esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Simulation Setup and Performance Metrics
### Performance Metrics  
#### 6.2.1 Throughput (bits/s)  
Throughput is the amount of successful transmissions over a communication link in a particular unit of time. Higher throughput is the requirement and characteristic of any network. In the proposed scheme, average throughput is measured across the network. A throughput variation graph using the OPNET is also presented in order to demonstrate the consistency of the proposed approach. Mathematically, throughput can be determined using the following equation:  
\[\text{Throughput}\ =\ \frac{\text{N}\ \times\ \text{S}\ \times\ 8}{\text{T}} \tag{7}\]  
where N is assumed to be the number of successful packets transferred, S is the size of the packet, and T is the time duration.
## 3 Results  
### Validation of the NPP Calculation  
Two methods were used to validate the estimated NPP. First, we compared estimated NPP with observation data, followed by corroboration with the results estimated by other models. The correlation analysis between BEPS-simulated NPP with the observed data gathered from May to September for the study period showed a moderate positive correlation (\(R^{2}\) = 0.59), and the corresponding root mean square error (RMSE) was 13.22 g-C m\({}^{-2}\)year\({}^{-1}\) for the overall dataset (Figure 3).  
In this study, the growing season total NPP over the Mongolian rangeland was determined to be 0.1713 Pg C year\({}^{-1}\) from 2003 to 2018, which is less than what was reported by [PERSON] et al. (0.3 Pg C year\({}^{-1}\)) [11] and [PERSON] and [PERSON] (0.71 Pg C year\({}^{-1}\)) [59]. They both used the CASA (Carnegie-Ames-Stanford Approach, a light-use efficiency) model to estimate NPP in Mongolia for the different periods. Such variation in NPP estimates between the current study and previous studies is due to differences in the study periods, resolution of remote sensing images, and the fundamental models employed for the studies. [PERSON] et al. [11] used NDVI products of 8 km spatial resolution for 30 years (1982-2011), while [PERSON] and [PERSON] [59] used the standard product of MOD13 QI vegetation indices with 250 m spatial and 16-day temporal resolutions from 2000 to 2004. Meanwhile, this study used LAI of 500-m spatial resolution. Moreover, the foliage clumping effect in the BEPS reduces sunit leaves and increases shaded leaves, which may be another reason.
## 2 Related Work  
### Traditional Detection Approaches  
Generally, the traditional surface defect detection mainly consists of two parts: feature extractor and classifier. This method uses hand-crafted features such as scale-invariant feature transform (SIFT) [27] and histogram of oriented gradient (HOG) [28]. Then these features are always input into a classification model, e.g., support vector machines (SVM) [29]. For instance, [PERSON] et al. [30] evaluate the performance of a number of wavelet features and used SVM to localize defects. To against noise, [PERSON] et al. [31] improve the local binary pattern (LBP) and then adopt SVM to classify hot-rolled steel strip surface defects. [PERSON] et al. [32] propose four types of statistical features, and then apply an enhanced twin support vector machine classifier to recognize steel surface defects. [PERSON] et al. [33] extract HOG feature and then introduce a random forest algorithm to perform defect classification. These traditional surface defect detection methods have achieved noticeable improvement. However, this type of extracted features suffers from drawbacks brought by hand-crafted features. For instance, these features are heavily dependent on expertise. Moreover, these methods still cannot achieve satisfactory detection accuracy and robustness.
# Construction of Three-Dimensional Semantic Maps of Unstructured Lawn Scenes Based on Deep Learning
## 3 Materials and Methods  
### Semantic Segmentation Module  
#### 3.1.1 Basic Principles of Deeplabv3+  
DeepLabv3+ [21] is a representative architecture in the field of semantic segmentation, having evolved from DeepLabv1, which was released by Google in 2014. It enhances the speed and accuracy of semantic segmentation by introducing concepts such as conditional random fields (CRFs), Atrous Spatial Pyramid Pooling (ASPP), and the encoder-decoder architecture. It mainly consists of two parts: an encoder module and a decoder module. In the encoding stage, the input image is first passed through a backbone network; some researchers use residual networks (ResNet) as backbone networks to obtain feature maps which are downsampled 16 times. Then, these downsampled feature maps are fed into the ASPP module. The ASPP module consists of a \(1\times 1\) convolution, an average pooling layer with global information, and three \(3\times 3\) dilated convolution layers with dilation rates of 6, 12, and 18, respectively. Finally, the feature maps obtained from the ASPP module are concatenated, and the channel number is compressed to 256 through a \(1\times 1\) convolution. In the decoding part, the output feature maps from the encoding part are upsampled four times using a bilinear interpolation algorithm. Then, they are concatenated with same-resolution feature maps extracted by the backbone network. Lastly, a \(3\times 3\) convolution refines the concatenated features, resulting in the segmentation output. The overall architecture of DeepLabv3+ is illustrated in Figure 1.
