esa-sceva/satcom-chunk-collection huggingface dataset:
_Algorithms_**2011**, \(3\), 200-222; doi:10.3390/a4030200  
_algorithms_  
ISSN **1999-4893**  
www.mdpi.com/journal/algorithms  
_Article_  
**Approximating Frequent Items in Asynchronous Data Stream**  
**over a Sliding Window**  
**[PERSON] \({}^{1,\star}\), [PERSON] \({}^{2}\), [PERSON] \({}^{1}\) and [PERSON] \({}^{1}\)**  
\({}^{1}\) Department of Computer Science, University of Hong Kong, Pokfulam, Hong Kong, China;  
E-Mails: [EMAIL_ADDRESS] (H.-L.C.); [EMAIL_ADDRESS] (T.-W.L.)  
\({}^{2}\) MADALGO (Center for Massive Data Algorithmics, a Center of the Danish National Research Foundation), Department of Computer Science, Aarhus University, Aarhus C DK-8000, Denmark;  
E-Mail: [EMAIL_ADDRESS]  
\({}^{\star}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.:+852-2859-8944; Fax: +852-2549-7908.  
_Received: 23 June 2011; in revised form: 23 June 2011 / Accepted: 10 September 2011 / Published: 22 September 2011_  
**Abstract:** In an asynchronous data stream, the data items may be out of order with respect to their original timestamps. This paper studies the space complexity required by a data structure to maintain such a data stream so that it can approximate the set of frequent items over a sliding time window with sufficient accuracy. Prior to our work, the best solution is given by [PERSON] _et al_. [1], who gave an \(O\big{(}\frac{1}{\epsilon}\log W\log(\frac{\epsilon B}{\log W})\min\{\log W, \frac{1}{\epsilon}\}\log|U|\big{)}\)-space data structure that can approximate the frequent items within an \(\epsilon\) error bound, where \(W\) and \(B\) are parameters of the sliding window, and \(U\) is the set of all possible item names. We gave a more space-efficient data structure that only requires \(O\big{(}\frac{1}{\epsilon}\log W\log(\frac{\epsilon B}{\log W})\log\log W\big{)}\) space.  
**Keywords:** asynchronous data streams; frequent items; sliding window; space complexity
## 3. Results and Discussion
### Biomechanical Modeling
#### 3.2.1 Pachymetry  
Influence of the Geometry  
In Figure 7, a reduction in the pachymetry is observed when the applied pressure was increased in both models (spherical and biconical geometries). This behavior corresponded to that obtained experimentally. There is a coincidence in the values of these models, except in the points closest to the maximum pressure (8.00 kPa), but they were still similar.  
Influence of the Boundary Conditions  
The pachymetry variation versus the pressure for the different applied boundary conditions is shown in Figure 8.  
Small differences were observed in the results obtained among the used models, and the bigger ones were less than 10 \(\upmu\)m. Pachymetry is a parameter that is barely affected by both the use of different geometric models and the analyzed boundary conditions.  
Model-Experiment Comparison  
It was necessary to obtain a stress-free cornea configuration [12] to compare the experimental and computational results, in which the displacements could be measured by starting at the initially applied pressure (corresponding to the initial geometry). The results are provided in Figure 9. Similar behaviors were noted, and the differences were less than 10 \(\upmu\)m, representing <1.5%.  
Figure 7: Pachymetry versus pressure for the spherical and biconical geometries.
