esa-sceva/satcom-chunk-collection huggingface dataset:
# A Spatial-Temporal-Semantic Neural Network Algorithm for Location Prediction on Moving Objects
## 4 Experiments
### Evaluation Based on External Comparison
\(epochs=20\) is used to train the data. The experiments were executed on a MacOS Sierra platform with a \(2.8\) GHz i7 CPU and \(4\) GB RAM. Evaluation based on performance. The size of time bin and the distance threshold in the courier dataset are set to \(16\) s and \(10\) m, which become \(30\) s and \(50\) m in the MTA bus dataset. The same location sequences from the STS feature extraction with \(maxLength=20\) are used as the input. After tuning the optimal parameters for each model, training data is imported and the results of the prediction of different prediction models are listed in Table 9.  
Evaluation based on the length of each sequence. The main difference between RNN and LSTM is the ability to learn long sequences. In the follow experiment, all other parameters and training data are kept the same, except for the \(maxLength\). \(maxLength\) is set from \(10\) to \(50\) and the precisions of RNN and LSTM on two datasets are shown in Figure 18. The horizontal axis represents the length of each sequence and the vertical axis is the precision of prediction. Figure 18: Figure of the comparison between RNN and LSTM of the ability to handle long sequences. **(a)** Results on the courier dataset; **(b)** Results on the MTA bus dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Datasets** & **Methods** & **Precision** & **Recall** & **F-Score** & **Training Time (s)** & **Predicting Time (s)** \\ \hline \multirow{4}{*}{**Courier**} & HMM & 0.6741 & 0.6678 & 0.6709 & 2433.40 & 12.85 \\  & XGBoost & 0.7152 & 0.7113 & 0.6897 & 221.33 & 0.33 \\  & RNN & 0.8370 & 0.8462 & 0.8380 & 61.03 & 0.59 \\  & LSTM & 0.8975 & 0.8890 & 0.8874 & 205.73 & 1.43 \\  & HMM & 0.5378 & 0.5204 & 0.5289 & 4320.42 & 20.67 \\  & XGBoost & 0.6012 & 0.5996 & 0.6004 & 450.85 & 1.07 \\  & RNN & 0.7501 & 0.7539 & 0.7520 & 135.67 & 1.21 \\  & LSTM & 0.8605 & 0.8571 & 0.8511 & 654.92 & 4.29 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The performance of different models on two datasets. Evaluation based on the size of datasets. Experiments are executed to evaluate the influence of data size on the performance of the LSTM-based model. Two datasets are used and the training data are sampled randomly by 20%, 40%, 60%, 80%, and 100% of the original size. Testing data remains the same. The number of samples are listed in Column 1, and the results are shown in Table 10.
# A New GNSS Single-Epoch Ambiguity Resolution Method Based on Triple-Frequency Signals
## 4 Experiments and Discussion
### Ambiguity Resolution of GPS/GLONASS Signals  
In this section, we fix the ambiguity of the GPS/GLONASS signals based on the BDS triple-frequency carrier phase observation with known ambiguity. To test the performance of the new method proposed in this study, we process the data using three schemes. The first one (S1) is using the new method to obtain the position of the rover; the second (S2) involves the robust estimation; and the third (S3) is the combined utilization of all instrument systems (GPS/GLONASS/BDS) with the three steps mentioned in Section 2. Before the data processing, the cycle slips were detected and repaired. In this section, we verify the ambiguity by comparing the estimated coordinate with the accurate coordinate. The ambiguity-fix rate (AFR) [33] was applied to quantify the efficiency performance of AR with the following definition:  
\[\text{AFR}=\frac{\text{Number of epochs with ambiguity fixed to integer}}{\text{Total number of epochs observed in the data sets}} \tag{36}\]  
Figures 5-7 display the deviation of the coordinate in the N, E, and U directions of the first two schemes. Figure a (Figures 5a, 6a and 7a) represents the result of least-squares estimation, and Figure b (Figures 5b, 6b and 7b) indicates the result of the robust estimation. From these figures, we can see that some epochs produce a big offset in three directions if we use the least-squares estimation directly. Some of the deviations even reach 1 m. Referring to Figure 3, which shows the resolution of BDS carrier-phase ambiguity, we find that the epochs with large coordinate deviation also have a large BDS mean square error. Thus, the wrong BDS ambiguity will result in an offset of the GPS/GLONASS AR model. Therefore, we can choose the BDS satellites with the correct ambiguity to restrict the calculation of the GPS/GLONASS ambiguity. However, it is difficult to obtain 100% correct BDS triple-frequency ambiguity due to measurement noise and other uncertain factors. To overcome these issues, we use robust estimation in the second scheme. Figure b (Figures 5b, 6b and 7b) show that the coordinate deviations in the N, E, and U directions are obviously smaller than those of Figure a (Figures 5a, 6a and 7a). Based on the robust estimation, the weight of the observation with outliers or mis-fixing ambiguities is reduced significantly and relatively high precise coordinates are obtained. Table 1 describes the distribution of coordinate deviation; 100% coordinate deviations of the second  
Figure 4: Variation of the absolute value of mean square error.
