esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards a Universal Semantic Dictionary
## 3 Proposed Method
### Cosine Similarity and Precision  
Cosine similarity ([[https://en.wikipedia.org/wiki/Cosine_similarity](https://en.wikipedia.org/wiki/Cosine_similarity)]([https://en.wikipedia.org/wiki/Cosine_similarity](https://en.wikipedia.org/wiki/Cosine_similarity))) is a measure of similarity between two non-zero vectors. It is calculated as the normalized dot product of two vectors, as shown in Equation (2). In fact, cosine similarity is a space that measures the cosine of the angle of two vectors. It is important to note that cosine similarity is not a proper distance metric, since the triangle inequality property does not apply. In word similarity tasks, however, this metric is used for measuring the similarity of two words represented as word vectors. Although cosine similarity values by definition are in range of [\(-\)1, 1], in word similarity tasks. it is particularly used in positive space, [0, 1], where parallel vectors are similar and orthogonal vectors are dissimilar.  
\[cos\_sim=\cos\theta=\frac{\vec{a}\cdot\vec{b}}{||\vec{a}||\cdot||\vec{b}||} \tag{2}\]  
Precision is a metric used for measuring the performance of translator systems, which intend to learn to translate from a source language into a target language. On the target side, a look-up space is defined, which could, for example, correspond to the most frequent 200K words of the target language, as in our experiments. After translating a word, the \(N\) word vectors of the look-up space that are closest to the translated one are regarded. The Precision @\(N\) metric denotes the percentage of how many times the real translation of a word is found among the \(N\) closest word vectors in the look-up space. Usual \(N\) values are 1, 5, and 10.
## 4 Results
### Tests with STFT
#### 4.4.1 Tests with Four Classification Classes and LFTO  
The validation and test accuracies for each setup of four classes are reported in Table 7 for the various cases. The confusion matrices for the cases with a decimated factor of 30 are shown in Figure 18.  
As can be seen from Table 7 and Figure 18, by comparing with the respective Table 5 and Figure 15, the accuracies reached with the spectrogram images tended to be better than those achieved with the scalograms, in the case of decimated data.
# Radial Gradient Seismic Metamaterials with Ultra-Low Frequency and Ultra-Wide Band Gap
## 3 The Radial Gradient Seismic Metamaterial Structure
### The Effect of Filling Rate on Band Gap  
In addition, under the premise of the same properties of the filling material, we studied the influence of the filling rate on the band gap. For the filled steel ring, the band calculation is carried out when the cross-sectional side length b2 is 2 m, 2.5 m, 3 m, and 3.5 m, as shown in Figure 5a. For the unit cell with a cross-sectional side length b2 = 2 m, the initial frequency and cutoff frequency of the first band are 5.2476 Hz and 9.625 Hz, respectively; the initial frequency and cut-off frequency of the first band of the unit cell with a side length of b2 = 2.5 m are 5.1766 Hz and 11.651 Hz, respectively, as shown in Figure 5b. The initial frequency and cutoff frequency of the first band of the unit cell with a side length of b2 = 3 m shown in Figure 5c are 5.3484 Hz and 13.882 Hz, respectively. The initial and cut-off frequencies of the first band of the unit cell with the cross-section side length b2 = 3.5 m shown in Figure 5d are 5.7752 Hz and 15.376 Hz, respectively. It can be observed that with the change in the equivalent stiffness of the structure caused by the increase of the filling rate, the boundary of the band gap moves to the high frequency direction, and the height of the sound ray increases with the increase in the equivalent stiffness. It is worth noting that the starting frequency of the first band gap almost does not change and that the cutoff frequency of the first band gap increases significantly with the increase of the filling rate, resulting in a significant increase in the width of the first band gap, while the second, third, and fourth band gaps gradually move to more than 20 Hz with the change in structural parameters.  
Figure 5: The effect of filling rate on the band gap of triangular structure. (**a**) The side length of the inner triangular filler is 2 m; (**b**) the side length of the inner triangular filler is 2.5 m; (**c**) the side length of the inner triangular filler is 3 m; (**d**) the side length of the internal triangular filler is 3.5 m.
