esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
The ability to accurately detect small targets in all ommatidium images greatly benefits the further application of compound eye cameras.  
Upon obtaining the pixel coordinates of all detected targets, the motion trajectory of the targets can be determined by integrating the geometric structural features of the compound eye camera and the results of camera calibration. As illustrated in Figure 13, we simulated the structure of 19 ommatidia within our compound eye camera and calculated the spatial coordinates of a target using the pixel coordinates of an ideal imaging result of a point in space. The error of the calculation results is shown in Figure 13b. The method employed involves establishing imaging equations using the pixel coordinates from a varying number of ommatidia and deriving the optimal result through the least squares method. The calculation results indicate that detecting all targets in a single-frame compound eye image and utilizing more target information can significantly reduce computational errors. Therefore, our method ensures a lower false alarm rate and a higher detection rate, which greatly aids in further obtaining the spatial information of the targets.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Method** & **4D\_TT** & **4D\_TR** & **ADMD** & **ASTV\_NTLA** & **ICEW** & **IPI** & **RCTV** & **RCTW** & **MPCM** & **NFTDGSTV** & **Top\_hat** & **OURS** \\ \hline Times & 0.6041 & 1.2686 & **0.0437** & 2.4624 & 1.4240 & 1.1535 & **0.1085** & **0.1187** & **0.1787** & 3.3907 & **0.0820** & **0.1821** \\ AUC & 0.9962 & 0.9925 & 0.9479 & 0.9473 & 0.9986 & 0.8776 & 0.9948 & 0.9886 & 0.9134 & 0.9617 & 0.9861 & **0.9999** \\ \hline \hline \end{tabular}
\end{table}
Table 4: The runtime for single-frame image detection and the corresponding detection results for each algorithm. The boldfaced values denote the best-performing results.  
Figure 12: The four rows of data in the figure correspond to (**a**) the image with 30 dB Gaussian white noise added and its detection results; (**b**) the image with 40 dB Gaussian white noise added and its detection results.  
Although the proposed method is evaluated using data from a custom-built mid-wave infrared (MWIR) compound eye camera, the underlying detection framework is not tightly coupled to any specific hardware configuration or waveband. The method is based on general assumptions about the data, such as the sparsity of target signals, the low-rank nature of the background, and the spatial redundancy among ommatidia--features that are generally applicable to infrared imaging in both MWIR and long-wave infrared (LWIR) systems.  
Therefore, we expect the method to generalize well to other compound eye platforms with different microlens arrangements or operating wavelengths. Further validation on diverse datasets and hardware will be part of our future work to enhance the robustness and applicability of the proposed approach.
## 5 Results and Discussion
### Impact of PNC Receiver Design
Performances below 10% EVM can be guaranteed for reduced filter bandwidth values of 2.5 and 20 MHz, respectively. The accompanying dotted lines in the figure are the predicted performance curves given by expression (7). The figure shows that this conservative estimate for the achievable system SNR exhibits good matching with the simulated system which also incorporates the intensity noise processes (RIN, shot noise, thermal noise), which are inherent to optical heterodyne implementations. The theoretical and simulated curves diverge as the system performance plots begin to reach the performance floor (evident due to the inclusion of intensity noise). Nevertheless, the results show that, with prior knowledge of the optical source linewidths (which would typically be known), the expression given in (7) can be used to indicate the minimum required carrier filter bandwidth for a target EVM, for the system described. Using the expression given by (6), this analysis can be extended to the system electrical SNR. Figure 6b shows how the received SNR varies with carrier filter bandwidth--for the same linewidth conditions given above. The dotted lines in the figure represent the predicted variance in SNR given by the expression (5). As expected, the prediction shows good alignment with the simulated results for lower filter bandwidth values. For example, given two optical sources with 100 kHz linewidth each and a target SNR of 20 dB, the predicted SNR curve (cyan dotted line) indicates that a minimum carrier filter 3-dB bandwidth of 2 MHz is required. This is matched well by the corresponding simulated curve which, for higher filter bandwidths, converges to the maximum achievable electrical SNR value for the given system conditions. In general, for the cases presented, the theoretical plots indicate that for a target electrical SNR value of \(\geq\)100 (equating to \(\leq\)10% EVM), the mm-wave carrier filter must have a 3-dB bandwidth which exceeds the combined optical linewidth value by a factor of at least 100. Figure 6c shows spectral profiles of the 60 GHz heterodyne generated carrier before and after filtering by the PNC receiver's carrier filter. The profile of this mm-wave carrier filter is also shown to be superimposed on the figure. In this case, \(F_{3 dB}\) was set to 50 MHz and \(LW_{opt}\) was set to 10 kHz--corresponding to the system performance point denoted with a star in Figure 6a.
