esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work and Research Methods
### Attention Mechanism  
The attention mechanism is designed to adaptively allocate attention weights based on task objectives. This enables the prioritization of relevant regions by neural networks while diminishing the significance of irrelevant parts. The attention mechanism covers five categories: time domain, space domain, layer domain, hybrid domain, and channel domain. Various lightweight attention mechanisms, such as SE (Squeeze Excitation) [20], ECA (Efficient Channel Attention) [21], and CA [22] attention have been developed. These mechanisms allow attention values to be adjusted for focusing the model on the target area, thereby mitigating background interference and ultimately enhancing recognition accuracy, especially when dealing with complex backgrounds. By intelligently guiding attention, essential information can be effectively highlighted by the model while the emphasis on irrelevant details is reduced.
# Constrained Optimization of FPGA Design for Spaceborne InSAR Processing
###### Abstract  
With the development of spaceborne processing technologies, the demand for on-board processing has risen sharply. Against this background, spaceborne Interferometric Synthetic Aperture Radar (InSAR) processing has become an important research area. In many cases, high processing capacity is required during on-board InSAR processing, yet Field-Programmable Gate Array (FPGA) resources on the satellites are limited. To improve the performance of spaceborne remote sensing processing, this paper designs a high-performing FPGA system for the coarse registration and interferogram generation process of InSAR. Moreover, to address this dual-constraint problem of resource and processing capacity, the paper proposes an FPGA design method based on the gradient descent theory, which can identify the optimum trade-off scheme between two such constraints. Finally, the proposed system design and method are implemented in FPGA. Experiments showed that the FPGA system outperformed the NVIDIA (Santa Clara, CA, USA) GTX Titan Black Graphics Processing Unit (GPU), and the optimum trade-off scheme only increases the entire time by 1.1% but reduces the FPGA BRAM usage by 8.7%. The experimental results proved the effectiveness and validity of the proposed system and method.  
FPGA; InSAR; double nonlinear constraints; gradient descent; spaceborne processing Article
# Development of Multimodal Physical and Virtual Traffic Reality Simulation System
## 5 Case Studies
#### 5.1.4 Results Analysis  
The analysis focused on three key measures to evaluate bicyclist responses to passing vehicles: critical distance, lane position change, and heart rate change. Critical distance was defined as the closest lateral distance between the passing vehicle and the bicyclist, providing an indicator of perceived safety. Lane position change was assessed by comparing the average lateral position of the bicyclist within the dedicated 2-meter-wide bicycle lane during the 5 s before and after the critical moment. In this lane, a position of 0 m represents the left edge, and 2 m represents the right edge, with the center of the lane at 1 m. Heart rate change was calculated as the difference in average heart rate during the same periods to evaluate potential physiological stress responses.  
The results showed that the mean critical distance across participants was 2.30 m, with a standard deviation of 0.12 m, suggesting some variability in individual safety perception. While some participants tolerated closer proximity, others exhibited more conservative distancing. A paired \(t\)-test comparing the average lateral position before and after the critical moment revealed a statistically significant shift (t = \(-\)21.07, \(p\) < 0.00001), indicating that bicyclists adjusted their position in response to the passing vehicle. On average, the lane position shifted from 0.94 m before the passing event to 1.14 m after, reflecting a subtle but consistent movement toward the right side of the lane--a defensive behavior likely intended to increase perceived safety.  
Similarly, a paired \(t\)-test conducted on heart rate data revealed a significant increase (t = \(-\)26.94, \(p\) < 0.00001), with the average heart rate rising from 69.45 bpm before the passing event to 72.75 bpm afterward. This finding indicates a clear physiological response to the proximity of the vehicle, suggesting heightened alertness or stress during the interaction.  
A summary of all quantitative results, including means, standard deviations, and statistical test outcomes, is presented in Table 1. These results support the hypothesis that bicyclists dynamically adjust both their physical positioning and physiological state in response to motor vehicle interactions. The combination of behavioral and physiological data emphasizes the importance of including real human behavior in simulation-based traffic safety research.
