esa-sceva/satcom-chunk-collection huggingface dataset:
# Image Retrieval Method Based on Image Feature Fusion and Discrete Cosine Transform
## 3 Materials and Methods
### Shallow Feature Fusion-Based Method
#### 3.1.5 Why We Choose Them?  
Since the R, G, and B components of the object color in the digital image are all related to the amount of light irradiated on the object, it is difficult to distinguish the object based on these components. HSV is more robust towards external lighting changes. In cases of minor changes in external lighting, hue values vary relatively less than RGB values. XYZ and YUV are computationally less expensive to reach from RGB and less \"good\" than HSV, HSL, or CIELAB. The CIELAB color space was found to perform at least as well as or better than the other color spaces when measuring image similarity [47]. The HSV color model is more correlated to how humans see colored objects than RGB [42].  
LBP has become one of the most prominent texture features, and many new variants have been proposed one after another. The advantages of LBP include avoiding the time-consuming discrete vocabulary pretraining phase in the BoW framework, its overall computational simplicity, and ease of implementation [48]. In addition, uniform patterns make using LBP computationally more efficient without compromising its accuracy. Uniform LBPs can also be applied to obtain rotation invariance [43].  
The shape feature reflects the spatial position relationship between the features, and it is more likely to contribute to human visual sensitivity than the color feature and texture feature. Considering the limitations of image retrieval applications based on shape features, we use another texture feature, DTCWT, as an alternative. The effectiveness of this method has also been well verified in previous work [49].  
Image local features play a key role in image retrieval. Different kinds of local descriptors represent other visual contents in an image. The local descriptors (SIFT, SURF, and HOG) maintain invariance to rotation, scale scaling, and brightness variation and holds a certain degree of stability against viewing angle changes, affine transformation, and noise [50]. However, it is impossible to extract these features for smooth-edged targets accurately [51], and their real-time performance is not high [52]. Therefore, the image representation methods (BoW, Fisher Vector (FV), VLAD model, etc.) aggregate local image descriptors by treating image features as words and ignoring the patches' spatial relationships.  
The handcrafted local descriptors dominated many domains of computer vision until the year 2012, when deep CNN achieved record-breaking image classification accuracy. Therefore, these local features are skipped in this paper, and only shallow features and depth features are used for image retrieval performance analysis.
# Cross-Layer Energy Optimization for IoT Environments: Technical Advances and Opportunities
## 4 Energy Efficient Techniques in the IoT
### Energy Efficient Techniques at Sensing Layer
#### 4.1.2 Energy Efficient Modulation Techniques  
Energy-Autonomous Wireless Communication for Millimeter-Scale Internet-of-Things Sensor Nodes (EAWCM) [34] has been suggested to target order of magnitude enhancements in energy efficiency while comprehensively analyzing wireless communication systems. This analysis jointly optimizes different parameters: modulation scheme, node dimension, carrier frequency, Rf/digital/analog circuit specifications, synchronization protocol and a miniaturized 3-D antenna for energy-based ultra-small nodes. In the millimeter-scale node transmission case, if \(P^{ckt}\) is the constant power utilization of the power oscillator circuit, then the energy per information bit can be expressed as:  
\[E^{b}=\frac{P^{ckt}K\times T^{pulse}}{\lceil C^{\prime}\rceil} \tag{3}\]  
and while using three modulation-coding parameters: \(T^{pulse}\) and \(C^{\prime}\), system data rate is formulated as:  
Figure 8: Flow of an energy management framework.  
\[R=\frac{\left[C^{\prime}\right]}{MT^{pulse}+(K-1)T^{ielle}+T^{charge}} \tag{4}\]
# Energy Efficient Communications for Reliable IoT Multicast 5G/Satellite Services
## 4 Multicast Nc Satellite Approach
In the considered multicast scenario, we assumed that an OFDM signal is transmitted and a repetition of multiple data symbols on multiple subcarriers is considered. At each multicast terminal, the decision variables carried by the \(m\) replicas of the same symbol are coherently combined, by improving the Symbol Error Rate (SER) and, as a consequence, reducing the Packet Error Rate (PER). Under the assumption of static or low mobile devices, the considered Land Mobile Satellite (LMS) channel [28] is a flat fading channel. By assuming, without loss of generality, that: (1) the communications from satellite to all the on-Earth nodes occur over independent and identically distributed (i.i.d.) links; (2) the channel propagation conditions are constant during the transmission of a data packet; and (3) ideal coherent detection at the Earth terminal, we can obtain for the \(i\)th node, a SER for each \(M\) order modulation that depends on the \(m\) repetition factor and on the average of SNR \(\overline{\tau}_{i}\), which can be generally expressed as \(P_{s,i}^{M}(m,\overline{\tau}_{i})\).
