esa-sceva/satcom-chunk-collection huggingface dataset:
# Wireless Data Acquisition System with Feedback Function
## 2 Materials and Methods
### Description of the Receiving Part of the Data Acquisition System
After receiving the header, the MCU receives data from the sensor, decrypts them, adds time and date stamps to them, displays them on the LCD indicator, and sends them to the PC for display on the monitor and recording on the hard drive. The process is then repeated cyclically for the remaining sensors. Figure 11: A simplified algorithm of the program that controls the operation of the DAS receiver. If an overcharge or overdischarge of at least one of the SWPP batteries is detected, the receiver MCU generates and sends IR codes to the DC of the IRC command decoder, as shown in Figure 2. Commands from the decoder are sent to the corresponding EU of backup protection located on the side where the batteries are located, thus preventing failure or a reduction in battery life. A time delay in cases where it is necessary before executing a particular command is set in the EU. The mentioned DC and EU perform based on the known principles of IR remote control and are therefore not described in this article. The control program allows you to poll several dozen sensors with an interval of 1 s, which is enough to organize full monitoring of the operation of the SWPP of ToU. At the same time, the absence of a response from any number of sensors does not lead to failures in receiving data from other sensors. The return of sensors to the system is detected and displayed automatically. If necessary, the polling frequency can be increased to 2 times per second by simply reconfiguring the interrupt period from the RTC.
## 2 Materials and Methods
### Data Analysis and Statistics
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Acquisition Scenario** & **Number of Epochs** & **Upscaling Method** & **Mean Square Error (MSE)** & **Mean Intensity Error (MIE)** & **Mean Structural Similarity (SSIM)** & **Peak Signal to Noise Ratio (PSNR)** \\ \hline \multirow{4}{*}{With-Prior} & \multirow{4}{*}{100 Epochs} & RUNet & **7.80 (2.02)** & **1.47 (0.21)** & 97.79 (0.36) & **39.84 (1.02)** \\  & & DUNet & 8.54 (0.89) & 1.57 (0.37) & **97.98 (0.30)** & 39.47 (1.53) \\  & & AUNet & 14.17 (5.77) & 2.01 (0.49) & 96.76 (0.64) & 37.60 (1.57) \\ \cline{2-6}  & \multirow{4}{*}{100 Epochs} & RUNet & 6.51 (1.64) & 1.34 (0.20) & 98.08 (0.25) & 40.59 (1.02) \\  & & DUNet & **6.35 (2.35)** & **1.32 (0.28)** & **98.40 (0.22)** & **40.73 (1.34)** \\  & & AUNet & 8.35 (4.06) & 1.53 (0.44) & 98.17 (0.27) & 39.73 (1.76) \\ \hline \multirow{4}{*}{With-Prior} & \multirow{4}{*}{100 Epochs} & Runet & **21.33 (9.22)** & **2.54 (0.56)** & 94.77 (0.52) & 35.81 (1.18) \\  & & DUNet & 21.59 (10.89) & 2.55 (0.64) & **95.00 (0.54)** & **35.82 (1.29)** \\ \cline{1-1}  & & AUNet & 24.32 (10.69) & 2.72 (0.64) & 94.45 (0.56) & 35.41 (1.23) \\ \cline{1-1} \cline{2-6}  & \multirow{4}{*}{100 Epochs} & RUNet & 19.63 (9.25) & **2.42 (0.55)** & 95.34 (0.47) & 36.14 (1.21) \\ \cline{1-1}  & & DUNet & 20.39 (10.30) & 2.47 (0.63) & 95.50 (0.50) & 36.04 (1.33) \\ \cline{1-1}  & & AUNet & **19.61 (9.38)** & 2.42 (0.60) & **95.57 (0.47)** & **36.15 (1.33)** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Upscaling a 1/2 downsampled (88 \(\times\) 104) synLQ with the three Unets. The best results are represented in bold. The numbers represent the mean value followed by the standard deviation in parenthesis.
