esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Validation and Evaluation of a Coupled Twist-Camber Morphing Wing Concept
## 1 Introduction
Within the scope of this project, computational and experimental models for leading edge, trailing edge, and winglet morphing were developed and served as the basis for more recent investigations on the aero-servo-elastic behavior of morphing wings including flutter analysis and dynamic stability of the morphing systems [20]. The major European collaborative programs Clean Sky 1 and 2 [21; 22] also addressed morphing technologies as an important part of the European effort to reduce fuel consumption and minimize emissions in commercial aviation ranging from regional to large passenger aircraft and also for future vertical take-off and landing aircraft. An example of the research on morphing within this program is the proposed morphing winglet concept [23; 24] with a fault-tolerant architecture that is intended to improve performance and alleviate wing loads in maneuver conditions. In this paper, a prototype of a morphing wing with coupled camber-twist change capability and its testing is described. This novel concept contributes to the state-of-the-art as it explores multiple and coupled morphing concepts while also exploring multiple functions for said concepts, thus improving the competitiveness of the morphing solution when compared to conventional ones. In particular, this work explores the functionality of the concept for roll control, performance improvements, and load alleviation. The paper is organized as follows; Firstly, the explanation on the morphing concept and wing manufacture is presented. Secondly, the ground testing procedure to determine the wing's actuated shapes is described and estimates of roll authority, load alleviation potential, aerodynamic efficiency differences, mass and actuation energy are shown. Thirdly, a section about flight testing describes the outcome of a small flight using the morphing wing prototype. Finally, the main conclusions are highlighted.
## 3 Experimental Results  
### Transmittance and Visual Analysis  
Figure 2 shows a spectral transmittance study performed on the glass and the EVA material used to encapsulate the samples. These data are shown together with the spectrum of the full solar irradiation (AM 1.5 G) and of the UV lamp used in the degradation processes. As is shown, the encapsulant built of glass and EVA acts as an effective UV block agent below 400 nm.  
Figure 3 shows the transformation observed on the MAPbI\({}_{3}\) and MAPbI\({}_{3}\) with ETL samples, encapsulated and non-encapsulated, during the degradation process, starting from their pristine stages (Figure 3a,d,g,i,k). As can be seen, the non-encapsulated MAPbI\({}_{3}\) samples show a similar visual degradation when exposed to both full solar irradiation and UV lamp irradiation. The non-encapsulated samples present decoloring into a dull brown color after 50 min of exposure (Figure 3b,e), turning to a yellowish shade after 70 min (Figure 3c,f). These results are attributed to a degradation mechanism triggered by UV irradiation in a humid environment [4, 46, 64, 65, 66]. However, the visual appearance of the  
Figure 2: Transmittance of the glass and EVA (Y-axis on the left) against the irradiance of the AM1.5 spectrum and the UV lamp (Y-axis on the right).  
encapsulated MAPbI\({}_{3}\) samples remained stable above 350 h under full solar irradiation (Figure 3g,h). Additionally, although the non-encapsulated samples of MAPbI\({}_{3}\), with and without the ETL layer, show a similar degradation, the ones with ETL acquire the characteristic yellowish shade after 70 min of UV irradiation (Figure 3j), indicating that the ETL layer somehow delays the degradation. In addition, the encapsulated MAPbI\({}_{3}\) with the ETL layer sample remained stable after 70 min exposure (Figure 3l).
