esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Real Data Experiments  
#### 3.1.1 Qualitative Comparisons Using Real S2 Data  
An unprocessed \(324\times 324\) pixel crop from real S2 data seen in Figure 3, showing parts of central Reykjavik in Iceland, was super-resolved using the methods and parameter tuning previously described. The image is part of an S2 tile from the Copernicus Open Access Hub that is 10,000 \(\times\) 10,000 pixels in the 10 m bands captured by Sentinel-2B on a descending orbit at 13:03 on the 17 th of October 2019, with Level-2A atmospheric corrections applied [51]. It was chosen for the favorable cloud conditions and presents a very clear view of the capital of Iceland and the surrounding area. Results for each method can be seen in Figures 4 and 5, as \(100\times 100\) pixel crops from bands B9 and B12, as seen in Figure 3. Very few outliers in the images, having intensity values over 10,000, the expected maximum value, have been clipped and the images normalized by dividing every value by 10,000. These bands are highlighted here because they are particularly hard to super-resolve. No ground truth is available, so quantitative image quality metrics can not be used, and the qualitative comparison is strictly visual.  
Figure 3: An RGB image generated [52; 53] using an S2 image of central Reykjavik, Iceland. A crop in the upper left corner of the image, shown on the right, is used in Figures 4 and 5.  
For the 60 m B9 in Figure 4, ATPRK, S2 Sharp, and SSSS appear to give comparatively good results. Rooftop outlines and street layouts are easily discernable and the image contrast looks natural. SupReME, DSen2 and Sen2 res, are each successively blurrier and Sen2 res returns much lower reflectance values than all the other methods resulting in much lower contrast. MuSA appears oversharpened with overbright outlier pixels. The methods all give fairly similar results for most of the 20 m bands but struggle with band B12 in particular. Building shapes are harder to discern and appear irregular compared to the other methods.  
Figure 4: Band B9 of real S2 image of Reykjavik super-resolved to 10 m resolution.  
Figure 5: Band B12 of real S2 image of Reykjavik super-resolved to 10 m resolution.  
other bands, as seen in Figure 5. Sen2 res and DSen2, remain slightly blurry compared to the other methods. S2 Sharp, SSSS, SupReME, and S2 SSC appear most natural and visually pleasing. MuSA again appears oversharpened. Making further qualitative judgements is hard due to how similar the results appear.
## 4 Experiments and Results
### Experimental Settings
#### 4.3.2 Hyper-Parameter Settings  
To demonstrate the effectiveness of the NDRE, we set most hyper-parameters in the NDRE following previous work [10; 14] and adjusted other hyper-parameters using cross-validation on the training data.  
After adjustment, we chose the following settings in our model: the dimension of word embedding \(d^{w}\) was set to 50, the dimension of position embedding \(d^{p}\) was set to 5, the dimension of convolution layer \(d^{c}\) was set to 256, the window size of the convolution kernel \(k\) was set to 3, the number of multi-heads \(A\) was set to 8, the dropout rate was set to 0.5, and the threshold \(\phi\) of similarity was set to 0.16 from experience. Following the approach in [10], we first pre-trained our sentence encoder and noise detection strategy with only data \(\mathbb{S}^{1}_{h,t}\), then fine-tuned the whole model with data \(\mathbb{S}_{h,t}=\mathbb{S}^{1}_{h,t}\cup\mathbb{S}^{2}_{h,t}\). The epoch of pre-training was about 50, and the epoch of fine-tuning was about 180; the batch size of pre-training was fixed to 64, and the batch size of fine-tuning was fixed to 2500; the learning rate of pre-training was set to 0.005, and the learning rate of fine-tuning was set to 0.001.