# URANS Analysis of a Launch Vehicle Aero-Acoustic Environment
## 3 Results and Discussion
### Noise Source and Propagation  
The identification of noise sources started with the study of shock waves, as these are among the main noise generation phenomena during lift-off. The instantaneous pressure was represented in logarithmic scale to visualize the regions where the sound pressure was higher. Because the jet flow was supersonic, there was a normal shock wave that generated the maximum pressure value, as shown in Figure 4a. As the flow moved, the wave was deformed by the presence of the deflector, resulting in the detached shock wave, as seen in Figure 4b. At this instant, the maximum pressure levels were due to the impact of the normal shock wave with the deflector. Later, the wavefront remained at the curvature between the deflector and the floor due to an oblique shock wave. Figure 4c shows a numerically obtained version of the Schlieren images, which capture the density variations of the fluid. The main shock waves observed were those caused by the jet and the moving flow on the platform, the detached wave on the deflector, and the oblique shock wave caused by the ramp curvature change. In this way, all of these disturbances were noise-generating sources.  
Figure 4b at t = 0.02 s depicts the evolution of the blast wave due to the rocket exhaust gases. There is a green wavefront that corresponded to the jet core boundary and a second  
Figure 4: VEGA lift-off environment shock waves’ identification. Normalized instantaneous pressure contour at (**a**) t = 0.0064 s and (**b**) t = 0.02 s. (**c**) Schlieren shock waves’ identification.  
Figure 3: Time-averaged pressure validation against experimental measurements along (**a**) the VEGA fairing and (**b**) one of the masts. Results are normalized.  
one that emerged from the nozzle and moved downstream while being disrupted by launchpad elements. The evolution of this second wavefront can be seen in the top images of Figure 5, between 0.0267 s and 0.0377 s. It was caused by the initial exit of the gases and their impact on the deflector. As it moved upstream, it was disrupted by the elements of the structure. Between 0.04 s and 0.16 s, the blast wave continued to advance upstream. At the same time, the waves generated by the interaction with the support structure separated from the wave generated at the output of the jet. Due to the faster sound propagation speed, the wavefront eventually overtook the fluid front and was attenuated. At the final time step, the maximum pressure values were found in the jet development region and the deflector due to the supersonic velocity and shock waves that appeared. On this basis, it can be concluded that the maximum pressure levels were produced by the initial normal shock wave and its subsequent impact with the deflector. The acoustic waves propagated upstream of the vehicle due to this impact and the effect of the flow movement.
## 4 ARIMA-BP-Based Selective Offloading Strategy
### A Selective Offloading Algorithm  
Based on the estimation of computation resources utilization of the edge cloud, we can obtain the remaining computation resources of the edge cloud by \(\hat{f}^{c}(t)=c_{s}-f^{c}(t)\). Then we develop a selective offloading algorithm. Initially, all mobile devices need to set their initial decisions during a time slot. Then, we lead into the condition below for prioritizing urgent tasks that need to be offloaded. This is known as selective offloading. **Condition 1**. : _if \(T^{l}_{n,k}>D^{l}_{n,k}\), the task \(u_{n,k}\) offloads to the edge cloud for processing._ \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **30 Step Ahead** & **60 Step Ahead** & **90 Step Ahead** \\ \hline ARIMA & 0.0139 & 0.0163 & 0.0317 \\ ARIMA-BP & 0.0120 & 0.0126 & 0.0224 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: RMSE of ARIMA and ARIMA-BP on CPU Usage. Figure 3: Prediction on CPU usage by ARIMA and ARIMA-BP. When Condition 1 is satisfied, it is usually the devices which are constrained to resource, and their tasks are sensitive to latency. We give priority to offload these tasks. Because computing locally cannot meet tasks' delay constraints. (i.e., \(T_{n,k}^{l}>D_{n,k}^{l}\)). Tasks must be accomplished within the specified time (i.e., the delay requirements) \(D_{n,k}\). Therefore, we can calculate the minimum computation resources needed for each task under the latency requirements when computed at edge cloud by:  
