esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Polygonal Vortex Flows in a Cylinder with a Rotating Bottom
### Analytical Modeling  
Along with the experimental works, analytical models were developed in an attempt to understand the phenomena involved in polygon shapes' formation and transition. Two-dimensional rotating flow was considered in [26] in two cases: a hollow potential vortex (which simulates a dry polygon) and a Rankine vortex (which simulates a wet polygon). By applying a linear stability analysis, they obtained the phase velocity (defined as the ratio of the angular velocity, \(\omega\), to the wave number, \(N\)) and the polygon shape for each case. The predicted phase velocities in stable states for \(N=3\) and \(N=4\) showed reasonable agreement with the measurements reported in [6].  
A hollow potential vortex but with three-dimensional perturbations was also considered in [27]. They solved numerically the corresponding Laplace equation along with the boundary conditions in a three-dimensional domain, and also suggested a simplified solution based on separate two-dimensional problems concerning rotation at the plane of the bottom disk and at the cylindrical surface, respectively. The linear stability analysis was applied to these two problems, yielding two types of waves: centrifugal waves for the first problem and gravity waves for the second one. It is shown that the instability occurs when the frequencies of the two kinds of waves are equal and the centrifugal wave travels backward relative to the mean wave. The predicted transition frequencies, obtained using both two- and three-dimensional models, reasonably agreed with the experimental results of [7]. In addition, the gravity waves were similar to the waves observed in [23]. The experimental findings of [28] support the predictions of [27] for transition between a circle and an ellipse shapes, with small discrepancies attributed to the region of solid body rotation that were not taken in account in the model. The model of [27] neglects the singular point between the rotating bottom and the fixed cylinder walls, implying that the instability is not affected by the flow details in the vicinity of the singular point. To support this idea, an experiment with liquid nitrogen rotating inside a hot kitchen pot was conducted [27]. The hot pot boiled the nitrogen layer in its immediate vicinity and thus practically eliminated the friction between the liquid and the boundaries, hence precluding singularity. Polygons were still observed, thus supporting the model assumption.  
The model presented in [27] was extended in [29] and considered the Rankine vortex, rather than the potential vortex. It was suggested in [30] that rotating waves are cnoidal waves which result from the nonlinear equation of Korteweg-de Vries. The free surface polygon shapes instability was also a subject of analytical study in [31]. The global stability problem of [27] was extended further in [32]--more complex wave structures were found.
# Toward Defect Engineering Strategies to Optimize Energy and Electronic Materials
## 5 Tuning Metal Oxides for Catalysis
### Codoping in TiO\({}_{2}\)  
Ever since the seminal study of [PERSON] and [PERSON] [83], titanium oxide (TiO\({}_{2}\)) has been intensively investigated because of its high chemical stability, strong catalytic activity, and long lifetime of photon generated carriers [83; 84; 85; 86; 87; 88; 89; 90; 91]. Anatase is the polymorph of TiO\({}_{2}\) with the highest photocatalytic activity, but has a large band gap (3.2 eV) and this limits its use in the limited ultraviolet range which is only 5% of the solar spectrum [89]. Ideally a photocatalyst should have a band gap of \(\sim\)2 eV, whereas band edges must be consistent with the redox potential of water [92].  
[PERSON] et al. [89] proposed that the band edges of TiO\({}_{2}\) can be modified by codoping aiming to shift the valence band edge up, while retaining the conduction band edge. [PERSON] et al. [89] considered a number of codopants such as V+N, Nb+N, Cr+C and Mo+C (refer to Figure 10). The DFT results indicate that Mo+C codoped TiO\({}_{2}\) is a strong candidate for photo-electro-catalytic (PEC) hydrogen production via water splitting, as it reduces the band gap to the visible light region and without practically impacting the conduction band minimum [89]. The study by [PERSON] et al. [89] has significantly impacted the community to determine appropriate codoping strategies to improve anatase and related materials [93; 94; 95; 96; 97].  