## 3 Results
### PCA-LDA-Based SVM
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Actual Group**} & \multicolumn{3}{c}{**Predicted Group**} & \multicolumn{3}{c}{**Evaluation Parameters**} \\ \hline  & & **POAG** & **PACG** & **Normal** & **Sensitivity** & **Specificity** & **Accuracy** \\ \hline \multirow{3}{*}{Experiment A} & POAG & 24 & 3 & 0 & 88.9\% & 97.8\% & \\  & PACG & 1 & 17 & 1 & 89.5\% & 94.4\% & 93.2\% \\  & Normal & 0 & 0 & 27 & 100\% & 97.8\% & \\ \hline \multirow{3}{*}{Experiment B} & POAG & 4 & 1 & 0 & 80.0\% & 100\% & \\  & PACG & 0 & 7 & 1 & 87.5\% & 92.9\% & 90.9\% \\ \cline{1-1}  & Normal & 0 & 0 & 9 & 100.00\% & 92.3\% & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification results and related evaluation parameters of the PCA-LDA-based SVM model. at each threshold. This was conducted by taking each category in turn as a positive sample and the other categories as negative samples to obtain a confusion matrix (of size 2 \(\times\) 2) for that category. The confusion matrix of all categories was superimposed to obtain the overall confusion matrix. Furthermore, the false-positive \(FPR_{t}\) and true-positive \(TPR_{t}\) under this threshold were calculated based on this confusion matrix. Finally, based on \(\{[FPR_{t_{1}},TPR_{t_{1}}]\), \([FPR_{t_{2}},TPR_{t_{2}}]\),..., \([FPR_{t_{n}},TPR_{t_{n}}]\}\), the ROC curves were drawn and the micro-average AUC was calculated. In addition, macro-average AUC was simply defined as the average of the AUC for each category. The values of both micro-average AUC and macro-average AUC were 0.99, which shows the PCA-LDA model can discriminate POAG, PACG and normal groups from each other through Raman spectra in glaucoma diagnosis.
# Study on the Effect of Insulation Materials on the Temperature Field of Piles in Ice-Rich Areas
## 5 Conclusions  
Based on the pile-soil heat transfer theory, a finite element numerical model was used to calculate the temperature disturbance by the pile's foundation by applying insulation around the pile perimeter. We conducted a comprehensive analysis and obtained the following conclusions.  
1. The finite element numerical calculation showed that the temperature of the pile foundation increased with the insulation thickness. In a real project, we must consider saving insulation material and reducing the construction period. Combined with the concrete ratio used in the project, we can reasonably choose the thickness of the insulation layer to achieve the best effect. In this project, the best thickness was 10 cm.
2. Analysis of the calculated thermal and physical parameters of the insulation material revealed that the use of insulation materials with a low thermal conductivity was conducive to the formation of a more suitable concrete curing temperature. Polyurethane foam had insulation performance and was suitable for the construction of bored piles in permafrost areas.
3. The effect of applying insulation around the pile perimeter on the concrete strength of pile foundations in ice-rich permafrost areas was verified. Test pile #2 with double sheathing and an insulation layer was used, and the strength at all ages was greater than that of test pile #1 without an insulation layer. The application of the insulation layer improved the maintenance temperature of the pile foundation in the permafrost region and ensured that the pile foundation was better maintained, thus improving the concrete strength. The technical measures of adopting double sheathing with polyurethane as an insulation layer effectively improved the concrete strength. This technical measure was applicable to the ice-rich permafrost area in the Daxinganling Mountains and also has reference value for the wetland permafrost area at middle and low latitudes.  
Conceptualization, Y.S., T.Y., Y.W. and X.W.; Methodology, Y.S.; Data curation, Y.S.; Writing--review & editing, Y.S. and T.Y. All authors have read and agreed to the published version of the manuscript. This study was supported by the Fundamental Research Funds for the Central Universities (2572015 BB03), and Technology Project (LJKY004-2017) of Longjian Road & Bridge Co.  
The authors declare no conflict of interest.