\[f_{n,k}^{min}=\frac{w_{k}}{D_{n,k}-T_{n,k}} \tag{14}\]  
The edge cloud server gets the remaining computation resources \(\tilde{f}^{c}(t)\) and broadcasts it to the mobile devices. Mobile devices which do not meet Condition 1 will be judged by Condition 2 after they get \(\tilde{f}^{c}(t)\). **Condition 2**. : _if \(\tilde{f}^{c}(t)\geq f_{n,k}^{min}\), the task merges into the set \(\Theta(t)\)._  
For all tasks that satisfy Condition 2, they will merge into the set \(\Theta(t)\), since the remaining computation resources of the edge cloud can meet the minimum resources they need (i.e., \(\tilde{f}^{c}(t)\geq f_{n,k}^{min}\)), and the tasks not satisfying the Condition 2 still to judge in the next time slot. While \(\Theta(t)\
eq\varnothing\), tasks in \(\Theta(t)\) compete for decision-making opportunity by sending requests to the edge cloud. During each time slot, only one task can get the opportunity to make the decision. Suppose the mobile device \(i\) obtain the chance, it shows to other devices that it gains the chance for decision-making by broadcasting. Then, the energy consumption of local computing and edge cloud processing of task \(u_{n,k}\) will be compared. If the energy consumption of the edge cloud computing is smaller (i.e., \(E_{n,k}^{c}<E_{n,k}^{l}\) ), the task would be offloaded to the edge cloud for processing: If not, the task would be processed locally. For mobile devices don't get the opportunity, they will continue to judge the Condition 2 in the next time slot. Algorithm 1 shows the process of getting offloading decision.
## 3 Results
### Distributions of the CCs and RICs of Different Influencing Factors
As for the southern tip of the Indian Peninsula and the Indian Ocean, the RICs of the mean zonal wind shear between 20 and 30 km (Figure 3g) are significantly larger than those of the other factors, generally reaching higher than 0.3, which demonstrates that the mean zonal wind shear between 20 and 30 km is the most important factor influencing the LS GWs in this region. Moreover, the mean meridional wind speed between 15 and 17 km (Figure 3b) and the mean zonal wind shear between 15 and 17 km (Figure 3c) also show some influences on the LS GWs in this region, with the RICs varying between 0.10 and 0.25. Over the Indian Ocean near the southwest of the Indian Peninsula, the influence of the convections (Figure 3i) on LS GWs is also non-negligible. In comparison, over the Bay of Bengal and its surrounding land area, the contribution of the mean meridional wind speed between 15 and 17 km (Figure 3b) to the LS GWs is comparatively significant, with the RICs varying between 0.10 and 0.25. It can be found that the RICs of the three factors, including the mean meridional wind speed between 20 and 30 km (Figure 3f), the mean meridional wind shear between 20 and 30 km (Figure 3h), and the mean meridional wind shear between 15 and 17 km (Figure 3d), are generally smaller than 0.2, indicating that these factors are not the dominant ones influencing the LS GWs over the whole studied area. Overall, based on the distributions of the RICs of different factors, it is clear that over the whole Indian region, the influences of the zonal wind speeds and the zonal wind shears in the two height ranges (Figure 3a,c,e,g) on the LS GWs are generally much greater than those of the meridional wind speeds and wind shears in the two height ranges (Figure 3b,d,f,h). Moreover, it can also be inferred from the RIC distributions that the LS GWs in the central and northern parts of the Indian Peninsula are more affected by the zonal wind speeds (Figure 3a,e) than by the zonal wind shears (Figure 3c,g), while the situation is opposite in the southern part of the Indian Peninsula and over the Indian Ocean, where the RICs of the zonal wind shears in the two height ranges are generally higher. In order to gain a deeper understanding of why the influences of the zonal wind-related factors on the LS GWs over the Indian region show spatial differences, two sample grid points, which are, respectively, located in the northern and the southern parts of the Indian Peninsula, are selected, and the impacts of zonal wind speeds and wind shears on the LS GWs over these two grid points are analyzed and are compared in Section 4.2.