## 2 Results and Discussion  
### RNA-Seq Output, Sequence Assembly and Gene Annotation  
#### 2.1.1 Transcriptome Sequencing Output and Sequence Assembly  
Next-generation sequencing was performed on RNA extracted from the _I. asprella_ root and provided 55,028,452 high-quality (HQ) reads out from 58,670,910 raw reads (a yield of 93.79%). The Q20 and GC percentages were 98.08% and 46.34%, respectively. _De novo_ assembly of these HQ reads  
Figure 2: Terpenoid backbone biosynthetic pathway.  
produced 110,049 contigs of 36,036,333 nucleotides (nt) and the average length of these contigs was 327 nt, with an N50 of 540 nt. Further assembly of these contigs generated 51,865 unigenes; and the mean length and N50 of the unigenes were 685 and 1028 nt, respectively. Furthermore, the 51,865 unigenes could be grouped into 16,517 distinct clusters and 35,348 distinct singletons, using homologous transcription cluster analysis. The distribution of contigs and unigenes is shown in Figure S1.
# Differentially Private Clustered Federated Load Prediction Based on the Louvain Algorithm
## 5 System Architecture and Design Scheme
### Privacy Analysis  
The introduction of differential privacy aims to address differential attacks and prevent honest but curious servers or participants from inferring and stealing sensitive private information of clients based on shared data.  
**Theorem 1**: _In Algorithm 1, for the local dataset \(D_{i}\) of client \(i\) participating in training, a small batch sample of size \(\lambda\) is randomly selected without replacement. Assuming that client \(i\) participates in training and uploads updates \(T_{i}\) times during \(T\) rounds of learning, then for client \(i\), Algorithm 1 satisfies the following condition \((\varepsilon_{i},\delta)\)-DP, where_  
\[\varepsilon_{i}=\frac{2T_{i}L}{qr\lambda\sigma^{2}}+2\sqrt{\frac{2T_{i}L}{qr \lambda\sigma^{2}}\log\frac{1}{\delta}} \tag{16}\]  
**Proof.** For client \(i\), assuming that two adjacent datasets \(X_{i}  
Therefore, \(\Delta_{2}(\tilde{g}(y_{i}^{t,L}))\leq\frac{2C_{i}^{t}}{\lambda}\). Noting that, in the absence of added noise,  
\[\begin{split} y_{i}^{t,L}&=y_{i}^{t,L-1}-\eta_{l} \tilde{g}(y_{i}^{t,L-1})\\ &=y_{i}^{t,L-2}-\eta_{l}\tilde{g}(y_{i}^{t,L-2})-\eta_{l}\tilde{ g}(y_{i}^{t,L-1})\\ &=\ldots\\ &=y_{i}^{t,0}-\eta_{l}\tilde{g}(y_{i}^{t,0})-\eta_{l}\tilde{g}(y_ {i}^{t,1})-\cdots-\eta_{l}\tilde{g}(y_{i}^{t,L-1})\end{split} \tag{18}\]  
By the definition of sensitivity and Equations (17) and (18), we have  
\[\begin{split}\Delta(y_{i}^{t,K})&=\eta_{l}\Big{\|} \tilde{g}(y_{i}^{t,0})(X_{i}^{t,0})-\tilde{g}(y_{i}^{t,0})(X_{i}^{t,0\prime})+ \tilde{g}(y_{i}^{1})(X_{i}^{t,1})-\tilde{g}(y_{i}^{1})(X_{i}^{t,1\prime})+ \cdots\\ &+\tilde{g}(y_{i}^{t,L-1})(X_{i}^{t,L-1})-\tilde{g}(y_{i}^{t,L-1} )(X_{i}^{t,L-1\prime})\Big{\|}\\ &\leq\frac{2\eta_{l}LC_{i}^{t}}{\lambda}\end{split} \tag{19}\]  