## 9 Performance Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scheme** & **Moduli** & **Data Size** & **Caching Time** & **Redundancy** & **Deeding Time (ms)** \\ \hline Asmuth-Bloom & & 6076 & 56 & 8.0068 & 69 \\ \hline D-RRNS & & & 8 & 1.0509 & 11 \\ \hline Asmuth-Bloom & & 23,974 & 186 & 8.0047 & 187 \\ \hline D-RRNS & & & 36 & 1.0513 & 32 \\ \hline Asmuth-Bloom & & & 740 & 8.0002 & 693 \\ \hline D-RRNS & & & 102 & 1.0492 & 114 \\ \hline Asmuth-Bloom & & & 778 & 8.0003 & 720 \\ \hline D-RRNS & & & 106 & 1.0482 & 122 \\ \hline Asmuth-Bloom & & & 829 & 8.0002 & 772 \\ \hline D-RRNS & & & 112 & 1.0047 & 125 \\ \hline Asmuth-Bloom & & & 137,016 & 1026 & 8.0001 & 944 \\ \hline D-RRNS & & & 139 & 1.04279 & 152 \\ \hline Asmuth-Bloom & & & 1092 & 8.0016 & 1011 \\ \hline D-RRNS & & & 148 & 1.04871 & 140 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._  
Figure 7: Encoding time and obtained redundancy for Asmuth-Bloom and DT-RRNS (**a**) Encoding time. (**b**) Decoding time. We see that the proposed DT-RRNS has several advantages. It increases the speed of the system. The encoding time is in the range of 1 to 180 ms, while Asmuth-Bloom is between 40 and 1100 ms. It has reduced data redundancy while maintaining the same level of security and reliability.
## 2 Fundamental Principles
### Measurement Range  
The useable measurement range of an LCI system is dependent on: the sampling rate of the signal due to [PERSON]'s theory, the sensitivity fall-off and the returned signal strength from measured regions such that the returned signal can be distinguished from the systems noise. This section looks at factors impacting on these areas.
## Materials and Methods
### Methods
#### 2.2.2 Analytical Testing
The first derivative curve was produced for the various samples tested and was used for comparisons instead of the mass loss curve, as it yields sharp distinctive peaks. Characterization of fiber-cement nanohybrids  
Flexural (three-point bending) strength tests were carried out in accordance with BS EN 12467. Mean strength values of three specimens were calculated, as well as standard deviation at day 7, 28, 56, and 90. For the paste characterization, arrest of hydration was performed following two different methodologies: oven drying and solvent exchange, as described by [PERSON] et al. [40]. For TGA/dTG, the oven drying technique was adopted. For the MIP investigation, solvent exchange was the technique employed for the arrest of hydration. Isopropanol was selected as the most appropriate solvent according to literature [41,42]. Thermogravimetric analysis (TGA) and derivative thermogravimetry (dTG) were carried out using Setaram TGA92. Each powder sample was placed in an alumina crucible and heated at a rate of 10 \({}^{\circ}\)C/min from 20 to 1000 \({}^{\circ}\)C in nitrogen atmosphere, as explained above for the characterization of nC4 at day 28, 56, and 90. X-ray diffraction (XRD) measurements were performed using a D8 ADVANCE x-ray diffractometer with CuK\(\alpha\) radiation controlled by a Dell PC. Spectra were obtained in the range \(4^{\circ}<20<60^{\circ}\). Analysis of peaks and \(d\)-spacing [according to Bragg's law (n\(\lambda\) = 2 dsin\(\theta\)) was carried out using EVA software, at day 28]. For the late age relative density measurements, BS EN 12390-7:2009 [18] was selected as a basis and the exact procedure followed is covered in literature [31]. Mean density values of three specimens were calculated, as well as standard deviation. Water impermeability test was modified to account for the much smaller specimen used in this research (slabs \(120\times 40\) mm and 10 mm thickness) and were carried out in accordance with BS EN 492:2012 at day 7, 28, and 56. A transparent tube of 250 mm length was used as water column with an internal bore of 29 mm diameter. A control water column was also adopted to ensure zero water evaporation in the laboratory testing environment. Mercury intrusion porosimetry (MIP) was executed with an Autopore III--Model unit 9420 supplied by Micromeritics, Hexton, Herts, UK. Stems of 3 mL capacity were filled with solids of the arrested hydration paste and tested at day 28.