## 3 Results and Discussion
### Textural Properties of Yanggaeng  
Table 6 presents the TPA results for hardness, adhesiveness, resilience, cohesion, springiness, gumminess, and chewiness. Hardness was highest in the Xyl group (1930.75 \(\pm\) 11.67 g), and this value was significantly higher than that of the Sug group (1800.21 \(\pm\) 8.01 g), while Alu (1631.22 \(\pm\) 80.84 g) and Ste (1549.30 \(\pm\) 21.60 g) showed lower hardness. Resilience was significantly higher in the Alu group (9.30 \(\pm\) 0.85%) than in the Ste group (6.60 \(\pm\) 1.36%), with Sug (7.50 \(\pm\) 0.35%) and Xyl (7.37 \(\pm\) 0.41%) showing intermediate values. Gumminess and chewiness were highest in the Xyl group (639.09 \(\pm\) 0.47 and 540.47 \(\pm\) 21.50, respectively), while Ste showed the lowest values for both gumminess (405.25 \(\pm\) 4.59) and chewiness (356.06 \(\pm\) 10.03). However, textural properties, adhesiveness, cohesion, and springiness were not affected by the introduction of alternative sweeteners. The addition of xylose resulted in the hardest texture, with greater gumminess and chewiness compared to that of other sweeteners. This difference may be attributed to the unique molecular structure and physical properties of xylose. Its molecular structure facilitates stronger intermolecular interactions that enhance gel formation and elasticity [39; 40]. Furthermore, xylose exhibits crystallization properties that likely contribute to the formation of a denser and more stable gel matrix [38], thereby increasing the hardness and chewiness of Yanggaeng. In contrast, stevia produced the softest texture, characterized by the lowest hardness, gumminess, and chewiness. This suggests that stevia weakens the structural integrity of the gel matrix. Additionally, stevia may contribute to a less elastic texture and a weaker gel structure than other sweeteners [41; 42].
## 4 Doppler Frequency Estimation in the Presence of Noise
### Evaluation of the Cramer-Rao Lower Bound  
Usually, a statistical estimator is characterized by its bias (mean error), variance (mean square error), and the threshold SNR (signal-to-noise ratio) [22]. Hence, here the Cramer-Rao lower bound (CRLB) is proposed as benchmark to compare the performance of different algorithms.  
Without considering the data-transition problem and ignoring the influence of the code delay estimation error (which will be discussed in Section 5.2), the received samples Equation (34) can be mathematically expressed as  
\[\widetilde{y}(nT_{s}) =y(nT_{s})+\eta(nT_{s})\] \[=A\cos\left(2\pi(f_{IF}+f_{d})nT_{s}+\varphi\right)+\eta(nT_{s}) \tag{44}\]  
which contains the unknown parameter vector \(\mathbf{\theta}=[A,f_{d},\varphi]\). The corresponding Fisher information matrix \(\mathbf{I}\) for an observation of \(N\) samples has elements [22]  
\[\left[\mathbf{I}(\mathbf{\theta})\right]_{ij}=\frac{1}{\sigma^{2}}\sum\limits_{n= 0}^{N-1}\frac{\partial y(n;\mathbf{\theta})}{\partial\mathbf{\theta}_{i}}\frac{ \partial y(n;\mathbf{\theta})}{\partial\mathbf{\theta}_{j}} \tag{45}\]  
where \(y(n;\mathbf{\theta})=A\cos(2\pi(f_{IF}+f_{d})nT_{s}+\varphi)\), and \(N\) is the number of samples used for the estimation of the unknowns. Therefore the Fisher matrix is  
\[\mathbf{I}(\mathbf{\theta})=\frac{1}{\sigma_{IF}^{2}}\begin{bmatrix}\frac{N}{2}&0& 0\\ 0&2\pi^{2}A^{2}T_{s}^{2}\sum\limits_{n=0}^{N-1}n^{2}&\pi A^{2}T_{s}\sum\limits _{n=0}^{N-1}n\\ 0&\pi A^{2}T_{s}\sum\limits_{n=0}^{N-1}n&\frac{NA^{2}}{2}\end{bmatrix} \tag{46}\]  
The CRLB is found as the \([i,i]\) element of the inverse of the Fisher matrix:  
\[\mathrm{var}(\mathbf{\theta}_{i})\geq\left[\mathbf{I}^{-1}(\mathbf{\theta})\right]_{ii} \tag{47}\]  
Therefore the CRLB for the Doppler frequency estimate is  
\[\mathrm{var}(\widehat{f}_{d}) \geq\left[\mathbf{I}^{-1}(\mathbf{\theta})\right]_{22}\] \[\geq\frac{12}{(2\pi)^{2}\;\rho\;T_{s}^{2}N(N^{2}-1)} \tag{48}\]  
where \(\rho=A^{2}/(2\sigma_{IF}^{2})\). As \(N=f_{s}T_{d}\gg 1\), Equation (48) can be written as  
\[\mathrm{var}(\widehat{f}_{d}) \geq\left[\mathbf{I}^{-1}(\mathbf{\theta})\right]_{22}\] \[\geq\frac{12}{(2\pi)^{2}\;\rho\;f_{s}T_{d}^{3}} \tag{49}\]  
which is the benchmark for our estimation algorithms.