From Equation (17) and Lemma 1, it is known that in Algorithm 1, each participating client satisfies \(\frac{2}{\lambda^{2}\sigma^{2}}\)-zCDP during a single local iteration. During one round of local iteration, the total number of accesses to the local dataset by the participating client is \(\frac{L\lambda}{q}\). According to Lemma 2 and Lemma 3, the participating client satisfies \(\frac{2L}{q\lambda\sigma^{2}}\)-zCDP during one round of local iteration. Combining Equation (19) and Lemma 1, Algorithm 1 satisfies \(\frac{2L}{q\lambda\sigma^{2}}\)-zCDP in the \(t\)-th round of training. If client \(i\) participates in training and uploads updates \(T_{i}\) times during \(T\) rounds of learning, then client \(i\) achieves \(\frac{2T_{i}L}{q\lambda\sigma^{2}}\)-zCDP throughout the learning process. By Lemma 4, the theorem is proved.
# Investigation of Nanogrids for Improved Navy Installation Energy Resilience
## 3 Methodology
### Step 3: Analyze Threats to Base Energy Security
#### 3.3.2 Step 3.2: Determine Percent Occurrence of Threat per Year  
After a final set of threats are determined, the probability of occurrence of these events initiating within any given year must be determined. In this methodology, probability of occurrence is the likelihood over a given time frame (often one year) that a threat will be realized. While natural disasters such as fires and floods have well-established probabilities of occurrence in the literature, it can be challenging to ascertain the probability of occurrence of human-caused threats such as insider threats or terrorist attacks [51].  
While it maybe desirable for some military installations to examine rare threats, we recommend that a threat only be carried forward for further evaluation if the probability of occurrence on a yearly basis is greater than two percent (\(Pr(S=s)\geq~{}0.02\)). If the potential threat is below this threshold, then we suggest it can be disregarded based on our professional experience. Existing sources of data and quantification process for probability of occurrence for failure scenarios are challenging due to to information available for military installations. The base energy manager's responsibility is to postulate probability of occurrence for specific threats on the location of their base [22]. A variety of useful resources to develop probability of occurrence of threats exists [52; 53]. It is important to note the limitations on historical data due to climate change. Increasing climate changed has caused considerable changes in weather patterns and poses risk to military installations. For instance, in 2019 the Pentagon reported that a total of seventy-nine military installations were at risk of flooding due to rising sea levels induced by climate change. Norfolk, one of the Navy's largest bases, is seeing the effects already. The main road to Norfolk Naval Station floods a few times a month with predictions that it will continue to flood more frequently up to two hours per day in the upcoming years [54; 55].
