esa-sceva/satcom-chunk-collection huggingface dataset:
# Photonic Crystal-Based Sensors for Detecting Alcohol Concentration
###### Abstract  
Polystyrene (PS) opal and titania (TiO\({}_{2}\)) inverse opal films were fabricated by the self-assembly colloidal crystal template technique. Based on Bragg's law, these sensors were used to detect the different concentrations of ethanol solution. The results indicated that TiO\({}_{2}\) inverse opal films were advantageous over PS opal film for detecting the ethanol concentration. TiO\({}_{2}\) inverse opal films sintered at 600 \({}^{\circ}\)C retained the highest sensitivity for ethanol concentration identification, since the anatase phase was transformed into the rutile phase, which resulted in an enhancement of the refractive index, _i.e._, an increase in the amount of the red shift.  
photonic crystals; inverse opal; polystyrene; titania; alcohol concentration +
Footnote †: journal: Applied Physics Letters  
Article
## Why Seabed 2030?
### At What Resolution Should We Map the Seafloor?
The inadequacy of our current baseline knowledge of seafloor bathymetry became particularly evident in the search for the Malaysia Airlines flight MH370, which disappeared 8 March 2014 (Figure 8). The satellite altimetry predicted bathymetry and database of available soundings in the southeastern Indian Ocean at the time of the crash proved so sparse that it was impossible to deploy the AUVs needed to find the wreckage without first conducting new surface ship deployed multibeam sonar bathymetric surveys [26; 27]. The search for MH370 facilitated the collection of ~710,000 km\({}^{2}\) of new surface ship-derived multibeam sonar data representing a remarkable improvement and addition to Indian Ocean bathymetry (Figure 9) as well as many new insights into the geology of the southeastern Indian Ocean [27]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Depth Range** & **Grid-Cell Size** & **\% of World Ocean** \\ \hline
0–1500 m & 100 \(\times\) 100 m & 13.7 \\
1500–3000 m & 200 \(\times\) 200 m & 11 \\
3000–5750 m & 400 \(\times\) 400 m & 72.6 \\
5750–11,000 m & 800 \(\times\) 800 m & 2.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Feasible resolution based on state-of-the-art 2\({}^{\circ}\times 2^{\circ}\) deep-water multibeam installed on surface vessel, calculated at 60\({}^{\circ}\) from nadir. Figure 7: Depth regions based on state-of-the-art 2\({}^{\circ}\times 2^{\circ}\) deep-water multibeam installed on surface vessels, calculated at 60\({}^{\circ}\) from nadir shown in Table 2.  
Figure 8: The need for high-resolution bathymetry in the deep ocean became strikingly evident outside of the scientific community following the disappearance of Malaysia Airlines flight MH370, en route from Kuala Lumpur to Beijing on 8 March 2014. The seafloor in the projected search area (green box) was very poorly mapped at the time the search for parts of the fuselage began [26]. Black lines are single-beam sonar ship track lines, and red lines show multibeam bathymetry available at the time the search began. The bathymetric data coverage was far from sufficient to navigate underwater vehicles for detailed inspection of the seafloor. Map is from the Nippon Foundation—GEBCO—Seabed 2030 Roadmap for Future Ocean Floor Mapping [20]. Figure 9: _Cont._
## 4 Results and Analyses of the Numerical Application of Global Cost and Carbon Impact of the PVCS
### Results of the Numerical Application of the PVCS Carbon Impact  
In this example, the assessment of the PVCS carbon impact is calculated using PV panels with a carbon emission coefficient of 0.04 kgCO2,eq/kWh, according to the NREL laboratory in the context of the LCA harmonization project [28]. Numerical applications of formulas give:  
\[\begin{array}{l}Imp_{PV,infra}=48,546\,\mathrm{kgCO_{2,eq}}\\ Imp_{site}=2\,\mathrm{kgCO_{2,eq}}\\ Imp_{maint}=447\,\mathrm{kgCO_{2,eq}}\end{array} \tag{45}\]  
The carbon impact of this PV system is therefore:  
\[Imp_{PV}=48,995\,\mathrm{kgCO_{2 eq}} \tag{46}\]  
In addition to this carbon impact, there is also the impact of batteries, charging terminals, infrastructure related to the shade, and electricity provided by the power grid. As summarized in Table 1, the numerical application provides a value of \(Imp_{PVCS}=85,961\,\mathrm{kgCO_{2,eq}}\) as the total carbon impact of the PVCS. Compared to the amount of power that PVCS supplies from the PV system and the power grid in the analysis period, this carbon impact is equivalent to an global emission coefficient of the PVCS of 0.068 kgCO2,eq/kWh.  