An efficient way to achieve the reduction of the band gap is via nitrogen (N) doping [85; 98]. As in the case of WO\({}_{3}\) considered above, H is introduced in TiO\({}_{2}\) to modify the band gap and enhance the photocatalytic activity [79; 80; 99; 100]. Hydrogen can be easily incorporated in TiO\({}_{2}\) during synthesis or by simply immersing it in water [98]. Importantly, previous theoretical studies by [PERSON] et al. [101; 102] showed that hydrogen substitutionals at oxygen sites lead to \(n\)-type conductivity. Codoping anatase with N and H has been considered by the community as an effective defect engineering strategy to reduce the band gap, increase the conductivity, and passivate surface dangling bonds aimed at forming high-efficiency devices (for example, organic photovoltaics [90]).  
Figure 10: Density of states results of undoped TiO\({}_{2}\) and codoped-TiO\({}_{2}\). Reproduced with permission from [89]. American Physical Society, 2009.  
Other dopants and codopants (such as fluorine) have also been employed, whereas simple methods such as vacuum annealing can also form oxygen vacancies and increase the activity.
## 4 Actuation System
### Definition of an Intermediate Baseline Shape  
Whenever a morphing system is designed, the baseline shape needs to be defined, i.e., the manufacturing geometry, which is stress free as long as no loads are applied.  
An option for the baseline shape is using one of the target shapes, either the aerofoil for cruise (S4) or for take-off/landing (S5), see Figure 13.  
However, deformation from S4 to S5 or vice versa leads to large strains in the skin. In the context of the work presented here, the material's stress exposure is quantified using the multiaxial stress failure criterion according to [PERSON] [30]. According to this criterion, an inverse reserve factor (IRF) is computed. An IRF of 1 or higher would indicate static failure of the composite structure. For cyclic loading, an IRF below 0.5 is acceptable. For deformation from S4 to S5, an IRF of fE1 = 0.752 (inter-fibre fracture with weakening) was computed by finite element (FE) analysis, which is critically high.  
Figure 12: Kinematic system design of the morphing leading edge [28].  
Figure 13: Derived intermediate baseline together with target shapes S4 and S5.  
In order to reduce material strain and required actuation forces, an \"intermediate baseline\" between the geometries S4 and S5 was derived, see Figure 13. From this \"intermediate baseline\" the MLE can be moved up into aerofoil S4 for cruise or down to aerofoil S5 for take-off and landing. This intermediate baseline needs to be compatible with the movement of the kinematic system. Hence, the intermediate baseline was derived from a simulated intermediate shape of the skin resulting from an intermediate position of the drive mechanism.  
Using the intermediate baseline, the material stress exposure in terms of IRF is reduced by 38% to a value of f\({}_{\text{E1}}\) = 0.4633, see Figure 14.  
The model parameters for calculation of the IRF are s = m = 0.5, \(p^{c}_{\perp\parallel}\), \(p^{t}_{\perp\parallel}\) = 0.35 and \(p^{c}_{\perp\perp}=p^{t}_{\perp\perp}\), \(p^{c}_{\perp\parallel}\), \(p^{t}_{\perp\parallel}\), \(p^{c}_{\perp\perp}\) and \(p^{t}_{\perp\perp}\) s and m are the weakening parameters expressing reduction of inter fibre fracture (IFF) strength due to longitudinal stress. The material strength parameters used can be found in [35].
## 2 5G Antenna Development Trends for Base Station (Access Point) and Mobile Terminal
### 5G Antennas for Base-Station Communication Scenario  
A conceptual scenario based on a practical cellular communication system is presented with a clear aim of understanding the transmission and reception of wireless communication signals at 900, 1800, and 2700 compared with a 450 beam shutoff range, and as a result, minor improvement in the exclusion zone is achieved [6]; as the beam shutoff range increases to 900, 1800, and 2700, the restriction zone then decreases significantly. Both zones can be achieved by loading the 5G BSs and ES actual parameters.  