## 5 Results and Discussion
### Accuracy Analysis of the Communities Detected  
To determine the quality of the communities formed, two methods were used, normalised mutual information (NMI) and modularity. Tables 3 and 4 represent the normalized mutual information results obtained when evaluated on networks with the ground truth available, and Table 5 depicts the modularity obtained for the networks without the ground truth available.  
\begin{table}
\begin{tabular}{l c c c c} \hline
**Input Network (LFR(N, k, maxk, mu, min_c, max_c))** & **No. of Nodes** & **No. of Edges** & **No. of Focal Nodes** & **Execution Time** \\ \hline (128, 10, 10, 0.1, 32, 32) & 110 & 1024 & 73 & 370 ms \\ (333, 10, 16, 0.2, 10, 30) & 333 & 2359 & 183 & 698 ms \\ (1500, 15, 15, 0.1, 20, 50) & 1500 & 10,473 & 1764 & 772 ms \\ (5000, 20, 40, 0.1, 30, 60) & 5000 & 25,784 & 2140 & 789 ms \\ (10,000, 20, 30, 0.2, 100, 200) & 10,000 & 54,396 & 5647 & 867 ms \\ \hline \end{tabular}
\end{table}
Table 2: Execution time obtained for the synthetic benchmark dataset of varying community size and network size.  
\begin{table}
\begin{tabular}{l c c c} \hline
**Network** & **Proposed Method** & **GA with NED Index** & **GA with Community Score** \\ \hline Karate club network [(62)] & 0.7324 & 0.5513 & 0.5426 \\ Dolphin social network [(63)] & 0.6507 & 0.5673 & 0.6201 \\ Books about U.S. politics [(64)] & 0.8311 & 0.7512 & 0.7937 \\ American college football [(28)] & 0.5520 & 0.6725 & 0.5844 \\ \hline \end{tabular}
\end{table}
Table 3: Normalized mutual information (NMI) obtained for real-world network datasets with the ground truth available.
# Visual Sentiment Analysis Using Deep Learning Models with Social Media Data
## 3 The Proposed Work--Block Diagram  
The effectiveness of our model in predicting sentiments using a fine-tuned transfer learning model is shown in Figure 1. This was done by fine-tuning the existing pre-trained VGG-19, ResNet50V2, and DenseNet-121 models. The reason for choosing the ResNet50V2 model is that it can reduce the vanishing gradient problem that happens when gradients are backpropagated, and the presence of residual or skip connection improves the learning [41]. It employs an identity connection or skip connection to increase system performance by lowering the error rate and uses a batch normalization layer before weight layers to improve classification performance. The DenseNet-121 model is favored by us because it can mitigate the vanishing gradient problem, requires fewer parameters, and has feature reuse capabilities. The validation error for the DenseNet model is much lower than that of the ResNet model, and the authors of [42] claim that DenseNet performed better based on extensive hyperparameter adjustments. We favor the VGG-19 model since it is an upgraded version of the VGG-16 model and is favored by many researchers for image classification tasks. Our work involved four stages-- Stage I, Stage II, Stage III, and Stage IV. In Stage I, the image samples that were extracted from the Crowdflower dataset were loaded with their sentiment labels, which were available in a text file. We extracted about 1000 images from the dataset using the URLs of the images along with their sentiment polarity labels. In Stage II, the pre-processing of images was done by converting the images into an RGB format, and the resizing of the images into different dimensions was performed as required by the pre-trained models. The models required an image dimension of 224 \(\times\) 224 \(\times\) 3, and the normalization of image pixels was done in the next step. In Stage III, the training and testing samples were created by dividing the input image samples (1000) into training (795) and testing (205) images. Nearly 80% of the samples were utilized for training, with the remaining 20% being used for testing. In Stage IV, the training process was started by building the pre-trained model architectures with additional layers, and the prediction of unknown samples was done next. The fine-tuning of the models was done by freezing some layers and unfreezing the remaining layers. The performance metrics of the models, such as accuracy, precision, recall, and F1 measures, were generated, and the comparative analysis of the different pre-trained models was done.  