# Identification of Coal Geographical Origin Using Near Infrared Sensor Based on Broad Learning
###### Abstract
Finally, we compare the proposed method with state-of-the-art methods in coal geographical origin identification. The results demonstrate that the proposed PSO-BL-based strategy achieves the best performance with an accuracy of 97.05%, which strengthens the contribution of the proposed method and further emphasizes the importance of the optimal parameters. In this study, we propose a novel method to identify coal geographical origin. The main contributions of this paper are three-fold:  
1. Inspired by the usage of NIR in agriculture, petrochemical and pharmaceutical industries, we employ NIR to identify coal geographical origin, which is unprecedented. This method is fast and non-destructive. 2. Considering the noisy NIR spectral and limited samples, we employ the BL as the modelling algorithm for its advantages of a simple structure, robustness to noise and excellent performance in previous studies. Compared with the traditional methods in the literature, this study improves the classification precision. 3. The performance of BL is largely dependent on the network structure. In order to obtain the optimal parameters for the BL model, the PSO algorithm is utilized. The proposed method is able to classify the coal geographical origin accurately. The rest of this paper is organized as follows. The experimental preparations, including the sources of data, data processing and outlier rejection, are presented in Section 1. Section 2 explains the theory of the BL model and presents the detail of our proposed PSO-BL model. Some comparison experiments are carried out to compare the performance of the PSO-BL model with that of the other five traditional discrimination methods, and the results are shown in Section 3. Section 4 concludes the whole paper.
# A Systematic Review of the Usability of Telemedicine Interface Design for Older Adults
## 2 Methods
### Information Sources  
The literature search for this review focused on four major databases: Scopus, Web of Science, PubMed, and CINAHL. Scopus is recognized as the largest database of peer-reviewed literature, covering various fields including health sciences, natural sciences, humanities, social sciences, and life sciences. Web of Science is an authoritative source that encompasses a broad range of disciplines from natural sciences to humanities. PubMed is a leading database for global medical and biomedical literature. CINAHL is specially tailored for nursing, allied health sciences, and related fields. To explore the influencing factors and usability of telemedicine interface design for the elderly, Boolean operations (AND) were used to combine keywords. The final search query was defined as: \"TS = ((\"elder\"*\" OR \"older adult**\" OR \"old people\" OR \"seniors\" OR \"aging\" OR \"the aged\" OR \"Geriatric\") AND (\"telemedicine\" OR \"mobile application** OR \"telehealth\" OR \"mobile health\" OR \"mhealth\" OR \"mehealth\" OR \"ehealth\" OR \"digital health\") AND (\"interface design\" OR \"user interface\" OR \"APP design\" OR \"application design\" OR \"UI\" OR \"UX\" OR \"user experience\" OR \"usability\" OR \"efficiency\" OR \"human factor\" OR \"design guidelines\"))\". All terms were searched across titles, keywords, abstracts, and full text.
# Joint Delay-Doppler Estimation Performance in a Dual Source Context
## 5 Validation and Discussion
### Results
#### 5.2.3 Scenario (c): \(\Delta\tau\) smaller than 1 chip  
In this scenario, we consider a relative delay of an eigth of a L1 C/A chip, which accounts for a path delay of about 37.5m. In this case, there is such a strong interference between the two signals (see Figure 2 (bottom)) that the CRE cannot be efficient anymore. The CRE limit is clearly shown in Figure 5. For both signals, the time-delay estimation never reaches the optimal performances set by the CRB. Interestingly, the chirp signal seems to be more robust for the Doppler estimation, reaching the CRB for the direct signal but not for the reflected one.  
Figure 6 shows the results for the dual source CMLE. One can observe a good fit between the MSE and the CRB. This result supports the fact that the CRE limitations can be overcome by the CMLE, and also validates again the CRB expression for the dual source estimation problem derived in this contribution.  
Figure 5: Scenario (c1): Estimation of the delay (**left**) and the Doppler frequency (**right**) for \(\Delta\tau=1/8\) chips with CRE. (**top**) is for a GPS L1 C/A signal, (**bottom**) is for an LFM chirp signal.  
Figure 6: _Cont_.
