esa-sceva/satcom-chunk-collection huggingface dataset:
# Advancements and Applications of Rim-Driven Fans in Aerial Vehicles: A Comprehensive Review
## 1 Introduction
Ducted fan technology, which has been a staple since the 1960s, has now witnessed a transformative advancement in the form of rim-driven fan (RDF) technology. This innovation, which eschews the conventional motor designs in favor of a more compact and efficient approach, offers the potential to revolutionize the way aircraft propulsion is conceptualized and engineered. By embodying a unique rotor configuration that departs from traditional center hub designs, RDF technology not only enhances thrust and exhaust air velocity but also ushers in opportunities for more streamlined and effective designs. As the aviation industry treats the path of electrification, the innovative prowess of rim-driven fan technology emerges as a beacon of hope, pointing towards a more sustainable and efficient future for air travel. The potential of rim-driven fan technology to revolutionize aircraft propulsion is a central theme in the subsequent sections. By embracing the unique rotor configuration that sets RDF technology apart, we unlock opportunities for enhanced thrust and exhaust air velocity while streamlining designs for increased effectiveness. This collective effort to explore the untapped potential of rim-driven fan technology is a vital step towards reconciling aviation's impact on the environment. By addressing the core challenges and opportunities that this technology presents, we are contributing to the larger mission of reducing carbon emissions and building an eco-friendlier aviation landscape. The journey to fully realizing the promise of rim-driven fan technology is an ongoing narrative, one that aligns seamlessly with the broader narrative of a sustainable and innovative aviation future. In the pages that follow, we delve deeper into the intricacies of rim-driven fan technology, exploring its principles, benefits, and the potential it holds for shaping the future of aviation propulsion. The leading sections of the document focus on providing readers with an overview of the RDF jet and its key features and benefits. This includes the rim-driven configuration and various concepts that underpin the design and development of the engine. The article also explores the innovation and evolution of the RDF jet, including the different types of electric motors, the design considerations, and the noise and vibration issues that have been addressed. The control mechanisms used, the bearing technology employed, and the use of FEA and CFD in the development process are also discussed. The article concludes with a brief summary of the main points covered, highlighting the significance of RDF technology as a driving force in reshaping aviation propulsion. As the industry moves forward, propelled by the momentum of electrification, the lessons and insights from this exploration will undoubtedly play a pivotal role in defining the trajectory of aviation's environmental transformation.
## 4 Numerical Verification for Design
### Results Analysis
#### 4.4.4 Review of the Safety of Protrusion Concrete  
In the case of the vertical hydrogen storage vessel reviewed in this paper, the unique characteristics of the structure, supported by a high center of gravity and anchors, may result in concentrated complex loads such as moment loads on the protrusion concrete during seismic loading. Accordingly, in Figure 12, where the anchor stress is most significant in the protrusion concrete due to the seismic loading, the splitting tensile strength is assessed at the anchor insertion area (C1), and the bending tensile stress is evaluated at the joint area connected to the slab (C2) to confirm structural integrity.  
The permissible splitting tensile strength and bending tensile stress for the reviewed protrusion concrete area were calculated based on Equations (2) and (3) [45; 46]. These values were used as references during the review process to assess the structural integrity:  
\[f_{sp}=0.57\lambda\sqrt{f_{ck}} \tag{2}\]  
Figure 11: Results of comparison of areas where stress is concentrated: (**a**) connecting point (S1), (**b**) support column (S2), and (**c**) anchor bolts (S3).  
Figure 12: Review area of protrusion concrete according to seismic loading.  
\[f_{r}=0.63\lambda\sqrt{f_{ck}} \tag{3}\]  
Here, the coefficient \"\(\lambda\)\" is used when lightweight concrete is cast, while for the case where regular ordinary concrete is used, the concrete coefficient was set to 1, considering the actual field installation conditions with typical aggregates. The maximum splitting tensile strength occurring in the upper part (C1) of the protrusion concrete, where direct contact with the anchor for the structural support is established, was measured as 2.87 MPa, as shown in Figure 13a. This indicates a safety margin of approximately 8%, considering the allowable splitting tensile strength of 3.12 MPa. The maximum bending tensile stress occurring in the joint area (C2) between the protrusion concrete and the foundation slab concrete was measured as 3.17 MPa, as shown in Figure 13b. This indicates a safety margin of approximately 8%, considering the allowable bending tensile stress of 3.45 MPa.  