## 3 Data Analysis of Similarity Calculation Method for Infield and Outfield Environmental Tests
### Data Analysis and Fitting of Environmental Tests
\begin{table}
\begin{tabular}{c c c c c} \hline
**Coefficients** & \(\mathbf{p1}\) & \(\mathbf{p2}\) & \(\mathbf{p3}\) & \(\mathbf{p4}\) \\ \hline Outfield springback & \(-0.0001176\) & \(0.005580\) & \(0.06518\) & \(40.92\) \\ \hline Outfield elongation & \(-2.011\times 10^{-7}\) & \(-6.544\times 10^{-5}\) & \(9.171\times 10^{-5}\) & \(0.9945\) \\ \hline Infield springback & \(-0.0002816\) & \(-0.003121\) & \(-0.1933\) & \(42.79\) \\ \hline Infield elongation & \(1.094\times 10^{-8}\) & \(-4.374\times 10^{-5}\) & \(0.002189\) & \(0.979\) \\ \hline \end{tabular}
\end{table} 
Table 1: Polynomial fitting coefficient. \begin{table}
\begin{tabular}{c c c c} \hline
**Coefficients** & \(\mathbf{a}\) & \(\mathbf{b}\) & \(\mathbf{c}\) \\ \hline Outfield springback & \(2.461\) & \(0.05436\) & \(38.28\) \\ \hline Outfield elongation & \(-0.02057\) & \(0.0435\) & \(1.016\) \\ \hline Infield springback & \(2.562\) & \(0.05555\) & \(39.90\) \\ \hline Infield elongation & \(-0.09687\) & \(0.02415\) & \(1.075\) \\ \hline \end{tabular}
\end{table} 
Table 2: Exponential fitting coefficient. \begin{table}
\begin{tabular}{c c c c c} \hline
**Coefficients** & \(\mathbf{a0}\) & \(\mathbf{a1}\) & \(\mathbf{b1}\) & \(\mathbf{w}\) \\ \hline Outfield springback & \(1.672\times 10^{9}\) & \(-1.672\times 10^{9}\) & \(6494\) & \(3.861\times 10^{-6}\) \\ \hline Outfield elongation & \(-0.7670\) & \(0.2274\) & \(0.004408\) & \(0.02331\) \\ \hline Infield springback & \(3.451\times 10^{8}\) & \(-3.451\times 10^{8}\) & \(-1.204\times 10^{4}\) & \(7.918\times 10^{-6}\) \\ \hline Infield elongation & \(-2.538\times 10^{5}\) & \(2.538\times 10^{5}\) & \(-117.0\) & \(-1.868\times 10^{-5}\) \\ \hline \end{tabular}
\end{table} 
Table 3: Fourier fitting coefficient. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Fitting Type** & **A Springback** & **B Springback** & **A Elongation** & **B Elongation** \\ \hline Polynomial fitting & & & & \\ outfield \(R^{2}\) & 0.9497 & 0.9823 & 0.9255 & 0.9348 \\ \hline Polynomial fitting & & & & \\ infield \(R^{2}\) & 0.9965 & 0.988 & 0.9823 & 0.9669 \\ \hline Exponential fitting & & & & \\ outfield \(R^{2}\) & 0.9471 & 0.9840 & 0.9157 & 0.9331 \\ \hline Exponential fitting & & & & \\ infield \(R^{2}\) & 0.9956 & 0.9889 & 0.9782 & 0.9667 \\ \hline Fourier fitting & & & & \\ outfield \(R^{2}\) & 0.9533 & 0.9855 & 0.928 & 0.9418 \\ \hline Fourier fitting & & & & \\ infield \(R^{2}\) & 0.9966 & 0.9944 & 0.9823 & 0.9684 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The goodness of fit. Figure 8: \(Cont.\)  
Figure 7: The goodness of fit. It can be seen from Table 4 and Figure 7 that all the methods are almost equivalent, with the exception of the elongation outfield test for material A, where the exponential fitting is the worst.
## 4 Methodology
### Temperature-Sensitive Variable Selection
#### 4.1.3 KR-BPNN Method  
Considering the range of \(K\) values, different \(K\) values will produce different clusters, marked as \(C_{ik}\) (where \(i\) is the serial number of the heat source area). The correlation degree between the temperature variables and thermal error is calculated using the Pearson correlation coefficient, based on which the absolute mean correlation coefficient \(\overline{R_{i}}\) can be obtained, according to Equation (4). Based on \(C_{ik}\), the temperature variables with the largest \(\overline{R_{i}}\) in each cluster can be found. They are combined as a set of temperature-sensitive variables in the present heat source area, marked as \(T_{ik}\). On this basis, the temperature-sensitive variables set \(T_{ik}\) is taken as the input of the BP neural network, and the thermal error term is the output.  