## 3 Related Work
### Permission-Based Detection and Feature Extraction  
Several studies have been carried out on Android permission requests [26], Android user privacy [27], attacks on Android and other mobile platforms [28, 29], including malware attacks [30], synchronous channels attack [31], side-channel attacks [32], runtime repackaging attacks [33], and Man-in-the-Middle attacks [34] accuracy. This section of the paper provides a brief examination of some of the related works carried out. [PERSON] and [PERSON] [1] discussed the Android permission systems by providing a general overview of the Android control mechanism. The study identified and grouped Android dangerous permissions based on their API levels. The research showed the significant influence of Android permissions at diverse protection levels. [PERSON] and [PERSON] [35] proposed an Android malware detection framework that collects Android applications' permissions by applying clustering algorithms. Their system monitors several permission features obtained from Android apps to determine whether the permissions are malicious or benign.  
In [36], [PERSON] proposed permission-based detection of Android malware using a feature selection approach based on linear regression. Using a minimum of 27 features, their research selected the most distinctive permissions to enhance the framework's performance instead of using all the permission feature vectors. In [37], [PERSON] et al. used four different algorithms to classify Android malicious applications as either malicious or benign based on their permissions manifest files. Using a dataset with a sample size of 5243, the best algorithm obtained a performance accuracy of 81.5% accuracy. The research concluded that hackers target manifest permission features as an attack vector to infect mobile devices. In [38], [PERSON] et al. proposed a detection framework for detecting Android malware using custom and native permissions features. Their findings show that the increasing number of Android permissions increases options to gain access and control over sensitive information and mobile devices.  
In [39], [PERSON] et al. proposed a framework for the detection of Android permissions that are anomalous. The detection system focused on the identification of relevant categories by applying a latent semantic index [40, 41]. The result of the study shows that dangerous permissions constitute a security vulnerability. The understanding of permissions relationships could help in the detection of malicious applications. In [42], [PERSON] et al., proposed an Android malware detection tool called SigPid. Their system is based on the analysis of permission usage. Using a three-level pruning approach, their study mined the permission dataset to effectively and distinguishingly identify significant permissions in malicious and benign applications. Although their study achieved a high detection accuracy of 93.62%, only 22 out of 135 permissions were considered. Additionally, the research did not consider the protection level or threat level of those permissions.
## 2 Materials and Methods
### 165 rRNA Amplicon Sequencing and Bioinformatic Analysis  
Based on the qPCR results, the fractions with the first and second highest relative abundance in \({}^{14}\)N\({}_{2}\)-treatment (light fraction 1; L1, light fraction 2; L2) and \({}^{15}\)N\({}_{2}\)-treatment (heavy fraction 1; H1, and heavy fraction 2; H2) from the Con and Fe plot samples (2-day, 4-day, and 6-day) were selected and subjected to amplicon sequencing targeting the V3-V4 region of the bacterial 165 rRNA gene as follows. The first-step PCR amplification was performed using a 20 \(\upmu\)L mixture: 10\(\times\) Ex buffer, 2.0 \(\upmu\)L; dNTPs (each 2.5 mM), 1.6 \(\upmu\)L; forward and reverse primers (10 \(\upmu\)M), each 0.5 \(\upmu\)L; DNA template, 1 \(\upmu\)L; Ex Taq (5 units/\(\upmu\)L), 0.2 \(\upmu\)L; Milli-Q water, 13.2 \(\upmu\)L. The amplification program was initial denaturation at 94 \({}^{\circ}\)C for 2 min, followed by 25 cycles of 94 \({}^{\circ}\)C for 30 s, 55 \({}^{\circ}\)C for 30 s, and 72 \({}^{\circ}\)C for 30 s, and a final elongation step at 72 \({}^{\circ}\)C for 5 min. The second-step PCR was conducted with primers 2 ndF/2 ndR [30] under the following amplification program: 94 \({}^{\circ}\)C for 2 min, followed by 10 cycles of 94 \({}^{\circ}\)C for 30 s, 60 \({}^{\circ}\)C for 30 s, and 72 \({}^{\circ}\)C for 30 s, with a final extension at 72 \({}^{\circ}\)C for 5 min. Sequencing