Accordingly, it is determined that the structural integrity for the AC 156 Amp. 100% artificial earthquake, satisfying the seismic design criteria for buildings, has been secured. However, it is predicted that issues may arise when applying more stringent seismic design criteria, as the safety margin with respect to the structure's own safety factor is relatively limited.
# NIRS-EMG for Clinical Applications: A Systematic Review
## 3 Near Infrared Spectroscopy
### Different NIRS Techniques
The information about the depth reached by photons into the tissue is, straightforwardly, linked to their arrival time: Photons which arrive early (\"early photons\") travelled shorter paths through the tissue and, thus, they explored only the surface layer. Hence, they are related with physiological changes occurring in the superficial capillary bed and are strongly influenced by the fat layer. Photons which arrive later (\"late photons\") travelled through longer paths inside the tissue and, thus, they investigated deeper layers where the muscular tissue is located. By exploiting these peculiar characteristic of time-resolved measurements, it is possible to remove the more superficial physiological and confounding contributions from the detected signal [61; 62]. Up to now, due to the high cost of components and the complexity of the technique, there are few commercially available instruments which exploit the TD-NIRS approach for muscle monitoring. The Hamamatsu Photonics K.K. company has developed three devices: The tNIRS-1, originally designed for cerebral measurements, but which has been used also in muscle studies, and the TRS-10 and TRS-20 systems [63]. A high-power version, the TRS-20 SD, has been used by [PERSON] et al. [64] to probe deep into quadricp muscles. Moreover, a research-oriented TD-NIRS device dedicated to muscle metabolism monitoring has been recently developed by [PERSON] et al. [65].
## 3 Materials and Methods
### Proposed Method
#### 3.2.1 Residual Connected UNet  
The standard UNet architecture utilizes an encoder-decoder structure. The encoder captures high-level features through successive convolutional layers and max pooling, providing a condensed input representation. The bottleneck layer bridges the encoder and decoder, capturing essential features. The decoder then uses up-sampling and convolutional layers to reconstruct the output, incorporating skip connections to retain fine-grained details. The final \(1\times 1\) convolutional layer maps the feature channels to the desired output, typically used for generating segmentation masks in medical imaging tasks. Figure 4 displays the base UNet model as referenced in [22].  
Incorporating residual connections [41] within the UNet model, both inside the encoder and between the encoder and decoder (i.e., skipping the RDAM block), addresses the vanishing gradient problem. The architecture of the Residual UNet ensures smoother gradient flow and preserves essential details, making it a robust foundation for further enhancements with attention mechanisms.  
The following equation mathematically expresses the residual connection:  
\[R(x)=F(x)+x \tag{1}\]  
Figure 3: Illustration of liver and tumor segmentation pipeline for Residual UNet–RDAM.  
Figure 5 illustrates the application of residual connections within the encoder and between the encoder and decoder at the attention mechanism stage of the proposed Residual UNet architecture. These residual connections ensure that the model can learn even complex features from liver and tumor images.
# In-Depth Analysis of GAF-Net: Comparative Fusion Approaches in Video-Based Person Re-Identification
## 5 Experimental Results
### Results on iLIDS-VID Dataset
#### 5.1.3 Comparison with the SOTA Methods
These comparative evaluations demonstrate the robustness and superiority of GAF-Net in harnessing gait and appearance features for the purpose of person Re-ID. The results confirm that our approach not only competes well with existing methods but also sets a new bar for accuracy in this challenging domain. Additionally, GAF-Net improves lightweight architectures significantly by integrating skeletal gait information.
# Secure Image Transmission Using Multilevel Chaotic Encryption and Video Steganography
## 5 Results and Testing
### Image Encryption Result  
In this part, the MCC/AES/Stego algorithm is subjected to standard security analyses, including statistical and differential analyses. Figure 7 illustrates the encryption and decryption results using the proposed.