## 6 Data Transmission
### Number of Packets Transmitted or Received  
#### 6.2.1 Scenario I: Without Retransmission  
Recall that in Section 3.2, we assume there are \(L\) layers in the WSN, and, in a data gathering cycle, \(a\) packets each with \(m\) bits are sensed and transmitted by one sensor in a single location. However, in each data gathering cycle, the numbers of packets received or transmitted by different sensors may be different. According to the possible data transmission paths obtained in Figure 3.2, the possible transmitters of each sensor in the outer layer and the corresponding possible receivers in the neighboring inner layer are determined. In Figure 3.2, one sensor location may have one or two possible transmitters and receivers. Sensor locations with two possible transmitters are the closest sensor locations to the base station in the neighboring inner layer. Locations with two possible receivers are those ones on a vertical line of the triangle as mentioned in Section 3.2. Therefore, three types of locations should be distinguished:  
* Location with two possible transmitters and two possible receivers, _i.e._, (1,1) and \((2x,x+1)\) (\(x=1,2,3,\ldots\));
* Location with two possible transmitters and one possible receiver, _i.e._, \((2x+1,x+1)\) and \((2x+1,x+2)\) (\(x=1,2,3,\ldots\));
* Location with one possible transmitter and one possible receiver.  
The numbers of packets transmitted and received are calculated as  
\[N_{t,non}=\begin{cases}\frac{(L-i+1)(L-i+2)}{2}a,\text{Case }1,\\ \frac{(L-i+1)(L-i+4)}{4}a,\text{Case }2,\\ (L-i+1)a,\quad\text{Case }3,\end{cases}\quad\text{and}\quad N_{r,non}= \begin{cases}\frac{(L-i)(L-i+3)}{2}a,\text{Case }1,\\ \frac{(L-i)(L-i+5)}{4}a,\text{Case }2,\\ (L-i)a,\quad\text{Case }3,\end{cases} \tag{17}\]  
respectively (\(N_{r,non}=N_{t,non}-1\)).
# A Reverse Shortest Path Tree-Based Multicast Joining Node Selection Method
## 3 Reverse Shortest Path Tree Based MJN Selection Method
### Construction of RSPT
#### 3.1.4. An Example of RSPT  
In Figure 2, node \(a\) constructs the RSPT with itself as the root node. It first floods requests to all other nodes in the network. Each node that receives the request will send the probe messages back to node \(a\). By following Algorithm 1, the RSPT can be constructed with nodes \(c\), \(e\), and \(g\) as the leaves.  
Figure 2: RSPT example.
## 4 Experimental Design
### Data Procurement  
In the real world, there is a reasonable upper bound to the size of computer networks, as universities, corporations, and other organizations usually do not exceed the sixth order of magnitude (hundreds of thousands) in terms of membership. Therefore, the ideal packet capture dataset should originate from a network with at least a few hundred hosts (or servers) in order to minimize the effects of noise caused by individual hosts. In addition, the data should be anomaly-free so as to allow ARMA to learn the baseline behavior of the network. In terms of length, it is recommended that ARMA is trained with at least 50 but ideally over 100 contiguous data points in order to effectively learn a time series' behavior [25]. Since in this study, we performed an hourly analysis of a network, this translated to at least 2 days of packet capture network data.  
After an extensive search through PCAP repositories and libraries, we selected the University of New Brunswick's ISCX 2012 Intrusion Detection Evaluation Data Set [26] for our experiments. It features seven complete labeled days of PCAP data from a large network. However, four out of seven days of data were known to contain anomalies, such as HTTP Denial of Service and internal infiltration, and therefore, we decided to drop these four days and to juxtapose the three anomaly-free days for our experiments in order to establish are clean baseline. The three anomaly free days were Friday (11 June), Saturday (12 June), and Wednesday (16 June). We treated the Wednesday data as if it were the weekday before the Friday data. This gave us a 72 h anomaly free data set that was nearly continuous and had two weekdays and one weekend.  