# Analysis and Demonstration of First Cross-Support Interferometry Tracking in China Mars Mission
## 2 Theorem and Methods
### Station Clock Estimation
#### 2.3.2 Fast Clock Estimation for Correlation  
This chapter describes a fast processing method for estimating clock information. The clock model is still be expressed as:  
\[Clock(t)=\text{rate}\cdot\text{t}+\text{offset} \tag{14}\]  
Step 1. Performing correlation without clock information for one quasar scan, we then obtain complex cross spectrum of each sub integration time.  
\[\varnothing_{i}(m),\text{ m}=1,2\ldots\text{M};i=1,2\ldots N \tag{15}\]  
where M is the FFT point during correlation, and \(N\) is the total number of sub integration.  
Step 2. Both steps of rate and offset are set using Equation (16).  
\[\begin{cases}offset_{step}=\frac{1}{2T_{s}}\\ rate_{step}=\frac{1}{NRT_{s}f}\end{cases} \tag{16}\]  
where \(F_{s}\) is the bandwidth of channel, \(T_{s}\) is sub integration time, \(f\) is the down-converting frequency of the channel, and \(l\) and \(k\) are step factors.  
Step 3. Iteration compensation and statistics  
With each clock \(offset\) and \(rate\), compensation \(\varnothing_{i}(m)\) using the following formular.  
\[\varnothing_{i-cor}(m)=\varnothing_{i}(m)\cdot\exp\left\{-\beta 2\pi\bigg{[}\bigg{(}f+(m-1)\frac{F_{s}}{M}\bigg{)}(i-1)T_{s}rate+(m-1)\frac{F_ {s}}{M}offset\bigg{]}\right\} \tag{17}\]  
Figure 4: Station clock information by GPS common view technique.  
Compute the cross spectrum \(\varnothing_{int}(m)\) and evaluation vector \(\delta_{int}\) of integration time.  
\[\varnothing_{int}(m)=\frac{1}{N}\sum_{i=1}^{N}\varnothing_{i}(m),\ \mathrm{m}=1,2 \ldots\mathrm{M} \tag{18}\]  
\[\delta_{int}=FFT\{\varnothing_{int}\} \tag{19}\]  
Search the maximum value of magnitude of \(\delta_{int}\), and the location of the maximum magnitude exists.  
Step 4. After all the iterations, count the maximum magnitude \(\delta_{int}\), and the iteration clock \(offset\) and \(rate\) are the clock information estimation results.  
The algorithm flow scheme with key steps is shown in Figure 5.
# _remote sensing_
## 3 Results
### Selection of Navigation Data Records  
The accuracy of the broadcast ephemeris is directly affected by the ephemeris's age (14; 15; 37). The BDS-3 broadcast ephemeris is generated using ISL observations, and this overcomes the non-global uniform distribution shortcoming of its ground observation sites. The cross-links of the constellation are used to update the navigation message (10; 12). The ephemeris age of BDS-3 has been greatly improved compared to that of BDS-2 (17). In this study, the proportions of orbit age and clock offset age from the D1 navigation messages of BDS-3 MEO satellites shorter than 12 h were 99.95% and 99.96%, respectively. The corresponding proportions from the B-CNAV navigation messages were both 90.17%. The statistical results showed that the ages of the two navigation messages were healthy and suitable for analyses.
# ObFuzzer: Object-Oriented Hybrid Fuzzer for Binaries
## 4 Design
### Static Analysis: Identifying and Scoring Information Related to Object Operations in the Program  
In order to implement an object-oriented approach to vulnerability discovery, we propose supporting static analysis and scoring approaches. The basic blocks of the program are scored for their effect in object-oriented vulnerability discovery and prepared for the subsequent dynamic vulnerability discovery.  
