esa-sceva/satcom-chunk-collection huggingface dataset:
# Field Measurement Study on Dynamic Characteristics of the Shanghai World Financial Center
## 2 Structure and Monitoring System
### Modal Identification Method
#### 2.4.4 E-Rdt  
The E-RDT method [31] is a time-domain data preprocessing method for extracting the free attenuation vibration signal of the structure from its random vibration response signal of the structure. To determine the change in the building with amplitude, these steps should be performed:  
(1) Modal decomposition of the original acceleration response time equation to obtain the first-order modal acceleration response time equation \(a_{1}\) and the second-order modal acceleration response time equation \(a_{2}\).  
(2) The envelope \(A(t)\) is found for the measured response times of the structure according to the Hilbert transform [32].  
\[A(t)=\sqrt{y^{2}(t)+\left[\frac{1}{\pi}\int_{-\infty}^{+\infty}\frac{y(\tau) }{t-\tau}d\tau\right]^{2}}, \tag{2}\]  
(3) Select multiple thresholds \(m_{j},\,j=1,\,2,\,3,\dots,\,\,\,n\); use \(m_{j}\) as the trigger condition to intercept the amplitude curve \(A(t)\), find the points \(A_{j}(t_{i})\) on the amplitude curve \(A(t)\) that intersect \(m_{j}\) and the corresponding moment's \(t_{i}\), \(i=1,\,2,\,3,\dots,\,\,n\), and also find the points \(y_{j}(t_{i})\) on the response time course \(y(t)\) corresponding to these moments, \(i=1,\,2,\,3,\dots,\,\,n\);  
(4) Take the response time course of length \(\tau\) backward from each \(y_{j}(t_{i})\) as a response segment \(y_{j}(t_{i}+\tau)\). If \(y_{j}(t_{i})<0\), then \(y_{j}(t_{i}+\tau)=-y_{j}(t_{i}+\tau)\).  
(5) To find the random decrement function \(D_{j}(\tau)\), which corresponds to the amplitude \(m_{j}\), all data response segments corresponding to \(\left(y_{j}(t_{i}+\tau)\big{|}y_{j}(t_{i})=m_{j}\right)\) with initial values smaller than \(m_{j}\) corresponding to \(m_{j-1},\,\,m_{j+1}(y_{j-1}(t_{i}+\tau)\big{|}y_{j-1}(t_{i})<m_{j})\), and \(\left(y_{j+1}(t_{i}+\tau)\big{|}y_{j+1}(t_{i})<m_{j}\right)\) are also added to the summation average of \(D_{j}(\tau)\), which corresponds to the sought amplitude \(m_{j}\). Then, the stochastic decrement function is obtained as follows:  
\[\begin{array}{c}D_{j}(\tau)=\frac{1}{N_{1}}\sum\limits_{i=1}^{N}\left[\left( y_{j-1}(t_{i}+\tau)\mid y_{j-1}(t_{i})<m_{j}\right)\right]+\frac{1}{N}\sum \limits_{i=1}^{N}\left[\left(y_{j}(t_{i}+\tau)\mid A_{j}(t_{i})=m_{j}\right)+ \right.\\ \left.\frac{1}{N_{2}}\sum\limits_{i=1}^{N}\left[\left(y_{j+1}(t_{i}+\tau) \mid y_{j+1}(t_{i})<m_{j}\right)\right]\right]\end{array}, \tag{3}\]  
(6) The random decay function is fitted to the theoretical free decay curve with three splines, and the structure's self-oscillation frequency and damping ratio corresponding to the threshold \(m_{j}\) are calculated.  
(7) The amplitude correlation of the power characteristic parameters is obtained by corresponding each of the received power characteristic parameters to the threshold \(m_{j}\).
# Smart Contract Vulnerability Detection Based on Hybrid Attention Mechanism Model
## 5 Experiments and Results
### Implementation Detail  
All experiments are conducted on a computer equipped with an Intel(R) Core(TM) i7-4710 MQ [EMAIL_ADDRESS] GHz, and 32 GB RAM. We exploit Keras with Tensorflow backend to implement our approach. In the Bi-GRU part of the model, the number of layers is two, the number of hidden layer units is set to 128, and the output dimension of single attention encoder is 256. The number of heads in the multi-head attention encoder is set to 8, and the output dimension d = 512. The hybrid attention encoder generates feature vectors of 768 dimensions. The optimizer is Adam and all parameters are set to default. The learning rate is initialized to 0.002. To avoid overfitting, dropout is set for every two layers of the network and the ratio is set to 0.1. Cross-entropy loss is used as the loss function, the batch size is set to 64, and the epoch is 100.  