###### Abstract  
The deployment of intrusion detection systems (IDSs) is essential for protecting network resources and infrastructure against malicious threats. Despite the wide use of various machine learning methods in IDSs, such systems often struggle to achieve optimal performance. The key challenges include the curse of dimensionality, which significantly impacts IDS efficacy, and the limited effectiveness of singular learning classifiers in handling complex, imbalanced, and multi-categorical traffic datasets. To overcome these limitations, this paper presents an innovative approach that integrates dimensionality reduction and stacking ensemble techniques. We employ the LogitBoost algorithm with XGBRegressor for feature selection, complemented by a Residual Network (ResNet) deep learning model for feature extraction. Furthermore, we introduce multi-stacking ensemble (MSE), a novel ensemble method, to enhance attack prediction capabilities. The evaluation on benchmark datasets such as CICIDS2017 and UNSW-NB15 demonstrates that our IDS surpasses current models across various performance metrics.  
intrusion detection system; LogitBoost; ResNet; machine learning; stacking ensemble Article
## 3 Results and Discussion
### Prototype Development System
#### 3.1.3 Comparison Between Optical Projector Measurements and Prototype System Measurements  
The optical projector can perform the measurement faster than the CMM; thus, we used the optical projector measurements for comparison with the prototype measurements.  
Figure 6 shows the position of the teeth, and each tooth is assigned a number. The optical projector measures the tooth height and tooth thickness of T9-T12 of the workpiece, and the average of three measurements was considered in this study and compared with the optical projector measurement results. Table 4 lists the measurement results of the tooth height; the heights, as measured using the optical projector, are 3.192, 3.183, 3.164, and 3.236 mm for T9, T10, T11, and T12, respectively. The heights, as measured using the prototype system measurement, are 3.181, 3.163, 3.151, and 3.215 mm for T9, T10, T11, and T12, respectively. The measurement error between the tooth height values measured using the prototype system and the optical projector is approximately \(-0.02\) mm.  
Table 5 lists the measurement results for tooth thickness; the thicknesses, as measured by using the optical projector, are 10.273, 10.425, 10.475, and 10.155 mm for T9, T10, T11, and T12, respectively. The thicknesses, as measured by using the prototype system, are 10.258, 10.415, 10.433, and 10.133 mm  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Tooth Height (Unit: mm)**} \\ \hline
**No. of Tooth** & **Avg.** & **Projector** & **Error** \\ \hline T9 & 3.181 & 3.192 & \(-0.011\) \\ T10 & 3.163 & 3.183 & \(-0.020\) \\ T11 & 3.151 & 3.164 & \(-0.013\) \\ T12 & 3.215 & 3.236 & \(-0.021\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of tooth-height result using optical projector.  
Figure 9: Repeatable error distribution in tooth-thickness tests.  
for T9, T10, T11, and T12, respectively. The measurement error between the tooth thickness values measured using the prototype system and the optical projector is approximately \(-0.04\) mm.  
As in the optical projector comparison result, the developed measurement system is highly precise and accurate, with errors within \(0.04\) mm.
## 2 Detection System and Data
### Guangdong-Hong Kong-Macao Lightning Location System  
The Guangdong-Hong Kong-Macao Lightning Location System (GHMLLS) has been collaboratively established by the meteorological agencies across Guangdong, Hong Kong, and Macao since 2005. Presently, the GHMLLS comprises 19 detector stations and employs a time-difference and direction-finding integrated location technique. This method is adept at gauging the precise moment of lightning events, their three-dimensional coordinates, polarity, and magnitude, and distinguishing between intra-cloud and cloud-to-ground lightning discharges, as documented by [22].  
[PERSON] et al. [23] conducted an assessment of the GHMLLS's operational performance using data collected from the Guangzhou high-rise building lightning observatory from 2016 to 2017. The findings indicated that the system achieved a detection efficiency of 93% for both first and subsequent strokes. The arithmetic mean (and median) value of the location errors was about 361 m (188 m), 252 m (167 m), and 294 m (173 m) for downward first negative strokes, downward subsequent negative strokes, and upward negative strokes, respectively.
# Metadata Quality for Biobanks
## 4 Biobank Metadata
### Motivation and Goals for Biobank Metadata  
#### 4.1.1 Metadata Motivation  
There are two main problems with the biobank data which make introducing biobank metadata necessary (we first discussed these problems in [5]):  
1. A significant percentage of data served by a biobank does not originate there (coming from external sources), so it serves as a data broker for this external data. In this capacity, biobanks use and publish the data originating from external sources, providing mediating services and descriptions if the data is incomplete. Such descriptions form a part of the biobank metadata. The main function of the biobank with respect to such external data is to provide appropriate metadata to describe it. The metadata, in this case, is in the realm of responsibility for the biobank.