Figure 4: The 30-year financial report: (a) Distribution of the global cost; (b) evolution of the global cost by year.  
Figure 3: (a) Maintenance cost, (b) exploitation cost.  
It is interesting to compare the carbon impact of the PVCS with the carbon impact of PGCS. This station therefore provides the same quantity of electricity over 30 years as the PVCS, and also includes the same number of charging terminals. Thus, its carbon impact \(Imp_{PGCS}\) can be calculated using this equation:  
\[Imp_{PGCS}=Imp_{CT}+Imp_{PG} \tag{47}\]  
where \(Imp_{CT}\) and \(Imp_{PG}\) are the carbon impact of the charging terminals and the public grid, respectively.  
The numerical application gives that the carbon impact for this PGCS is equal to 77,436 kgCO2,eq. The comparison of these two carbon impacts is given by calculating the variation rate.  
\[Variation\ rate=\frac{Imp_{PVCS}-Imp_{PGCS}}{Imp_{PGCS}}\times 100=11\% \tag{48}\]  
In this example, the numerical application indicates that the carbon impact of the PVCS is 11% higher than that of the PGCS. Once the carbon impact of the PVCS is established, it is necessary to estimate the action levers allowing one to reduce the carbon impact of the system.
# Reliable Design for a Network of Networks with Inspiration from Brain Functional Networks
## 4 Evaluation
### Network Construction
#### 4.1.1 Intranetwork Assortativity  
When node degree distribution is fixed on configuring topological connectivity within a network, the nodal degree is the only metric that can evaluate the centrality of the nodes. Consequently, we focus on the degree assortativity \(\eta\) to configure the connectivity of a VN. We begin by introducing the remaining degree distribution \(q(k)\), which is defined as  
\[q(k)=\frac{(k+1)p(k+1)}{\sum_{j}jp(j)}. \tag{16}\]  
The remaining degree distribution is related to the degree distribution \(p(k)\) that describes the probability that the degree of a randomly chosen node corresponds to \(k\). The remaining degree of a node in a path corresponds to the number of links of a node excluding the link it was arriving from. For a given \(q(k)\), we can introduce the joint probability distribution \(e(j,k)\), which indicates the probability that the two endpoints of a randomly chosen link have remaining degrees \(k\) and \(j\). Consequently, the degree assortativity \(\eta\) is defined as  
\[\eta=\frac{1}{\sigma_{q}^{2}}\left[\sum_{j,k}jke(j,k)-\left(\sum_{j}jq(j) \right)^{2}\right], \tag{17}\]  
where \(\sigma_{q}\) is the standard deviation of the remaining degree distribution \(q(k)\). \(\eta\) can take any value in the interval \([-1,1]\): \(\eta>0\) and \(\eta<0\) indicate an assortative network and a disassortative network, respectively, while \(\eta=0\) indicates that the nodes are connected with each other randomly irrespective of their degrees. The degree distribution limits the range of feasible values of \(\eta\).  
Having constructed an initial VN topology with a specific degree distribution, we then set a target value of \(\eta^{\prime}\) and rewire the links continuously [47; 48] until the assortativity \(\eta\) of the current topology approximates the given \(\eta_{target}\). Note that we rewire a topology so that it is not split into submodules; if the generated VN topology is separated into more than one module, the former is reconstructed so that it is fully connected.
