esa-sceva/satcom-chunk-collection huggingface dataset:
# Augmented Reality in Surgery: A Scoping Review
## 2 Theoretical Background
For the visualization of the VOs onto the real world, several AR display technologies exist, usually classified in head, body and world devices, depending on the place where they are located [7; 17]. World devices are located in a fixed place. This category includes desktop displays used as AR displays, and projector-based displays. The former are equipped with a webcam, a virtual mirror showing the scene framed by the camera and a virtual showcase, allowing the user to see the scene, alongside additional information. Projector-based displays cast virtual objects directly onto the corresponding real-world objects' surfaces. With body devices, we usually refer to handheld Android-based platforms, such as tablets or mobile phones. These devices use the camera for capturing the actual scenes in real time, while some sensors (e.g., gyroscopes and accelerometers and magnetometer) can determine their rotation. These devices usually resort to fiducial image targets for the tracking-registration phase [18]. Finally, the HMDs are near eye displays, wearable devices consisting in sort of glasses that have the advantage of leaving the hands free to perform other tasks. HMDs are mainly of two types: video see-through and optical see-through. The first ones refer to special lenses that let the user see the external real world through a camera whose frames are in turn combined with VOs. In this way, the external environment is recorded in real time and the final images overlaying the VOs are produced directly over the user's lenses. Differently, the optical see-through devices consist of an optical combiner or holographic waveguides, the lenses, that enable the overlay of images transmitted by a projector over the same lenses through which a normal visualization of the real world is allowed. In this way the user visualizes directly the reality augmented with the VOs overlaid onto it [7; 19]. Figure 2 shows an example of HMD. Figure 1: Workflow of augmented-reality-enabled systems. The different techniques are summarized in Figure 3. The aim of this study was to describe the state of the art relating to the use of AR in the surgery domain. The description and analyses of the various procedures used to create the virtual images represented a further objective. This scoping review aims to provide a summary of the surgical fields in which this new technology finds its best application providing doctors with an overview of the key aspects behind viewing accurate virtual images superimposed on the real world. The research highlighted that the marker-based tracking and the rigid registration are currently the most used systems to acquire data, as reported in the following paragraphs.
## 2 Materials and Methods
### Paper Properties
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline
**Properties** & **Standard ISO** & **S1** & **S2** & **S3** & **S4** \\ \hline Grammage [g/m\({}^{2}\)] & ISO 536 (2019) & 78.8 \(\pm\) 1.2 & 76.8 \(\pm\) 0.9 & 102.6 \(\pm\) 2.1 & 109.7 \(\pm\) 3.8 \\ Thickness [mm] & ISO 534 (2011) & 0.103 \(\pm\) 0.005 & 0.121 \(\pm\) 0.002 & 0.186 \(\pm\) 0.003 & 0.207 \(\pm\) 0.005 \\ Density [kg/m\({}^{3}\)] & ISO 534 (2011) & 766 \(\pm\) 27 & 635 \(\pm\) 10 & 551 \(\pm\) 8 & 529 \(\pm\) 17 \\ Specific volume [cm\({}^{3}\)/g] & ISO 534 (2011) & 1.307 \(\pm\) 0.046 & 1.576 \(\pm\) 0.025 & 1.817 \(\pm\) 0.026 & 1.892 \(\pm\) 0.062 \\ Roughness [mL/min] & ISO 8791-2 (2013) & 198 \(\pm\) 14 & 860 \(\pm\) 48 & 1191 \(\pm\) 98 & 1786 \(\pm\) 124 \\ Roughness (Ra)\(-\)TR200 [um] & ISO 21920-2 (2021) & 4.681 \(\pm\) 0.712 & 5.565 \(\pm\) 0.688 & 7.082 \(\pm\) 0.271 & 7.420 \(\pm\) 1.145 \\ Porosity [mL/min] & ISO 5636-3 (2019) & 1159 \(\pm\) 42 & 418 \(\pm\) 44 & 1490 \(\pm\) 55 & 1507 \(\pm\) 32 \\ Water absorption—Cobb [g/m\({}^{2}\)] & ISO 535 (2023) & 20.4 \(\pm\) 1.6 & 20.4 \(\pm\) 1.8 & 15.7 \(\pm\) 0.8 & 21.8 \(\pm\) 5.5 \\ D65 whiteness with UV [\%] & ISO 2470-1 (2016) & 101.91 \(\pm\) 0.43 & 35.22 \(\pm\) 0.22 & 28.78 \(\pm\) 0.15 & 28.08 \(\pm\) 0.08 \\ D65 whiteness without UV [\%] & ISO 2470-1 (2016) & 85.74 \(\pm\) 0.11 & 35.29 \(\pm\) 0.32 & 28.75 \(\pm\) 0.15 & 28.05 \(\pm\) 0.08 \\ Opacity [\%] & ISO 2471 (2008) & 94.47 \(\pm\) 0.20 & 96.58 \(\pm\) 0.33 & 99.77 \(\pm\) 0.25 & 99.90 \(\pm\) 0.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Properties of commercial office paper (S1), Japanese knotweed paper (S2), giant goldenrod paper (S3), and black locust paper (S4).
# Research on Panorama Generation from a Multi-Camera System by Object-Distance Estimation
## 2 Related Work  
### Feature Point-Based Panoramic Imaging Method  
For a set of overlapping images acquired with a multi-camera panoramic system, it is possible to generate a panoramic image relying on feature point-based methods. In the approach, the SIFT feature points are detected and matched on the overlapping regions of each pair of images from adjacent cameras [19; 20; 21]. Assuming that the scene is flat, the homographic estimations [22; 23] are performed based on corresponding feature-points for each two adjacent cameras. At this point, all the transformations are modified relative to the master image, which is required for the generation of the panoramic images. The panoramic imaging approach is an extension of feature-based image registration and stitching of a single pair of images, which can be applied in scenes with rich texture features. However, this method heavily depends on achieving a sufficient distribution and accurate matching of feature points, as well as requiring substantial overlap between adjacent cameras. Additionally, it requires less variation in object-distance information from the surroundings, as the homographic transformation only exists between two planes.
# Seismic Random Noise Attenuation via Low-Rank Tensor Network
## 2 Materials and Methods
### Data and Data Processing  
We compare the attenuation performance of our proposed LRTNet with a recent Python package, Pyseistr 0.0.4.4.0 [51], and an LRA method, TNN-SSTV, on an open synthetic dataset and two real field datasets (F3-2020 and Penobscot).  
Synthetic Dataset: The synthetic dataset, released by [PERSON] et al. in FaultSeg [52], contains 200 training and 20 validation pairs of 3D seismic volumes (\(128\times 128\times 128\) per volume) with corresponding faults. To standardize the denoising evaluation, we implement the following preprocessing method:  
* We select the first 20 volumes from the training set to train our LRTNet. This decision is based on preliminary experiments where we observed that using the full 200-volume training set required a similar number of epochs to converge, with only a marginal improvement in average SNR (less than 0.5 dB). Given the significant computational cost and time required for training on the full dataset (the limitation of efficiency is discussed in Section 4), we opted for the smaller subset to balance performance and computational efficiency. For each volume, we generate a corresponding noisy volume by adding Gaussian noise at 0 dB SNR, while retaining the original noise-free volume as the ground truth label. The choice of 0 dB SNR is motivated by the observation that all methods perform well at lower noise levels, while performance degrades at higher noise levels. At 0 dB SNR, our method demonstrates a clear advantage over baseline approaches, making it a balanced and representative noise level for evaluation.