## 2 Principle of Operation
### Xor Gate  
The XOR gate configuration, as depicted in Figure 2[8; 18], utilizing CR-SOAs-MZI, operates by launching two input data streams, denoted as A and B, from ports 1 and 2 of the CR-SOA-MZII, respectively, via wavelength selective couplers (WSCs). Simultaneously, a continuous wave (CW) beam is introduced into port 3, where a 3-dB optical coupler (OC) evenly divides it into two identical copies. Within the CR-SOAs, the alteration of the gain and phase of the CW beam constituents is induced by data A and B through cross-gain and cross-phase nonlinear effects. Cross-gain modulation, a significant nonlinear effect, results in the modulation of the amplifier gain due to the presence of optical signals. In the context of CR-SOAs, as input signals A and B propagate through the amplifier, their intensities influence the gain experienced by the CW beam. This modulation of gain  
Figure 1: Schematic diagrams of the states and carrier transitions of both (**a**) CR-SOA [7; 8; 9] and (**b**) QDs [19; 20].  
affects the amplification characteristics of the CW beam, crucial for subsequent interference processes. Similarly, cross-phase modulation, another pivotal nonlinear effect, involves the modulation of the phase of the CW beam induced by the input data streams. As A and B propagate through the CR-SOAs, they induce changes in the phase of the CW beam, altering its phase characteristics. This modulation is essential for controlling the interference pattern at the output port of the MZI, ultimately determining the logic output of the XOR gate. When both A and B share the same logic states ('0' or '1'), they induce consistent changes in the gain and phase of the CW beam. This leads to destructive interference at port 4 of the MZI, resulting in a '0' output. Conversely, when A and B have different logic states, distinct modulation occurs, leading to constructive interference at port 4 and providing a '1' output. Subsequently, an optical bandpass filter (OBPF) is applied to eliminate any undesirable wavelengths, ensuring that the XOR output of the CR-SOAs-MZI aligns with the attached XOR truth table. To further clarify, the consistent change leading to destructive interference is due to the inherent properties of the MZI structure and the phase shifts induced by the input signals. Specifically, when both input signals A and B are '0' or '1', they induce the same phase shift in the CW beam components within the MZI arms, causing them to interfere destructively at the output port. This is further ensured by the additional \(\pi\) phase shift introduced in the MZI structure, which aligns the phase changes to produce the desired interference pattern.
## 4 Results and Discussion
### Spatial Distribution
Figure 8 shows other comparison examples between the HJ-1B/IRS and MODIS derived LST in the growing season: (a) 16 May 2012 (_in situ_ data were not available on this day in the former discussion); (b) 19 June 2012; and (c) 2 September 2012. The result with the obvious color changes indicates that the LST greatly changes during the growing season. Figure 7: _Cont._  
On 16 May 2012, the plants were seeded in the Zhangye oasis (in the solid square) when the landscape was covered by sparse vegetation. Therefore, the LST image on this day is not as smooth as that of the fully covered vegetation days, such as 19 June 2012 and 2 September 2012. However, the difference between the results from two sensors is not as obvious. Furthermore, the large difference shown in Figure 8b may be heavily affected by the cloud on the retrieval images from two sensors. In the solid square, the view zenith angles (VZAs) of HJ-1B/IRS are all approximately 30 degrees. Meanwhile, the VZAs of MODIS vary from 10 degrees to 50 degrees in the same area. The VZA comparisons are shown in Table 4. Apparently, the minimum VZA differences between MODIS and HJ-1B/IRS occurred on 22 August 2012, in which the LST differences (Figure 7c) of MODIS and HJ-1B/IRS are also smaller than those on other days (Figure 8). [PERSON] _et al._[2] demonstrated that different  