## 2 Materials and Methods
### Whole Cartilage Manual Segmentation  
Three-dimensional (3D) MR images can be used to measure soft-tissue structures, including the intra-articular hyaline cartilage. A single 3D knee MR scan generates up to 160 2D slices in the sagittal view. On each slice, the boundary of knee cartilage can be manually delineated and the thickness information can be measured (number of pixels from each cartilage boundary position to its closest boundary position on the other side). However, the manual segmentation of the 3D sequence is quite time-consuming. It may take up to six hours to label one knee sequence with 160 images. For the Imorphics dataset used in this work, it took around one year for the experts to manually segment the cartilage of the 81 knees (81 \(\times\) 160 = 12,960 images) [18]. Additionally, the person who operates the segmentation software needs intensive training [7], which also increases the cost of manual segmentation.
# Stereo Digital Image Correlation in MATLAB
## 3 Implementation
### Displacement Transformation Implementation
#### 3.5.1 CSTrans Function  
CSTrans, outlined in Table 13, uses MATLAB's estimateCameraParameters function to determine the calibration parameters according to Section 2.2 and the fundamental matrix according to Section 2.4. The canonical form of the projection matrices are used such that the world CS is aligned with the camera CS of the first camera.  
Lines 6-18 cycle through image pairs of the image set performing displacement transformation according to Section 2.9. Line 9 identifies subsets which do not contain NaN values in either their position or SFPs (subsets which pass stereo matching) which are to be analyzed during displacement transformation. MATLAB's undistortPoints function is used to remove distortion from the subset positions while the subroutine Triangulation uses polynomial and linear triangulation to determine the 3D coordinate in the world CS corresponding to a subset pair. The if statement of line 10 avoids recomputing the reference subset positions for the absolute reference image strategy.  
CSTrans is susceptible to particular issues which can cause a fatal error. Refer to Appendix C for a discussion of a function which improves the robustness of CSTrans.
## 3 Signal Spatial Reconstruction Method for Relay Cooperative Networks
### Physical Layer Adaptive High Order Modulation  
BPSK modulation is more widely used in the research of bidirectional relay physical layer network coding. However, there is constellation ambiguity in high order amplitude and phase modulation. In order to solve this problem, the relay node rearranges the constellation points of the received signals. We can therefore reduce the number of signal constellation points through constellation point merging. The Euclidean distance between adjacent constellation points becomes larger, and the system bit error rate performance is improved.  
The design of \(M\)-QAM constellation mapping must be combined with PNC to ensure the existence of mapping function G. Reference [41] gives the conditions for the existence of the PNC mapping function G: if there is \((C,\odot,M:C\to X)\) to make \(G:c_{r}\to s_{r}\) exist, it must satisfy \(\forall c_{i},c_{j}\in C\) and \(x_{i}=M(c_{i}),x_{j}=M(c_{j})\). If there is \(c_{i}\odot c_{j}=s_{r}\), then \(G\big{(}x_{i}+x_{j}\big{)}=s_{r}\). That is \(G\big{(}M(c_{i})+M\big{(}c_{j}\big{)}\big{)}=s_{r}\), where \(C\) is the source symbol set, \(X\) is the set of modulated signals, \(c_{r}=c_{i}+c_{j}\) is the symbol set after \(x_{i}+x_{j}\) demodulation, and \(\odot\) is the PNC mapping operator.  
Since \(M\)-QAM is the superposition of two in-phase and quadrature \(\sqrt{M}\)-PAM signals, only the design of \(M\)-QAM in-phase \(\sqrt{M}\)-PAM modulation constellation mapping needs to be considered. The algorithm shall ensure that the corresponding PNC mapping function G exists. For \(\sqrt{M}\)-PAM modulation, the only necessary and sufficient condition for PNC mapping to be solvable is that any \(L=\sqrt{M}\) consecutive constellation points of a superimposed signal constellation are mapped to different symbols. Table 1 lists the results of PNC using an \(M\)-QAM modulation-demodulation-remapping scheme, where \(x_{1}+x_{2}\) represents the superimposed in-phase signal received by the relay node, \(s_{r}=G(c_{1},c_{2})\) represents the symbol after the PNC mapping, corresponding to the PNC mapping function G, and \(M_{R,L}(s_{r})\) represents the \(\sqrt{M}\)-PAM modulated signal of \(s_{r}\).  