We processed PCAP data into netflow files, filtering by protocol and writing a new netflow every hour in a new data set. This resulted in 72 netflow files that were subsequently analyzed in order to aggregate ports used by IP addresses and then label each node as either \"host\" or \"server\". In the case of HTTP, the presence of port 80 indicated a server. The approach can be easily updated for different protocol labeling requirements (including HTTPS). It is important to note that filtering by protocol substantially reduces the amount of data traffic, as well as nodes, in netflow files. Filtering by HTTP, our netflows averaged around 20 nodes, with 2 HTTP server devices apparent, and the rest labeled as \"hosts\".  
After labeling hosts, we condensed the netflow files further into \"edge hour\" files, which represents the network in edge list form, aggregating by source ip, destination ip, and bytes transferred, disregarding the port identities.
# Architecture Exploration of a Backprojection Algorithm for Real-Time Video SAR
## 4 Experimental Results
### Performance Analysis of BP-VISAR Architectures with Multi-Stream Based Acceleration  
To evaluate the performance of the multi-stream based BP-VISAR architectures, we implemented N-way concurrent Arch. S3, i.e., Arch. S3-N by grouping the output frames and allocating the tasks of generating the groups of the frames to the concurrent streams of the BP-VISAR pipeline. We measured the overall processing times and visually profiled the internal status of the GPU device for Arch. S3 and Arch. S3-N.  
Arch. S3 is executed as one stream in the GPU device as in Figure 11(a) where kernels and memory data transfer are executed in the issue order by the host. Therefore, when the execution engine runs RC or IF kernels for the \(n\)th frame, the copy engine is idle, and vice versa.  
Arch. S3-3 is executed as three streams in the GPU device as in Figure 11(b). The kernels and memory data transfers in each stream are still executed in the issue order by the host.  
Figure 11: BP-VISAR frames of different frame resolutions (sub-aperture processing in overlapped mode, frame \(29,\alpha=0.8\)).  
Figure 12: Visual profiling results of Arch. S3 and its multi-stream and multi-GPU extensions. Suffix \(n\) of H2D.\(n\), RC.\(n\), IF.\(n\), D2H.\(n\) represents the BP-VISAR frame number. Target frame resolutions are \(2048\times 2048\) for (**a**,**b**) and \(4096\times 4096\) for (**c**), respectively.  
However, there is simultaneous execution of a kernel and memory data transfer from different streams. For example, the RC kernel for frame 2 (RC.2) operates simultaneously with the memory data transfer for frame 1 (D2H.1) and frame 4 (H2D.4). Memory data transfer for frame 5 (H2D.5) operates simultaneously with the RC and the IF kernels for frame 3 (RC.3 and IF.3), etc. These concurrent executions of the execution engine and the copy engine can further accelerate the overall processing time by increasing the resource utilization of the GPU device. As a result, there is performance improvement in Arch. S3-3 by 6-31 % and 12-38 % in non-overlapped and overlapped modes compared to Arch. S3, respectively.  
For the multi-stream execution of BP-VISAR, a number of streams saturate the performance due to the finite resources of the execution and the copy engines in the GPU device. The number is dependent on the structure of the BP-VISAR pipeline in a stream and the GPU resources. Table 8 shows the average processing time for one BP-VISAR frame generation with the number of streams in Arch S3-N. In Arch. S3-N, three or four streams of the BP-VISAR pipeline are optimal for speed. In this study, we used three streams for Arch. S3-N for the performance comparison with others.