Figure 1: Flow diagram of the image sentiment prediction model.
# Asymmetric Quantum Multicast Network Coding: Asymmetric Optimal Cloning over Quantum Networks
## 3 Results
### \(1\to 2\) Asymmetric UQC Multicast Protocol  
In this subsection, we present a new protocol that multicasts optimal asymmetric UQCs from the source node \(s\) to two target nodes \(t_{1}\) and \(t_{2}\) on a quantum network. We present the protocol in Section 3.1.1 and prove that the it creates optimal asymmetric UQCs in Section 3.1.2.
## 2 Methods  
The experiments were carried out at SACLA BL1, which covers a photon energy range of 20 to 150 eV in the fundamental FEL radiation. The FEL radiation was linearly polarized in the horizontal direction, and the repetition rate was 60 Hz. A schematic diagram of the experimental setup for soft X-ray absorption spectroscopy in transmission mode with a gas cell is presented in Figure 1. The FEL pulses are transported and focused with a carbon-coated Kirkpatrick-Baez (KB) focusing mirror system [23]. The focus size was about 10 \(\upmu\)m in diameter. The photon energy cutoff for the KB mirror system at an incident angle of 1.5\({}^{\circ}\) is roughly 1 keV, allowing soft X-ray absorption spectroscopy using the third- and fifth-order harmonics. In these experiments, we used photon energies of 82, 98, and 108 eV as the fundamental FEL radiation. In this energy region, pulse energies are roughly 100 \(\upmu\)[23]. At a photon energy of 100 eV, the pulse energy of the third harmonic is estimated to be 0.3% [23] of the fundamental FEL radiation (i.e., around 300 nJ). According to the previous theoretical work [27], we estimate the pulse energy of the fifth-order harmonic to be around 0.01% of the fundamental FEL radiation (10 nJ). Full details of the beamline are described in [23]. Aluminum filters of thicknesses 0.5, 0.4, and 1.4 \(\upmu\)m were used to selectively absorb the fundamental FEL radiation and optimize signal-to-background ratios of the Ar 2p, CO\({}_{2}\) C 1s, and CO\({}_{2}\) O 1s NEXAFS spectra, respectively. For example, the 0.5-\(\upmu\)m-thick filter provides transmissions of \(0.14\times 10^{-5}\) at 86 eV (fundamental) and \(0.4\times 10^{-2}\) at 246 eV (third order).  
The gas cell confines sample gas between two 100-nm-thick Si\({}_{3}\)N\({}_{4}\) membrane windows, and for the experiments described here we used an absorption length of 10 mm. To avoid nonlinear optical effects, the gas cell was positioned 400 mm upstream of the FEL focus point. The FEL beam size was estimated to be about 1.4 mm in diameter at the gas cell position. The sample gas pressures for the Ar 2p, CO\({}_{2}\) C 1s, and CO\({}_{2}\) O 1s NEXAFS measurements were 720, 350, and 580 Pa, respectively.  