## 5 Research on the Frequency-Domain Characteristics of AE Signals
### Wavelet Packet Energy Analysis of AE Signals
#### 5.2.3 Energy Distribution of the P-Signals  
In Section 5.1, the frequency-domain characteristics of the P-signals obtained by FFT are extracted. In this section, the P-signals of granite samples under each confining pressure are extracted by the wavelet packet energy analysis method, and the proportions of all P-signals in the sub-bands of granite samples under various confining pressures are obtained, as shown in Figure 11.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sub-Band Label** & **Frequency Band Range/kHz** \\ \hline
1 & 0–31.25 \\
2 & 31.25–62.5 \\
3 & 62.5–93.75 \\... &... \\
16 & 478.75–500 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Corresponding labels to band ranges.  
Figure 11 shows that the frequency band energy of the P-signals under various confining pressures is mainly concentrated in the No. 10-13 frequency band (281.25-406.25 kHz), accounting for more than 50% of the total signals. This result confirms the rationality of defining the signal with frequency centroid greater than 275 kHz as the P-signal. The No. 12 frequency band (343.75-375 kHz) accounts for the highest proportion of the single frequency band. This result validates the definition of the P-signal as the signal with a peak frequency greater than 375 kHz. The No. 10 frequency band (281.25-312.5 kHz) occupies the second-highest proportion of all frequency bands because the second peak frequency of most signals is concentrated in this area. However, the energy proportion of the single frequency band is less than 25%. As the confining pressure increases, the energy proportion of low-frequency signal decreases.  
The energy proportions of all the P-signals under various confining pressure values in each frequency band obtained through the wavelet packet analysis are the same as the conditions proposed in the previous section for defining P-signals. That is, the results confirm that the peak frequency and frequency centroid of the precursor signals should be greater than 350 and 275 kHz, respectively.
## 3 Results and Discussion
### Adsorption of MB in Actual Water Samples  
In order to evaluate the adsorption performance of CPP-derived BCs for MB, different water samples such as deionized water, industrial wastewater and lake water of Yudai River (Changsha, China) were used to prepare 50 mg/L MB solutions (Figure 10B). The adsorption capacity of CPP200 for MB solution prepared by lake water was 46.21 mg/g, which decreased to 28.54 mg/g for MB solution prepared by industrial wastewater. The decrease in adsorption capacity might be attributed to the existence of some metal cations (such as Zn\({}^{2+}\), Pb\({}^{2+}\), Fe\({}^{3+}\), Ca\({}^{2+}\), etc.) in those water samples. The industrial wastewater tested in this experiment was provided by a local metallurgical plant, which mainly contained some inorganic metal ions such as Zn\({}^{2+}\) and Pb\({}^{2+}\). In addition, the industrial wastewater was more acidic (pH = 3.73), which competitively inhibited electrostatic interactions between CPP200 and MB.
## 3 Two-Layer Singular Value Decomposition  
In this section, the two-layer singular value decomposition method is proposed. An inequality which reveals the number characteristics of singular values for the multi-sinusoid FM signal is derived in Section 3.1, the influence of noise on singular values is introduced in Section 3.2, and the two-layer singular value decomposition signal denoising technique is proposed based on the number characteristics of singular values for multi-sinusoid FM signal in Section 3.3.
# Challenges and Future Trends in the Treatment of Psoriasis [PERSON] and [PERSON]
## 4 Future Aspects in the Treatment of Psoriasis
### Biologic Agents and Small Molecule Inhibitors under Development
#### 4.1.1 IL-23 Inhibitors  
Mirikizumab, an interleukin (IL)-23 inhibitor, has shown promising results in clinical trials. By targeting the IL-23/Th17 pathway, mirkikizumab effectively suppresses inflammation and leads to significant improvements in psoriatic skin lesions [53]. In psoriasis pathophysiology, the IL-23/IL-17 axis plays a crucial role, and currently available biologics are designed to target this molecule.