In order to reasonably evaluate the test results of the model, we select the evaluation metrics that are widely used in machine learning tasks, including Accuracy, Recall, Precision, and F1-score. Some of the indicators are calculated according to the confusion matrix, which is defined in Table 3.  
Based on the confusion matrix, the specific formulas of the above evaluation indicators are as follows:  
\[Precision=\frac{TP}{TP+FP} \tag{26}\]  
\[Recall=\frac{TP}{TP+FN} \tag{27}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Training Set** & **Testing Set** & **Total Samples** \\ \hline Vulnerable sample & 6281 & 1570 & 7851 \\ Non-vulnerable sample & 13,685 & 3221 & 17,106 \\ Arithmetic vulnerability & 1086 & 272 & 1358 \\ Reentrancy & 1152 & 288 & 1440 \\ Timestamp dependency & 1542 & 385 & 1927 \\ Unchecked Return Value & 1696 & 423 & 2119 \\ Tx.origin & 806 & 201 & 1007 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Smart contract samples distribution.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Actual** & **Predicted** & **1** & **0** \\ \hline
1 & True Positive(TP) & False Negative(FN) \\ \hline
0 & False Positive(FP) & True Negative(TN) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Confusion matrix.  
\[F_{1}=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{28}\]  
\[Accuracy=\frac{TP+TN}{TN+TP+FP+FN} \tag{29}\]  
where \(TP\) means that the actually vulnerable contract code is detected as \"vulnerable\"; \(FP\) indicates that the actual contract code without vulnerability is detected as \"vulnerable\"; \(FN\) shows actual vulnerabilities are detected as \"non-vulnerable\". Accuracy represents the proportion of correctly predicted data in total samples; Precision shows the proportion of correctly predicted samples in actual correct samples; Recall is the proportion of actual positive samples in predicted positive samples. The F1-score considers both the precision rate and recall rate, which can well reflect the detection effect of the model.
# Robust and Secure Data Transmission Using Artificial Intelligence Techniques in Ad-Hoc Networks
###### Abstract  
The paper presents a new security aspect for a Mobile Ad-Hoc Network (MANET)-based IoT model using the concept of artificial intelligence. The Black Hole Attack (BHA) is considered one of the most affecting threats in the MANET in which the attacker node drops the entire data traffic and hence degrades the network performance. Therefore, it necessitates the designing of an algorithm that can protect the network from the BHA node. This article introduces Ad-hoc On-Demand Distance Vector (ADOV), a new updated routing protocol that combines the advantages of the Artificial Bee Colony (ABC), Artificial Neural Network (ANN), and Support Vector Machine (SVM) techniques. The combination of the SVM with ANN is the novelty of the proposed model that helps to identify the attackers within the discovered route using the AODV routing mechanism. Here, the model is trained using ANN but the selection of training data is performed using the ABC fitness function followed by SVM. The role of ABC is to provide a better route for data transmission between the source and the destination node. The optimized route, suggested by ABC, is then passed to the SVM model along with the node's properties. Based on those properties ANN decides whether the node is a normal or an attacker node. The simulation analysis performed in MATLAB shows that the proposed work exhibits an improvement in terms of Packet Delivery Ratio (PDR), throughput, and delay. To validate the system efficiency, a comparative analysis is performed against the existing approaches such as Decision Tree and Random Forest that indicate that the utilization of the SVM with ANN is a beneficial step regarding the detection of BHA attackers in the MANET-based IoT networks.  
MANET; AODV; black hole attack (BHA); Artificial Bee Colony algorithm; artificial neural network +
Footnote †: journal: Physics Letters A
###### Abstract  
Accurate assessment of above-ground biomass (AGB) is essential for optimizing crop growth and enhancing agricultural efficiency. However, predicting above-ground biomass (AGB) presents significant challenges. Traditional point cloud networks often struggle with processing crop structures and data characteristics, hindering their ability to predict biomass accurately. To address these limitations, we propose a point-supervised contrastive deep regression method (PSCDR) and a novel network, BMP_Net (BioMixerPoint_Net). The PSCDR method leverages the benefits of deep contrastive regression while accounting for the modal differences between point cloud and 2D image data. By incorporating Chamfer distance to measure point cloud similarity, it improves the model's adaptability to point cloud features. Experimental results on the SGCBP public dataset show that PSCDR significantly reduces prediction errors compared to seven other point cloud models. Furthermore, the BMP_Net network, which integrates the novel PFMixer module and a point cloud downsampling module, effectively captures the relationship between point cloud structure, density, and biomass. The model achieved test results with RMSE, MAE, and MAPE values of 75.92, 63.19, and 0.115, respectively, outperforming PointMixer by 37.94, 30.07, and 0.079. This method provides an efficient biomass monitoring tool for precision agriculture.  