## 3 Experimental Results and Analysis
### Dynamic Impact Test Results
#### 3.2.1 Dynamic Stress-Strain Behavior
From Equation (4), the frost heave force is affected by the porosity, saturation, freezing rate, and bulk modulus of ice and rock skeleton. Here, \(\beta\), \(K_{\text{r}}\), and \(K_{\text{i}}\) are set to 0.09, 20.83 GPa, and 5 GPa, respectively [32]. The step function defaults to 1 and \(S_{\text{r}}\) values range from 0.8 to 1. Substituting the parameters into Equation (4) yields the relationship between frost heave stress, saturation, and freezing rate, which is shown in Figure 11.  
As shown in Figure 12, the frost heave force is positively related to saturation; yet, even at 100% saturation, the effective heave force can still occur at lower freezing rates. As the freezing rate increases, the intensity factor of the frozen stress in the pore space exceeds the critical intensity factor \(K_{\text{IC}}\), leading to the initiation and propagation of cracks and thus internal damage to the sample [33]. As a result, the dynamic strength of the sample increases and decreases with increasing saturation. However, due to the weak cementation of the rock in Xinjiang [34], the critical saturation of the red sandstone in the actual tests is between 50% and 80%, which is less than the predicted threshold of 91% for frost deformation failure. Following Figure 8f, the tangential modulus was adopted as the dynamic elastic modulus (\(E_{\text{d}}\)) in this study. The variation of \(E_{\text{d}}\) with impact velocity and saturation is shown in Figure 13. In Figure 13a, the \(E_{\text{d}}\) of the sample at low temperatures initially shows an increasing trend with saturation. For example, when the saturation of the sample increases from 0 to 50%, the \(E_{\text{d}}\) increases by 72.6% from 2.59 GPa to 4.47 GPa at an impact velocity of 7 m/s. However, as saturation increases (>50%), the increasing trend is suppressed and the \(E_{\text{d}}\) of the sample reduces by 18.5% to 2.11 GPa at complete saturation. The increase in deformation resistance is due to the pore ice acting as a support effect for the pore wall and enhancing the cementation between the wall and the ice, facilitating  
Figure 12: Frost heaving force versus saturation and freezing rates. the propagation of stress waves. As a result, the pore ice reduces the stress concentration at the pore tip [2], leading to an increase in dynamic modulus. However, as saturation increases, new microcracks appear in the sample under the influence of freeze heave forces and the enhancement of pore ice gradually decreases, leading to a decrease in dynamic elastic modulus. Moreover, as shown in Figure 13b, the \(E_{\text{d}}\) of the sample increases linearly with the impact velocity at low temperatures. Under the five saturation conditions, the \(E_{\text{d}}\) increases rapidly by 32.8 %, 43.1 %, 37.1 %, 51.5 %, and 23.4%, respectively, when the impact velocity increases from 4 m/s to 7 m/s, indicating that the stiffness property of the frozen sample has a significant impact on the strengthening effect.
## 4 Methodology
### Real-Time Map Update and Localization
In order to increase the speed of checking each point for inclusion in the convex hull, a simple x-y axis-aligned bounding box is used to quickly rule out all points of the PGM map that could not fall within the hull since they do not fall within the enclosing AABB. This greatly reduces the number of points that need to be checked for the more computationally intensive task of inclusion in the convex hull. Similarly, current door positions are marked within the navigation. However, door positions will change often, perhaps even during a single surveillance task. Therefore, door positions are recorded in their own file, which can be quickly updated. When a door position is updated, the base map is copied from the most recently updated map containing long-term objects, and then, each door is added using the last known door position. In this way, long-term objects, such as furniture and appliances, remain in their place, and doors are able to change position frequently. After the new map has been copied and modified, it is made immediately available within the ROS ecosystem (e.g., for use by the navigation system) by spawning a new map server node. In order to avoid naming conflicts, each map server is set to broadcast on its own map topic name. A relay node is used to determine the map server that should broadcast to the principal/map topic. This process is illustrated in Figure 8, in which an initial map without a sofa is used for navigation. After the robot identifies and classifies the sofa, it is inserted into the PGM map, which is then served to the navigation system.
