusions  
We discussed an effective license plate detection system in this study that used a smart integration of Faster R-CNN with image processing methods. To shorten the time it takes to detect the license plates, the suggested method first uses the Faster R-CNN to detect diverse vehicles in images. The detected vehicle image is next analyzed by the License Plate Localization Module (LPLM), which searches for the presence of license plate(s). For the PKU-G1-G5 categories, including extreme reflected glare situations, the LPLM analyzes the detected vehicle image, turns it into an HSV, applies morphological processes, and ultimately performs an aspect ratio test to spot the license plate(s). Extensive testing on difficult PKU vehicle datasets demonstrates the developed method's resiliencein comparison to prior approaches in plate(s) detection accuracy, precision, recall, and execution time. Moreover, in the PKU dataset, modifications in crucial characteristics, such as image resolution, illuminations, vehicle shapes, license plate angle, and color variations, had the least impact on the proposed technique.  
In the future, important questions, for instance, partially occluded or broken license plates, dirty with soil, or large angle variation, can be further inspected. Once the license plates are accurately detected, a robust recognition algorithm can also be used to process the detected plates. Since, the developed method processes both the RGB and HSV color components, it can also be made capable to process only one-color domain images. Moreover, the proposed method can also be utilized in computationally low video frames. Finally, for real-time security applications, a parallel processing scheme of the proposed method can also be developed.  
Conceptualization, Z.M. and K.K.; methodology, Z.M. and M.S.; software, U.K. and S.H.A.; validation, Z.M., S.S.A.A., and K.K.; formal analysis, K.K. and M.S.; investigation, M.S. and U.K.; resources, Z.M., U.K., and M.S.; data curation, Z.M.; writing--original draft preparation, Z.M. and M.S.; writing--review and editing, Z.M. and K.K. and M.S.; visualization, S.S.A.A.; supervision, Z.M.; project administration, Z.M. and M.S.; funding acquisition, S.H.A. and S.S.A.A. All authors have read and agreed to the published version of the manuscript.  
This research is partially supported by COMSATS University Islamabad, Pakistan, Iqra University, Pakistan, and Universiti Teknologi PETRONAS joint research Project 015 ME0-228.  
Not applicable.  
Data Availability Statement: Data can be provided on demand.  
The authors declare no conflict of interest.
###### Abstract  
This research paper presents an advanced exploration into the utilization of ultrasound-assisted extraction (UAE) combined with natural deep eutectic solvents (NADESs) to achieve higher concentrations of polyphenols from Grasevina grape pomace. Focusing on optimizing extraction parameters to maximize the yield of polyphenols, this study evaluates their potential industrial applications, particularly within the food and cosmetics sectors. The effectiveness of betaine/glucose (BGlc) NADESs in producing stable, ready-to-use extracts with enhanced bioavailability and eco-friendly attributes is underscored. The integration of UAE with BGlc NADESs has shown significant scalability and applicability for industrial use, as evidenced by the extracts' collagenase-inhibitory effects, determined using a ninhydrin-based colorimetric assay showing the significant inhibition of gelatine degradation and scratch tests on cultured skin cells, demonstrating enhanced cell migration and wound healing, indicating their potential in anti-aging cosmetic products. Additionally, the results from PAMPA tests demonstrated that NADES extraction significantly enhances the intestinal absorption of polyphenols from grape pomace extracts compared to conventional solvents, highlighting the potential of NADESs to improve the bioavailability of these compounds and offering promising implications for their application in the food industry. Furthermore, the research highlights the practicality of directly incorporating these extracts into products, such as anti-aging creams and functional foods, supporting sustainability initiatives within the cosmetic and food industries. This work aims to provide a comprehensive guide to green extraction techniques on a preparative scale, showcasing the versatility and innovative applications of NADES-extracted compounds across various industries, thereby paving the way for the development of eco-conscious and effective products.  
