esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Linear Deviations of the Four Scanner Configurations
### Linear Deviations of the Scanner (b) ab-ba  
(1) Using Figure 2(b1), from Appendix B.1, with Equation (A17), using Equations (A12) to (A14), _the maximum linear deviation_ (obtained for the \(\varphi=0\) case) is  
\[y_{bP}^{max}=[e-b(tan\theta_{1}+tan\theta_{2})]\cdot\frac{sin(\varepsilon_{3} +\theta_{2})\cdot cos(\varepsilon_{4}-\theta_{2})}{cos\varepsilon_{3}\cdot cos \varepsilon_{4}}+b\cdot tan\theta_{2}\cdot tan\varepsilon_{4}, \tag{17}\]  
where the angles \(\varepsilon_{4}\) and \(\varepsilon_{3}\) are given by Equation (A18).  
From Figure 2(b1), the maximum linear deviation from the second diopter perpendicular on the O.A. to the screen (also for the \(\varphi=0\) case) is  
\[y_{bS}^{max}=L\cdot tanD_{b}^{max}, \tag{18}\]  
where the angular deviation \(D_{b}^{max}\) is given by Equation (3).  
(2) Using Figure 2(b2), from Appendix B.2, with Equation (A21), _the minimum linear deviation_ (obtained for the \(\varphi=\pi\) case) is  
\[y_{bP}^{min}=[e-b(tan\theta_{1}+tan\theta_{2})]\cdot\frac{sin(\varepsilon_{3} -\theta_{2})\cdot cos(\varepsilon_{4}+\theta_{2})}{cos\varepsilon_{3}\cdot cos \varepsilon_{4}}+b\cdot tan\theta_{2}\cdot tan\varepsilon_{4}, \tag{19}\]where the angles \(\varepsilon_{3}\) and \(\varepsilon_{4}\) are given in this case by Equation (A22).  
From Figure 2(a2), the minimum linear deviation from the second diopter perpendicular on the O.A. to the screen (also for the \(\varphi=\pi\) case) is  
\[y_{bS}^{min}=L\cdot tanD_{b}^{min}, \tag{20}\]  
where the angular deviation \(D_{b}^{min}\) is given by Equation (3).  
As in Section 3.1, for identical prisms, _the radius of the blind spot_ is the segment \(I_{3}M^{\prime}\) from Equation (A19) (Figure 2(b2)), from Equation (19),  
\[\Delta_{b}=(e\cdot cos\theta-2b\cdot sin\theta)\bigg{(}\frac{n\cdot cos\theta} {\sqrt{1-n^{2}\cdot sin^{2}\theta}}-1\bigg{)}\cdot sin\theta. \tag{21}\]  
In the particular case of the small angles approximation, this expression becomes  
\[\Delta_{b}^{lin}=(e-2b\theta)\Big{(}n/\sqrt{1-n^{2}\theta^{2}}-1\Big{)}\theta. \tag{22}\]
# Effect of Dry-Wet Cycle Periods on Properties of Concrete under Sulfate Attack
## 2 Experimental Program
### Drying-Wetting Cycle Design  
The dry-wet cycles were designed with reference to GB/T 50082-2009 [28] and ASTM C1012-18a [29]. Anhydrous sodium sulfate was used to prepare a 5% sodium sulfate solution as the test erosion solution. As shown in Table 3, this study employed four different dry-wet cycle periods (3, 7, 14, and 21 d) and a constant dry-wet duration ratio of 3:1. During the dry-wet cycle tests, the specimens were submerged in a polyethylene test box containing the sodium sulfate solution at room temperature as shown in Figure 3. During the drying process, the specimens were placed vertically upwards outdoors to dry naturally, maintaining good ventilation. The Na\({}_{2}\)SO\({}_{4}\) solution was refreshed every 30 days to keep the solution concentration constant.  
Figure 1: XRD spectrum for fly ash.  
Figure 2: Particle size distribution of fly ash and cement.
# M2 ER: Multimodal Emotion Recognition Based on Multi-Party Dialogue Scenarios
## 3 Evaluation
### Results of Unimodal Experiments  
This section primarily outlines the related experiments conducted on MELD, comparing our proposed method with commonly used baseline models for emotion recognition. Specifically, we compare the Text-CNN (text modality only) [48], bcLSTM [49], and DialogueRNNN [50] models with our proposed model.  
In these experiments, due to the imbalanced distribution of emotions within MELD, we utilized micro-F1 and weighted-average F1 (w-avg F1) as evaluation metrics. Table 4 presents the results for the seven emotion categories on the test set.  