Figure 7: Input parameters for the HJ-1B/IRS LST retrieval at a 300 m resolution in the UTM projection (zone number 47N) for the study area; the location of the HiWATER Matrix is indicated by the white solid square on 22 August 2012. **(a)** HJ-1B/IRS TOA radiance; **(b)** estimated LSE; **(c)** MOD05 WVC data; **(d)** DEM obtained from Shuttle Radar Topography Mission (SRTM) and **(e)** MCD12Q1 land cover data. VZAs between different sources of measurements may result in different land surface emissivity values, as well as different LST retrieval results. In this study, although the VZA differences between HJ-1B and MODIS are expected to be small for fully covered croplands [17,33], the differences are still an important source for bias in the LST retrievals and impede the validation between different sensors. Additionally, low spectral information impedes accurate atmospheric correction of satellite data because single-channel sensors observe much less information on atmospheric conditions than multi-channel sensors. The proposed single-channel method in this paper is mainly suitable in semi-arid regions with low atmospheric WVC. Figure 8: _Cont._
# Traffic Flow Prediction Method Based on Seasonal Characteristics and SARIMA-NAR Model
## 5 Conclusions  
In this paper, the time series of traffic flow of an expressway in southwest China was taken as the research object to establish two combination models using the linear and nonlinear component combination method and the MSE weight combination method. Through comparative analysis, the results show the following:  
* The SARIMA-NAR combination model 1 is superior to the SARIMA, NAR, and commonly used non-combination models, such as grey prediction and exponential smoothing models, have fewer errors than the SARIMA-NAR combination model 2. In the SARIMA-NAR combination model 2 using the MSE weight combination method, the predicted MAPE is smaller than other common non-combination models. However, it has a mediocre performance in evaluating other indicators.
* The time series of traffic flow has obvious seasonal characteristics. The SARIMA-NAR combination model has low requirements for data information and fewer restriction. In addition, the prediction results are highly effective and accurate, and the prediction process is more convenient and efficient.
* The SARIMA-NAR combination model can adapt to the fluctuations in traffic flow under the influence of the epidemic, and the model prediction has a high accuracy rate, especially the SARIMA-NAR combination model 1.
* The SARIMA-NAR combination model 1 and combination model 2 have different prediction characteristics. Further, based on the improved weight combination method, the advantages of these two combination models can be combined to realize the fusion prediction of the linear and nonlinear component combination method and the MSE weight combination method.  
Conceptualization, Y.W.; data curation, R.J.; formal analysis, R.J. and F.D.; Funding acquisition, Y.W.; Investigation, F.D. and Y.Y.; Methodology, R.J.; Writing--original draft, R.J.; Writing--review & editing, Y.Y. All authors have read and agreed to the published version of the manuscript.  
The research was funded by the National Natural Science Foundation of China (Grant No. 51778633, 51308552).  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare that there are no conflict of interest regarding the publication of this paper.
## 3 Chemical Physical Properties of \(a\),_o-Dialkovyperfluoropolyethers
### Boiling Point  
The boiling points (_b.p._) of DM-FPEs are reported in Figure 10 as a function of the number of backbone chain atoms (\(N\)), _i.e._, carbon plus oxygen atoms. They increase regularly with \(N\). In Figure 10, DM-FPEs boiling points are also compared with those of \(a\),_o-dihydroroethers (HFEs) [31] and perfluoropolyethers (PFPEs) [32].  
\begin{table}
\begin{tabular}{c l c c} \hline \hline
**Sample** & **Structure** & **MS** & **Purity (\%, by GC)** \\ \hline DM01 & CH\({}_{3}\)OCF\({}_{2}\)CF\({}_{2}\)OCH\({}_{3}\) & 162 & 99.8 \\ DM02 & CH\({}_{3}\)OCF\({}_{2}\)CF\({}_{2}\)OFF\({}_{2}\)CF\({}_{2}\)OCH\({}_{3}\) & 278 & 99.9 \\ DM03 & CH\({}_{3}\)OCF\({}_{2}\)CF\({}_{2}\)OCF\({}_{2}\)CF\({}_{2}\)OCF\({}_{2}\)OCH\({}_{3}\) & 394 & 99.0 \\ DM13 & CH\({}_{3}\)OCF\({}_{2}\)CF\({}_{2}\)OCF\({}_{2}\)OCF\({}_{2}\)OCF\({}_{2}\)CF\({}_{2}\)OCH\({}_{3}\) & 460 & 96.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Structure, molecular weight (MW) and purity of DM-FPEs samples.  
Figure 9: Reaction mechanism for the synthesis of DM-FPEs. For clarity, here using R\({}_{\rm F}\)-C(O)F to represent DAF-PFPEs.  