In this study, an angular protection scheme for fixed-earth stations (FEs) and 5G base stations is analyzed. Relative location, relative distance, as well as angular changes are analyzed. The angular protection scenario is the effective way to solve the coexistence-related issues of FSS and 5G BSs. The implementation of both the angular protection and distance protection should lead to more strengthening in the angular protection. The whole scenario is shown in Figure 3. The problem caused by interference can be solved effectively  
Figure 2: Single-beam BSA, two narrow-beam BSAs, and a twin-beam BSA [5].  
by a distance protection scheme. Four types of interferences are reported in [7] between FSS and 5G system. The types of interferences reported are as follows.  
From the FSS earth station to 5G base station.  
From the FSS Es to the 5G user.  
From 5G BS to FSS devices.  
From 5G users to FSS devices.  
In the work previously discussed, we have focused on the calculation of interferences in the elevation, and azimuth planes are presented by the formulas reported in [2; 16] as follows.  
\[\mathrm{I_{FSS\;ES\to BS}=P_{BS\;tx}+G_{FSS\;ES}+G_{BS}-PL}\]  
where \(\mathrm{I_{FSS}}\) is the power density (dB/MHz) of the transmit station.  
The gain of the antenna of the ES in the direction of the transmit station (in dBi) is represented by \(\mathrm{G_{FSS\;ES}}\), where \(\mathrm{G_{BS}}\) represents the gain of 5G BS by using MIMO antenna array in the direction of ES considering the antenna pattern(dBi) with beam-forming capability.  
To properly compute the range of the beam shut-off angle \(\phi_{\mathrm{off}}\) (i.e., the size of the beam angle that must be shut off) the BS interference was calculated in each step. If the \(\mathrm{I/N}\) interference threshold value remains less than that of the interference with the current beam sweep angle \(\phi_{\mathrm{s}}\), then the beam angle must need to be shut off for protection of ES. The shut-off angle \(\phi_{\mathrm{off}}\) of the beam increases by \(1^{\circ}\), as shown in Figure 4.
## 6 Conclusions  
In this work, we discuss and perform a comparative analysis of code-agnostic decoding algorithms. The key performance metrics of reliability and energy efficiency for the decoding schemes are considered. The comparative analysis of the decoders is done with the CA-SCL decoder of 5G NR polar code, setting it as a benchmark. It is observed from the derived results that although GRAND and its variants provide a higher coding gain compared to the state-of-the-art SCL decoder, they require a high SNR value for performance optimization of short-length codes. Adaptive-OSD achieves the target BLER at relatively low SNR for short-block-length codes and also provides a significant coding gain over the SCL decoder for 5G NR polar codes. The energy efficiency of the successive cancellation automorphism list (SCAL) decoder is higher compared to other decoders. The performance enhancement of candidate URLLC decoders is a potential research direction; hence, a comparative analysis of the schemes provides a vision for their evaluation and effective implementation. The trade-offs between reliability, latency, and complexity for the decoders can be controlled by adjusting the decoding parameters, resulting in further improvements in their performance. A potential future direction of research work lies in balancing the performance parameter trade-offs of decoders to make them more energy efficient for a sustainable telecommunication ecosystem.  
Conceptualization, A.G. and P.T.; methodology, A.G.; software, A.G.; validation, A.G., P.T. and G.S.; formal analysis, P.T.; investigation, A.G. and P.T.; resources, P.T.; data curation, A.G. and P.T.; writing--original draft preparation, A.G. and P.T.; writing--review and editing, P.T. and G.S.; visualization, A.G.; supervision, P.T. and G.S.; proofread, G.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The original contributions presented in this study are included in the article. The authors declare no conflicts of interest.
