esa-sceva/satcom-chunk-collection huggingface dataset:
# Green Hydrogen Storage in an Underground Cavern: A Case Study in Salt Diapir of Spain
## 3 Methodology and Results
### Storage Design
#### 3.1.2 Cavity Shape  
The shape of the cavity affects the spatial distribution of stresses and, thus, the convergence of the salt rock and the volume loss of the cavern. Proper design of its shape will increase stability and safety, so models should be made once the geomechanical parameters obtained from laboratory samples are known, although the actual shape of the cavern will be more complex than the simulated one.  
The caverns can have cylindrical, elliptical, or truncated cone shapes, extending the roof or the bottom of the cavity. The best performance (higher stability, lower stress state, and lower convergence) is obtained for cylindrical caverns with rounded edges [12; 22], i.e., with hemispherical shapes at the top and bottom of the cylinder, giving rise to a capsule shape.
# Design of a Flat-Panel Metasurface Reflectarray C-Band Antenna
## 3 Reflectarray Antenna Measurement  
### Source Antenna: Circular Waveguide Antenna  
A cylindrical horn antenna was designed for the feeding antenna based on dominant mode analysis [22]. The size of the cylindrical can is 35 \(\times\) 88 mm\({}^{2}\) (diameter \(\times\) height), and it has a single dominant TM\({}_{11}\) mode in the band of 5.0-6.6 GHz, which is broad enough to cover the whole operation frequency of the proposed metasurface reflector. The length of a feeding probe is 12 mm, and the distance between the probe and the metal wall is 20 mm. The fabricated source horn antenna and its measurement data are shown in Figure 5.  
All antenna parameters were measured in an anechoic chamber. The designed antenna has a broad impedance bandwidth of 5.8-6.0 GHz (Figure 6a) and high antenna gain value of 6.8-7.8 dBi (Figure 6b). The measured cross-pol suppression level was higher than 20 dB at the operation frequency band. All the simulation results show good agreement with the measurement data.  
Figure 5: Fabricated 16 \(\times\) 16 reflectarray in the anechoic chamber: (**a**) front and (**b**) side views.
# Adaptive Equalization for Dispersion Mitigation in Multi-Channel Optical Communication Networks
## 3 Technical Background
### Feed Forward Equalization  
The mitigation of the model dispersion can be achieved at the user end with signal processing, majorly employing adaptive equalization techniques, through which the compensation is achieved when linear time invariant model of the implemented equalizer approaches the channel-response of the SMF. The two similar but opposite responses cancel out the effect of each other and hence the received signal is free from any channel effects. The effects of model dispersion in the form of linear and slow-varying system are given in Equation (1) [27; 28]:  
\[r(z)=\int_{n=1}^{N}\kappa[(\tau,z)\psi(z-\tau)d\tau+\theta(z)], \tag{1}\]  
where \(\psi(z)\) describes modulated signal and can be extended as  
\[\psi(z)=\sum_{t}d[t]p(z-tZ). \tag{2}\]  
Figure 4: Modes of propagating pulse in optical fiber.  
Figure 3: Signal broadening issue due to chromatic dispersion.  
Here, \(p(z)\) denotes pulse shape, \(z\) is propagated per bit interval, \(\kappa(\tau,z)\) shows the equivalent impulse response of the transmission fiber, and \(\vartheta(z)\) is the additive noise.  
If \(g(z)\) denotes the convolution of the transmitted pulse with the channel impulse response [29], we can write  
\[g(z)=\kappa(z)*p(z). \tag{3}\]  
As \(\kappa(z)\) is time-invariant, i.e., \(\kappa(\tau,z)=\kappa(\tau)\), considering the PBRS output \(d[t]\), the \(r(z)\) can be expressed as follows (Equation (4) [27]:  
\[r(z)=d[k]g(z-kZ)d\tau+\vartheta(z). \tag{4}\]  
Thus, an optical matched filter on the receiver side is used to the transmitted pulse \(g(z)\). Subsequently, sampling the outcome of this matched filter at intervals, the communicated bit sequence \(d[t]\) is obtained, i.e,  
\[z=tZ. \tag{5}\]  
This shows that distortion among the optical pulses can be suppressed by using adaptive equalization [30]. A finite impulse response (FIR) filter is used for the FFE method [31], whose output is shown in Equation (6) as  
\[Y[t]=Y[nZ]=\sum_{t}^{N}[C_{k}r(tZ-kz)], \tag{6}\]  
where the delay-line spacing for \(N\) number of taps for the equalizer is \(Z\) and the \(k\)th coefficient of filter is \(C_{k}\), which is evaluated by using any of the LMS or MMSE schemes. The schematic diagram of FFE is shown in Figure 5.