biomass prediction; point cloud; contrastive learning; deep learning Article
## 3 The Proposed Scheme
### Data Extracting Phase  
In the data extraction process, some information needs to be coordinated with the sender, such as the value of \(n\) and \(k\). Next, the stego image can be transmitted from the sender to the receiver. The receiver is able to recover the secret message using the following steps:  
```
Input: A stego image (\(I^{{}^{\prime}}\)) Output: The secret image (\(S\)) Step 1. Check the first pixel's LSB for zero or one. Step 2. The second pixel to the last pixel of the stego image are divided into non-overlapping groups of \(n\) adjacent pixels \((x_{1},x_{2},\ldots,x_{n})\); then, we compute secret value \(t\) by using Equation (3). Step 3. The decoded binary stream (RLE) will be transformed into an eight-bit gray-level value to reconstruct the secret image. Example 2: From the results of Example \(1\), the receiver receives the stego image \((155,156,156,161,\ldots)\) and \((n,k)=(3,3)\) from the sender. After the data extraction process, the secret message can be recovered, and the secret image can be reconstructed. After following these steps, we can extract the hidden secret images of the stego image. Step 1. Let the pixels of the stego image be \((155,156,156,161,\ldots)\). The first pixel \(155\) is odd, meaning the LSB of \(155\) is one. Step 2. From Step 1, we know the sequence of RLE begins at one. Step 3. All stego pixels are divided into three adjacent pixels as a non-overlapping group; then, each group will use Equation (3) to compute the value \(t\). The extracted value is \((1112131121211121322\ldots)\). Therefore, the RLE sequence is \([1(1)0(1)1(1)0(2)1(1)\)\(0(3)1(1)0(1)\ldots]\). Step 4. Decoding RLE sequence \([1(1)0(1)1(1)0(2)1(1)0(3)1(1)0(1)\ldots]\) into a binary stream \((1010010001011011\ldots)\). Transform the binary stream into an eight-bit gray-level image to reconstruct the secret image.
```  
**Algorithm 2**Data extracting.
# Stomatal and Non-Stomatal Turbulent Deposition Flux of Ozone to a Managed Peatland
## 3 Results and Discussion  
### Flux Data Quality Control  
The eddy covariance method is widely employed to analyze fluxes of energy (H, LE) and \(F_{\text{CO}_{2}}\), and respective data quality assurance protocols are well established. We applied these protocols in our data analysis as described in Section 2.3 for the computation of all fluxes including those of O\({}_{3}\). The resulting data gaps are reported in Table 1. Altogether, 12% of the time series showed data gaps from power outages at the site. Additionally, 5-13% of the data were flagged because they did not fulfill the requirements of stationarity and/or integral turbulence characteristics (QC_2). Further, 18-23% of the good-quality data were flagged because of low u* values.  
The analysis of co-spectra is an additional diagnostic procedure to evaluate the quality of flux estimates in terms of spectral characteristics of the turbulent transport and the instrumental setup. The dashed blue line in Figure 2 shows a theoretical co-spectrum model [49] of the vertical wind component with temperature, which represents H. Deviations from the modelled co-spectrum in the high-frequency range result from dampening of ambient fluctuations within the intake tube and the time lag resulting from sensor separation [53], while low- and mid-frequency deviations are rather caused by non-stationarities, trends, or by low-frequency fluctuations within the analyzed time series (e.g., [54]). High similarity of measured data with the modelled co-spectra thus indicates a good coverage of the ambient turbulence by the employed measurement setup. Figure 2 shows that the ensemble co-spectra \(w^{\prime}O^{\prime}_{3}\) are in good agreement with the low- to mid- frequency range of the modelled co-spectrum, while the high-frequency range is dampened and becomes noise dominated at normalized frequencies above 2.3. On the other hand, the co-spectra \(w^{\prime}T\) were noise dominated only at normalized frequencies above 3.0, but not dampened (Figure 2). Dampening effects of the ozone flux, as shown in Figure 2, are accounted for in each half-hour flux computation through spectral corrections as described in Section 2.3. The spectral correction factors had a median of 1.23, an inner quartile range of 0.13, and the 10 th and 90 th percentiles were 1.12 and 1.40.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **QC\_0** & **QC\_1** & **QC\_2** & **NA** & **Below u*-Threshold** \\ \hline \(F_{\text{O}_{3}}\) & 59 & 24 & 5 & 12 & 23 \\ \(F_{\text{CO}_{2}}\) & 53 & 25 & 9 & 12 & 21 \\ LE & 50 & 25 & 13 & 12 & 18 \\ H & 60 & 20 & 8 & 12 & 22 \\ \hline \end{tabular}
\end{table}
Table 1: Frequency (%) of data sets within the quality control classes QC_0, QC_1, and QC_2 for \(F_{\text{O}_{3}}\), \(F_{\text{CO}_{2}}\), LE, and H. The sum of the three classes plus the data that were not available due to power failures (NA, 12%) is 100% for each flux. The last column contains the frequency (%) of good data (QC_0 and QC_1) below the u*-threshold.