# Wind Lidar and Radiosonde Measurements of Low-Level Jets in Coastal Areas of the German Bight
## 2 Methods  
### LLJ Definition  
In this study, the definition of the LLJ has the following criteria [26; 27]: The vertical profile of horizontal wind speed consists of a maximum and a subsequent minimum, with a wind speed difference of at least \(2\,\mathrm{m}\,\mathrm{s}^{-1}\) and \(25\%\) of the maximum wind speed. The term LLJ is used here independently of the mechanism leading to the formation of the enhanced wind speed in the boundary layer, in contrast to other definitions, e.g., [17]. To classify wind profiles as LLJ, the criteria of the wind speed difference between wind maximum and minimum have to be fulfilled at least for three consecutive lidar profiles averaged over 10 min. As only one radiosonde profile is available every 12 h, the last criterion, requiring three consecutive LLJs within 30 min for the lidar data, is not applicable to radiosonde profiles.
## 4 Chaotic Ensemble Particle Swarm Optimizer (C.EPSO)
### Chaotic Maps  
The randomization process in all the natural inspired optimization techniques is performed using Gaussian distribution. lately, a novel avenue has been created to improve this process by replacing Gaussian distribution by chaotic maps to avail from their randomization properties. In this approach, merging the features of chaos maps with the original algorithms achieves rapid convergence to the optimal solution with better accuracy especially in case of difficult problems such as multi-modal functions [33]. In this work, ten different one-dimensional chaos maps are utilized to adjust some control parameters of the basic version of EPSO algorithm and subsequently achieving a better convergence rate and more accurate results. The novel chaotic variants are named Chaotic Ensemble Particle Swarm Optimizer (C-EPSO). The utilized chaos maps are listed in Table 1. The distribution values is equal to the maximum number of iterations and the initial value is equal to 0.7.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number** & **Function Name** & **Chaotic Map** \\ \hline
1 & Chebyshev & \(x_{i+1}=\cos(i\cos^{-1}(x_{i}))\) \\ \hline
2 & Circle & \(x_{i+1}=mod(x_{i}+b-\left(\frac{\varphi_{i}}{\pi}\right)\sin(2\pi x_{k},1))\), \(a=0.5\) and \(b=0.2\) \\ \hline
3 & Gauss/mouse & \(x_{i+1}=\left\{\begin{matrix}1&x_{i}=0\\ \frac{1}{mod(x_{i},1)}&\text{Otherwise}\end{matrix}\right.\) \\ \hline
4 & Iterative & \(x_{i+1}=\sin\left(\frac{\varphi_{i}}{\pi}\right),a=0.7\) \\ \hline
5 & Logistic & \(x_{i+1}=ax_{i}(1-x_{i}),a=4\) \\ \hline
6 & Piecewise & \(\left\{\begin{matrix}x_{i+1}=\\ \frac{x_{i}-P}{0.5-P}&0<=x_{i}<P\\ \frac{1-P-x_{i}}{0.5-P}&P<=x_{i}<0.5\\ \frac{1-P-x_{i}}{P}&0.5<=x_{i}<1-P\\ \frac{1-x_{i}}{P}&1-P<=x_{i}<1\end{matrix}\right.\) \\ \hline
7 & Sine & \(x_{i+1}=\frac{\varphi_{i}}{\pi}sin(\pi x_{i}),a=4\) \\ \hline
8 & Singer & \(x_{i+1}=\mu(7.86x_{i}-23.31x_{i}^{2}+28.75x_{i}^{3}-13.302875x_{i}^{4})\), \(\mu=1.07\) \\ \hline
9 & Sinusoidal & \(x_{i+1}=ax_{i}^{2}\sin(\pi x_{i}),a=2.3\) \\ \hline
10 & Tent & \(x_{i+1}=\left\{\begin{matrix}\frac{x_{i}}{\pi}&x_{i}<0.7\\ \frac{10}{3}(1-x_{i})&x_{i}>=0.7\end{matrix}\right.\) \\ \hline \hline \end{tabular} where (\(x_{i+1}\)) refers to the map distributions over the number of iterations \(i\).  
\end{table}
Table 1: Chaotic maps formulas [33].