## 5 Parameter Analysis
### Tunnel-Surcharge Spacing \(d\)  
Assuming that the parameters of the new and existing tunnel models remain unchanged, five curves of tunnel buried depth and maximum longitudinal displacement were generated for different load-tunnel spacing \(d\). Figure 13 shows the variation curve of the maximum longitudinal displacement \(w_{\text{max}}\) for increasing tunnel buried depths \(z_{0}\) and different load-tunnel centerline spacing \(d\). As shown in the figure, for a given spacing \(d\), the maximum longitudinal displacement \(w_{\text{max}}\) of the tunnel decreases gradually with a varying slope as the tunnel buried depth \(z_{0}\) increases. The maximum longitudinal displacement \(w_{\text{max}}\) of the tunnel is different for different tunnel-load spacing \(d\). When \(d=0\), the tunnel is located at the center of the surcharge, and the external load disturbance is the most significant. Therefore, the maximum longitudinal displacement of the existing tunnel caused by the surcharge is large, and it increases rapidly as the buried depth decreases. As can be seen from the figure, as the tunnel buried depth \(z_{0}\) increases, the maximum displacement change rate of the tunnel gradually decreases for different tunnel-surcharge spacing \(d\). The reason is that the external load corresponding to the surcharge load has little influence on the tunnel with a small embedded depth.  
Figure 12: Relative positions of the tunnel and surface surcharge.
# New Hybrid Techniques for Business Recommender Systems
## 8 Experiment 2: Performance of Hybrid Recommender  
To find the right combination of the graph-based and CBR recommender, we experimented with different values of \(\beta\), starting with a very low value. The lower values of \(\beta\) indicate a higher weight to the graph-based recommender. As can be seen in Table 3, the performance of the hybrid recommender appears to be better than either of the individual recommendation techniques; however, the precision issue of the graph recommender still shows its negative impact for very low values of \(\beta\). The verbosity threshold for our experiments was at 14, and it can be observed that the performance suddenly dips at 15 input elements for \(\beta\) = 0.1 (where MAP = 0.843 for a verbosity of 10 and MAP = 0.804 for verbosity 15).  
On the other hand, although a high \(\beta\) resolves the precision problem, the performance is not optimum because for, e.g., \(\beta\) = 0.9, the graph recommender's ability to provide more recall is not sufficiently leveraged.  
From the results for \(\beta\) = 0.3, one can conclude that the right value of \(\beta\) can cure both the recall problem of the CBR recommender and the precision problem of the graph recommender and thus can give an optimum performance among the individual recommendation techniques and the various configurations of the hybrid strategy put together.  
Finally, we take a look at the time complexity of the hybrid recommender strategy, which is dependent on the complexities of the recommender systems that we combined.  
\[O_{hyb}=max(O_{GBR}(k_{GBR}*N_{GBR}),O_{CBR}(k_{CBR}*N_{CBR})) \tag{4}\]  
where:  
\(k_{GBR}\) = no. of iterations over the graph;  
\(N_{GBR}\) = no. of edges in the graph;  
\(k_{CBR}\) = no. of local similarity measures;  
\(N_{CBR}\) = no. of cases in the case base.  
The time complexity of the graph-based recommender is similar to that of PageRank with Priors [53], i.e., depending on the no. of iterations and the no. of edges in the graph, whereas the complexity of the CBR recommender depends on the no. of cases in the case-base and the no. of local similarity measures, which are roughly equal to the no. of attributes used to describe the cases. Thus, the overall complexity of the hybrid recommender is the maximum from either of these recommendation techniques, as depicted in Equation (4).