It can be observed that the emotion recognition performance in the text modality is generally better than that in the audio and video modalities. The text modality achieved a w-avg F1 of 60.05%, which is an improvement of 9.14%, 6.4%, and 5.3% compared to the Text-CNN, bcLSTM, and DialogueRNN models, respectively. The audio modality achieved a w-avg F1 of 43.39%, which is an improvement of 10.03% and 3.8% compared to the bcLSTM and DialogueRNNN models, respectively. These results demonstrate the effectiveness of the text and audio modality feature extraction models, surpassing the performance of popular baseline models.  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multicolumn{2}{c}{\multirow{2}{*}{**Model**}} & \multicolumn{8}{c}{**Emotion**} \\ \cline{2-10}  & & **Anger** & **Disgust** & **Fear** & **Joy** & **Neutral** & **Sadness** & **Surprise** & **w-avg F1** \\ \hline text-CNN & text & 34.49 & 8.22 & 3.74 & 49.39 & 74.88 & 21.05 & 45.45 & 55.02 \\ \hline \multirow{2}{*}{bcLSTM} & text & 42.06 & 21.69 & 7.75 & 54.31 & 71.63 & 26.92 & 48.15 & 56.44 \\  & audio & 25.85 & 6.06 & 2.9 & 15.74 & 61.86 & 14.71 & 19.34 & 39.08 \\ \hline \multirow{2}{*}{DialogueRNN} & text & 40.59 & 2.04 & 8.93 & 50.27 & 75.75 & 24.19 & 49.38 & 57.03 \\  & audio & 35.18 & 5.13 & 5.56 & 13.17 & 65.57 & 14.01 & 20.47 & 41.79 \\ \hline \multirow{2}{*}{M2 ER} & text & 40.45 & 15.24 & 5.55 & 53.31 & 77.57 & 37.85 & 52.42 & 60.05 \\  & audio & 31.51 & 9.02 & 5.25 & 29.08 & 64.84 & 13.06 & 20.13 & 43.39 \\ \cline{1-1}  & video & 24.43 & 6.62 & 4.54 & 22.89 & 63.68 & 20.07 & 29.44 & 42.73 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Scores for unimodal emotion classification on the test set. To facilitate a clearer comparative analysis, this table was transformed into a bar chart as shown in Figure 11.
## 1 Introduction
### Research Gap and Contribution  
To ascertain the level of scholarly interest in the study of daylighting strategies within mosque architecture, as well as the implications of incorporating smart glazing systems in such architectural contexts, a comprehensive bibliometric analysis was conducted utilizing keyword-based searches in the Web of Science (WOS) database spanning the years from 2000 to April 2023. The resulting dataset was then visualized using the VOS-viewer tool. In Figure 3, the bibliometric map provides insights into various sub-domains of research related to mosque daylighting typology and the adoption of smart window technologies within mosque structures. Notably, the size of each node on the map corresponds to the frequency of distinct keywords, while the distance between nodes reflects the degree of co-occurrence of keywords. Among these research sub-domains, the daylighting design and its performance [49; 50; 51; 52] emerge as the most extensively investigated area. Following closely is the exploration of smart window technologies and their typologies [53], alongside inquiries into their optical and thermal properties and their integration within architectural settings. It is worth noting, however, that there is a noticeable limitation in terms of the co-occurrence of keywords related to mosques and the correlation between different daylighting typologies and the integration of smart windows within buildings, suggesting potential avenues for further exploration in this field.  
The bibliometric analysis and review of related works reveals a substantial knowledge gap regarding the evaluation and integration of advanced daylighting technologies, such as smart switchable glazing, to address issues of glare and the distribution of daylight within mosque spaces. Specifically, there is a lack of research on the adoption of electrochromic glass and other smart glazing materials that dynamically control sunlight penetration year-round. This gap is pronounced in the context of hot regions, where such innovative strategies could mitigate the harsh effects of sunlight, optimizing both the daylight quantity and quality in mosque interiors. By addressing this gap, the study aims to provide a comprehensive examination of how the incorporation of EC glazing can enhance daylighting strategies in mosques. This involves a systematic exploration of the various daylighting typologies employed in mosque architecture and their compatibility with EC glazing technology. The research will assess the quantitative aspects of daylight, such as its intensity and distribution, to gauge the effectiveness of EC glazing in controlling sunlight penetration. This highlights an urgent need for research that not only considers traditional and passive daylighting strategies but also embraces these emerging technologies to enhance the environmental performance and user comfort in mosque designs suitable for hot climates.