# RMRRASL: Robust Matrix Factorization with Robust Adaptive Structure Learning for Feature Selection
## 3 Robust Matrix Factorization with Robust Adaptive Structure Learning
### Model Optimization
#### 3.2.1 Fix \(A\) and \(W\); Update \(S\)  
For Equation (12), removing the irrelevant term with \(S\) yields the following objective function:  
\[\begin{array}{l}\varphi(S)=||Z-ZSA||_{2,1}+\alpha\,tr(S^{T}Z^{T}LZS)\\ \text{s.t.}\ S\geq 0,S^{T}S=I_{k\times k}\end{array}. \tag{13}\]  
However, the constraint conditions in Equation (13) make it difficult to optimize; hence, we can alleviate the constraint condition \(S^{T}S=I_{k\times k}\) to \(||S^{T}S-I_{k\times k}||_{2}^{2}\) as a penalty term. Thus, Equation (13) is transformed into the following objective function:  
\[\begin{array}{l}\varphi(S)=||Z-ZSA||_{2,1}+\alpha\,tr(S^{T}Z^{T}LZS)+\lambda ||S^{T}S-I||_{2}^{2}\\ \text{s.t.}\ S\geq 0\end{array}, \tag{14}\]  
where the parameter \(\lambda\) > 0 is used to constrain the orthogonality of vector \(S\).  
Let \(M=Z^{T}LZ\); according to the above definitions and matrix correlation operations [29], Equation (14) can be simplified to Equation (15) through a series of algebraic operations:  
\[\begin{array}{l}\varphi(S)=||Z-ZSA||_{2,1}+\alpha\,tr(S^{T}MS)+\lambda tr( (S^{T}S-I)(S^{T}S-I)^{T})\\ =tr((Z-ZSA)^{T}U(Z-ZSA))+\alpha\,tr(S^{T}MS)+\lambda tr((S^{T}S-I)(S^{T}S-I)) \\ =tr((Z^{T}-A^{T}S^{T}Z^{T})U(Z-ZSA))+\alpha\,tr(S^{T}MS)+\lambda tr(S^{T}SS^{T} S-2S^{T}S)\\ =tr(Z^{T}UZ-2A^{T}S^{T}Z^{T}UZ+A^{T}S^{T}Z^{T}UIZSA+\alpha S^{T}MS+\lambda S^{T}SS^{T} S-2\lambda S^{T}S)\\ \text{s.t.}\ \ S\geq 0\end{array}, \tag{15}\]  
where \(U\in R^{n\times n}\) denotes the diagonal matrix defined as follows:  
\[U_{ii}=\frac{1}{||(Z-ZSA)^{i}||_{2}+\varepsilon}, \tag{16}\]  
where \(\varepsilon\) is a small constant.  
To solve Equation (15), a Lagrange multiplier \(\Lambda\) with non-negative constraint \(S\geq 0\) is introduced, and the Lagrangian function \(\varphi(S,\,\Lambda)\) can be defined as follows:  
\[\begin{array}{l}\varphi\big{(}S,\Lambda\big{)}=tr(Z^{T}UZ-2A^{T}S^{T}Z^{T}UZ +A^{T}S^{T}Z^{T}UZSA+\alpha S^{T}MS)\\ +\lambda tr(S^{T}SS^{T}S-2S^{T}S)+tr(\Lambda S)\\ \text{s.t.}\ \ S\geq 0\end{array}. \tag{17}\]  
The partial derivative of Equation (17) with respect to matrix \(S\) can be obtained as follows:  
\[\frac{\partial\varphi(S,\Lambda)}{\partial S}=-2Z^{T}UZA^{T}+2Z^{T}UZSAA^{T}+ 2\alpha MS+4\lambda SS^{T}S-4\lambda S+\Lambda. \tag{18}\]  
Equation (19) can be obtained according to the KKT condition (\(\Lambda_{ij}S_{ij}=0\)) [33].  
\[(-2Z^{T}UZA^{T}+2Z^{T}UZSAA^{T}+2\alpha MS+4\lambda SS^{T}S-4\lambda S+\Lambda )S_{jij}=0. \tag{19}\]  
The update formula of the matrix \(S\) is as follows:  
\[S=S\odot\frac{Z^{T}UZA^{T}+\alpha M^{-}S+2\lambda S}{Z^{T}UZSAA^{T}+\alpha M^ {+}S+2\lambda SS^{T}S}, \tag{20}\]  
where \(M=M^{+}-M^{-}\), \(M^{+}=\frac{|M|+M}{2}\), and \(M^{-}=\frac{|M|-M}{2}\).
# AIR-YOLOv3: Aerial Infrared Pedestrian Detection via an Improved YOLOv3 with Network Pruning
## 4 Experiment
### Model Test on PC and TX2  
Experimental results show that the channel pruning ratio affects the compression rate significantly and the number of the pruned residual structures affects the inference time significantly. When the channel pruning ratio is 90% and the number of residual structures pruning is 12, we obtain the optimal model.  