* Validation Data: We use all 20 validation volumes. We apply identical noise injection (0 dB Gaussian) to create noisy volumes, with denoised outputs compared against the pristine volumes for metric calculation. Real Field Datasets:
* F3-2020: This is a marine 3D survey of the offshore Netherlands (original size: \(651\ \times\ 951\ \times\ 458\)). To avoid redundant processing, we extract a random sub-volume (\(80\times 90\times 100\)) containing representative geological features. No artificial noise is added, as the raw data inherently contain field-acquisition noise.
* Penobscot: This is a publicly available North Atlantic survey with complex subsurface structures. Following the same rationale, we select a \(400\times 200\times 100\) sub-volume from the \"1-PSTM stack agc\" data. Denoising is applied directly to the native noisy volume without preprocessing.
# On Transform Domain Communication Systems under Spectrum Sensing Mismatch: A Deterministic Analysis
## 4 Simulation Results
### AWGN Channels  
In Figures 7 and 8, various mismatch factors are introduced to model various degree of SSM conditions. In spite of difference modulations methods in TDCS (CCSK or QPSK), Monte-Carlo simulation results match with our theoretical analysis perfectly. One observation is that, when the mismatch factor \(\alpha\) increases, BER performance of the TDCS is deteriorated gradually, as both symbol detections suffer from the presence of SSM.  
This deterioration seems to be slightly worse than expected when \(\alpha=50\%\). This is because that the CCSK is a waveform modulation, in which symbol information relies on the PCF vector, and is recovered by detecting the tag of the max peak. As \(\alpha\) increases, CCSK signaling no longer maintains its pseudo-orthogonal property, resulting in a severe rise of the side lobes in the PCF vector. In this way, Equation (11) serves as upper bound rather than an accurate result.  
Figure 8: BER performance of TDCS-QPSK in AWGN channels (various mismatch factor \(\alpha\)).  
Figure 7: BER performance of TDCS-CCSK in AWGN channels (various mismatch factor \(\alpha\)).
# Forecasting Daily Temperatures with Different Time Interval Data Using Deep Neural Networks
## 3 Proposed Neural Network Architectures
### Lstm  
The LSTM model is an advanced RNN model that reflects temporal dependencies between input data better than RNN by introducing the gates for controlling temporal information to the hidden state. The LSTM model has dealt with a vanishing gradient problem of the RNN model and is one of the most powerful artificial neural network models for sequential data such as text, signal, and time series. The LSTM model consists of connections of cells which contains a hidden layer and an output layer. A hidden layer receives an input \(x_{t}\) and a context \(h_{t-1}\), values of the hidden layer of the previous cell, as inputs and then derives a hidden context vector \(h_{t}\) from the inputs and an output layer calculates output vector \(y_{t}\) with the hidden context vector as the following equations:  
\[i=\sigma(W_{i}x_{t}+U_{i}h_{t-1}+b_{i}) \tag{2}\]  
\[o=\sigma(W_{o}x_{t}+U_{o}h_{t-1}+b_{o}) \tag{3}\]  
\[f=\sigma\left(W_{f}x_{t}+U_{f}h_{t-1}+b_{f}\right) \tag{4}\]  
\[c_{t}=\sigma(W_{c}x_{t}+U_{c}h_{t-1}+b_{c}) \tag{5}\]  
\[h_{t}=tanh(i\cdot x_{t}+fc_{t-1})\cdot o \tag{6}\]  
\[y_{t}=W_{i}h_{t}+b_{t} \tag{7}\]  
where the gate vectors and cell state vectors \(i\), \(o,f,c_{t}\), and weight matrices and bias vectors \(W,U,b_{i}\), \(b_{o}\), \(b_{i}\), \(b_{c}\) are learnable parameters, and \(\sigma\) denotes a sigmoid function. After deriving \(h_{t}\), the LSTM cell returns the output vector \(y_{t}\) by combining it with \(W_{1}\) and \(b_{1}\) in the output layer.  