After receiving the superimposed signal, the relay node first demodulates the signal. Table 1 shows that if the user node adopts an \(M\)-ary QAM modulation scheme, the signal received by the relay node is equivalent to \(N=\left(2L-1\right)^{2}\) any QAM. That is, the in-phase branch is equivalent to the PAM in \(H=2L-1\) any. The mapping function G calculates the code word \(s_{r}=c_{1}\odot c_{2}=G(c_{r})=\left(c_{1}+c_{2}\right)\)mod\(L\) after PNC mapping. Finally, the symbol \(s_{r}\) is modulated by \(L\)-PAM to obtain the modulated signal \(x_{r}\).  
In the BC stage, node 1 demodulates the received signal with L-PAM to estimate \(\hat{s}_{r}\). Node 1 demaps signal \(\hat{s}_{r}\) according to its own information \(c_{1}\), and estimates the information \(\hat{c}_{2}=\left(\hat{s}_{r}-c_{1}\right)\)mod\(L\) of node 2.
# Research on the Correlation Filter Tracking Model Based on the Deep-Prunned Feature Network
## 3 Methods
### PCIP-Based Pre-Pruning of Deep Feature Network  
It is assumed that the feature extraction network in the tracking model has \(CL\) convolutional layers, the \(ith(i\in(1,2,\ldots,CL))\) convolutional layer has \(CKN_{i}\) convolutional kernels, and the size of the convolutional kernels is \(ck_{i}\times ck_{i}\)[30]. Following design policy, the feature network is pre-trained offline to provide effective deep features of the target for the downstream correlation filter tracking task. As shown in Figure 2, offline pre-training is accompanied by a pre-pruning of the feature network using the PCIP pruning algorithm [2]; then, the pre-pruned feature network is obtained.  
Using \(F_{i,j}\) to denote the feature map corresponding to the \(jth\) convolutional kernel in the \(ith\) convolutional layer, of which the size is \(R_{i,j}\times C_{i,j}\), then the importance score \(Score_{i,j}\) of the \(jth\) convolutional kernel in the \(ith\) convolutional layer can be derived from Equation (1) [2].  
\[Score_{i,j}=Softmax\big{(}G_{i,j}\big{)}=\frac{e^{\frac{G_{ij}}{T}}}{\sum e^{ \frac{G_{ij}}{T}}} \tag{1}\]  
where \(G_{i,j}=\frac{1}{R_{i,j}\times C_{i,j}}\sum_{r=1}^{R_{i,j}}\sum_{c=1}^{C_{i,j} }F_{i,j}(r,c)\) and \(T\) is the temperature parameter in the Softmax function. Based on the result of Equation (1), the convolutional kernels in layer \(i\) are re-ordered according to \(Score_{i,j}\), \(p_{i,j}\) denotes the reordered order of the \(jth(j=1,2,\ldots,CKN_{i})\) convolutional kernel in layer \(i\). Assuming that the pruning rate in the pre-training stage is defined as \(PR\), the convolutional kernels in layer i after reordering are divided into \(CKN_{i}*PR\), significant convolutional kernels, and \(CKN_{i}*(1-PR)\), unimportant convolutional kernels.  
Set the \(jth\) convolutional kernel in layer i to be \(Kernel_{ij}(k)\) at the \(kth\) iteration; it is then iteratively updated according to Equations (2)-(4) [2]:  
\[Kernel_{ij}(k+1)=Kernel_{ij}(k)+\eta\
abla_{Kernel_{ij}}\text{Loss} \tag{2}\]  
\[Kernel_{ij}(k+1)*\beta_{ij}\to Kernel_{ij}(k+1) \tag{3}\]  
\[\beta_{ij}=\frac{1}{1+e^{\alpha*(p_{ij}-CKN_{i}*PR)}} \tag{4}\]  
where \(\beta_{ij}\) denotes the penalty coefficient, which is used to moderately penalize the unimportant convolutional kernels, while the important convolutional kernels remain unchanged; a is used to regulate the trend of \(\beta_{ij}\). When \(\alpha\) is smaller, the trend of change is smoother, and vice versa, and steeper; Loss denotes the loss function of the whole model. \(\
abla_{Kernel_{ij}}\text{Loss}\) is the gradient of the loss function in \(Kernel_{ij}(k)\).  