## 6 Conclusions  
This paper discusses a UAV-assisted communication scenario in an earthquake-stricken cellular cell. As the ground base station is devastated and blocked, a UAV is dispatched as a temporary aerial base station. An ISATR (iterative scheduling algorithm of trajectory and resource) is constructed to solve optimization questions to enhance the UAV's communication efficiency. A trajectory prediction model is derived via cellular automata and provides location data of ground users in evacuation for the UAV's decision. Path planning and resource allocation including bandwidth distribution and transmission power control are involved in the decision space, and the total throughput of A2G channels is considered as the objective function in optimization. With our designed multi-stage subtask iteration optimization algorithm, the total throughput is enhanced. Compared to the traditional optimization method GA and path planning method A*, our method has an advantage in higher optimization performance. Finally, we have an enhancement of approximately 40% total throughput tested in several typical environments, compared to non-optimized cases, which indicates that the proposed method can serve as an effective algorithm for pre-planning emergency UAV scheduling tasks.  
Conceptualization, Z.Z. and Y.L.; methodology, Z.Z., Y.W., and Y.L.; software and validation, Z.Z. and X.Z.; formal analysis, H.Z.; investigation, Z.Z. and W.D.; resources, Y.W.; data curation, Y.L.; writing--original draft preparation, Z.Z. and X.Z.; writing--review and editing, Y.L. and Y.W.; visualization, Z.Z. and H.Z.; supervision, W.D.; project administration, W.D.; funding acquisition, W.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (U20B2042).  
The data used to support this study have not been made available.  
The authors declare no conflicts of interest.
# A Design Method of Code Correlation Reference Waveform in GNSS Based on Least-Squares Fitting
## 3 Results
### Boc(7,1)  
Similar result are obtained with BOC(7,1) signals. The fitting parameters are listed in Table 5.  
For high-order BOC signals, the limited bandwidth and multi-peaked auto-correlation function cause a shift in the zero-crossing point of the fitted S-curve. The code correlation reference waveform diagrams of the BOC(7,1) signal for the 20.46-MHz bandwidth are shown in Figure 30. It is more difficult to find a suitable reference waveform for high-order BOC signals with a larger bandwidth, which is the opposite of the BOC(1,1) signal.  
Figure 28: Code correlation reference waveform diagram for the BOC(6,1) signal coherent discriminator (24.552-MHz bandwidth).  
Figure 29: Fitted S-curves for the BOC(6,1) signal (24.552-MHz bandwidth).  
The resulting S-curves for the 20.46-MHz bandwidth case are shown in Figure 31. Like the BOC(6,1) signal, the resulting S-curves can avoid the false lock points. However, the peak in the left side of linear region is reduced, and it is difficult to restrain the fluctuations outside the linear region.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scenario** & **Infinite Bandwidth** & **20.46 MHz** & **16.368 MHz** \\ \hline Fitting range (\(\pm\)chips) & 1 & 1 & 1 \\ Linear region (\(\pm\)chips) & 0.1 & 0.1 & 0.1 \\ Offset (chips) & 0.01 & 0.03 & 0.02 \\ Waveform range (chips) & \(-\)0.5\(\sim\)0.5 & \(-\)0.6\(\sim\)0.4 & \(-\)0.3\(\sim\)0.3 \\ Gate width (chips) & 0.01 & 0.05 & 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Simulation parameters for the BOC(7,1) signal.  
Figure 30: Code correlation reference waveform diagram for the BOC(7,1) signal coherent discriminator (20.46-MHz bandwidth).  
Figure 31: Fitted S-curves for the BOC(7,1) signal (20.46-MHz bandwidth).