How to locate, track and analyze objects in the program is a critical issue in object-oriented vulnerability discovery. The program creates and uses objects all the time during execution and it is impractical to analyze every object. Fortunately, ObFuzzer can analyze only the crash-related objects, which significantly narrows the scope of our static analysis.  
To define crash-related objects, this paper proposes potential crash points. The potential crash points are the instructions (in practice, we adjust the granularity to the basic block level) that read, write, or execute on memory. As shown in the previous examples, a large number of crashes of object-oriented vulnerabilities occur in incorrect operations on memory. After that, compared to stack memory, heap memory is less frequently and more widely used. So, heap memory's security is more difficult to guarantee. ObFuzzer focuses on operations on heap memory in the static analysis. Therefore, ObFuzzer only recognizes the potential crash points by identifying the read, write and execute operations on the object stored in the heap memory. Since we care about vulnerabilities that crash at the potential crash points, we only need to analyze the data related to the potential crash point pointer. ObFuzzer can prune the basic blocks with little relationship with the potential crash point object and focus only on the basic blocks related to the potential crash point object. In this way, ObFuzzer can shift our focus from the program execution path to the order of the object operations in the program. Thus, the traditional procedure-oriented fuzzing technique can be transformed into the object-oriented fuzzing technique.  
The static analysis module mainly completes three functions: identifying the potential crash point of the program, identifying the basic blocks related to the potential crash point's object and scoring previously identified information.
## 2 Materials and Methods
### Data Pre-Processing  
In the realm of ECG signal analysis, data pre-processing is pivotal to ensure the efficacy of subsequent models [37]. There are two primary models under consideration:  
(i) The denoising model: the ECG beats are resampled to ensure uniformity in the data. We have up-sampled the QTDB to 360 Hz, the same as NSTDB's frequency sampling. We have only experimented with the complete ECG beat, which consisted of P-waves, QRS-complexes, and T-waves. If the length of each beat is not fixed, padding is applied to standardize the length of each beat, accommodating the model's input requirements. Padding is used to ensure that all model input sizes are consistent. Both the ECG signal noise removal and the ECG signal delineation model receive input in the form of vectors with a size of 512 nodes. Typically, the number of nodes in a beat varies between 200 and 300. To establish consistency, the size of 512 nodes was chosen for the input and output layers during the model training process. The reason 512 nodes were chosen is that the lift is not smaller than the range of variations in the number of nodes in one beat. Then, the dataset is then split into training, validation, and test sets, ensuring that the model can be appropriately trained and evaluated.  
Lastly, the ground truth signals are separated from the model inputs to facilitate accurate training and validation. The ground truth for the denoising model consists of the raw QTDB signal, which serves as the clean reference signal. To generate the noisy input signal for the denoising model, the amplitudes of the QTDB and NSTDB signals are combined. The NSTDB contributes baseline wandering noise, simulating the slow fluctuations caused by patient breathing. Additionally, to mimic electrode motion artifacts, a pseudo-random number generator is employed. To inject noise into the ECG signals, we adopted the approach used by the NSTDB as described in [11]. Noise is randomly introduced with amplitudes ranging from 0.2 to 2 times the maximum peak value of the ECG signal.  
(ii) The delineation model: for the delineation process, we down-sampled the LUDB to 360 Hz, the same as the frequency sampling of the NSTDB. The delineation model shares some of these pre-processing steps, including resampling beats, padding beats, and splitting the dataset. However, a key additional step in the delineation model's pre-processing is the inference of beats from the best denoising model. This step leverages the outputs of the denoising model to enhance the accuracy and reliability of the delineation process, ensuring that the model works with the cleanest possible signals. A sample of ECG in the QTDB and LUDB for the delineation process is presented in Figure 2.