The quantitative comparison of mask R-CNN, YOLOv3, AIR-YOLOv3, and Tiny-YOLOv3 is shown in Table 3. The detection models were transplanted to the Jetson TX2, which is the most widely used embedded platform on UAVs because of its portability and low power consumption [44].  
Figure 8: Influence of layer pruning and channel pruning: (**a**) AP curve, (**b**) inference time and (**c**) model size.  
As shown in Table 3, the input size of the model is set to \(416\times 416\), which achieves the balance of detection speed and accuracy [9]. On the airborne TX2 compared with original YOLOv3 [9], the inference speed has been increased by nearly two times from 3.7 FPS to 8 FPS, and the model size is compressed by 228.2 MB, while the AP of the AIR-YOLOv3 is just 1.7% lower.  
Compared with the well-known two-stage detection algorithm mask R-CNN [16], the inference speed on the airborne TX2 has been increased by nearly 11.4 times from 0.7 FPS to 8 FPS, while the AP of the AIR-YOLOv3 is just 4.0% lower. Compared with the Tiny-YOLOv3, which is the mobile version of YOLOv3, the AIR-YOLOv3 offers 18.1% higher AP, and 17.4 MB smaller model size.  
The AIR-YOLOv3 model runs on the Jetson TX2 to detect infrared pedestrian objects, and it can accurately predict the location of the object in the image, as shown in Figure 9.  
In a word, AIR-YOLOv3 makes a good compromise for the detection accuracy and inference speed. At the same time, it has a smaller model size, which is conducive to the deployment on the mobile airborne computing platform.
## 5 Discussion  
### Controllability Metric Performance Comparison  
This subsection provides a comparative analysis of different controllability Gramian-based metrics applied under damping ratio constraints to show which metric for \(J_{1}\) turns out to be the most appropriate. The evaluation is based on optimization criteria, controllability (\(J_{1}\)) and RoCoF (\(J_{2}\)), and analysis of modal parameters: damping ratios (\(\zeta\)), time constants (\(\tau\)), and damped frequencies (\(f_{d}\)) for knee points and points closest to the origin on each Pareto front. Results are summarized in Table 2. Among all the metrics, EV\({}_{\text{max}}\) achieved the lowest \(J_{2}\) values (\(J_{2\text{min}}=0.7214\), \(J_{2\text{max}}=0.7267\)), meaning it was best at minimizing RoCoF while maintaining moderate controllability (\(J_{1\text{min}}=0.8748\), \(J_{1\text{max}}=0.9009\)). Its dynamic behavior showed lower damping (\(\zeta_{3,4}=0.0062\), \(\zeta_{1,2}=0.0108\)), slower response times (\(\tau_{1,2}=12.35\) s, \(\tau_{3,4}=13.96\) s), with damped frequencies \(f_{d3,4}=1.05\) Hz, and \(f_{d1,2}=2.09\) Hz. TR had a slightly higher \(J_{2}\) but the lowest \(J_{1\text{min}}\) (\(0.6555\)), indicating strong controllability. It had balanced dynamics, moderate damping, faster response (\(\tau_{1,2}=8.55\) s, \(\tau_{3,4}=10.54\) s), and damped frequencies \(f_{d3,4}=1.39\) Hz, and \(f_{d1,2}=2.56\) Hz. DET also showed good controllability (\(J_{1\text{min}}=0.8208\), \(J_{1\text{max}}=0.8899\)) and a stable RoCoF response, but it responded slightly slower than TR. EV\({}_{2\text{min}}\) had decent damping and response (\(\tau_{3,4}=10.05\) s, \(\tau_{1,2}=10.41\) s) but did not perform as well as EV\({}_{\text{max}}\) or TR overall. EV\({}_{\text{min}}\) had the narrowest \(J_{2}\) spread, but did not match the best criteria in controllability. Overall, EV\({}_{\text{max}}\) was best for minimizing RoCoF while keeping a balanced dynamic response, and TR offered the strongest overall balance across all performance indicators.  