# Novel Pointing and Stabilizing Manipulator for Optical Space Payloads
###### Abstract  
Considering that the Line-Of-Sight (LOS) of a small optical payload is mainly connected with the angular motions while insensitive to linear motion, a novel pointing and stabilizing manipulator was proposed for small payloads on a large spacecraft. By integrating fine and coarse actuators in parallel, the proposed manipulator could isolate spacecraft vibration and independently adjust the LOS on a large scale at the same time. On this basis, the study revealed the key kinematic and dynamic characteristics and then designed an operation scheme, including the large-scale angular motion algorithm and the active isolation algorithm. Finally, the proposed pointing solution was comprehensively verified through simulation.  
pointing mechanism; fine and coarse integration; active isolation; parallel configuration
## 5 Experimental Results and Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**TID2008**[21]} & \multicolumn{3}{c}{**CSIQ**[8]} \\ \hline
**FR-IQA Metric** & **PLCC** & **SROCC** & **KROCC** & **PLCC** & **SROCC** & **KROCC** \\ \hline
2 stepQA [50] & 0.757 & 0.769 & 0.574 & 0.841 & 0.849 & 0.655 \\ CSV [51] & 0.852 & 0.851 & 0.659 & 0.933 & 0.933 & 0.766 \\ DISTS [52] & 0.705 & 0.668 & 0.488 & 0.930 & 0.930 & 0.764 \\ ESSIM [19] & 0.658 & 0.876 & 0.696 & 0.814 & 0.933 & 0.768 \\ GSM [29] & 0.782 & 0.781 & 0.578 & 0.906 & 0.910 & 0.729 \\ IW-SSIM [16] & 0.842 & 0.856 & 0.664 & 0.804 & 0.921 & 0.753 \\ MAD [8] & 0.831 & 0.829 & 0.639 & **0.950** & **0.947** & **0.796** \\ MS-SSIM [26] & 0.838 & 0.846 & 0.648 & 0.913 & 0.917 & 0.743 \\ PSNR & 0.447 & 0.489 & 0.346 & 0.853 & 0.809 & 0.599 \\ ReSIFT [53] & 0.627 & 0.632 & 0.484 & 0.884 & 0.868 & 0.695 \\ RVSIM [54] & 0.789 & 0.743 & 0.566 & 0.923 & 0.903 & 0.728 \\ SSIM [23] & 0.669 & 0.675 & 0.485 & 0.812 & 0.812 & 0.606 \\ SUMMER [55] & 0.817 & 0.823 & 0.623 & 0.826 & 0.830 & 0.658 \\ \hline _SG-ESSIM_ & **0.853** & **0.888** & **0.708** & 0.836 & 0.944 & 0.786 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of _SG-ESSIM_ to several other state-of-the-art algorithms on TID2008 [21] and CSIQ [8]. The highest values are typed in **bold**, while the second highest ones are underlined. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**KADID-10k**[20]} & \multicolumn{3}{c}{**TID2013**[7]} \\ \hline
**FR-IQA Metric** & **PLCC** & **SROCC** & **KROCC** & **PLCC** & **SROCC** & **KROCC** \\ \hline
2 stepQA [50] & 0.768 & 0.771 & 0.571 & 0.736 & 0.733 & 0.550 \\ CSV [51] & 0.671 & 0.669 & 0.531 & 0.852 & **0.848** & **0.657** \\ DISTS [52] & 0.809 & 0.814 & 0.626 & 0.759 & 0.7  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline  & **2 stepQA [50]** & **CSV [51]** & **DISTS [52]** & **ESSIM [19]** & **GSM [29]** & **MAD [8]** & **MS-SSIM [26]** & **ResIFT [53]** & **RVSIM [54]** & **SSIM [23]** & SG-ES  
In Figure 3, the execution time (logarithmic scale) versus SROCC scatter plot measured on KADID-10k [20] is depicted. From this figure, it could be seen that the proposed _SG-ESSIM_ method was the fourth fastest algorithm out of the examined thirteen ones. Considering execution time and estimation performance together, the _SG-ESSIM_ provided a competitive result against the state-of-the-art. Similarly, Figure 4 depicts the execution versus the SROCC on the CSIQ [8] database. Here, we could also observe that considering execution time and performance together, the proposed method was able to achieve a competitive result.