## 3 Materials
### Satellite Image Pre-Processing
#### 3.3.2 MCD43A4 NBAR Product  
The MCD43A4 version 5 provides 500 m reflectance data adjusted using the bidirectional reflectance distribution function to model the values as if they were collected from a nadir view [48]. The NBAR algorithm produces 8-day composite data using 16-day reflectance data from the Aqua and Terra satellites. Validation at stage 3 has been achieved for the MODIS C5 NBAR product. All images covering the study area were downloaded from The National Aeronautics and Space Administration (NASA) ([[https://earthdata.nasa.gov](https://earthdata.nasa.gov)]([https://earthdata.nasa.gov](https://earthdata.nasa.gov))). We reprojected the MODIS images to the UTM-WGS84 projection to match the HR reflectance data.
###### Abstract  
Enhancements to the analytical method for the determination of tricyclazole in rice samples have been applied to monitor residues during unmanned aerial spraying. The acetonitrile extraction technique QuEChERS was improved by the incorporation of ethyl acetate and 0.1% formic acid, which significantly elevated the recovery rates. Furthermore, the purification process was refined by integrating both primary-secondary amine (PSA) and C18 in the dSPE method, achieving a substantial improvement in reducing matrix effects (MEs) and increasing recovery efficiency. The optimized method demonstrated an impressive % ME value at \(-\)3.1%, with a limit of quantitation (LOQ) established at 0.01 mg/kg, and recovery rates between 94.7 and 95.6% at 0.01, 0.1, and 2 mg/kg. Using two types of adjuvarants (stickers) during multi-copter spraying markedly improved the initial tricyclazole deposition on rice panicles, with residue levels initially increasing from 0.35 mg/kg to between 0.68 and 1.60 mg/kg. Residues in hulled rice at harvest (10 days post-application) remained well below the maximum residue limit (MRL) of 0.7 mg/kg, ranging from 0.02 to 0.11 mg/kg, thus affirming the safety and efficacy of adjuvarants in residue management.  
rice; LC-MS/MS; QuEChERS; tricyclazole; unmanned arial vehicle (UAV); adjuvant
# Power Profile Index: An Adjustable Metric for Load Monitoring in Road Cycling
###### Abstract
Furthermore, power profiles can be automatically updated from training software databases without the need of performing specific tests, resulting in a better description of reality (describing the current athlete status based on the actual work performed during training or competition during a desired time window), as well as time savings. A new external load index based on PO outcomes, the Training Stress Score (TSS), was also developed with power meters. This value is obtained from an estimate of Functional Threshold Power (FTP), the maximum power a cyclist can maintain at a near constant pace for approximately one hour [34]. Nonetheless, the TSS presents important limitations, one of its main problems being that it does not sufficiently weight intensity with respect to volume. Because of this, short, high-intensity exercises (e.g., time trials) score much lower than longer, very low-intensity exercises, as PO and time are directly related in a hyperbolic way [35]. Moreover, the TSS cannot be individualized nor adjusted to a particular exercise intensity zone, both being critical factors for an accurate management of the relationship between training dose and training outcome or response [32]. In addition, while FTP has been suggested as a non-invasive and practical alternative for estimating physiologically relevant events, such as maximal lactate steady state (MLSS) [36], recent investigations demonstrate a large variability between traditionally established events (e.g., ventilatory and lactate thresholds) [37; 38]. Thus, two individuals cycling at 80% of their FTP could, be exercising at dissimilar physiological levels, resulting in different intensity, adaptations, fatigue, and recovery times [39; 40]. A plausible approach to make the index adjustable could be the use of the theoretical available time to exhaustion (TTE) at different cycling physiological events, namely maximal oxygen uptake (VO\({}_{\text{2 max}}\)), respiratory compensation point (RCP), MLSS, and first ventilatory threshold (VT1) [38; 41; 42; 43; 44; 45; 46; 47]. It has also been suggested to use the hyperbolic energy production curve for different times of efforts in order to contemplate changesin energy production during the exercise session [48]. However, this proposal has not yet been incorporated into a training intensity quantification method. Besides, adding an exponential factor to weight intensity based on the stage type (e.g., high mountain, middle mountain, flat, long time trials, or prologues) could help to increase the accuracy of the estimations. This study proposes a new quantification metric, the Power Profile Index (PPi), which includes a weighting factor based on the individual PO profile, relative exercise intensity, and stage type. The study comprised two experiments with data from professional cyclists (1) to validate the weighting factors, and (2) to cross-validate the PPi against current load quantification methods.