## 4 Results
### Estimation of the Post-Liquefaction Consolidation Subsidence  
The phenomenon behind the observed post-seismic subsidence was investigated by calculating the post-liquefaction consolidation subsidence according to the procedure described in Section 3.2, implemented in Python code, and applied to all the available cone penetration tests over the study area.  
The results revealed a significant spatially heterogeneous distribution of post-liquefaction consolidation subsidence (Figure 7a). This distribution, with larger values often located in proximity to liquefaction evidence at the ground, but also in areas where liquefaction evidence at the ground was not observed, is a key finding. From a spatial point of view, the computed punctual values differ from the InSAR-derived ground subsidence, as shown in Figure 7a. However, some similarities arise when looking at the statistical distribution of the calculated values.  
Figure 6: Estimation of theoretical vertical tectonic uplift (green curve—U) and theoretical post-liquefaction consolidation subsidence (blue curve—S), whose algebraic sum provides the best-fit curve (yellow curve—U + S) to the observed InSAR-derived ground subsidence (dashed black curve) over the study area. The deviation from the observed InSAR median trend is represented by shades of orange-colored bands defining the quantiles calculated every 10%.  
The histogram of the calculated post-liquefaction consolidation subsidence (Figure 7b) is positively skewed. Many of the available cone penetration tests provide vertical movements lower than \(-0.5\) cm. However, most surveys identify subsidence values between \(-0.4\) cm and \(-2.3\) cm, corresponding to the first and third quartile of the distribution of the computed values, respectively, with a mean value of \(-1.65\) cm, slightly larger than its median value (\(-1.42\) cm). This subsidence interval also includes the asymptotic median post-liquefaction subsidence value estimated from the analysis of InSAR data, as shown in Figure 6 (i.e., \(-2.1\) cm as in Figure 6). Such a result highlights that the observed ground displacements are compatible with the estimated consolidation subsidence and provides further evidence about the cause of the observed displacements, which can be attributable to post-liquefaction consolidation.
# Communication Technologies for Smart Grid: A Comprehensive Survey
## 4 Challenges of Smart Grid Communication
### Privacy  
Communication in SGs are often linked to information related to individual customers and their lives. This is why securing authentication, authorization, and confidentiality is so important in a SG environment. It is of greatest importance not to disclose private data to anyone other than consented entities. Private data include consumer identification, address, and energy usage information [47]. Smart meters are expected to provide high accuracy reading of power consumption at defined time intervals to the utilities companies. This data is used for billing purposes and grid management. However, measurement data from smart meter may be used for other purposes. Usage pattern analysis can be useful for power saving, but involves a significant risk. The data holds a great amount of information about individual consumers [55,139]. Non-intrusive Appliance Load Monitoring (NALM) technologies uses extracts detailed information on appliance use based on energy measurements [140]. By analyzing data and usage patterns, it may be possible to predict when people are at home or away from home, or what appliances are in use. This information is could be of interest for the police, tax authorities, insurance companies, etc. [19,141]. NIST have acknowledged that the major benefit of SGs is the ability to receive richer data from smart meters and devices, is also the biggest weakness from a privacy standpoint.
# Systemic Evaluation of PSS in the Early Concept Phase Using Graph-Based Reasoning
## 5 Validation
### GEAR Method--Step 4: Evaluation and Derivation of Measures  
The findings were presented to the domain experts and validated for plausibility, and potential measures were discussed. A recommendation was made for the most promising PSS concept variation based on the current state of knowledge, which is only pursing internal powder recycling. This is due to it having the highest expected value for the combined EVCs. However, this assessment has to be considered in light of knowledge gaps. Therefore, it was discussed which knowledge gaps need to be closed most urgently. A critical knowledge gap for the PSS concept variation with exclusively internal recycling is the uncertainty regarding the impact of a lower powder specification. Closing this gap would allow for the more precise assessment of how much powder can actually be use for internal recycling. However, this requires extensive experiments with various powder qualities, which are both resource-intensive and time-consuming. The company is already conducting relevant studies in this area, so the model can be updated with insights from these studies in the future. For the introduction of external recycling, the knowledge gap related to \"willingness to pay\" plays a significant role. This gap can be addressed relatively easily through comparative market studies. In case a service provider cannot be found with favorable conditions, this PSS concept variation might improve the expected utility and become the best option. Furthermore, the symbolic representation and probabilistic evaluation of the EVCs allowed for more targeted discussions of potential measures and better justification for associated efforts, ultimately leading to an improved understanding of the PSS concept. In summary, the graph model allows for early prediction and evaluation of the PSS concept's EVC achievement, considering existing knowledge gaps. It enhances traceability and accounts for uncertainties in the PSS concept. The symbolic representation creates an executable model of expert understanding, ensuring consistent reasoning about PSS concept variations and knowledge gaps.