## 2 Methods
### Inverse problems
In order to turn the inverse problem to well-posed, the goal is relaxed by asking for some reliable estimate only, say \(\tilde{\mathbf{f}}\), which, in a practical sense, may be considered \"very close\" or \"best approximation\" to the exact image \(\mathbf{f}\), given the measured sample data \(g_{m},\,m=1,2,...,M\), the known response function \(r_{m}(t),\,m=1,2,...,M\), of the measuring instrument, and some information about the noise components \(e_{m},\,m=1,2,...,M\), such as their covariance matrix \(\mathbf{C}=\{C_{ij}\}_{i,j=1,2,...,M}\). In this respect, [PERSON] and [PERSON] [18] suggested the ME principle to assign a probability distribution to any image along with a \(\chi^{2}\)-constraint for handling the errors \(e_{m},\,m=1,2,...,M\). The \(\chi^{2}\)-distribution measures how well a model \(\tilde{\mathbf{f}}\) fits the measured data \(\mathbf{g}\):  
\[\chi^{2}\left(\tilde{\mathbf{f}}\right)=\sum_{i=1}^{M}\sum_{j=1}^{M}\left[g_{i}- \sum_{n=1}^{N}R_{in}\tilde{f}(t_{n})\right]\,C_{ij}^{-1}\,\left[g_{j}-\sum_{n =1}^{N}R_{jn}\tilde{f}(t_{n})\right] \tag{6}\]  
or, in the form of a matrix product:  
\[\chi^{2}\left(\tilde{\mathbf{f}}\right)=\left(\mathbf{g}-\mathbf{R}\tilde{\mathbf{f}} \right)^{T}\,\mathbf{C}^{-1}\,\left(\mathbf{g}-\mathbf{R}\tilde{\mathbf{f}}\right) \tag{7}\]  
where \(\mathbf{C}^{-1}\) is the inverse of the covariance matrix of noise. An approximate equality holds if neglecting the off-diagonal elements, which is strictly true when the noise is not correlated among pixels:  
\[\chi^{2}\left(\tilde{\mathbf{f}}\right)\cong\sum_{m=1}^{M}\left[\frac{g_{m}-\sum_{ n=1}^{N}R_{mn}\tilde{f}(t_{n})}{\sigma_{m}}\right]^{2} \tag{8}\]  
where the noise standard deviation, \(\mathbf{\sigma}\), has the components:  
\[\sigma_{m}=\left(C_{mm}\right)^{\frac{1}{2}},\,m=1,2,...,M \tag{9}\]
## 3 Results
### Experimental Results  
For the evaluation, the experimental setup was prepared as described in Figure 7, in which the position of the FAD was placed arbitrarily. Three novice people who had no experience on the distal screwing operation performed the experiment. The subjects first tried to roughly relocate the C-arm to put the FAD within the range of C-arm view, attempting to make the two planes parallel. After that, the subjects tried to align the guiding pattern to the IMN hole. During these experiments, we measured the following parameters: (1) Parallelization time to make the planes of the C-arm and the FAD parallel. (2) Fine-positioning time to adjust the IMN hole position from the misaligned state to the aligned state. (3) Total operation time, which is the sum of the parallelization time and the fine-positioning time. (4) Number of X-ray images taken during the fine-positioning step. (5) The amount of X-ray radiation that is indicated by the C-arm device. Three subjects performed the guiding process three times each, and the measured data are listed in Tables 1 and 2. As shown in Table 1, the average parallelization time was measured to be 41.1 s with a standard deviation (STD) of 19.0 s. The average fine-positioning time was measured to be 207 s with a STD of 107 s and the total operation time to be 248 s with a STD of 105 s. Please note that in order to eliminate X-ray exposure to the subjects, when the FAD position was modified, the subjects left the X-ray shielding room and executed X-ray shooting remotely. In a real situation, however, an operating surgeon would shoot the X-ray in situ while changing the FAD position, and thus the time would be expected to be reduced significantly. Owing to this, the fine-positioning time measured was much longer than the expected time in a real operating room. To show the effectiveness of the proposed system, we also provide the X-ray images taken in Table 1. The number of X-ray images taken ranges from 3 to 10 times, with an average of 6.67 and a STD of 2.65 times. The average dose of X-ray exposure was 2.14 with a STD of 0.97 cGy-cm\({}^{2}\), which are significantly smaller than the normal procedure as shown in [19], where the data were investigated from 39 cases, and which showed the number of X-ray images was 243 and the dose of X-ray exposure was 172 cGy-cm\({}^{2}\). In addition, we also measured the tilted angle error and the translation error following the method aforementioned in Section 3.2. As shown in Table 2, the tilted angle error during the parallelization step was measured to be 1.24 \(\pm\) 0.715\({}^{\circ}\), and the translation error during the fine-positioning step was measured  
Figure 10: X-ray images before and after fine adjustment. (**a**) Misaligned state. (**b**) Aligned state.  
to be 0.378 \(\pm\) 0.120 mm. We believe these results are also sufficient for the distal screwing process.