Using the maximum eigenvalue metric enhances controllability along the easiest direction in the state space, which is useful for achieving fast, high-priority responses, and EV\({}_{\text{max}}\) results in the lowest frequency deviations overall. The trace (TR), being the sum of all eigenvalues, reflects the total average controllability across all state directions. TR shows a good balance between maximizing controllability, minimizing RoCoF, and damping of oscillations. This makes it suitable for systems requiring coordinated dynamic performance across multiple modes. Maximizing the determinant (DET) of the controllability Gramian aims to improve the controllability in all directions, significantly increasing the volume of the reachable state space. DET with the knee point parameters has the highest frequency deviations, but it results in the best overall controllability.
# An Algorithm to Assist the Robust Filter for Tightly Coupled RTK/INS Navigation System
## 4 Results
### The Performance of the TC RTK/INS Integration without the Outlier Resistance
We divide the positioning results into several groups by epochs. Except for the last one, each group contains the positioning results of  
Figure 8: The positioning accuracy comparison between RTK and the TC RTK/INS integration. Figure 7: The positioning continuity comparison between RTK and TC RTK/INS. 100 epochs. Then, we calculate the Root-Mean-Square Error (RMSE) for positioning results in each group. Figure 8c,d show the RMSE results. In Figure 8c, Group 6 corresponds to the positioning results for the 501 st epoch to the 600 th epoch in Figure 8a. Figure 8a shows, during this period, that some large errors exist in RTK results, especially the results corresponding to the 524 th epoch. In contrast, the TC RTK/INS integration provides more accurate positioning results for the 501 st epoch to the 600 th epoch. Accordingly, the TC RTK/INS integration provides smaller RMSE for results in Group 6. Group 9 is related to the positioning results for the 801 st epoch to the 900 th epoch in Figure 8a. Figure 8a shows, during this period, that the positioning errors provided by the integration are generally larger than those given by RTK, especially at the 873 th epoch. As a result, the TC RTK/INS integration provides a larger RMSE for results in Group 9. We can observe the same phenomenon comparing Figure 8b,d. Figure 8 demonstrates the importance of outlier detection and exclusion. The outlying measurements result in biased estimation and inaccurate INS states correction, which will be accumulated along with the INS mechanization. RKF can help improve the performance of the TC RTK/INS integration. A more specific quantitative analysis will be presented in the next section.
## 3 Observed Damage Mechanisms in Eastern T\(\ddot{\text{u}}\)rkiye's Masonry Buildings
### 3.2. _Inadequate Cranftsmanship in Masonry Construction_
#### 3.2.1 Irregular Laying of Units  
A fundamental tenet in masonry engineering is the organized offsetting of joints to guarantee strong interlocking among courses. For example, the utilization of stretcher bond or running bond configurations in brick masonry minimizes the vertical alignment of joints, consequently fostering even distribution of stress in the face of shear and out-of-plane forces. Nevertheless, conventional construction practices in eastern Turkkiye frequently overlook these design principles. Instead, masonry units are arranged based on immediate practicality, resulting in vertical joints being stacked directly above each other. This vertical alignment accelerates the propagation of cracks, allowing fissures to extend uninhibited from one course to its adjacent counterpart. This occurrence was commonly documented in reconnaissance assessments conducted after the earthquakes in the region, wherein entire wall sections fractured along uninterrupted vertical mortar joints.  
In the context of stone masonry, the irregularity of shapes and the lack of thorough finishing necessitate that masons frequently engage in a process of \"fitting\" stones on-site through a method of trial-and-error. Stones that do not align properly are either forcibly wedged into place or are left with substantial interstitial voids that are subsequently filled with a thick, inferior-quality mortar. This technique, often termed random-rubble or \"bag-placed\" stonework, results in significant voids within the wall's cross section [46]. In a typical instance, blocks may only make contact at their corners, producing a fragile mortar matrix in between. In the event of an earthquake, these mortar-filled voids tend to fail prematurely, while the stone blocks may pivot or rotate, ultimately leading to either partial or complete structural failure.  
Even when utilizing rectangular bricks or HCTs, it is common for constructors to overlook the necessity of half or quarter unit overlapping at corners and T-junctions. In  
Figure 9: Observed damage due to poor-quality mortar.  
the case of interior corners or L-junctions formed by intersecting walls, the omission of \"toothing\" or interlocking of bricks leads to a significant vertical seam. This seam serves as a critical point of vulnerability under lateral forces, resulting in the potential for corners to separate or fragment with minimal lateral displacement. Observations following the regional earthquakes indicated that entire corner sections were dislodged where these vertical seams extended from the foundation to the roof level. Field photos of this damage type are shown in Figure 10.