In order to judge which set of temperature-sensitive variables is most appropriate for the prediction of thermal errors, three metrics, mean absolute error (_MAE_), mean square error (_MSE_) and root mean square error (_RMSE_), are selected. The definitions of the metrics are shown in Equation (5).  
\[\left\{\begin{array}{l}MAE=\frac{1}{n}\sum\limits_{i=1}^{n}{|y^{\prime}_{i}- y_{i}|}\\ MSE=\frac{1}{n}\sum\limits_{i=1}^{n}{(y^{\prime}_{i}-y_{i})^{2}}\\ RMSE=\sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}{(y^{\prime}_{i}-y_{i})^{2}}} \end{array}\right. \tag{5}\]  
where, \(y^{\prime}_{i}\) is the predicted value, \(y_{i}\) is the actual value, and \(n\) is the size of sample.  
As a result, the set of temperature-sensitive variables with smaller _MAE_, _MSE_, and _RMSE_ is appropriate for the prediction of thermal errors.
## 3 U-Net Model and Network Structure Improvement
### Improved U-Net Model Structure  
The improved U-Net model framework is shown in Figure 5, where the CBAM attention mechanism module is added before the jumps are connected to the decoder, and the original convolution kernel is replaced by a depth-separable convolution module so that the model pays attention to the important detail information in the image, especially the texture, shape, and boundaries of the crop diseases, and the low-level and high-level semantic information captured in the encoder portion of the U-Net model is better fused with the features in the decoder so that more original feature information can be retained after upsampling and ultimately the accurate and reliable segmentation of the melting pool contour boundaries is realized. The low-level and high-level semantic information captured by the encoder part of the U-Net model can be better fused with the features in the decoder so that the upsampled image can retain more original feature information and ultimately realize the accurate and reliable segmentation of the contour boundary of the crop diseases.  
To enhance the performance of U-Net for crop pest recognition, the network was modified by integrating the Convolutional Block Attention Module (CBAM) into both the encoder and decoder paths. The CBAM introduces a dual attention mechanism that adaptively emphasizes relevant features while suppressing irrelevant ones. This modification helps the network better distinguish between pest-infected regions and healthy plant tissues, improving the accuracy of segmentation results.  
Figure 4: Structure of CBAM.
## 10 Comparative Analysis of Propagation Models
### Comparison of Propagation Models on 3.6 GHz--ITU-R P.1546-6, PEM, ITU-R P.452-17
However, in both cases, the significant relative errors must be taken into account. In flat terrain, the hybrid model is particularly recommended, as it yields highly accurate results while requiring substantially less computational effort. The differences in the test point results are illustrated in Figures 32 and 33. The grouped standard deviations of terrain elevation and field strength levels, as well as the Pearson correlation coefficients, are presented in Table 12.  
The results indicate a strong positive correlation between the standard deviation of terrain elevation and that of field strength levels for both the deterministic PEM and the hybrid ITU-R P.452-17 models. Based on these findings, it can be stated that both models exhibit good terrain sensitivity. In contrast, the empirical ITU-R P.1546-6 model shows a weaker and negative correlation. This implies that higher variability in terrain elevation is associated with lower variability in field strength levels. This outcome is primarily due to the field strength levels calculated for TestBS_3, where a substantial terrain elevation standard deviation (139.91 m and 213.65 m) corresponds to a significantly lower field  
Figure 33: Differences in field strength levels at the test points (PEM – ITU-R P.1546-6, 3.6 GHz). Figure 32: Differences in field strength levels at the test points (PEM – ITU-R P.452-17, 3.6 GHz). strength standard deviation of 7.44 dB and 10.84 dB, respectively. These findings once again highlight the limitations and shortcomings of the empirical model in accurately capturing variations resulting from terrain profile fluctuations.
## 5 Uniaxial and Biaxial Compression Tests
### Damage Evolution  
Figures 8-11 show the failure processes of rectangular specimens under different confining pressures. The figures show the five steps of damage: primary damage, secondary damage, pre-peak damage, post-peak damage, and final damage.  
Figure 7: Finite element model of the rock specimen in compression tests, (**a**) model geometry; (**b**) mesh grids and initial elastic modulus.  
Figure 8: Failure process of rock specimen under axial compression, \(p_{\rm c}=0\) MPa, (**a**) damage variable; (**b**) damage mode.  
Figure 9: Failure process of rock specimen under biaxial compression, \(p_{\rm c}=10\) MPa, (**a**) damage variable; (**b**) damage mode.  