Spectra of radiation transmitted through the gas cell were recorded using a flat-field imaging grazing incidence spectrometer (XUV639, manufactured by Shinkukogaku Co. Ltd., Tokyo, Japan), which was set on the same axis as the incident FEL axis. The FEL pulse was dispersed by an aberration-corrected concave grating with a groove density of 600 lines/mm and focused on the detection plane of a thermoelectrically-cooled CCD camera (DO420-BN, Andor Technolgy Ltd). Assuming that 1 electron-hole pair can be made per 3.6 eV [28; 29], the CCD intensity was converted to the number of photons detected. Error bars in absorption spectra were estimated from statistical errors of the number of photons detected.
## 4 Countermeasures against Thermal Covert Channel Attacks
### Selective Noise  
To fight TCC attacks and address the system's overheating issue, we proposed a new countermeasure, which essentially consists of selective noise. In other words, this operation is designed based on the recorded current temperature of bit \"1\". If the current temperature shows any changes or fluctuation, then the system decides to add extra noise or not, which is known as selective noise. We will discuss it in more detail in the next section. By adding selective noise, we could first change the pattern of the thermal signal, and then avoid increasing the unnecessary temperature, which results in high power consumption.
## 5 Discussion of Experimental Results
### First-Stage Experimental Results
When the amount of data or the accuracy of recognition reaches a certain level, data enhancement technology can be used to improve it. Therefore, in the second stage of the experiments, we only fine-tuned the parameters of the experiments related to the Probe data enhancement. Further looking at the data, for Probe attack detection, using the Gaussian noise enhancement could improve the SVM classification accuracy to 71%, both the MLP classification accuracy and KNN classification accuracy to 70%, and the CNN classification accuracy to 64%. Among them, SVM had the highest accuracy rate of 71%, and CNN had the highest improvement rate of 7%. When WGAN-GP was used to enhance the Probe data, the accuracy of both the SVM classification and the MLP classification could be improved to 72%, and the accuracy of the CNN classification could be improved to 72%. Among them, the SVM and MLP classifiers had the highest accuracy, both at 7%. CNN had the highest improvement rate of 15%, followed by MLP at 7%. If SMOTE was used to enhance the Probe data, the accuracy rate of the MLP classification could be improved to 73% as the highest, and the accuracy rate of the SVM classification could be improved to 72%. The accuracy rate of the CNN classification was only 60% for the Probe data identification. Overall, for most of the classifiers, the WGAN-GP enhancement had a higher accuracy rate and improvement rate than the Gaussian noise enhancement and the SMOTE enhancement in the case of sparse data detection, particularly for Probe attack detection. From the experimental results, we concluded that using WGAN-GP to enhance the rare data of the Probe attack could enhance the classification of MLP with the best results, and the accuracy rate could be significantly improved to 74%, which was 4% higher than that in the case of the Gaussian noise enhancement, 1% higher than that in the case of the SMOTE enhancement, and 7% higher than that in the unenhanced case.
# High-Level Smart Decision Making of a Robot Based on Ontology in a Search and Rescue Scenario
## 3 Methods
### Reasoning Based on SWRL Rules
#### 3.1.2 The Constructions of SWRL Rules  
The steps to build SWRL rules base in SAR are as follows:  
* Extract the key rule knowledge of robot automatic search and rescue from relevant books, literature and manuals, and form the rule knowledge in the form of natural language;
* Declare this rule knowledge in the formal description language and specify the Precondition of the SWRL rule;
* Determine the types and instances of the concepts involved in the rule knowledge.  
We take the initial state of SAR tasks as an example and design the following rule knowledge:  
Rule_1: If the initial position of SAR robot is the center of SAR map, the robot will perform the center search route.  
Rule_2: If the initial position of SAR robot is the Corridor of SAR map, the robot will perform the cross search route.  
Rule_3: If the initial position of SAR robot is the Room of SAR map, the robot will perform the square search route.  
For the above rule knowledge, we analyze the Atom of SWRL rule contained in the ontology as shown in Table 1.  