# Modeling and Analysis of High-Power Ti:sapphire Laser Amplifiers-A Review
## 2 Amplification Model
### Effects of Transverse Profiles
Using Equations (19) and (20), a multi-pass amplifier simulation can be performed to calculate the extraction efficiency based on the edge steepness and the spatial uniformity of the pump beam and the amplified beam as shown in Figure 8. The simulation results exhibit that the energy extraction efficiency increases as the edge steepness of the pump beam decreases and the spatial distribution of the output beam becomes uniform as the spatial distribution of the pump beam becomes uniform. Thus, sharpening the edge of the pump beam and making the spatial distribution uniform are methods to uniformly amplify a seed beam with a high energy. In addition, we can consider using a square pump beam for realizing uniform and efficient amplification in a laser amplifier. Employing a beam homogenizer using lens arrays, a highly steep and uniform square pump beam can be illuminated into the gain medium with a high efficiency. The square shape makes the fill factor of the lens array close to 100% and multiple circular beams can be pumped into the laser medium by shaping them into a square beam using a beam homogenizer as shown in Figure 9. In addition, the use of a square beam can reduce the size of the laser system. A square beam can reduce the size of the optical part by 11% compared to a circular beam and can use an area of 27% at the same size [20]. This reduces the risk of damage to the optical components and helps to miniaturize the pulse compressors or optical components. Figure 8: (**a**) Edge steepness of the pump beam and the energy extraction efficiency, (**b**) The spatial uniformity of the pump beam and the amplified beam according to the design parameters of the beam shaping system; \(\lambda_{pump}=532\,\mathrm{nm},J_{sat}=0.83\,\mathrm{J/cm^{2}}(@800\,\mathrm{ nm})\).
# Open-Pit Bench Blasting Fragmentation Prediction Based on Stacking Integrated Strategy
## 2 Ensemble Machine Learning (ML) Models
### Base Learner
#### 2.3.2 XGBoost  
Extreme Gradient Boosting (XGBoost) was proposed by [PERSON] et al. based on improvements to the Gradient Boosting Decision Tree (GBDT) algorithm [39]. Based on decision trees, the XGBoost algorithm leverages advantages such as parallel tree boosting, regularization, and efficient tree pruning to handle regression and classification problems more effectively [40]. The decision trees in XGBoost are sequentially constructed, with each new tree considering the prediction errors of the previous tree. Adjustments are made based on the modified sample distribution to train the next tree. The core idea is to optimize the objective function. XGBoost can be seen as a soft computing library that combines new algorithms with gradient-boosting decision tree algorithms to improve prediction accuracy [41]. Figure 3 shows the pseudocode for the XGBoost algorithm.
# Robust Device-Free Intrusion Detection Using Physical Layer Information of WiFi Signals
## 5 Evaluation
### Performance Evaluation
#### 5.2.3 Intrusion Detection with Different Window Sizes  
As RDFID is a window-based human detection approach, the detection performance is also evaluated under different window sizes. To examine the advancement of RDFID, it is compared to two other human detection approaches, FRID and PADS. In the construction phase of the training set, a 30 s data segment is randomly divided from the regular walking data of the first volunteer in scenario (a). The test data contains the regular walking data of the other three volunteers, while the window size ranges from 1 s to 5 s.  
The results are the mean values of the three people. The FN and FP rate of the three approaches under different window sizes are shown in Table 4. It is indicated from the table that the FN rate of RDFID is as high as 11.7% when the window size is 1 s, but it decreases to 5.2% when the window size changes to 2 s. Moreover, the FN rate of RDFID keeps decreasing as the window size increases. It is because the 1-s window is too narrow for human motion, and people can only walk less than two steps within the window. As a result, the periodicity in the extracted frequency-domain features is not significant enough, which leads to a higher FN rate. Although the FN rate of FRID is lower than that of RDFID when the window size is 1 s, it decreases slower when the window size increases. On the other hand, as the training and test data is from the same scenario in this experiment, the variation of the support vector of the features is insignificant; the FN rate of PADS can achieve a low level.  
The FP rate of the three approaches all undergoes a low fluctuation, which indicates that the FP rate of the three approaches can be less affected by the window size. However, as the extracted feature in RDFID has a better discernibility between static and dynamic, this approach achieves the lowest FP rate.  