## 2 Related Work
### Other Techniques in Video Anomaly Detection
It incorporates a convolutional block attention module (CBAM) to improve interpretability and focus on crucial information in the video data. Unlike traditional 3D-CNNs that use multiple frames simultaneously, this method merges various frames into one image, reducing memory usage to enable more precise detection of anomalies. A weakly supervised video anomaly detection technique to bridge the gap between normal and abnormal instances and tackle the problem of false alarms was proposed by [PERSON] et al. [63]. They utilized a unique multiple instance learning (MIL) framework based on a memory unit that comprised a multi-head attention feature augmentation (MHFA) module, a loss function with KL divergence, and Gaussian distribution estimation. [PERSON] and [PERSON] [64] proposed a classification approach called MST-RNN-LSTM, \"Modified Spatiotemporal Recurrent Neural Network using Long Short-Term Memory\", for detecting anomalies in video surveillance as part of IoT-based smart city initiatives. Cuboids were created by processing the normalized frames for motion tracking, and a discrete wavelet transform (DWT) with principal component analysis (PCA) was applied to the cuboids. The training of the features and the classification were performed by an RNN-LSTM model. A similar work for video anomaly detection in surveillance systems, proposed by [PERSON] and [PERSON] [65], utilized a 2D CNN-based decoder, a time-distributed 2D encoder, and an architecture resembling U-Net. In addition, a memory module was included to save the most pertinent prototypical patterns of normal activities to facilitate inaccurate predictions for abnormal inputs. In summary, the video anomaly detection domain is rapidly evolving, with significant contributions from transformer-based and other advanced techniques. These developments are substantial for enhancing the capabilities of surveillance systems and contributing to public safety and security. A weakly supervised data augmentation network was conducted by [PERSON] et al. [66] to support attention-guided data augmentation and enrich the input pictures. The method used the multi-scale feature extraction technique to extract visual information from video footage at different scales. Then, the model was trained by incorporating the enhanced convolutional block attention module (CBAM) into the base U-Net architecture to suppress interference from non-anomalous areas in the video, enabling the model to focus on anomaly-relevant regions. The state of the art shows that recent research focuses on vision transformers as leading models for detecting video anomalies in different fields, but there is a great demand for intelligent surveillance systems for preserving public safety and security.
# Remote Sensing of Forest Burnt Area, Burn Severity, and Post-Fire Recovery: A Review
## 3 Descriptive Analysis
### Referred FAO Ecozones and Tree Species  
When considering the FAO Global Ecological Zone map ([[http://foris.fao.org/static/data/fra2010/ecozones2010.jpg](http://foris.fao.org/static/data/fra2010/ecozones2010.jpg)]([http://foris.fao.org/static/data/fra2010/ecozones2010.jpg](http://foris.fao.org/static/data/fra2010/ecozones2010.jpg)), accessed on 20 May 2022) for defining forest regions of the world, most of the reviewed studies were conducted within subtropical dry forest (21.4%, 92 cases), followed by borealiferous forest (9.3%, 40 cases), borealundra woodland (8.4%, 36 cases), temporal continental forest (8.4%, 36 cases), and temperate oceanic forest (7.7%, 33 cases) (Figure 7a). Many studies were also focused on the subtropical mountain system (7.5%, 32 cases), the boreal mountain system (6.3%, 27 cases), the temperate mountain system (4.7%, 20 cases), the tropical rainforest (5.6%, 24 cases), and the temperate steppe (4.4%, 19 cases). Fewer studies were carried out in tropical mountain system (1.4%, 6 cases), subtropical desert (1.2%, 5 cases), tropical shrubland (0.9%, 4 cases), and temperate desert (0.7%, 3 cases). To some degree, the global distribution of the research forest sites within the FAO ecozones indicate spatial similarities between the locations of BA around the world.  
With regard to forest tree species discrimination, the reviewed studies have mainly described pine (37.0%), followed by oak (19.5%), fir (9.5%), spruce (9.3%), aspen (6.3%), birch (5.2%), larch (4.3%), eucalypt (3.7%), chestnut (1.7%), cedar (1.3%), hemlock (1.1%), and juniper (1.1%) (Figure 7b). Among the 28 pine species described in the articles, the most often mentioned were Ponderosa pine (_Pinus Ponderosa_) (26 cases), Maritime pine (_Pinus pinaster_) (24 cases), Black pine (_Pinus nigra_) (19 cases), Scots pine (_Pinus sylvestris_) (15 cases), Alepppo pine (_Pinus halepensis_) (14 cases), Lodgepole pine (_Pinus contorta_) (11 cases), Turkish pine (_Pinus bruita_) (5 cases), and others (Table 1). Among the 32 oak species, the most frequently studied were Evergreen oak (_Quercus alex_) (18 cases), Cork oak (_Quercus suber_) (10 cases), Kermes oak (_Quercus coccifera_) (8 cases), and others. Sixteen studies were exclusively examining BA covered by the black spruce (_Picea Mariana_), 19 studies estimated Douglas fir (_Pseudotsuga menziesii_), and 17 were focused on the trembling aspen (_Populus tremuloides_) forest stands. Furthermore, there were studies describing burnt territories dominated by Siberian larch (_Larix sibrica_) (9 cases), Dahurian Larch (_Larix smelin_) (7 cases), and Siberian silver birch (_Betula Platyphylla_) (6 cases). Only four studies estimated blue gum (_Eucalyptus globulus_) and Darwin stringybark (_Eucalyptus tetrodonta_) species.  