## 4 Intruder Tracking Algorithm
Parameter \(\beta\) (\(\beta\epsilon[0,1]\)) is used to regulate the growth of \(\alpha\), which must never grow beyond its upper bound (_max_alpha_\(\epsilon\) [0,1]). ```
input:interval,max_alpha,beta begin setmicroint=interval/5;#microgroupinterval setlast_id=0; set\(\overrightarrow{P_{e}}[0]\)= \(\overrightarrow{P_{s}}[0]\);#initiallocationequaltothelocationofthefirstsensorreporting foreachreportidreceiveddo{ if(timestamp[id]-timestamp[0]<$interval){#forfirstgrouponly setalpha=\(\frac{timestamp[id]-timestamp[id-1]}{timestamp[id]-timestamp[0]}\times beta\) if(alpha>max_alpha)setalpha=max_alpha; set\(\overrightarrow{P_{e}}[id]=\alpha\cdot\overrightarrow{P_{s}}[id-1]+(1-\alpha)\cdot \overrightarrow{P_{s}}[id]\) }else{#group-basedestimation if(timestamp[id]-timestamp[last_id+1]<microint){#microgroupdetected set\(\overrightarrow{P_{mgr}}=\) average(\(\overrightarrow{P_{s}}[last\_id+1]\)to\(\overrightarrow{P_{s}}[id]\))#microgroupestimation }else{#newmicrogroup set\(\overrightarrow{P_{mgr}}=\) \(\overrightarrow{P_{s}}[id]\);#est.grouppos.equaltocurrentsensorpos. setlast_id=id-1; } set\(\overrightarrow{V_{mgr}}=\)estimate_intruder_speed(last_id,id)#reportsfromlast_idtoid set\(\overrightarrow{P_{speed}}=\) \(\overrightarrow{P_{e}}[last\_id-1]+\overrightarrow{V_{mgr}}\times(timestamp[id]-timestamp[last\_id])\) set\(alpha=\frac{timestamp[id]-timestamp[last\_id]}{interval}\times beta\) if(alpha>max_alpha)setalpha=max_alpha; set\(\overrightarrow{P_{e}}[id]=\alpha\cdot\overrightarrow{P_{mgr}}+(1-\alpha)\cdot \overrightarrow{P_{speed}}\) }
```  
**Algorithm 1** Intruder tracking: position estimation process. Finally notice that, for error estimation purposes, the output of this algorithm consists of a sequence of estimated intruder coordinates (\(\overrightarrow{P_{e}}\)), which are stored alongside with the corresponding timestamp, to be compared against the real sequence of intruder coordinates at the end. This strategy allows determining the tracking accuracy of our solution in any scenario.
## 4 Discussion
### Uncertainty Analysis
#### 4.2.3 Feature Importance Evaluation  
Feature importance analysis elucidates the degree to which each variable impacts the dependent variable. Using the Random Forest feature importance algorithm, we systematically evaluated and ranked all features to find the impact factors of XCO2 and their weights, presenting the outcomes in Figure 11. Detailed definitions of each variable can be found in Tables S1 and S2 in the Supplementary Materials. A higher feature importance value indicates a greater influence on XCO2. Notably, the nighttime light data (NTL) emerges as the most influential variable, followed by CH\({}_{4}\) column-mean molar fraction (tcch4), total column ozone (tco3), total column water (tcov), and surface solar radiation (ssr), all significantly contributing to XCO2. To enhance the interpolation accuracy of the XCO2 dataset, we propose choosing the independent variables with higher accuracy and reliability, particularly those with substantial weight. Moreover, given China's pronounced spatial heterogeneity, regional variations in optimal independent variables and modeling approaches should be considered. Zoning the study area based on topographic or climatic factors could thus refine the models and improve dataset accuracy.  
Figure 10: Spatial distributions of the difference between CNN-STK XCO2 and Mapping-XCO2 for each season. The maximum underestimations of CNN-STK XCO2 relative to Mapping-XCO2 appear in bright blue boxes, while the maximum overestimations appear in magenta boxes.