We designed the LSTM structures in this study as three stacked LSTMs connected to the fully connected layer. We describe LSTM model architectures in Figure 1, and Tables 3 and 4 summarize the details of the daily input and hourly input LSTM model structures, respectively. If the number of input variables is 17, the first cell receives a 17-dimensional vector as an input and extends it to a 20-dimensional vector before it passes to the second cell. In the case of hourly input data, however, the LSTM model keeps the number of vector dimensions as the number of input variables \(m\). As we noted earlier, we determined the remaining structure of the LSTM model through trial and error. Note thatand \(m\) shown in Tables 3 and 4 denote the length of time of the input data and the number of input variables, respectively.
## 3 Results and Discussion
### Effect of Resin Types  
The NOL ring failure data of different resin types were sorted in Figure 7. From the tensile strengths and interlaminar shear strengths of the three levels, it is known that the breaking strengths of NOL rings prepared with HBV-SL-03-01 resin and HBV-SL-03-02 resin are higher than those of NOL rings prepared with HBV-SL-03-03 resin. It was found that the viscosity of HBV-SL-03-01 and HBV-SL-03-02 resins was the same, both higher than that of HBV-SL-03-03 resin. It is known that resin viscosity is one of the factors affecting the mechanical properties of NOL rings. The resin with higher viscosity is easier to wrap on  
Figure 5: Tensile strength of different fibers when winding with HBV-SL-03-03 resin.  
Figure 6: Tensile strength of different fibers when winding with HBV-SL-03-01 resin.  
the surface of carbon fiber, which can more effectively protect the carbon fiber during the process, reduce fiber wear, and improve the quality of the composite. In addition, as the viscosity of the resin increases, the crosslink density between the resin molecules increases, which also leads to a decrease in the tensile strength of the composite [27].
# On Channel Estimation in LTE-Based Downlink Narrowband Internet of Things Systems
## 4 NB-IoT Downlink Channel Estimation
### LMMSE Estimation with SVD and Overlap Banded Technique  
The banded submatrix utilization is not efficient at the edge subcarrier because of exploiting comparatively low correlated channel statistics for estimating the channel. To overcome this drawback, overlapped subcarriers are introduced, as in [29], to each small matrix as depicted in Figure 6a. We estimate the coefficients of the channel by the submatrices in the overlapped situation, and hence it is expected that the edge subcarrier performance will improve for all submatrices. We consider the overlap distance as \(S/2\), and the total submatrices are \(R=(2L/S)-1\) for the overlapped strategy. Therefore, the channel autocorrelation matrix can be split into sections (b) and (c), as shown in Figure 6.  
Figure 5: Banded structure of the channel autocorrelation matrix \(\Phi_{\texttt{TV}}\).  
Depending on the above depiction, Figure 6b gives us the vector of the channel estimation for LMMSE, as shown below:  
\[\mathbf{\Psi}_{r,LMMSE}^{(b)}=\mathbf{\
abla}_{r.LMMSE} \tag{27}\]  
where \(r=2.floor[(p-1)/S]+1\), for \(1\leq p\leq L\). From Figure 6c, the LMMSE estimation vector becomes  
\[\mathbf{\Psi}_{r,LMMSE}^{(c)}=\mathbf{\
abla}_{r.LMMSE} \tag{28}\]  
where \(r=2.floor[(p-1)/S+1/2]\) for \((S/2)+1\leq p\leq L-(S/2)\). Therefore, channel estimation in the overlap area of subvectors (i.e., \((S/2)+1\leq p\leq L-(S/2)\)) will be calculated at two submatrices that will be obtained from (b) and (c) of Figure 6. Hence, we get the final LMMSE overlap banded estimated channel vector from Equation (27) and Equation (28) as given below:  