## 4 Our Approach
### Feature Vector Normalization  
There have been many successful attempts to employ various types of normalization methods in order to achieve the stability and speed-up of the training process [35; 36; 37]. One of the main difficulties in sign language translation with the small dataset is the large visual variance as the same sign can look very different depending on the signer. Even if we utilize the feature vector which is obtained by estimating the keypoints of human body, the absolute positions of the keypoints or the scale of the body parts in the frame can be very different. For this reason, we apply a special normalization method called the _object 2D normalization_ that suits our purpose well.  
After extracting high-level human keypoints, we normalize the feature vector using the mean and standard deviation of the vector to reduce the variance of the data. Let us denote a 2D feature vector by \(V=(v_{1},v_{2},\dots,v_{n})\in\mathbb{N}^{n\times 2}\) that consists of \(n\) elements where each element \(v_{i}\in\mathbb{N}^{2},\ 1\leq i\leq n\) stands for a single keypoint of human part. Each element \(v_{i}=(v_{i}^{x},v_{i}^{y})\) consists of two integers \(v_{i}^{x}\) and  
Figure 4: An overall architecture of our approach that translates a sign language video into a natural language sentence using sequence to sequence model based on GRU cells.  
\(v_{i}^{y}\) that imply the \(x\)- and \(y\)-coordinates of the keypoint \(v_{i}\) in the video frame, respectively. From the given feature vector \(V\), we can extract the two feature vectors as follows:  
\[V_{x}=(v_{1}^{x},v_{2}^{x},\ldots,v_{n}^{x})\text{ and }V_{y}=(v_{1}^{y},v_{2}^{y}, \ldots,v_{n}^{y}).\]  
Simply speaking, we collect the \(x\)- and \(y\)-coordinates of keypoints separately while keeping the order. Then, we normalize the \(x\)-coordinate vector \(V_{x}\) as follows:  
\[V_{x}^{*}=\frac{V_{x}-\bar{V_{x}}}{\sigma(V_{x})},\]  
where \(\bar{V_{x}}\) is the mean of \(V_{x}\) and \(\sigma(V_{x})\) is the standard deviation of \(V_{x}\). Note that \(V_{y}^{*}\) is calculated analogously. Finally, it remains to concatenate the two normalized vectors to form the final feature vector \(V^{*}=[V_{x}^{*};V_{y}^{*}]\in\mathbb{N}^{2n}\), which is used as the input vector of our neural network.  
It should be noted that we assume that the keypoints of lower body parts are not necessary for sign language recognition. Therefore, we only use 124 keypoints from the 137 keypoints detected by OpenPose since six keypoints of human pose correspond to lower body parts such as both feet, knees and pelvises, as shown in Figure 2. We randomly sample 10-50 keyframes from each sign video. Hence, the dimension of input feature vector is \(248\times|V|\), where \(|V|\in\{10,20,30,40,50\}\).