The highest boiling points are those of DM-FPEs, the intermediates are HFEs and the lowest ones are PFPEs. The above-mentioned result reflects the typical low intermolecular interactions of perfluorinated compounds. Partially fluorinated ethers, like HFEs and DM-FPEs, display higher boiling temperatures and this is indicative of greater intermolecular polar interactions [11].  
Considering three similar fluoromethylelthers, CF\({}_{3}\)OCF\({}_{3}\) (b.p. 214 K; \(\mu\) = 0.49 D), CF\({}_{2}\)HOCF\({}_{3}\) (b.p. 238 K, \(\mu\) = 1.18 D) and CH\({}_{3}\)OCF\({}_{3}\) (b.p. 250 K, \(\mu\) = 2.56 D), the boiling points and the dipole moments have analogous tendencies [33]. The progressive substitution of fluorine atoms with hydrogen in the methoxy end caps leads to a stronger dipole and consequently, to an increase of the boiling point, as shown in Figure 10. The boiling point differences between PFPEs and HFEs and between PFPEs and DM-FPEs are not constant but decrease continuously with the chain length. Since the attractive forces are due to polar terminal groups, their relative intensity decreases as \(N\) increases and, as a result, the boiling points of HFEs, DM-FPEs and PFPE tend to an asymptote at higher \(N\) values.
# A Graph Convolution Collaborative Filtering Integrating Social Relations Recommendation Method
## 2 Related Work
### Social Recommendation Algorithm  
Users in rating platforms are often allowed to create explicit relationships between other users affiliated with the same platform. Examples of these relationships are friendship and trust. Some researchers [10; 11] suggest to incorporate social relations in the recommendation process to better deal with data sparsity in the rating matrix. The resulting recommendation algorithm is often called social recommender system while the user-user social network is often called social graph.  
Most traditional social recommender systems leverage CF technique. In Figure 1 we report the general structure of a social recommender systems.  
Figure 1 shows that a social recommender has two inputs, namely interactive information and social information.  
According to different fusion mechanisms of these two types of data, social recommender systems can be classified into two categories: regularization-based and feature  
Figure 1: Social recommendation framework based on CF.  
sharing based methods. The regularization-based social recommendation algorithm has the hypothesis that users trust friends in their social circle more than strangers and tend to conform to their preferences. The regularization-based recommendation algorithm projects social data and ratings into the same space and restricts each other so that users can consider their social influence before making decisions. Two important examples of regularization-based algorithms are SocialMF [12] and CUNE [13]. Regularized social recommendation algorithms indirectly simulate social network, while, due to the indirect modeling of social information, there is a low degree of overlap and correlation between user-item interaction information and social information, which leads to a weak integration of social information and ratings.  
The basic assumption of feature sharing recommendation algorithms is that user feature vectors in interactive space and social space can be projected into the same space. TrustSVD [14] and SoRec [15] are two examples of feature-sharing social recommendation algorithms. Feature sharing based recommendation algorithm can generate accurate prediction. However, the current mainstream algorithms only use original social information, it means they cannot make full use of social data.
# Failure Detection for Semantic Segmentation on Road Scenes Using Deep Learning
## 1 Introduction
### Safety Problem of Real-World Application  
When applying semantic segmentation networks to real-world applications for safety issues, it is a problem that there are no clear criteria to judge failure cases. In other words, allowing the system to detect failures is important for self-driving [25; 26; 27] in autonomous driving systems. For example, predicted semantic segmentation map from neural network, Figure 1 (right), displays the misrecognition of the sidewalk as a lane. If such misconception occurs in the network of actual autonomous driving vehicles, it can function as a fatal flaw which leads to serious accidents (e.g., fatality or car accident). Therefore, the function of notifying them and handing over authority to drivers is essential when the driving system has delivered the wrong results to drivers. To prevent such accidents, we propose a neural network method by predicting the mean intersection of union (mIoU), which is a widely used evaluation metric in the semantic segmentation task, indicating how accurately each pixel of the image is classified.
# Intrusion Detection Method Based on Preprocessing of Highly Correlated and Imbalanced Data
## 3 Results
### Construction of Features Reducing Procedure SP_PCA  
The analysis of the data revealed a significant number of correlated features, as shown in Figure 2.  