\[\overline{\mathbf{\overline{\Psi}}}_{LMMSE.overlap.banded}=[\overline{\mathbf{\overline{ \Psi}}}_{1,LMMSE}^{T},\overline{\mathbf{\overline{\Psi}}}_{2,LMMSE}^{T},\dots, \overline{\mathbf{\overline{\Psi}}}_{R,LMMSE}^{T}]^{T} \tag{29}\]  
where \(\overline{\mathbf{\overline{\Psi}}}_{r.LMMSE}\) can be defined as  
\[\overline{\mathbf{\overline{\Psi}}}_{r,LMMSE}=\left\{\begin{array}{ll}\mathbf{ \Psi}_{r,LMMSE}^{(b)}[1:1+3S/5],&\text{r}=1\\ \mathbf{\Psi}_{r,LMMSE}^{(c)}[2S/5:1+3S/5],&\text{r}=2,4,\dots,R-1\\ \mathbf{\Psi}_{r,LMMSE}^{(b)}[2S/5:1+3S/5],&\text{r}=3,5,\dots,R-2\\ \mathbf{\Psi}_{r,LMMSE}^{(b)}[2S/5:S],&\text{r}=R\end{array}\right.\]
# An Efficient Single-Anchor Localization Method Using Ultra-Wide Bandwidth Systems
## 5 Experiment and Analysis
### Performance Evaluation  
We randomly selected \(N_{t}=20\) test points in E1 and \(N_{t}=14\) test points in E2 to evaluate the fixed point localization performance, and each test point has \(N_{m}=1000\) measurements. Then we select a fixed trajectory in E2 to evaluate the tracking performance. The localization accuracy of the static agents and the moving agents are evaluated under fixed points and trajectories, respectively.
# Towards 6G Satellite-Terrestrial Networks: Analysis of Air Mobility Operations
## 5 Results
### Link Budget
#### 5.1.3 Path Loss vs. Elevation Angle  
Figure 14 shows the path loss of the signal, considering the free-space loss, weather attenuation, and Doppler effect. The minimum path loss occurs when the elevation angle is 90 degrees. Hence, the best signal quality is achieved when the satellite is directly above the UAV. As the satellite moves away from the UAV, the path loss gradually increases.
# A Distributed Indexing Method for Timeline Similarity Query
## 4 The Structure of DTI-Tree  
The intervals of a timeline are stored on a TI-Tree. They consist of some objects [\(T_{id}\), \(I_{id}\), \(I_{s}\), \(I_{e}\), \(I_{t}\)], where [\(I_{s}\), \(I_{e}\)] is the spatial location on a 2D plane (Figure 7a), defined by the start value and the end value of the data. Each object belongs to a triangle partition, as shown in Figure 7a. The DTI-Tree is a combination of T-Tree and TI-Trees that are processed by each slave node in the cluster. Let \(M\) be the master node and have \(n\) slave nodes. Each slave node is recorded as \(S_{i}\) and \(i\in[0\), \(n-1]\). Each slave node has one or \(K\) TI-Trees. They may be recorded as  
\[TI-Trees(S_{i})=\sum_{k=0}^{K-1}(TI-Tree(S_{i},k)) \tag{2}\]  
\[TI-Trees\ =\sum_{i=0}^{n-1}(TI-Tree(S_{i})) \tag{3}\]  
Therefore,  
\[\mathrm{D}TI-Tree\ =T-Tree+\sum_{i=0}^{n-1}(TI-Tree(S_{i})) \tag{4}\]  
For example, if the data in triangle \(OEM\) (Figure 7a) are stored in two slaves, then the dataset will be partitioned into two parts, triangle \(OKE\) and \(EKM\). One T-Tree will be constructed in the master node, and Tow TI-Trees, TI-Tree1 (OKE) and TI-Tree2 (EKM) will be constructed in slave 1 and slave 2, as shown in Figure 9. Because the' TI-Tree is a virtual binary tree without non-leaf nodes, all of the leaf nodes with codes (The triangle codes are in Figure 7a) in a TI-Trees form a linear leaf node list. Each triangle contains one or more interval points, which are parts of a timeline storing in HDFS. DTI-Tree has five layers: master, slave, leaves, intervals, and timelines, as shown in Figure 9. A timeline is possible to be partitioned to two or more slaves, for example, the timeline T1 in the Figure 9 has two partitions, one is in the slave1, and the other is in the slave 2. The following are the construction, maintenance, and query of DTI-Tree.  