Figure 10 shows the PD of the three approaches when the window size is different. It can be seen that PADS achieves a higher PD when the window size is no larger than 3 s, but the PD of RDFID increases fast as the window size gets larger, and gets the highest of the three approaches when the window size is larger than 3 s.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**FN**} & \multicolumn{3}{c}{**FP**} \\ \hline
**Window Size** & **1** & **2** & **3** & **4** & **5** & **1** & **2** & **3** & **4** & **5** \\ \hline RDFID & 11.7 & 5.2 & 4.6 & 3 & 2.1 & 2.1 & 2.3 & 1.7 & 1.8 & 1.7 \\ \hline FRID & 9.8 & 7.4 & 5.6 & 4.9 & 4.4 & 7 & 7.6 & 6.8 & 7.2 & 7.3 \\ \hline PADS & 6.3 & 4.6 & 4.6 & 3.8 & 3.2 & 3.5 & 3.8 & 3.5 & 4 & 3.3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: FN/FP of human detection under different window sizes (%).  
Figure 10: PD of human detection under different window sizes.
## 5 Hardware Design of the Proposed Algorithm
### Hardware Architecture of the proposed 3D Shape Reconstruction System  
The hardware architecture of the proposed algorithm, presented in Figure 8, follows the same processing chain as the workflow algorithm. It consists of two main stages: spot detection and 3D reconstruction.  
The first subsystem presents two pipelined sequential treatment blocs of the stereo images. Each bloc provides twelve 2D coordinates of the laser projections of the corresponding image. These coordinates are sent to the next processing bloc when all image pixels are covered and checked. The second stage takes as inputs the corresponding stereo coordinates and the calibration parameters. It permits to generate the 3D coordinates corresponding to the world points identified through the laser spots projections. An additional bloc is inserted to synchronize the reading of corresponding 2D coordinates and their sequential sending to the reconstruction process. We take profit from stereovision to optimize the reconstruction result. Two pipelined architectures are designed to simultaneously treat  
Figure 8: Hardware architecture of the proposed algorithm.  
the stereo RGB images and reconstruct the 3D laser spots world positions for each system. The system output is the average of the two generated results. A description of the functioning of each subsystem will be given in the following sections.
## 3 Results and Discussion
### Optimal Coverage  
In order to evaluate the Wi-Fi network radio budget in the proposed site, an access point with a consumption of 12 W and high radiation power of 27 dBm was selected. The technical characteristics are shown in Appendix A (Table A1). Before the calculation, to find the optimal coverage, characteristics of the equipment were introduced in the simulator including access points, antennas, and cable losses. Figure 3 summarizes the input-output data using MATLAB code. If the receiver sensitivity is lower than the received power (\(-28\) dBm), the connection is enabled.  
Using the MATLAB code, we estimated that at least five Wi-Fi access points to cover the whole campus are required. The impact of using lower and higher numbers of access points was also assessed. Figure 4 provides the optimal site for the five Wi-Fi access points.  
Figure 4: Geographic map of the ENST campus (Algiers, Algeria).  
Figure 3: Input-output data of the MATLAB calculator interface.
## 3 Nasal Acoustic Signal Processing Methods
### Nasal Cavity Acoustic Signal Preprocessing Method
#### 3.1.2 Elimination of DC Offset Component From Incident and Reflected Waves  
As the electronic components in the nasal acoustic signal acquisition device are susceptible to environmental interference, the DC component from the incident and reflected waves will be shifted. This DC offset component can affect the amplitude range of both the incident and reflected waves, as well as the input impulse response sequence. Consequently, the related parameters in the nasal cavity three-dimensional structure hierarchical reconstruction method are further affected. In the final nasal cavity three-dimensional structure reconstruction, the presence of the DC offset component tends to cause cross-sectional area values at the same depth to exceed standard values. Based on the principles of the acoustic wave reflection method, this phenomenon becomes more pronounced over time. Hence, the elimination of the interference caused by the DC offset component is particularly crucial [17; 18].  
In this research, before the first-stage incident wave is recorded by the microphone, there exists a segment of acoustic waves, the amplitude of which remains relatively stable. Hence, these values are chosen as the DC offset component. This DC offset component is then separately eliminated in the time domain for both the incident and reflected waves, correcting the amplitude range of the input impulse response to be obtained subsequently. Ultimately, this correction enhanced the accuracy of the nasal cavity three-dimensional structure reconstruction results.