# Object-Wise Video Editing
## 2 Object Removal and Background Inpainting
### Potential Point Set Collection
#### 2.2.3 Frame Selection  
In the ray segment refinement step, we assume that our refined ray segments are located in a good position and have intersected with an occluded background. In the real world environment with complex shape background, neighboring frames could have failed to observe the desired occluded background even though the ray segment have an intersection with the background surface. However, we avoid the failure frames in our final inpainting calculation. In Figure 10, we have shown three failure cases where the desired background can not be observed.  
Figure 10a is a \"Invisible Background due to Close Foreground\" case, where neighbor frames may not see occluded background \(b\). This situation mostly happened with the nearest neighboring frames when the foreground object is bigger in size. If all points of the truncated epipolar line from a single frame consist of a foreground region, this frame is not considered for potential point collection. In this case, our NDD distribution curve does not touch the horizontal axis (point index along the epipolar line) and monotonically increases or decreases which indicates that the background point \(b\) can not be observed from this viewpoint.  
Figure 9: Ray segment range shift: If ray segment does not intersect background, we shift our ray segment toward the side where calculated NDD values become smaller so that we can meet the minima point touching the axis.  
Figure 10b is a \"Self Occlusion\" case. If the background contains very complex shape, desired background point \(b\) can be occluded by a different part of the background from the viewpoint of the neighboring frame. In this case, the NDD curve has a local minima point but never touches the horizontal axis. We have to discard this frame in the potential point collection step as well. For the detection of this situation, we compare the NDD score at the local minima of NDD curve with single 3D pixel size. If the score at local minima is smaller than single 3D pixel size then self-occlusion occurred on that frame. Figure 10c is a \"Overlaid Object Clutter in Background\". In some case, the current frame has multiple backgrounds behind the foreground object at point \(p\). In this situation, our NDD curve has multiple touches with the horizontal axis shown in Figure 10c. To inpaint the point \(p\), we have to find the closest background point from the NDD curve. We choose the closest local minima point on the curve to collect the potential point for \(p\) and discard the farther minima points from the potential points collection.  
Therefore, our frame selection step discards all potential points from the potential point collection step from the failure frames detected by the NDD curve. Finally, we get a refined and highly qualified potential point set \(\hat{S}(p)=\{\hat{s}|\text{qualified points on all truncated epipolar lines for point $p$ of the current frame}\}\) for point \(p\) after performing both \"Ray Segment Refinement\" and \"Frame Selection\" steps.
# Dual-Band MIMO Antenna with Enhanced Isolation for 5G NR Application
###### Abstract  
A two-port multiple-input and multiple-output (MIMO) antenna with dual-band characteristics operating at the fifth-generation (5G) new radio (NR) sub-6 GHz n7/n38/n41/n79 bands is proposed. The proposed MIMO antenna is composed of two symmetric antenna elements and a defected ground plane. The antenna element consists of an incomplete circular patch with two L-shaped branches. By applying the defected ground structure and the slotted stub, the current distribution on the ground plane is changed to reduce the mutual coupling between the antenna elements. The measured \(-\)10 dB reflection coefficients cover 2.34-2.71 GHz and 3.72-5.10 GHz, while the measured isolation is larger than 20 dB at the whole operating frequency band. The paper has investigated different performance parameters in terms of the envelope correction coefficient (ECC), diversity gain (DG), radiation patterns, antenna gain, and efficiency. The proposed MIMO antenna is suitable for 5G applications.  