Figure 9: DTI-Tree index structure (example of Figure 7a).
## 5 Experiment
### Experimental Setup  
We compared our models with server KG embedding algorithms in our experiments:  
1. TransE [6]. TransE was the first model to introduce translation-based embedding, which interprets relations as the translations operating on entities.
2. DistMult [10]. DistMult is based on the bilinear model, where each relation is represented by a diagonal rather than a full matrix. DistMult enjoys the same scalable properties as TransE and it achieves superior performance over TransE.
3. ComplEx [11]. ComplEx extends DistMult by introducing complex-valued embeddings so as to better model asymmetric relations. It has been proven that HolE is subsumed by ComplEx as a special case.
4. ConvE [12]. ConvE is a multi-layer convolutional network model for link prediction [24] of KGs, and it reports state-of-the-art results for several established datasets. Unlike previous work which has focused on shallow, fast models that can scale to large knowledge graphs, ConvE uses 2D convolution over embeddings and multiple layers of nonlinear features to model KGs.
5. ConvKB [13]. ConvKB applies the global relationships among same-dimensional entries of the entity and relation embeddings, so that ConvKB generalizes the transitional characteristics in the transition-based embedding models.
6. R-GCN [14]. R-GCN applies graph convolutional networks to relational knowledge bases, creating a new encoder for link prediction and entity classification tasks.  
The experimental results from those baselines were obtained from the codes provided by the authors. In our method, we made configurations by selecting a learning rate \(\alpha\) among [0.01, 0.05, 0.10] and an output dimension \(D\) among [100, 200, 400]. For WN18 RR, the configuration was as follows. The learning rate \(\alpha\) was 0.01 and the output dimension \(D\) was 400, with 3000 training iterations using the Adam [27] optimizer. For FB15k-237, the configuration was as fallows. The learning rate \(\alpha\) was 0.10, and the output dimension \(D\) was 200, with 1000 training iterations using the SGD optimizer. We trained the model until it converged.
# Difference DV_Distance Localization Algorithm Using Correction Coefficients of Unknown Nodes
## 3 Theoretical Background
\tag{13}\]  
Then, the Equation (13) can be transformed into the linear equation form of \(\mathbf{AX}=\mathbf{B}\), and we have:  
\[\mathbf{X}=\left[ \begin{array}{c}x\\ y\end{array} \right];\mathbf{A}=\left[ \begin{array}{ccc}2(x_{1}-x_{n})&2(y_{ 1}-y_{n})\\ 2(x_{2}-x_{n})&2(y_{2}-y_{n})\\ \vdots&\vdots\\ 2(x_{n-1}-x_{n})&2(y_{n-1}-y_{n})\end{array} \right];\mathbf{B}=\left[ \begin{array}{c}x_{1}^{2}-x_{n}^{2}+y_{1}^{2}-y_{n}^{2}-d_{1}^{2}+d_{n}^{2} \\ x_{2}^{2}-x_{n}^{2}+y_{2}^{2}-y_{n}^{2}-d_{2}^{2}+d_{n}^{2}\\ \vdots\\ x_{n-1}^{2}-x_{n}^{2}+y_{n-1}^{2}-y_{n}^{2}-d_{n-1}^{2}+d_{n}^{2}\end{array} \right]\]  
Finally, the least square method can be used to solve the coordinates \(\mathbf{X}\) of unknown nodes, as shown in Equation (14):  
\[\mathbf{X}=(\mathbf{A}^{T}\mathbf{A})^{-1}\mathbf{A}^{T}\mathbf{B} \tag{14}\]