The deep feature network is pruned while training offline and iteratively updated using Equations (1)-(4). After offline pre-training and pre-pruning, the pre-pruned feature network is obtained with \(CL\) convolutional layers. Each convolutional layer has \(PPCKN_{i}=CKN_{i}*PR\) convolutional kernels, all of which have the same size \(ck_{i}\times ck_{i}\).  
Figure 2: PCIP-based pre-pruning of the deep convolutional feature network.
## 3 Samples and Methods
### Analytical Methods  
#### 3.2.1 Mineral Composition Analysis by X-ray Diffraction (XRD)  
According to the standard method, \"Analysis Method for Clay Minerals and Ordinary Non-clay Minerals in Sedimentary Rocks by the X-ray Diffraction\" (SY/T 5163-2018) [42], a 0.4 g sample was utilized. The sample powder was carefully placed into the groove of the sample slide, ensuring that the measured surface of the sample remained rough and maintained a flat and even alignment with the surface of the sample slide to avoid preferential crystal powder orientation. The TD-3500 diffractometer manufactured by Dandong Tongda Technology Co., LTD, Dandong, Liaoning, China, was used for detection under the following conditions: 30 kV acceleration voltage, 30 mA beam current, Cu K\(\alpha\) radiation, and Ni filter. The parameters for detection included a scanning range from 3.00\({}^{\circ}\) to 45.00\({}^{\circ}\) 20, a wide angle of 0.02\({}^{\circ}\), a sampling time of 0.6 s, a scan speed of 0.033 s, and a total time (h:m:s) of 0:21:00.  
The sample was detected to obtain X-ray diffraction spectra. Subsequently, the X-ray diffraction spectra were processed using the TD-3500\({}^{\circ}\)s data processing software (MDI JADE 6.5, ICDD, Livermore, CA, USA). The software utilized the Inorganic Crystal Structure Database (ICSD) based on the sample's characteristics.
# MAC/PHY Comprehensive Visible Light Communication Networks Simulation
## 1 Introduction
The MAC-layer mechanisms have been developed for OMNET++, while the optical wireless channel impulse response is calculated using a Modified Monte Carlo Ray tracing (MMCRT) algorithm [12]. The resulting platform allows one to accurately evaluate the performance of VLC networks, taking into account both Line-Of-Sight (LOS) and Non-LOS (NLOS) contributions on each node. Depending on the characteristics of the scenario (materials, geometry, etc. ), the user nodes may be aware (or not) about the transmissions of neighboring nodes. This enables the effective use of Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) that IEEE 802.15.7r1 defines. Nonetheless, as it will be shown during the results sections of this work, this effectiveness is lost depending on the distance between nodes and their optical front-end sensitivities, ultimately diminishing the network performance. Hence, this work provides scientific evidence about the necessity of accurately modeling VLC network taking into account both the logical processes within the MAC layer and the physical nature of visible light propagation in indoor scenarios. The remainder of this paper is structured as follows. In Section 2, an in-depth analysis regarding the state-of-the-art contributions related to MAC layer simulation for VLC networks is presented. Section 3 presents a clinical description of the schemes and processes related to IEEE 802.15.7r1 MAC layer. Moreover, Section 4 introduces the structure of the proposed simulation platform and describes in detail each comprising part. The description of the experiments carried out in this work are presented in Section 5, and the results are illustrated in Section 6. Finally, Section 7 extracts and presents the conclusions of this work and provides insight into potential future research directions.
# Interferometric DEM-Assisted High Precision Imaging Method for ArcSAR
## 5 Accuracy Analysis
### The DEM Accuracy Analysis
#### 5.1.2 Phase Error due to the Difference in Phase Distortion of the SAR Images  
The defocusing can cause phase distortion in SAR images. If there is the difference in phase distortion of the SAR image pair used for interference, the phase error will be introduced during the interference process. In this section, we analyze this kind of phase error. We define the distortion phase as \(p_{\mathrm{d}}\). The \(h\)-\(p_{\mathrm{d}}\) curves of the SAR image pair are shown in Figure 14.  