2. In biobanks, the direct access to the data can be problematic due to privacy and other reasons, so that the data can be not accessible to the researchers by direct search. To solve this problem, it is necessary to establish metadata to facilitate the search. The main function of the biobank with respect to such inaccessible data is to provide an appropriate metadata which * can be searched in place of the original data to find collections or biobanks with certain characteristics; * can support further decisions on discovered artifacts (e.g., if the discovered samples and data can support an envisioned study). In our treatment, we exploit the similarity with the library management domain [5], where library search also checks only the descriptions of books without going into the books themselves.
## 4 Discussion and Analysis
### Performances of Friction Pairs with Single Texture Patterns
#### 4.2.4 Micro Morphology of Friction Pairs  
The friction pairs in the 2-B1, 2-B2, and 2-B3 groups were scanned by an electron microscope, as shown in Figure 9. Figure 9a shows that most of the surfaces of the 2-B1 group were lightly worn, but the wear in the areas corresponding to the sharp corners of the texture was more severe. This was because a large amount of wear debris was generated and gathered at the tips by the triangle geometry during the operation of the friction pair, while the rotation of the ring caused the wear debris stored in the tips to spread, thereby causing abrasive wear. Figure 9b shows that the surface wear of the 2-B2 group was more severe. The cutting edge of the elliptical texture was shorter along the direction of rotation and was prone to stress concentration, which caused damage to the surface structure and resulted in large flaking. Figure 9c shows that the surface wear of the 2-B3 group was mainly in the form of adhesive wear. Large furrows appeared on the surface material of the matrix. Due to the geometric characteristics of the spiral, the wear debris generated by the friction pair was transported from the outer diameter to the inner diameter and moved in the circumferential direction along the direction of rotation. Therefore, the wear debris was evenly distributed on the surface of the friction pairs during this process, and it did not tend to produce significant abrasive wear. Meanwhile, the evenly distributed wear debris of graphite had a certain lubricating effect. In this case, the graphite on the surfaces of the graphite rings in these three groups that was welded with silicon carbide detached from the matrix to form wear debris. The rest was in a semi-detached state. In summary, the wear of M120D was mainly composed of three processes: (1) The texture edge has a cutting effect on the surface of M120D; (2) Adhesive wear occurred on the untextured part of the surface of the friction pairs; (3) The secondary wear was caused by the debris inside the texture.  
Figure 9: Surface morphologies of specimens with triangular texture (**a**), elliptical texture (**b**), and spiral groove texture (**c**) after testing.
# Analysis of the Quantization Noise in Discrete Wavelet Transform Filters for 3D Medical Imaging
###### Abstract  
Denoising and compression of 2D and 3D images are important problems in modern medical imaging systems. Discrete wavelet transform (DWT) is used to solve them in practice. We analyze the quantization noise effect in coefficients of DWT filters for 3D medical imaging in this paper. The method for wavelet filters coefficients quantizing is proposed, which allows minimizing resources in hardware implementation by simplifying rounding operations. We develop the method for estimating the maximum error of 3D grayscale and color images DWT with various bits per color (BPC). The dependence of the peak signal-to-noise ratio (PSNR) of the images processing result on wavelet used, the effective bit-width of filters coefficients and BPC is revealed. We derive formulas for determining the minimum bit-width of wavelet filters coefficients that provide a high (PSNR \(\geq\) 40 dB for images with 8 BPC, for example) and maximum (PSNR = o dB) quality of 3D medical imaging by DWT depending on wavelet used. The experiments of 3D tomographic images processing confirmed the accuracy of theoretical analysis. All data are presented in the fixed-point format in the proposed method of 3D medical images DWT. It is making possible efficient, from the point of view of hardware and time resources, the implementation for image denoising and compression on modern devices such as field-programmable gate arrays and application-specific integrated circuits.  
Keywords:discrete wavelet transform; medical imaging; 3D image processing; quantization noise +
Footnote †: journal: Applied Mathematics and Mathematical Modeling, North-Caucasus Federal University, Stavropol 355017, Russia; [EMAIL_ADDRESS]