was performed using the Illumina MiSeq platform (Illumina, San Diego, CA, USA) for a 2 \(\times\) 300 paired-end (PE) configuration at Bioengineering Lab. Co. (Kanagawa, Japan). All raw sequence data were cleaned in QIIME 2 (ver. 2021.2) using DADA2 to remove low-quality sequence reads before regenerating the operational taxonomic unit (OTU) tables. The erroneous molecular OTUs were removed using LULU [31]. With a cutoff value of 97%, the clustered sequences corresponding to OTUs were annotated against the Greengenes database (ver. 13_8). Notably, the genus _Geobacter_ annotated by the public database is paraphyletic in taxonomy and contains species currently belonging to several genera in the order _Geobacterales_[20; 32]. Several novel _Anareomyxobacter_ strains were isolated from the environment and genome-sequenced [21; 23]. Thus, the sequences in the _Geobacter_ and _Anareomyxobacter_-related OTUs were reannotated against the NCBI-nr database. The 16S rRNA relative abundances for each bacterial genus in the \({}^{15}\)N-labeled DNA combined fraction (H1 and H2) and the \({}^{14}\)N-labeled DNA combined fraction (L1 and L2) were calculated by normalizing the number of amplicon sequence reads using the copy numbers of 16S rRNA genes obtained by qPCR. Genera with higher relative abundances in \({}^{15}\)N-labeled DNA than in \({}^{14}\)N-labeled DNA were selected, and a heatmap depicting the transitions of bacterial community composition incorporating \({}^{15}\)N was generated using the Pheatmap package in R-version 4.2.1 [33].
## 3 Optical Indices
### Spectral Resolution and SNR  
The lowest limits of spectral resolution (R) and SNR must satisfy the accuracy requirements of atmospheric-retrieval results, that is, the ability to separate two adjacent J components of the molecular band, which is considered to be the recognition of spectral features [23]. However, a higher resolution leads to larger spectral-line dispersion. Larger dispersion results in the spectral energy of the required band being dispersed into more pixels, which leads to an increase in the ratio of detector noise to total noise.  
The impact of SNR and spectral resolution on the retrievability of model parameters (planetary temperature, molecular abundances, and cloud parameters) from transit spectra was investigated by utilizing retrieval code TauREx [24]. The error of spectral retrieval was simulated as a function of the spectral resolution and SNR, and results were obtained in the case of hot Jupiter and warm Neptune [25]. Results indicate that a spectrum with an SNR of approximately 10 (denoted as \(\sim\)10) and R \(\sim\) 100 could retrieve the chemical  
Figure 2: Normalized radiance of several exoplanet candidates and zodiacal background.  
molecule well. There were noticeable improvements in temperature retrieval from SNR 5-10 and R 30-100. SNR \(\sim\) 20 and R \(\sim\) 200 had very accurate results, but there was no obvious improvement with a higher SNR and R.  
Combined with the present technical level, the goal SNR was set to be \(SNR_{goal}\sim 7\), \(R\sim\) 100 (2-4 \(\upmu\)m), \(R\sim\) 30 (4-8 \(\upmu\)m). Dim targets need to be revisited many times to achieve the above requirements. For a planetary system with an obvious signal, the SNR can be up to 10-20, which is conducive to deeper measurements.
# Time-Aware and Feature Similarity Self-Attention in Vessel Fuel Consumption Prediction
## 3 Methodology
### Feature-Similarity Attention
#### 3.3.1 Total Similarity Based on Feature Importance  
To obtain the total similarity, we use feature-wise distance and the feature importance. The feature-wise distance is \(L1\) distance between pairs of the features. We defined the truncated sequence, \(S=\{s_{1},s_{2},\cdots,s_{n}\}\), which consists of \(n\)th sequential data. Each of \(s_{i}\) has the \(k\) number of features except timestamp. Among \(k\), we only use the \(k-p\) number of features to estimate feature similarity. \(k\) is the total number of vessel features, and \(p\) is the number of vessel formulation features. In truncated sequence \(S\), vessel formulation features always have the same values since the types of vessels are the same. That is why we exclude \(p\) from total features. The feature-wise distance, \(D\in\mathbb{R}^{n\times n\times|k-p|}\) is applied to each feature between data in the sequence \(S\) and the equation is as below:  
\[D=\left|x_{f}^{i}-x_{f}^{j}\right| \tag{8}\]  
Figure 4: Attention process to combine with self-attention.  