It can be found that the phase distortions of the above SAR image pair are similar, which means that most of the distortion phase will be offset during the interference process. Therefore, the phase error introduced by the process of interference is small, as shown in Figure 15.  
\(\sigma_{\mathrm{pd}}\) represents the phase error due to the difference in phase distortion of the SAR image pair.
# Three Steps towards Better Forecasting for Streamflow Deep Learning
## 2 Methodology
### Long Short-Term Memory (LSTM)  
LSTM is an improved version of a recurrent neural network (RNN) [30]. It is a deep learning algorithm that has been set up to perform forecasting in the field of hydrology and water resources [31]. It eliminates the issue of overfitting and can yield better generalization than standalone models. The network captures long-term dependencies and deals with vanishing gradient limitations that exist in the original RNN [32]. The LSTM network (see Figure 1) comprises blocks of memory cells, an input gate, an output gate and a forget gate. The network operates like a chain [33] and can deal with delays such as seasonal and trend patterns [34]. The input gate manages the extra information added to the cell state.  
The forget gate eliminates information from the cell state. The ability of LSTM to store or remove information outperforms other neural networks [35]. Information can be carried over multiple time steps and provide for learning of sequential dependency in the input data, making it relevant even for long time series [36]. This gives an advantage to the LSTM when it comes to modeling time series, particularly hydrologic variables, which employ common hyperparameters, such as precipitation, flow or water level, for streamflow prediction, water quality modeling and flood forecasting [37]. Although the training process is longer than other data-driven models, LSTM can yield higher accuracy [38].
## 1 Introduction
These methods are effectively used in spatiotemporal variation of vegetation analysis indicated by vegetation index such as Normalized Difference Vegetation Index (NDVI) [25; 26; 27; 28], but have rarely been adopted in tree cover variation analysis. In this study, we utilized spatial analysis in GIS, trend analysis, and time-series analysis methods to analyze the tree cover variation in the Loess Plateau after the GGP. Analysis spatially and temporally based on MODIS VCF tree cover data is used to answering the following questions: (1) How does tree cover change spatiotemporally after the GGP in the Loess Plateau, and how will it change in the future? (2) Is tree cover an effective indicator in assessing the effectiveness and sustainability of the GGP? We hope the results will not only give a case for tree cover spatiotemporal variation analysis, but also potentially enhance understanding of current conditions and sustainability. This study also facilitates an assessment of the GGP's effects on environmental restoration, and offers a valuable reference for future detecting the tree cover change through GIS and RS tools.
# Towards Understanding Neural Machine Translation with Attention Heads' Importance
## 1 Introduction
### Contributions  
Our primary contributions are as follows:  
* We offer a comprehensive examination of the three types of attention mechanisms in the Transformer model: encoder self-attention, decoder self-attention, and encoder-decoder attention. This thorough analysis enriches our understanding of the model's attention mechanisms;
* Our study investigated the distribution and importance of attention heads. This helps researchers retain important attention heads and remove unimportant ones, thereby reducing model parameters and improving model speed;
* Our findings reveal a link between attention heads and the understanding of specific POS features, offering theoretical insights for future studies. Specifically, we suggest that focusing on the acquisition of noun and verb knowledge in Chinese-English machine translation could potentially improve model performance;* We analyze the decision-making processes of attention heads based on POS, dependency relations, and syntactic trees, which may inform and inspire model design. Our research indicates that certain linguistic elements, such as nouns, adjectives, and adjective modifiers, are pivotal, whereas others, like determiners, are less critical. This discovery suggests that in Chinese-English machine translation, the model focuses more on certain information, which enhances the model's interpretability.
## 4 Results
### Detection of Morphological Changes (SAR)
The temporal evolution of the most prominent morphological changes visible in SAR images are sketched on the right. (**a**) Caldera collapse of Kilauea (Hawai’i) during the first months of the 2018 flank eruption. (**b**) Anak Krakatau (Indonesia) island growth during the months preceding the 2018 tsunamigenic landslide, and horseshoe-shaped caldera after the landslide. In this figure speckle is removed from SAR images using a non-local means filter [78] (NDSAR, [[https://github.com/odhondt/ndsar](https://github.com/odhondt/ndsar)]([https://github.com/odhondt/ndsar](https://github.com/odhondt/ndsar))), and tone mapping of SWIR images is fixed so that colors and contrasts are the same in each image. See Supplementary Material S3 and S4 for video animations spanning several months.