## 5 Results and Discussion
### Comparison of Model Results
#### 5.3.3 Comparative Analysis of Model Regression Results Considering Average House Prices  
To explore the impact of average house price on the peak hourly passenger flow within a weekday of Shenzhen rail transit, in the study in this paper, the impact factors without average house price (\(X_{2}\)) are re-substituted into the OLS model as well as the GWR model to be analyzed against the methods of Section 5.3.1 and 5.3.2, and the results of the comparative analyses before and after the substitution of the average house price (\(X_{2}\)) are finally summarized and obtained (as shown in Table 7 below).  
From the analysis in Table 7, it can be seen that the average house price has a significant negative correlation effect on the spatial trend of Shenzhen rail transit passenger flow, and the consideration of the average house price as an influencing factor can significantly improve the accuracy of the model in predicting the peak hour passenger flow of Shenzhen rail transit. In particular, the GWR model, which describes spatial heterogeneity, shows higher prediction accuracy than the traditional OLS model and can better capture the influence mechanism of built environment factors on the passenger flow of urban rail transit stations.
## 4 Results
### Effect of Regularization Parameter on Local Explainability Metrics
#### 4.3.2 Computational Efficiency  
Tables 5 and 6 compare the computational efficiency of our frameworks with LIME on the HELOC and ML-100k datasets. Although our frameworks include an additional training phase, the average time to generate explanations for individual instances is significantly lower than LIME (0.011s vs. 0.3812s). This efficiency advantage becomes more apparent when generating explanations for multiple instances, with our frameworks being over 20 times faster than LIME for explaining 100 instances. While the additional training overhead is non-trivial, it occurs only during training and is offset by the 30\(\times\) speedup in explanation generation at inference time compared to LIME. For a model serving 10,000 predictions daily, the training overhead is amortized within hours, making our approach highly practical for production deployments where real-time explanations are required. We observe consistent efficiency advantages across different dataset sizes and domains.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Neighborhood Size** & **Neighbored Fidelity \(\uparrow\)** & **Stability \(\downarrow\)** & **Computation Time (s)** \\ \hline \(k=3\) & 0.8833 \(\pm\) 0.1939 & 0.2152 \(\pm\) 0.0175 & 0.0121 \(\pm\) 0.0014 \\ \(k=10\) & 0.9350 \(\pm\) 0.0381 & 0.0505 \(\pm\) 0.0098 & 0.0127 \(\pm\) 0.0009 \\ \(k=100\) & 0.9670 \(\pm\) 0.0013 & 0.0015 \(\pm\) 0.00006 & 0.0144 \(\pm\) 0.0061 \\ \hline \hline \end{tabular}
\end{table}
Table 4: HELOC Dataset: Effect of neighborhood size on neighborhood fidelity, stability, and computation time for the pre hoc framework (\(\lambda=0.25\)). \(\uparrow\) means higher score is better, \(\downarrow\) means lower score is better.  
Figure 3: Effect of explainability regularization parameter \(\lambda_{1}\) on point fidelity, neighborhood fidelity, and stability for the pre hoc framework on the ML-100k dataset results. Comparison of the pre hoc framework with \(k=10\), for \(\lambda=\{0.01,0.05,0.1,0.25,0.5,1\}\).
# An Indoor DFEC Ranging Method for Homologous Base Station Based on GPS L1 and BeiDou B1 Signals
## 3 Implementations and Evaluation
### Wired Connection Test
From the above analysis, we can find that, under the condition that the signal link is not affected by the outside world, the whole cycles estimate of the distance difference between the channels of the pseudo-satellite transmitting base station and the receiving terminal is the same, which together introduces one-cycle ranging error. In the following analysis, we will introduce the space environment impact and analyze the ranging estimation situation in the case of wireless transmission
## 3 Image Cryptography  
In general, secure data transmissions can be achieved through symmetric or asymmetric cryptography. Both of them present advantages and drawbacks that should be properly evaluated for each type of WSN application. For the particular case of image sensing, the additional burden for the transmission of large amounts of data should also be considered. The next subsections describe symmetric and asymmetric cryptography in the context of wireless sensor networks. Moreover, the relevant issue of key management is also addressed.