This problem can be described as follows: Let the initial data consist of objects, each of which is described by a set of p parameters \(x^{(1)}\), \(x^{(2)}\), \(\ldots\), \(x^{(p)}\), so the initial information is n objects of p-dimensional data:  
\[X_{1}\ =\ \left(x_{1}{}^{(1)},\ x_{1}{}^{(2)},\ \ldots,\ x_{1}{}^{(p)} \right)^{T}\!\!,\ X_{2}\ =\ \left(x_{2}{}^{(1)},\ x_{2}{}^{(2)},\ \ldots,\ x_{2}{}^{(p)} \right)^{T}\!\!,\ \ldots,\ X_{n}\ =\ \left(x_{n}{}^{(1)},\ x_{n}{}^{(2)},\ \ldots,\ x_{n}{}^{(p)} \right)^{T}\]  
\begin{table}
\begin{tabular}{c c} \hline Normal & 37,000 \\ Exploits & 11,132 \\ Fuzzers & 6062 \\ DoS & 4089 \\ \hline \end{tabular}
\end{table}
Table 1: Information regarding the balance of data classes.  
Figure 1: Model performance evaluation after balancing data using the different resampling algorithms.  
To solve this problem, the authors in [32] proposed a special procedure, SP_PCA, for reducing the correlation of the initial data, which is based on the following algorithm:  
Step 1. Convert all categorical features into numerical values using the factorize() method. Then, fill in any missing data.  
Step 2. Analyze the features, and remove any uninformative ones from the dataset.  
Step 3. Build the correlation matrices.  
Step 4. If there are features that correlate beyond the specified threshold (e.g., 90%), process them using principal component analysis (PCA). To accomplish this, we create data frames with the two features that have the highest correlation and apply the PCA method. Each pair of features is transformed into a new feature. After forming the new features, we remove the old ones and incorporate the new features into the main dataset.  
Step 5. Build the model, and evaluate its performance. If the model's performance has not significantly changed and remains above the specified threshold, return to step 4; otherwise, proceed to step 6.  
Step 6. If the model's accuracy has significantly decreased, analyze the features that were processed with the principal component analysis in step 4, and decide on their restoration. Finish the process.  
The proposed method SP_PCA allowed us to reduce the feature space from 42 (3 features were removed manually as uninformative) to 31 features.  
In our research, the correlation threshold value is 90%. Our goal was to reduce data redundancy, so we used a higher threshold. It was selected iteratively experimentally and varied from 87% to 96%.  
Next, we will evaluate the effectiveness of these algorithms.  
Figure 2: Correlation matrix.
## 3 Proposed Methods
### \(\ell_{0}\)-Smoothing Based on Box-Type Gradient Constraint  
#### 3.2.1 Minimization Problem  
Let \(\mathbf{s}_{\text{in}}\) and \(\mathbf{s}_{\text{ref}}\) be a vectorized input color image and a vectorized reference image for the proposed box-type gradient constraint, respectively. The image-smoothing problem with the proposed gradient constraints (12) and (13) is defined by  
Figure 2: Examples of the proposed gradient constraints: vertical and horizontal axes \(d_{v}\) and \(d_{h}\) represent gradient values of vertical and horizontal directions, respectively. The blue solid arrow and the dotted red arrow indicate the gradient vectors of reference and ideal images, respectively. The dotted blue box and circle represent areas that satisfy the gradient constraints, respectively. (**a**) Box-type gradient constraint. (**b**) Ball-type gradient constraint.  
\[\min_{\mathbf{s}}\|\mathbf{Ds}\|_{0,1}+\frac{\lambda}{2}\|\mathbf{s }-\mathbf{s}_{\text{in}}\|_{2}^{2} \tag{16}\] \[\text{s.t. }0\leq(\mathbf{Ds})_{i}\leq(\mathbf{Ds}_{\text{ref}})_{i}, \ i\!\in\!\Omega_{+},\ \ (\mathbf{Ds}_{\text{ref}})_{j}\leq(\mathbf{Ds})_{j}<0,\ \ j\!\in\!\Omega_{-},\]  
where \(\lambda\) is a balancing weight of the two costs. The first term in (16) is defined by (2). The second term is a data-fidelity term calculating the squared error between the input and output images. Note that the objective function, excluding the gradient constraints from (16), is equivalent to that of the \(\ell_{0}\) gradient minimization introduced in [31].  
To find a solution of (16), we use ADMM described in Section 2.2. Since the first term of (16) is a nonconvex function, there is no guarantee that the solution obtained by ADMM is a global minimum solution of (16). However, as stated in [37, 61], we experimentally confirmed that a reasonable solution satisfying the constraints can be obtained by carefully decreasing the step size of the algorithm in each iteration.