The established SWRL rules are as follows:  
Rule_1: WheeledRobots(?WR) ^ hasability (?A,?WR) ^ Center(?ER) ^ hasPosition(?WR,?ER) ^ SearchTask(?ST) ^ CenterSearch(?SR) -> ChooseSearchRoute(?SR,?ST)  
Rule_2: WheeledRobots(?WR) ^ has Ability (?A,?WR) ^ Corridor(?ER) ^ hasPosition(?WR,?ER) ^ SearchTask(?ST) ^ CrossSearch(?SR) -> ChooseSearchRoute(?SR,?ST)  
Rule_3: WheeledRobots(?WR) ^ hasability (?A,?WR) ^ Room(?ER) ^ hasPosition(?WR,?ER) ^ SearchTask(?ST) ^ SquareSearch(?SR) -> ChooseSearchRoute(?SR,?ST)  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Atom** & **Description** \\ \hline hasAbility(?A,?WR) & WR has the A ability \\ hasAction(?ST,?WR) & WR performs the ST task \\ hasPosition(?WR,?ER) & WR locates in ER \\ WheeledRobots(?WR) & WR is a wheeled robot \\ ChooseSearchRoute(?SR,?ST) & the ST task choose the SR search route \\ Victims(?V) & V is a victim \\ Center(?ER) & ER is the center of SAR map \\ Corridor(?ER) & ER is the corridor of SAR map \\ Room(?ER) & ER is the room of SAR map \\ SquareSearch(?SR) & SR is the square search route \\ CrossSearch(?SR) & SR is the cross search route \\ CenterSearch(?SR) & SR is the center search route \\ SearchTask(?ST) & ST is the search task \\ \hline \hline \end{tabular}
\end{table}
Table 1: The Atom of Semantic Web Rule Language (SWRL) rules contained in the ontology.
# Deep Reinforcement Learning for the Agile Earth Observation Satellite Scheduling Problem
## 4 Experiments
### Experimental Setting for AEOSSP
#### 4.2.3 GDNN Training  
In the proposed method, we train GDNN with tasks scaled of 40, 60, 80, and 100 by the PPO. The training processes are shown in Figure 8. Results show that the average profit increases rapidly in the first 5000 episodes, indicating that the network parameters are continuously being updated. When 10,000 episodes are reached, the profit improvement begins to level off and remains stable. In general, after 50,000 training episodes, the network converges stably.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Instance Scale**} & \multicolumn{2}{c}{\(\mathbf{32\times 2}\)} & \multicolumn{2}{c}{\(\mathbf{64\times 3}\)} & \multicolumn{2}{c}{\(\mathbf{128\times 4}\)} \\ \cline{2-7}  & **ASP** & **AST (S)** & **ASP** & **AST (S)** & **ASP** & **AST(S)** \\ \hline
40 & 176.4 & 0.240 & **183.7** & 0.263 & 183.5 & 0.309 \\
60 & 263.9 & 0.443 & 266.3 & 0.488 & **269.8** & 0.637 \\
80 & 349.7 & 0.757 & **356.6** & 0.868 & 354.0 & 1.180 \\
100 & 433.2 & 1.244 & **439.4** & 1.466 & 437.6 & 1.843 \\ \hline \hline \end{tabular}  
* **Bold** indicates the best ASP in all algorithms.  
\end{table}
Table 6: Experimental results of different dimensional networks in instances with different scales.  
Figure 8: GDNN training process.
# Wind Lidar and Radiosonde Measurements of Low-Level Jets in Coastal Areas of the German Bight
## 3 Study Sites and Data Base
### Norderney  
At Norderney, the wind lidar was installed at the measurement site of the German Weather Service (DWD) from 9 August 2019 to 8 August 2020. The data availability, defined as the percentage of wind profiles where at least half of the data points of the wind profiles were valid, was 80%. The site will be referred to as NO.  
Routine automated radiosondes are launched twice per day by DWD. To compare the different measurement periods for the one-year data sets at Heligoland and Norderney, radiosonde measurements up to 500 m altitude are analysed for the LLJ occurrence for both time periods. Besides wind data, information on temperature and humidity is available from the soundings.