where \(i=\{1,2,\cdots,n\}\), \(j=\{1,2,\cdots,n\}\), \(f=\{1,2,\cdots,k-p\}\), and \(x_{f}^{i}\) is \(f\)th feature of \(i\)th data in sequence \(S\). From feature-wise distance \(D\), we observe how each feature of data in sequence is different. However, it does not contain enough information since \(D\) is simply calculated by \(L1\) distance. We estimate the total similarity \(TS\in\mathbb{R}^{n\times n}\) to differently reflect feature-wise distance based on feature importance. \(TS\) can compare the overall distance between data. We apply a weighted sum on \(D\) to make the \(k-p\) number of the features distance to total similarity, where the weight is the feature importance learnable parameters, \(W=\{w_{0},w_{1},\cdots,w_{k-p}\}\).  
\[TS=\sum_{f=1}^{k-p}\left|x_{f}^{i}-x_{f}^{j}\right|*w_{f} \tag{9}\]  
From Equation (9), the total similarity between data is represented by the feature-wise distance and feature importance. Although each distance of the feature pair is equal, \(W\) parameters differently represent \(TS\) depending on the feature importance. Finally, \(TS\) is expressed as \(\alpha_{f}=softmax(TS)\).
# Investigation of Biochars in Terms of Vitamin E Adsorption Capacity
## 3 Results and Discussions
### Adsorption Capacity of \(\upalpha\)-Tocpheryl Acetate
#### 3.4.2 Correlation of Adsorption Capacity and Respective Mesopore Class  
To determine which mesopore class is best suited for the adsorption of \(\alpha\)-tocopherol acetate, a regression analysis of the relative adsorption capacity of \(\alpha\)-tocopherol acetate to the BJH surface area of the six mesopore classes was conducted (Table 2). The highest \(r^{2}_{adj}\) with 0.899 was obtained for the surface area of the second-lowest mesopore classes, ranging from 3.22 to 4.03 nm. The same holds true for the correlation coefficient \(r\) with 0.954. Both \(r\) and \(r^{2}_{adj}\) decrease with an increasing mesopore class. This indicates that the relationship between the adsorption capacity and the mesopores is stronger for mesopores with a smaller diameter. The assumption could be made that the correlation coefficient would be higher for micropores below 2.58 nm, as \(\alpha\)-tocopherol acetate might be smaller, assuming a size of 1.19 \(\times\) 0.95 nm for vitamin E [25]. However, due to the weak correlation coefficients of the micropore volume (<2.58 nm) with the relative adsorption capacity of \(\alpha\)-tocopherol acetate (\(r=0.228\), \(r^{2}=0.052\), and \(r^{2}_{adj}=-0.066\)), we confirmed the findings of [PERSON] et al.'s [34] study, who showed that the adsorption of vitamin E in mesoporous materials is larger than that in microporous adsorbents. One reason for this could be that adsorption into micropores is slower than that into mesopores. Slower or no loading of micropores could be due to molecules docking in channels to larger pores, and thereby blocking micropores [34]. The fact that adsorption mainly occurs in mesopores is advantageous because, according to [PERSON] et al. [35], micropores are not accessible during animal digestion. This suggests that vitamin E is more likely to desorb from mesopores than from micropores.  
Figure 5: The adsorption of \(\alpha\)-tocopherol acetate and its dependence on the total Barrett–Joyner–Halenda mesopore volume, ranging from 2.58 to 20.01 nm. Different small letters indicate significant differences between biochars, determined by One-Way ANOVA and [PERSON]’s post hoc test (\(p<0.05\)).