# Multi-Feature Guided Low-Light Image Enhancement
## 4 Experiment and Result Analysis  
### Experimental Details  
We first train Illumination-Net and Noise-Net, and then use Adam optimizer [28] to fine-tune the entire architecture end-to-end. For the noise estimation net, batch size is set to be 4 and patch size to be 48 \(\times\) 48, while for the illumination awareness net, batch size is set to be 8 and patch size to be 96 \(\times\) 96. In the optimizer, we use the default parameters: \(\beta_{1}=0.9\), \(\beta_{2}=0.999\), \(\alpha=0.001\), and \(\epsilon=10^{-8}\). We implemented the proposed model under the Pytorch deep learning framework. We used the LOL dataset as the training dataset to train 20 epochs on the NVidia Titan-X GPU and conduct the final test. The Low Light paired dataset (LOL) contains 500 low/normal-light image pairs taken from real scenes, and it is proposed in the work [1]. For the entire net, batch size is set to be 16 and patches to be 256 \(\times\) 256. \(\omega_{\text{a}}\), \(\omega_{\text{b}}\), and \(\omega_{\text{c}}\) are set to 10, 1, and 1, respectively. The learning rate decay strategy is adopted; after each epoch, the learning rate becomes 90% of the original. For image enhancement, we adopt standard enhancement strategies of random scaling, cropping, and horizontal rotation.
# Data Analysis of the TK-1G Sounding Rocket Installed with a Satellite Navigation System
## 4 Data Processing and Analysis
### Result Analysis
#### 4.2.2 Analysis of the Meteorological Data
Figure 9: The temperature profiles (10–60 km) obtained from the rocket sonde (red) and the SABER (blue). The temperature profiles obtained from the rocket sonde and the T799 is shown in Figure 10. In the figure, the result of rocket sonde shares coincident tendency with the result of T799, but large deviation appears in the height range of 42-50 km. Differences between the atmospheric pressure and density obtained by the rocket sonde and those collected in other ways are displayed in Figures 11 and 12. From Figure 11, it is found that the atmospheric pressure measurements differ a little bit from the reference atmosphere and SABER data, and their relative deviation are both within \(\pm\)2.4%. (deviation percentage = (rocket probe value \(-\) the value of the data used for comparison)/rocket probe value \(\times\) 100%). From Figure 12, we can see that the difference between the density measured by the rocket sonde and that of either the reference atmosphere or that gauged by SABER remains small; their relative  
Figure 11: The deviation between the atmospheric pressure that was obtained from the rocket sonde and the reference atmosphere (**left**, 10–60 km). Also, the deviation between the atmospheric pressure that was obtained from the rocket sonde and the SABER (**right**, 20–60 km). Figure 10: The temperature profiles (10–60 km) obtained from the rocket sonde (red) and the T799 (blue). deviations are both within \(\pm 4\%\). However, large disparities exist between the density measured by the rocket sonde and the density obtained from the T799; their relative deviations are within \(\pm 10\%\).
## 1 Introduction
[PERSON] [14] investigated the damping capacity of the Fe-18 Cr alloy at different annealing temperatures and found that the annealing temperature significantly affects the damping capacity. [PERSON] [15] investigated the effects of alloying elements Cu, Mo, and Ti on the damping capacity of the Fe-13 CR-2 Al-1 Si alloy, and the results show that the alloy can maintain high damping performance over a wider range of strain amplitudes after replacing 1 Si with 0.5 Cu + 0.5 Mo and 0.5 Ti + 0.5 Mo. [PERSON] [16] investigated the effect of the element Gd on the damping properties of Fe-21 Cr-4 Al alloys and found that with the addition of Gd, ferromagnetic damping and dislocation damping would exist in the region of the appropriate strain amplitude, which increases the internal friction, thus improving the damping capacity of the alloy. The studies on high damping alloys have been focused on the effects of alloy elements and heat treatment on their damping capacity, but the research on applying the damping properties of materials to the mounting structure of aero-engine lubricating oil tanks for vibration reduction is relatively few, and the research on the reduction method and damping properties is also not comprehensive enough. Figure 1: The AL-31 FN aero-engine. On this basis, this paper proposes the application of a high damping alloy in the mounting structure of an aero-engine lubricating oil tank for the vibration problem. Utilising the excellent damping performance of the high damping alloy to reduce the vibration transmission from the aero-engine casing to the lubricating oil tank, the vibration of the lubricating oil tank and the mounting structure responsible for its fixed connection is effectively suppressed, which improves the reliability, efficiency, and durability of the lubricating oil tank and the mounting structure. In this paper, the ferromagnetic high damping alloy material, Silentalloy, with the composition of Fe-12 Cr-3 Al, is selected. Based on the real parameters of the aero-engine lubricating oil tank mounting structure, the vibration reduction performance is comparatively analysed using the finite element simulation and damping test.