The mesoscale failure process can be analyzed through evolutionary diagrams of the damage variable, in which red represents the damaged elements. It can be observed that the loading step of the primary damage varied with different confining pressures. For low confining pressures, such as 0 MPa and 10 MPa, the primary damage occurred after a certain number of loading steps when the stresses of some elements exceeded their strength owing to an increase in the axial load. For confining pressures greater than or equal to  
Figure 11: Failure process of rock specimen under biaxial compression, \(p_{\mathrm{c}}=40\) MPa, (**a**) damage variable; (**b**) damage mode.  
Figure 10: Failure process of rock specimen under biaxial compression, \(p_{\mathrm{c}}=20\) MPa, (**a**) damage variable; (**b**) damage mode.  
20 MPa, primary damage occurred in the first step when the axial load was not applied, and the damage was only caused by the confining pressure. The greater the confining pressure, the greater the number of primary damaged elements. The confining pressure also affected the loading step of the secondary damage. The greater the confining pressure, the later the secondary damage occurred. Owing to the randomness of the material properties of the specimens, the early damaged elements were arbitrarily scattered. In general, the damage developed slowly and accumulated insignificantly before the peak load. The damage spread rapidly and accumulated significantly once the peak load was attained, finally forming a macrocrack penetrating the rock specimen. Subsequently, the damage stopped because the specimen deformation mainly occurred in the completely damaged area. Moreover, the damage became more concentrated as the confining pressure increased.  
The failure mechanism can be analyzed from the evolutionary diagrams of the damage mode. For low confining pressures, early damage before the peak load was dominated by compression-shear damage with minimal pure tensile damage and sporadic tensile-shear damage. Late damage after the peak load was dominated by pure tensile damage. With an increase in the confining pressures, the proportion of compression-shear damage in early damage increased gradually. Moreover, late damage also gradually changed to compression-shear-dominated damage.
## 2 Theory and Model
### Fast Calculation of Far-Field Intensity Distribution with PSF Convolution
\begin{array}{c}OTF_{L}(\lambda zf_{x},\lambda zf_{y})\\ =\left\langle\exp,\left(i\psi_{jitter}(x,y)\right),\exp\left(-i\psi_{jitter}(x- \lambda zf_{x},y-\lambda zf_{y})\right)\right\rangle\\ \times\left\langle\exp(i\psi_{turbulence}(x,y))\exp\left(-i\psi_{turbulence }(x-\lambda zf_{x},y-\lambda zf_{y})\right)\right\rangle\\ \times\frac{\int\int_{-\infty}^{\infty}p(x,y)p(x-\lambda zf_{x},y-\lambda zf_{ y})exp\left(i\psi_{p+TB}(x,y)\right)exp\left(-i\psi_{p+TB}(x-\lambda zf_{x},y- \lambda zf_{y})\right)dxdy}{\int\int_{-\infty}^{\infty}p^{2}(x,y)dxdy}\\ =OTF_{jitter}(\lambda zf_{x},\lambda zf_{y})OTF_{turbulence}(\lambda zf_{x}, \lambda zf_{y})OTF_{p+TB}(\lambda zf_{x},\lambda zf_{y})\end{array}  \tag{5}\]  
where \(OTF_{jitter}\), \(OTF_{turbulence}\), and \(OTF_{p+TB}\) are, respectively, the \(OTF\) for jitter effect, turbulence effect, and thermal blooming effect with aberrations and finite aperture effect. In the actual calculation process, it is convenient to use the inverse Fourier transform of \(OTF\), that is, to calculate the \(PSF\) in the spatial domain. In the spatial domain, the system \(PSF\) can be expressed in the form of convolution:  
\[PSF_{\text{FC}}(x,y)=PSF_{jitter}(x,y)\otimes PSF_{turbulence}(x,y)\otimes PSF _{p+TB}(x,y) \tag{6}\]  
where \(PSF_{FC}\) is the system \(PSF\) of the fast calculation model with \(PSF\) convolution, \(PSF_{jitter}\), \(PSF_{turbulence}\), and \(PSF_{p+TB}\) is, respectively, the \(PSF\) for jitter effect, turbulence effect, and thermal blooming effect with aberrations and finite aperture effect, and \(\otimes\) represents convolution operator.