Keywords:dual-band; 5G NR; high isolation; MIMO antenna +
Footnote †: journal:
# Analysis of Blur Measure Operators for Single Image Blur Segmentation
###### Abstract  
Blur detection and segmentation for a single image without any prior information is a challenging task. Numerous techniques for blur detection and segmentation have been proposed in the literature to ultimately restore the sharp images. These techniques use different blur measures in different settings, and in all of them, blur measure plays a central role among all other steps. Blur measure operators have not been analyzed comparatively for both of the spatially-variant defocus and motion blur cases. In this paper, we provide the performance analysis of the state-of-the-art blur measure operators under a unified framework for blur segmentation. A large number of blur measure operators are considered for applying on a diverse set of real blurry images affected by different types and levels of blur and noise. The initial blur maps then are segmented into blurred and non-blurred regions. In order to test the performance of blur measure operators in segmentation process in equal terms, it is crucial to consider the same intermediate steps involved in the blur segmentation process for all of the blur measure operators. The performance of the operators is evaluated by using various qualitative measures. Results reveal that the blur measure operators perform well under certain condition and factors. However, it has been observed that some operators perform adequately overall well or worse against almost all imperfections that prevail over the real-world images.  
blur measure; blur segmentation; focus measure; sharpness measure +
Footnote †: journal: Applied Sciences
## 4 Methods Used for Fault Diagnosis
### DC Fault Diagnosis and Protection Using CPU  
This protection strategy works based on the change of current in each zone to identify and interrupt the fault branch. In a ring bus type or multi-terminal low-voltage DC system, the current of each small branch is measured and sent to the Digital Signal Processing Unit (DSPU) for further processing. For prevention of any error and delay, an accurate communication link is essential to transfer the measured data from each of the local devices to DSPU and the related breakers. Furthermore, in a short-circuit situation, the main valve of the semiconductor switches (IGB, MOSFET, etc.) in the converters will be closed and may not feed the fault current. Instead, the free-wheelingdiode of the converter will provide a free-wheeling path to bypass the huge current which is generated by the discharge of distribution-line parameters. Thus, to maintain the safety for the converters, an auxiliary breaker is essential to interrupt the short-circuit current while the capacitor is discharging.
# Detection of Forest Tree Losses in Cote d'Ivoire Using Drone Aerial Images
## 2 Materials and Methods
### Overall Methodology
#### 2.2.5 Geometric Corrections  
In a change analysis approach, it is necessary to considerably reduce geometric errors. Geometric errors are related to uncontrolled movements (e.g., wind force) of the drone with respect to the previously defined flight line and to variations in altitude during shooting [34]. The digital surface models obtained were therefore georeferenced. The method used is image-to-image georeferencing: the DSM at date T2 is georeferenced from the DSM at date T1 on all the sites studied. The transformation applied was polynomial, and the resampling method was nearest neighbor.
## 4 Examples  
The applicability and accuracy of the procedure proposed are verified by various structural behaviors such as geometric nonlinearity, snap-through buckling, dynamic response and fracture. Of these, the beam-column joint is assumed to be linear (e.g., in Examples 3.1-3.4), bilinear (e.g., in Examples 3.5) or nonlinear semi-rigid connection (e.g., in Examples 3.6) accordingly. The geometrically nonlinear analysis of a column with elastic support under static loading is performed in Example 3.1 to preliminarily test the correctness of the proposed procedure. The static analysis of a beam with elastic supports is first carried out in Example 3.2, and then dynamic response of this structure is supplemented to emphasize the accuracy of the proposed procedure under dynamic loading. Example 3.3 further employed the proposed procedure into the static and dynamic response of steel frames with linear semi-rigid connections to validate the applicability of the proposed procedure in steel frames. In Example 3.4, the snap-through buckling behavior of steel frames is investigated in detail to reflect the advantages of the proposed procedure. Different from the above examples, Example 3.5 introduces the bilinear semi-rigid connection model proposed by [PERSON] et al. [8] into the MDEM for the static analysis of single-story and three-story frames with semi-rigid connections. In order to capture the nonlinear behavior and hysteresis performance of semi-rigid connections more accurately, the Example 3.6 adopts the Richard-Abbort four-parameter model [5] given in Section 2.2 for dynamic analysis of the double-span, six-story Vogel steel frame under cyclic loading. In addition, the effect of semi-rigid connections on the collapse behavior of the structure is studied.  