Keywords:antioxidants; Grasevina grape pomace; natural deep eutectic solvents (NADESs); polyphenols; sustainable extraction; ultrasound-assisted extraction +
Footnote †: journal: Applied Sciences
## 1 Introduction
Therefore, an efficient transmit delay calculation method is necessary to overcome these challenges. In this paper, a new efficient transmit delay calculation method, in which the number of operations does not increase proportionally to the number of plane-wave transmissions, is proposed. By applying  
Figure 1: Beam shapes (dotted lines) and the corresponding image scanlines to be reconstructed (dashed lines) in (**a**) conventional focused ultrasound imaging and (**b**) plane-wave imaging techniques. the proposed method, the total number of operations for a transmit delay calculation can be substantially reduced. The proposed method was evaluated using the total number of delay calculations and the corresponding computation time in various conditions, i.e., a different number of transmit elements or number of plane-waves.
# Combined Access Barring Scheme for IoT Devices Using Bayesian Estimation
## 4 Performance Evaluation and Discussion
This is because after periodicity of bitmap transmission, the class which is unbarred already has devices to be served. In Figure 6, we plot access delay for \(U\) = 30,000 for ACB, EAB and proposed CAB. Again, for the proposed CAB, both Algorithms 1 and 2 based scenarios are plotted.We see that the access delay decreases as the number of preambles increases. We also observe that the access delay shown by CAB is significantly less than EAB but slightly larger than ACB. Moreover the delay calculated by estimation of backlog devices (Algorithm 2) is very close the delay found by observing actual number of devices (Algorithm 1). As described above that the TST or delay shown by CAB is large as compared to ACB which is due to the periodicity of bitmap. If the bitmap allows all the classes all the time, then CAB becomes ACB. In Figure 7, we plot TST by varying periodicity of bitmap broadcast (\(T_{P}\)). We see that the TST becomes larger and larger as \(T_{P}\) increases as we vary \(T_{P}\) from 160 to 1280 for both Algorithms 1 and 2. Figure 4: Total service time (TST) vs. number of preambles, \(R\). Figure 5: Transmission probability vs. time. In Figure 8, we plot the number of collisions for ACB, CAB and EAB. We see that the ACB shows largest number of collisions while CAB exhibits much less collisions than ACB. CAB shows slightly larger collisions than EAB which is due to the reason that CAB does not use DTX. Hence we see that for energy sensitive devices, CAB performs much better than ACB because collisions and retransmissions have direct relation with energy usage. Large collisions means large retransmissions and loss of energy. Thus the proposed CAB is useful for energy constrained devices as well. Figure 8: Number of collisions vs. number of preambles, \(R\). Figure 6: Average access delay vs. number of preambles, \(R\). Figure 7: TST vs. periodicity of bitmap broadcast, \(T_{P}\). Although Figure 8 implicitly shows that the energy efficiency of ACB is the worst as compared to EAB and CAB, we have added Figure 9 for explicitly showing the results of energy efficiency (EE). Instead of expressing EE as energy expended per unit transmission, we find it more useful to present the results on energy wasted per unit transmission (energy inefficiency (EE) i.e., how much energy is wasted in the collision when it could have been usefully utilized in success. We see that ACB has higher value of energy waste as compared to CAB and EAB, whereas CAB has slightly higher waste of energy than EAB.
# Data-Free Area Detection and Evaluation for Marine Satellite Data Products
## 2 Materials and Methods  
### Study Area  
The areas selected for this study are the Yellow Sea and the Bohai Sea of China (32\({}^{\circ}\) to 41\({}^{\circ}\), 117\({}^{\circ}\) to 126\({}^{\circ}\); Figure 1). The Bohai Sea is the northernmost offshore sea of China and the internal sea of the People's Republic of China. It is surrounded by land on three sides, with Liaoning Province to the north, Tianjin City and Hebei Province to the west and Shandong Province to the south. The eastern side is divided from the Yellow Sea by a joint line from the old Tieshan Cape on the Liaodong Peninsula through Miao Island to the Penglai cape at the northern end of the Shandong Peninsula. Moreover, the Yellow Sea is located between China and the Korean Peninsula. It is the largest marginal sea in the western part of the Pacific Ocean, and it is connected to the Bohai Sea through the Bohai Strait. Both seas cover an area of approximately 460,000 square kilometers and are rich in marine biological resources, making them an important part of China's marine economy [33].