## 2 Related Work
### ABAC Integration in Blockchain Systems  
ABAC has gained significant traction for its ability to enforce fine-grained, context-aware policies. Unlike RBAC, ABAC leverages dynamic attributes--such as organizational role, project status, or environmental context--to enable more expressive and scalable access decisions [19].  
Implementing ABAC in blockchain environments presents challenges related to policy enforcement, system performance, and the protection of confidential data. A smart contract-based architecture for ABAC enforcement on Ethereum has demonstrated the feasibility of fine-grained access control in decentralized settings [5]. However, public blockchains often suffer from scalability constraints and inherent privacy limitations, making them less suitable for enterprise-grade applications.  
Permissioned blockchains like Hyperledger Fabric are increasingly favored for implementing enterprise-grade ABAC due to their modular architecture, decentralized identitymanagement, and reduced consensus overhead. These features enable scalable and fine-grained policy enforcement while also supporting efficient attribute revocation and trust management--capabilities that are critical for secure access governance in dynamic environments such as the Internet of Things (IoT) and organizational networks [20]. Recent work has also demonstrated enhanced policy expressiveness and scalability through hierarchical attribute structures and sharding techniques in smart contract-based ABAC systems [21] as well as improved throughput in Hyperledger environments for IoT-focused applications [22].  
On-chain enforcement of attribute-based policies in Hyperledger Fabric has been demonstrated using a modular ABAC framework, improving policy expressiveness over traditional access models [11].  
These studies build upon these foundations by deploying a full ABAC-enforced system using Hyperledger Fabric for the tokenization of mining licenses. It contributes a reusable smart contract suite, structured access logs, and an evaluation framework for analyzing latency, scalability, and auditability in enterprise conditions.
## 5 ANOVA and Parametric Influence on Performances  
Analysis of variance (ANOVA) on the process parameters and the effects of the main process parameters on the performance measures was performed. This and [PERSON]'s test (F-ratio) were performed to justify the adequacy of the developed regression models as in Equations (2) and (3). Values of \"Prob > F\" less than 0.05 indicate the model's significance and terms. Values greater than 0.1 indicate that the model terms are insignificant.  
The model F-values in Table 6 indicate that the developed models were very significant. The two-way interaction terms such as EC*V, EC*EC, and V*FR for the MRR and NP models were insignificant when the values of \"Prob > F\" were greater than 0.05. The terms V*V and EC*FR for the MRR and NP models, respectively, were not significant, but the coefficient of determination values for both models were good, as shown in Table 7. The normal probability curves in Figure 7 confirm the adequacy of the model equations. The high coefficient of determination values (R2 = 90.34% for the MRR and R2 = 92.29% for the NP) for both responses confirm its adequacy for optimization study.  