Figure 5: MDEM flowchart for static and dynamic analysis of steel frames with semi-rigid connections.
## 3 Elastographic Methods
### Point SWE (pSWE)
#### 3.2.1 Liver Fibrosis  
Quantification of liver fibrosis correlates with the histological fibrosis stage in children with chronic liver disease [63]. The accuracy of pSWE in determining the extent of liver fibrosis in pediatric patients with short bowel syndrome has been evaluated. The AUROCs to differentiate moderate/severe liver fibrosis from mild disease were 0.83 and 0.86 for the median and mean SWV, respectively [10].  
In children without inflammation, SWV was higher when fibrosis was present compared to the absence of fibrosis (average SWV 1.8 vs. 1.4 m/s). A SWV cut-off of 1.7 m/s had 100% positive predictive value and 24% negative predictive value for detecting liver fibrosis or inflammation [15].  
Fibrosis related to several causes can be diagnosed in children and adolescents' liver grafts. In a small series (30 subjects), the AUROCs for SWV, APRI, and AST/ALT (aspartate aminotransferase/alanine aminotransferase) ratio index for significant fibrosis were 0.76, 0.74, and 0.69, respectively. Through multivariate logistic regression analysis, the only independent predictor of significant fibrosis was SWV. SWV assessment may serve as a potential method for assessing significant fibrosis in pediatric patients with liver transplants, particularly in combination with AST/ALT ratio [64].  
Graft fibrosis is a common finding from biopsies after pediatric liver transplantation. LSMs had good accuracy for diagnosing graft fibrosis after pediatric living donor liver transplantation. SWVs significantly increased with increased portal and pericellular fibrosis grades [65]. For the diagnosis of significant fibrosis, the AUROCs were 0.760 and 0.849 for the midline and intercostal values, respectively, and the optimal cut-off values were 1.30 and 1.39 m/s for midline and intercostal values. The pericellular pattern of fibrosis was frequently observed in this setting, and moderate pericellular fibrosis was detectable by SWV [65].
## 4 Simulation Results
### P-V and I-V Characteristics Curves for Uniform and Shading Pattern 1, 2, and 3  
Figure 9 shows the characteristic curves of a PV panel under an acute and three PSCs containing three peaks on each PV curve. A detailed explanation of the chosen shading patterns and corresponding waveforms given below:  
1. Figure 9a: P-V and I-V characteristics curves generated from the pattern 4a at STC weather condition (1000 W/m\({}^{2}\) and 25 \({}^{\circ}\)C). This uniform weather condition generates a single MPP coordinate (183.9 V, 1497 W) on the linear curves.
2. Figure 9b: Three PV modules; M1, M2, and M3 are exposed to 1000, 900, and 800 W/m\({}^{2}\) irradiances to generate a GMPP\({}_{\text{r}}\) (1.28 V and 0.144 kW) and other two LMPPs (LMPP\({}_{\text{a}}\) and LMPP\({}_{\text{b}}\)).
3. Figure 9c: The M1, M2, and M3 PV modules are responsible for 1, 0.3, and 0.9 kW/m\({}^{2}\) with the GMPP\({}_{\text{m}}\) value being 0.924 kW. The connected bypass diodes along with each PV module enable bypassing the maximum current flow generated by non-shaded modules. Therefore, two more local MPPs (GMPP\({}_{\text{c}}\) and GMPP\({}_{\text{d}}\)) occurred on the P-V graph.  
Figure 8: Proposed PI coordinated MPPT algorithm.  
Conventional MPPT schemes are inefficient for locating the GMPP smoothly because of multiple power peaks on the same P-V wave shape (Figure 9b-d). The PV system is highly degraded because of the wrong tracking direction of an operating point under the three discussed PSCs, especially when conventional and evolutionary algorithms are used to realize the GMPP.