# A CNN-RNN Combined Structure for Real-World Violence Detection in Surveillance Cameras
## 2 Related Works  
With the widespread use of surveillance cameras and the emerging need for automatically abnormal event detection, several methods have been proposed to solve various typesof anomaly detection in video datasets [5; 6; 7; 8; 9]. Supervised learning methods aim to separate data classes, whereas unsupervised techniques explain and understand data characteristics. Between the two, supervised anomaly detection techniques outperform unsupervised anomaly detection techniques using labeled data [28]. In supervised anomaly detection, the separating boundary is learnt from training data, and then test data are classified into either normal or abnormal classes using the learned model.  
In 2015, [PERSON] et al. [29] presented a model for learning spatiotemporal features with 3D convolutional networks. This model is called C3D, and in this model, each segmented video goes through a three convolution layer 3D ConvNet to classify different actions. After four years, [PERSON] et al. [30] used this model with multiple instance learning (MIL) in their paper to find abnormal events.  
However, deep neural network architecture has recently been successfully used in various computer vision tasks, including anomaly detection problems. Mainly, supervised deep learning for anomaly detection includes two parts: a feature extraction network followed by a classifier network [31]. This paper implements Convolutional Neural Networks (CNN) to extract essential features from each input video data frame. By taking advantage of the Recurrent Neural Network (RNN) structure, the system can investigate a series of frames to find any abnormal events.
## 3 Results
### Model Validation
#### 3.2.2 Vegetation Maps  
The unsupervised classification of the wetlands of Donana National Park derived seven classes displayed in Figure 10. Classes can be grouped into (i) vegetated areas (green areas); (ii) sand depression areas (yellow and orange areas) and (iii) flooded areas (blue areas). Visual comparison of the outcomes with different land cover classification maps of the region [48; 58; 74; 75] showed good performances of the unsupervised classification algorithm. The flux tower is located within the green zone (dense biomass area) and measures carbon fluxes from the densely vegetated areas on the west side of the wetlands (see Figure 10).  
By a visual comparison with the vegetation classification map of 2009 provided by ICTS-RBD (Figure 11), we could see that those green areas correspond to the regions dominated by _Bolloschoenus maritimus_ vegetation, commonly named saltmarsh bultush. The vegetated-marsh area of the Donana National Park was also identified in 2017 by [PERSON] et al. [58] (highlighted with the red line in Figure 11). Being one of the most recent classifications we could find in literature, we decided to adapt our model to the same  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **MAE [gC m\({}^{-2}\) day\({}^{-1}\)]** & **RMSE [gC m\({}^{-2}\) day\({}^{-1}\)]** & **Sest** \\ \hline Our model & 0.52 & 0.63 & 0.27 \\ MOD17 & 0.94 & 1.44 & 0.62 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Summary of our model and MOD17 GPP product errors.  
Figure 9: Time series of our model GPP predictions, MOD17 GPP prediction and in situ measurements of GPP.  
region identified by [PERSON] et al. [58]. This area dominates the region surrounding the eddy covariance tower and has similar biomass production.
## 3 Results
### Cyclic Fatigue Resistance  
Both the experimental temperature (\(p\) < 0.001) and brand of NiTi-file (\(p\) < 0.001) significantly affected the NCF, and their interaction was significant (\(p\) < 0.001). The NCF and fracture length data are described in Table 2. When the cyclic fatigue resistance test was performed at RT, CM exhibited a higher NCF than EDM (\(p\) = 0.001), and EDM had a higher NCF than OF (\(p\) = 0.023), whereas when the test was subjected at BT, EDM had a much higher NCF than those of CM and OF (\(p\) < 0.001), and those two did not show a significant difference each other (\(p\) = 0.565). The NCF of EDM did not significantly alter according to environmental temperature, while the NCF of CM and OF was lower at BT than RT (\(p\) < 0.001 and \(p\) = 0.0002, respectively). The fracture length of the tested files were not affected by the experimental temperature. The fracture length of CM was longer than those of EDM (\(p\) = 0.002) and OF (\(p\) < 0.001) at BT; the fracture length of CM was longer than that of OF (\(p\) = 0.003) at RT.  