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Source**} & \multirow{2}{*}{**DF**} & \multicolumn{2}{c}{**Adj SS**} & \multicolumn{2}{c}{**Adj MS**} & \multicolumn{2}{c}{**F-Value**} & \multicolumn{2}{c}{**\(p\)-Value**} \\ \cline{2-10}  & **MRR** & **NP** & **MRR** & **NP** & **MRR** & **NP** & **MRR** & **NP** & **MRR** & **NP** \\ \hline Model & 9 & 9 & 0.037278 & 437.944 & 0.004124 & 48.660 & 10.39 & 13.29 & 0.001 & 0.000 \\ Linear & 3 & 3 & 0.025497 & 347.518 & 0.008499 & 115.839 & 21.32 & 31.64 & 0.000 & 0.000 \\ EC & 1 & 1 & 0.002700 & 168.372 & 0.002700 & 168.372 & 6.77 & 45.99 & 0.026 & 0.000 \\ V & 1 & 1 & 0.005345 & 34.468 & 0.005345 & 34.468 & 13.41 & 9.42 & 0.004 & 0.012 \\ FR & 1 & 1 & 0.017452 & 144.678 & 0.017452 & 144.678 & 43.78 & 39.52 & 0.000 & 0.000 \\ Square & 3 & 3 & 0.007095 & 79.698 & 0.002365 & 26.566 & 5.93 & 7.26 & 0.014 & 0.007 \\ EC*EC & 1 & 1 & 0.001154 & 3.003 & 0.001154 & 3.003 & 2.89 & 0.82 & 0.120 & 0.386 \\ V*V & 1 & 1 & 0.001136 & 61.290 & 0.001136 & 61.290 & 2.85 & 16.74 & 0.122 & 0.002 \\ FR*FR & 1 & 1 & 0.006615 & 33.779 & 0.006615 & 33.779 & 16.59 & 9.23 & 0.002 & 0.013 \\
2-Way Interaction & 3 & 3 & 0.004685 & 10.728 & 0.001562 & 3.576 & 3.92 & 0.98 & 0.044 & 0.442 \\ EC*V & 1 & 1 & 0.00193 & 0.002 & 0.000193 & 0.002 & 0.48 & 0.00 & 0.502 & 0.981 \\ EC*FR & 1 & 1 & 0.004082 & 7.681 & 0.004082 & 7.681 & 10.24 & 2.10 & 0.009 & 0.178 \\ V*FR & 1 & 1 & 0.000410 & 3.045 & 0.000410 & 3.045 & 1.03 & 0.83 & 0.334 & 0.383 \\ Error & 10 & 10 & 0.003987 & 36.607 & 0.000399 & 3.661 & & & & \\ Lack of Fit & 5 & 5 & 0.002498 & 28.127 & 0.000500 & 5.625 & 1.68 & 3.32 & 0.292 & 0.107 \\ Pure Error & 5 & 5 & 0.001489 & 8.480 & 0.000298 & 1.696 & & & & \\ Total & 19 & 19 & 0.041264 & 474.551 & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Analysis of variance for quadratic models of MRR and NP.  
Figure 7: Normal probability plots for MRR and NP.
## 4 Error Analysis
### Error From Compensation Angle
Figure 15: Deviation of compensation angle caused by different geomagnetic information in north direction (**a**), east (**b**) direction, south (**c**) direction, and west (**d**) direction. From Figure 10b, pitch ranges from 50\({}^{\circ}\) to \(-\)67\({}^{\circ}\). Meanwhile, inclination is 59.263\({}^{\circ}\). Thus, Figures 16 and 17 indicate that when the body of projectile tends to be parallel to geomagnetic vector (shown in Figure 18), the compensation angle is influenced by the noise to a large degree. Moreover, for course correction is conducted at the terminal part of trajectory, large deviation in the previous part (when yaw is 180\({}^{\circ}\) shown in Figure 17a,b) has less effect on the guidance and control. In practice,  
Figure 16: SD and mean of deviation of compensation angle caused by the noise. Figure 17: Compensation angle and deviation when yaw is 0\({}^{\circ}\) and 180\({}^{\circ}\): (**a**,**c**) Description of compensation angle in the north and south direction and (**b**,**d**) description deviation of compensation angle. attitude of projectile keeps changing in flight so that period of large deviation would not last as long as what is shown in Figure 17.  
To conclude, in the range of howitzer, the change of geomagnetic information has less effect on the compensation angle. Deviation caused by it is less than 0.15\({}^{\circ}\). Meanwhile, SD of noise from pitch and yaw should be less than 5\({}^{\circ}\) so that SD of deviation of compensation keep within 6\({}^{\circ}\). Large deviation caused by certain azimuth will be discussed in detail next.