Figure 7: Distribution of studies: **(a)** according to the Global Ecological Zoning framework; **(b)** by the main tree species on the burnt sites.
## 3 Proposed Method
### Result Representation  
This block is responsible for displaying the classified data along with its explanation, allowing users to visualize the detected intrusions and anomalies as presented in Figure 1. It is based on Mongo DB due to its real-time data storage capacity. Furthermore, a Flask server was utilized for result representation. This database stores historical data and has a configuration interface for user customization. The result representation component ensures that the system's findings are presented in an understandable and actionable manner for users.
# Fast Spectral Approximation of Structured Graphs with Applications to Graph Filtering
## Appendix C Proof of Theorem 2
The complexity for \((ii)\), \(T_{d}(n,d)\), is obtained considering that there are \(d\) levels in the tree, and that at the \(i\)th level \(2^{i}\) decompositions on graphs with \(n/2^{i}\) nodes are performed, i.e., \(\mathcal{O}(d\cdot 2^{i}\cdot T_{g}(n/2^{i}))\). As for graphs in \(\mathcal{S}_{1/2}\) the decomposition requires linear time, i.e., \(T_{g}(m)=\mathcal{O}(m)\), we obtain linear complexity in \(n\) and \(d\), i.e.,  
\[T_{d}(n,d)=\mathcal{O}(nd). \tag{13}\]  
As a result, the total complexity can be expressed as  
\[ \begin{array}{rcl}T(n,d)&=&T_{e}(n,d)+T_{d}(n,d)\\ &=&\mathcal{O}(2^{d}T_{f}(\frac{n}{2^{d}}))+\mathcal{O}(nd).\end{array}  \tag{14}\]  
From (14), it is observed that the time for computing the eigendecomposition diminishes for larger \(d\), while the time for computing the graph decomposition increases with \(d\), hence their competing nature. As most of the (if not all) algorithms for general matrix eigendecomposition have cubic time complexity, \(T_{f}(m)=\mathcal{O}(m^{3})\), we obtain the following total complexity. \[T(n,d)=\mathcal{O}(n^{3}/2^{2d})+\mathcal{O}(nd). \tag{15}\]Now, substituting the optimal depth parameter, \(d^{*}\), in (A10) we obtain a total complexity  
\[T(n,\delta)=\mathcal{O}(n^{1+4\delta}+(1-2\delta)n\log n),\] (A11)  
depending on the size of the graph and the accuracy of the approximation. Furthermore, considering that  
\[\log n=o(n^{\omega}),\ \ \forall\ \omega>0,\] (A12)  
we obtain  
\[T(n,\delta)=\mathcal{O}(n^{1+4\delta}),\] (A13)  
as we wanted to show.
## 3 Results and Discussions
### Radiation Characteristics  
For a centre-fed design with a broadside beam (scan angle as 0 degrees), the 15 \(\times\) 15 element reflectarray using the optimised design parameter produced a gain of 21.3 dBi, with a beam width of 14 degrees in both E and H planes. The gain then starts to deteriorate gradually. With an increase in scan angle, the gain tends to decrease significantly, reaching very low levels above 60 degrees. As in our case, the scan angle is set to 30 degrees. Hence, from the radiation pattern curve, it can be observed that there is a gain of 19.8 dBi for both planes. The beamwidth increases from 14 degrees to 17 degrees from a scan angle 0 to 30 degrees. The reflectarray's radiation characteristics are measured in an anechoic chamber using a horn antenna as the feeder. Both the simulated and measured results for E-plane (XZ-plane, \(\phi\) = 0\({}^{\circ}\)) and H-plane (YZ-plane, \(\phi\) = 90\({}^{\circ}\)) are depicted in Figure 8.  
Figure 7: Simulated and measured S11 vs. Frequency.