The fractured surface showed a typical pattern of brittle fracture. All fractured instruments exhibited several crack points at the edges of the fractured surfaces, and multiple fatigue striations were observed near them (Figure 2). Magnified views of box areas in Figure 2A-C,G-I are presented in Figure 2D-FJ-L, respectively. Multiple fatigue striations, which represent brittle fracture, are observed. Some of them (Figure 2D-FJ) appear to have been fractured several thin layers, where multiple cracks might initiate the fracture. Dimple areas, in a characteristic ductile fracture pattern, were examined in all fractured instruments, marked with a yellow boundary in Figure 2A-C,G-I. EDM had a particularly large area which characterized the ductile fracture (Figure 2B,H) compared to other instruments. Each brand of instrument had a similar fractographic pattern irrespective of the experimental temperature.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **HyFlex CM** & **HyFlex EDM** & **OneFlare** \\ \hline \multirow{2}{*}{Bending resistance (N)} & RT & 1.799 (0.095) a  & 4.586 (0.384) b  & 3.967 (0.197) c,* \\ \cline{2-4}  & BT & 1.784 (0.151) a  & 4.854 (0.493) b  & 4.371 (0.493) c,* \\ \hline \multirow{2}{*}{NCF} & RT & 2410 (684.7) a,*  & 1508 (447.5) b  & 899 (176.5) c,* \\ \cline{2-4}  & BT & 670 (266.2) a,*  & 1372.7 (446.5) b  & 522.5 (193.1) a,*  \\ \hline \multirow{2}{*}{Fracture length (mm)} & RT & 4.61 (0.51) a  & 4.24 (0.33) a,b  & 4.07 (0.17) b  \\ \cline{2-4}  & BT & 4.78 (0.51) a  & 4.22 (0.21) b  & 4.0 (0.24) b  \\ \hline \end{tabular} Different lowercase letters in the same row indicate significant difference among NiTi files (\(p\) < 0.05). * indicates significant difference according to experimental temperature within each NiTi file (\(p\) < 0.05).  
\end{table}
Table 2: The bending resistance, number of cycles to failure (NCF) and fracture length of HyFlex CM (CM), HyFlex EDM (EDM), and OneFlare (OF).
###### Abstract  
Recently, advancements in convolutional neural networks (CNNs) have significantly contributed to the advancement of hyperspectral image (HSI) classification. However, the problem of limited training samples is the primary obstacle to obtaining further improvements in HSI classification. The traditional methods relying solely on 2D-CNN for feature extraction underutilize the inter-band correlations of HSI, while the methods based on 3D-CNN alone for feature extraction lead to an increase in training parameters. To solve the above problems, we propose an HSI classification network based on hybrid depth-wise separable convolution and dual-branch feature fusion (HDCDF). The dual-branch structure is designed in HDCDF to extract simultaneously integrated spectral-spatial features and obtain complementary features via feature fusion. The proposed modules of 2D depth-wise separable convolution attention (2D-DCAttention) block and hybrid residual blocks are applied to the dual branch, respectively, further extracting more representative and comprehensive features. Instead of full 3D convolutions, HDCDF uses hybrid 2D-3D depth-wise separable convolutions, offering computational efficiency. Experiments are conducted on three benchmark HSI datasets: Indian Pines, University of Pavia, and Salinas Valley. The experimental results show that the proposed method showcases superior performance when the training samples are extremely limited, outpacing the state-of-the-art method by an average of 2.03% in the overall accuracy of three datasets, which shows that HDCDF has a certain potential in HSI classification.  
hyperspectral image classification; hybrid residual unit; depth-wise separable convolution; spatial-spectral features +
Footnote †: journal: Applied sciences
# Hardware Acceleration of Satellite Remote Sensing Image Object Detection Based on Channel Pruning
## 3 Methodology
### YOLOv4-MobileNetv3 Model Channel Pruning
The output of a channel close to 0 approximates a constant and can be clipped. After sparse training at 300 epochs, channel pruning was performed on the sparse YOLOv4-MobileNetv3 network model using a pruning rate of 0.85. The experimental  
Figure 8: The distribution of \(\gamma\) coefficients in each BN layer after sparse training. Figure 7: The distribution of \(\gamma\) coefficients in each BN layer before sparse training. results show that the parameter size of the model after pruning is 9.42 M, and the parameter size is reduced by 2.05 M.