# Acoustic Improvement of Stator-Rotor Interaction with Nonuniform Trailing Edge Blowing
## 6 Summary and Conclusions  
The noise reduction effect on stator-rotor interaction arising from different levels of NTEB mass flow was investigated. Unsteady-flow simulations were conducted to illustrate the mechanism that NTEB can compensate the momentum loss and influence the axial thrust on the rotor blades. Flow field measurement contours obtained within the stator-rotor interaction region revealed that the pressure loss decreases with successively increasing levels of NTEB flow. Acoustic measurements of the sound pressure levels in forward and backward radiation have shown that NTEB leads to a noise reduction by more than 10 dB at the maximum acoustic mode. Forward radiation is always reduced with NTEB; however, in backward radiation, there are some small increases, but overall a reduction.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & & **2 m\({}^{3}\)/h** & **3 m\({}^{3}\)/h** & **4 m\({}^{3}\)/h** & **5 m\({}^{3}\)/h** & **6 m\({}^{3}\)/h** \\ \hline \multirow{2}{*}{F-OASPL} & \(1^{\text{st}}\) BPF & 0.35 & 0.78 & 1.51 & 2.10 & 2.45 \\  & \(2^{\text{nd}}\) BPF & 0.66 & 1.28 & 2.49 & 4.46 & 4.60 \\ \hline \multirow{2}{*}{B-OASPL} & \(1^{\text{st}}\) BPF & \(-1.02\) & \(-0.73\) & 2.83 & 3.65 & 0.65 \\  & \(2^{\text{nd}}\) BPF & 0.92 & 1.42 & 3.00 & 5.05 & 9.01 \\ \hline \end{tabular}
\end{table}
Table 2: Overall sound pressure level (OASPL) reduction of the first two orders (dB). F-OASPL: overall sound pressure level of forward radiation noise, B-OASPL: overall sound pressure level of backward radiation noise.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline  & & **ch1** & **ch2** & **ch3** & **ch4** & **ch5** & **ch6** & **ch7** & **ch8** & **OASPL** \\ \hline \multirow{4}{*}{F-OASPL} & \(2\) m\({}^{3}\)/h & 0.28 & 0.15 & 0.16 & \(-0.10\) & 0.43 & 0.45 & 0.04 & 0.15 & 0.21 \\  & \(3\) m\({}^{3}\)/h & 0.43 & 0.42 & 0.29 & \(-0.39\) & 0.55 & 0.46 & 0.13 & 0.14 & 0.27 \\  & \(4\) m\({}^{3}\)/h & 0.80 & 1.51 & 1.08 & 0.57 & 1.44 & 1.41 & 0.58 & 0.51 & 0.98 \\  & \(5\) m\({}^{3}\)/h & 1.16 & 2.39 & 1.89 & 1.04 & 2.18 & 2.35 & 1.03 & 0.89 & 1.59 \\  & \(6\) m\({}^{3}\)/h & 1.06 & 3.27 & 2.36 & 0.07 & 2.82 & 2.53 & 0.68 & 1.05 & 1.68 \\ \hline \multirow{4}{*}{B-OASPL} & \(2\) m\({}^{3}\)/h & \(-0.25\) & \(-0.06\) & \(-0.40\) & 0.22 & 0.28 & 0.30 & \(-0.36\) & 0.25 & \(-0.08\) \\  & \(3\) m\({}^{3}\)/h & 0.57 & 0.23 & \(-0.11\) & \(-0.14\) & 0.51 & 0.21 & \(-0.44\) & 0.29 & 0.03 \\ \cline{1-1}  & \(4\) m\({}^{3}\)/h & 1.11 & 0.83 & 0.47 & 0.34 & 1.41 & 0.92 & 0.08 & 0.72 & 0.60 \\ \cline{1-1}  & \(5\) m\({}^{3}\)/h & 0.89 & 1.17 & 0.35 & 0.47 & 1.86 & 1.29 & 0.52 & 0.70 & 0.78 \\ \cline{1-1}  & \(6\) m\({}^{3}\)/h & 1.13 & 1.13 & 0.55 & 0.42 & 3.02 & 1.41 & 0.11 & 0.21 & 0.76 \\ \hline \end{tabular}
\end{table}
Table 3: OASPL reduction of circumferential channels (dB).  
**Funding:** This research was funded by China Scholarship Council (No. 201809110025).  
**Acknowledgments:** We acknowledge the valuable comments by editors and anonymous reviewers.  
**Conflicts of Interest:** The authors declare no conflict of interest.