# Comparison of Manual versus Semi-Automatic Segmentations of the Stenotic Carotid Artery Bifurcation
## 4 Discussion
### Effect of Differences on the Flow Field  
The exploratory CFD simulations showed that the WSS field can be significantly affected by differences in the segmentation. The velocity field is much less prone to the presence of small differences (at the smallest diameter), but moderate differences that weakly change the morphology of a certain vessel section can change the velocity field considerably as was similarly demonstrated by others [36; 37]. Our results for the TaWSS field show the effect of how a more localized difference (Case 042) could alter the results even qualitatively and how a broadband difference could blur the WSS field. Automatic segmentation can spare time and effort for the examiner, but the clinically relevant stenotic and post- stenotic part of the target vessel should be prepared manually, even if it is time-consuming. Our results highlight the importance of the experienced medical investigators and the co-working of experts from different fields. Thus, knowledge transfer with a medical practitioner is essential for the non-medical examiners to construct lumen segmentations that are applicable for CFD analysis without major inconsistency in the critical stenotic segments. The CFD analysis evaluation needs special skills to substantiate appropriate results, proper settings of the analysis are essential [18; 38] beside the knowledge of the physiologic and pathologic processes.
## 3 Experimental Phenomena and Analysis
### Failure Modes
According to the steel yield formula \(\sigma_{1}^{2}+\sigma_{2}^{2}+\sigma_{1}\sigma_{2}=f_{y}^{2}\)[21]. According to the steel yielding formula, the steel pipe UHPC is mainly subjected to the lateral tensile force brought by the concrete and the longitudinal pressure brought by the axial compression; the damaged angle of the steel pipe tends to be horizontal, which indicates that the steel is subjected to the greater influence of the circumferential tensile force during the steel yielding, and also indicates that the degree of the constraint of the core concrete is high, and the steel pipe provides more constraints, and the core concrete interacts with the steel pipe under the three-phase force of the steel pipe hoop. Analyzing the comparison of group (c) in the figure, it can be seen that when the coarse aggregate dosage is lower, the coarse aggregate content does not significantly affect the damage morphology of the steel pipe-concrete composite; however, when the coarse aggregate dosage is increased to 40%, some localized areas of the steel pipe-UHPC specimen exhibit severe shear bulging. This could be attributed to the fact that an excessively high coarse aggregate content may disrupt the uniform distribution of the steel fibers within the concrete, leading to inconsistent elastic modulus within the concrete matrix and causing premature failure of certain local regions; the failure and extrusion of these localized concrete areas then induces severe bulging deformation in the surrounding steel pipe. As can be seen from the comparison group of different steel fiber dosages in Figure 4(d), the presence of steel fibers can not only significantly delay the damage of the specimen, but also affect the damage morphology to a certain extent. For the specimen without steel fibers, the shear damage curve is more pronounced, the shear line is more prominent, and the formation of the folds is thicker. With the increasing steel fiber content, the deformation folds become shallower, and the overall deformation of the specimen due to extrusion becomes more proportional rather than exhibiting localized prominence. The damage pattern of the specimen is a result of the collective behavior of the outer steel pipe and the inner UHPC filling; it is presumed that the appropriate addition of steel fibers to the UHPC leads to a more uniform deformation of the concrete, allowing for more comprehensive interaction between the concrete and the steel pipe, whereas the concrete without steel fibers is more likely to undergo localized deformation damage under compression, leading to the extrusion of the steel pipe and the formation of a distinct shear line.
# Miniaturized MIMO Antenna Array with High Isolation for 5G Metal-Frame Smartphone Application
## 3 Results and Discussion  
A prototype of the MIMO antenna system was manufactured and tested, as shown in Figure 11, to verify the feasibility of the proposed 8-element MIMO antenna system. Metallic copper was attached to the bottom and side panels of the smartphone's body made of RF-4. Microstrip feeders were printed on the top, as shown in Figure 11a. Moreover, the metal frame and the ground plane of the mainboard were welded together. Eight 50 \(\Omega\) SMA connectors were soldered with the feeder for convenient antenna testing. The red circled part shown in Figure 11b is the etched C-shape decoupling slot. As shown in Figure 11c,d, the gap between the periphery of the frame and the bottom plate is welded to complete by solder. The S-parameter test results were obtained by the Agilent E5071C vector network measurement analyzer, as shown in Figure 12a. The far-field results were obtained by a SATIMO anechoic chamber, as shown in Figure 12b.