# Deep Learning of GNSS Acquisition
## 5 Results
The impact of low \(C/N_{0}\) values on ROC curves is further explained by the histograms of the test statistic \(\mathcal{T}(\mathbf{Z}_{i}^{(m,n)})\), which are shown in Figures 8 and 9 for the cases of \(K=1\) and \(K=6\), respectively. Recall that one would like to have the histograms under \(\mathcal{H}_{0}\) and \(\mathcal{H}_{1}\) as _distant_ as possible, which happens for large \(C/N_{0}\) but clearly does not for low \(C/N_{0}\) values. In Figure 9, the empirical distributions can be clearly distinguished in comparison with Figure 8, however, a sample from \(\mathcal{T}(\mathbf{Z}_{i})\) cannot be statistically discerned between both distributions. More precisely, without the noncoherent fusion rule, it is hard for the DNN to distinguish the difference between noise and signal subimages, particularly when the \(C/N_{0}\) is low, which is shown in Figure 8. Although for large \(C/N_{0}\) the separation increases, it is still far from desirable ROC regions. On the other hand, when noncoherent integration is considered, Figure 9 shows that there is no overlap between the two histograms, which causes an increased detection accuracy. In summary, when the signal power is high enough (or when noncoherent integration is used to increase that power), the DNN classifier performs remarkably well, even exceeding current state-of-the-art well-known performance results.
# Three-Dimensional Broad Learning Gravity Data Inversion Using Single-Anomaly Training Samples
## 4 Discussion
Undoubtedly, the proposed inversion method is more suitable for gravity inversion than the traditional method. Figure 12: Inversion results of the numerical density model with four rectangular-shaped anomalies by the traditional inversion method. (**a**) Three-dimensional representation of the inverted density model. (**b**) Gravity anomaly data forwarded from the inversion result. (**c**) Residuals between inverted and observed surface gravity anomaly data. Figure 13: Comparison of boundary detection capability for anomalies between the traditional and proposed methods. (**a**) Vertical slice at Y = 1500 m by the traditional method. (**b**) Vertical slice at Y = 1500 m by the proposed method. (**c**) Comparison of boundary detection capability for anomalies. Note: Each black rectangular box in (**a**,**b**) represents the actual boundary of an anomaly, while the density value curve in (**c**) depicts the density values along the white dashed line in (**a**,**b**). It is known that machine learning methods rely on input-output training samples to learn the functional relationship needed for various modeling tasks, including gravity inversion, which is also a modeling problem. The number of density anomalies is an inevitable consideration during the construction of training samples. This study proposes constructing single-anomaly training samples to eliminate the need for such consideration, removing the dependency on prior information about the number of anomalies. The numerical and field data examples showcased the effectiveness of using single-anomaly training samples for gravity inversion. The success of this sample construction approach depends on the generalization ability of machine learning, which is precisely what modeling problems expect from machine learning. The linear superposition property of gravity anomalies is specifically considered to investigate the underlying rationale behind the expected generalization ability of single-anomaly training samples. Figure 14 visualizes this property, illustrating that the surface gravity anomaly produced by two anomalous bodies is equivalent to the sum of the gravity anomalies generated by each individual body. Evidently, this linear superposition relationship can be leveraged by machine learning. Consequently, the proposed approach for generating training samples enables the automatic determination of the number of anomalies. Meanwhile, the BL method still faces limitations in terms of depth resolution due to inherent non-uniqueness [11; 46; 48; 49]. The inversion results for the Vinton salt dome indicate that, although there is an improvement in depth resolution compared with traditional inversion methods, discrepancies remain when compared with the geological data, particularly at the bottom boundary. Reference [43] proposed a sample selection method to enhance the mapping capabilities of a BL network. This suggests that the performance of our method could be further improved by incorporating this strategy.
# Comparing Link Budget Requirements for Future Space-Based Interferometers
## 1 Introduction
Transponder configuration (**left**) sees stabilized light from an optical source on satellite A transmitted to satellite B where it is used to phase lock a free-running source at a frequency offset. Light from satellite B is then transmitted back to perform scientific measurement. The dual-stabilized configuration (**middle**) stabilizes the optical source on satellite B to a second ultra-stable cavity, offering improved laser frequency suppression over the transponder configuration. The retro-reflector configuration (**right**) replaces the optical components of satellite B with a single retro-reflecting element. As such, the light transmitted from satellite A is reflected directly back, suffering twice the optical loss due to beam divergence on transmission. This article explores the impact of the mission architectures illustrated in Figure 1 on relaxing the optical power requirements of phasemeter tracking. This work does not consider the impact of optical power on the sensitivity of the science measurement--however the lowest optical power explored here of 100 attowatts contributes 7.4 \(\mathrm{nm}/\sqrt{\mathrm{Hz}}\) of ranging noise, which is below the current noise requirements of next-generation missions [7]. The findings of this work outline how optical power and carrier-to-noise density ratio requirements can be reduced for different schemes when the phasemeter is optimized for low-power operation. Section 2 overviews the phasemeter performance and discusses the mechanisms that cause phase tracking to fail. Section 3 outlines a set of simulations used to explore the phasemeter performance in each configuration discussed above and presents the simulation results.