## 4 Results and Discussion  
In the following, we present experimental and numerical results for the performance of the 3 rd-order IVSTF-NLE and the DBP-SSF method, varying the number of steps per span. For brevity, we denote the different versions of the DBP-SSF NLEs as DBP-SSF\({}_{\text{Nsteps}}\), where \(N_{steps}\) is the number of steps per span. In both, experimental and simulation, studies we have only detected the 3 rd-band of the central OFDM super-channel, since inner bands are more affected by inter-channel nonlinear impairments compared to the outer bands. To compare the performance of different NLEs, we use the \(Q^{2}\)-factor as a figure of merit, which is related to BER by \(Q^{2}\) (dB) = 20 log\({}_{10}\) [\(\sqrt{2}\) erfc\({}^{-1}\) (2 BER)] [16]. We estimated the net \(Q^{2}\)-factor improvement (\(\Delta Q^{2}\)) in dB which is obtained as follows: First, we estimated the maximum \(Q^{2}\)-factor value when only linear equalization is applied (max.\(Q^{2}_{\text{w. LE}}\)). Second, we applied both, linear and nonlinear, equalization and we evaluated the corresponding maximum \(Q^{2}\)-factor value (max.\(Q^{2}_{\text{w. NLE}}\)). We define as \(\Delta Q^{2}=Q^{2}_{\text{w. NLE}}-Q^{2}_{\text{w. LE}}\) the net \(Q^{2}\)-factor improvement, thus, the higher the \(\Delta Q^{2}\), the better the performance of the NLE. For both, experimental and simulation, results four trials were conducted and then the average was calculated and recorded as the final value.  
Figure 4: Simulation setup of a 4-band, single-polarization, OFDM super-channel using as a transmission link either an SSMF or a LEAF. (Abbreviations: pseudo-random bit sequence (PRBS), OFDM transmitter (OFDM Tx), digital-to-analog converter (DAC), standard single-mode fiber (SSMF), large effective area fiber (LEAF), erbium-doped fiber amplifier (EDFA), polarization-diversity coherent receiver (Polariz. Divers. CohRx), analog-to-digital converter (ADC), nonlinear equalizer (NLE), OFDM receiver (OFDM Rx), and bit-error-rate (BER).
# A Drone-Enabled Approach for Gas Leak Detection Using Optical Flow Analysis +
## 4 Experiments
### Experiment A: Isolated Environment  
This experiment is designed to investigate the performance of the proposed gas leak detection method in an isolated environment. To do so, a FLIR GF620 cooled thermal camera in a fixed position with around one-meter distance from the target is utilized to record the generated gas emanations. The experiment is conducted in two sessions using two types of gases and containers including alcohol and gasoline. The details of the experiment are listed in Table 1 and the sample photos are shown in Figure 3.
## Materials and Methods  
### Site Description  
The study was conducted in central Missouri (38\({}^{\circ}\)44.650N, 92\({}^{\circ}\)12.000W, 219 m a.s.l.) at the Missouri Ozark Forest Flux Site (MOFLUX, [43]), a broadleaf deciduous forest dominated by isoprene-emitting oaks, in the Basketball Wildlife Research and Education Area of the University of Missouri, which is included in the AmeriFlux Network. The main tree species include white, post and black oaks (_Quercus alba_ [PERSON], _Q. stellata_ [PERSON] and _Q. velutina_ [PERSON], respectively), shagbark hickory (_Carya ovata_ ([PERSON]), sugar maple (_Acer saccharum_ [PERSON]) and eastern red cedar (_Juniperus virginiana_ [PERSON]). The area has a warm, humid, continental climate [44], with mean January and July temperatures of \(-1.3\) and \(25.2\)\({}^{\circ}\)C, respectively, and a mean annual precipitation of 1083 mm (National Climatic Data Center 1981-2010 Climate Normals, Columbia Regional Airport, Columbia, MO, USA). We compared PRI time series derived from the Multi-Angle Implementation of Atmospheric Correction (MAIAC, MODIS Terra, col. 6) product to measurements of isoprene emission based on eddy covariance (measured continuously by proton transfer reaction mass spectrometry (PTR-MS) and a fast isoprene sensor (FIS)).