esa-sceva/satcom-chunk-collection huggingface dataset:
# A Person Re-Identification Method Based on Multi-Branch Feature Fusion
## 3 Multi-Branch Feature Fusion Network
### Multi-Scale Feature Fusion Global Branch  
This branch is designed hierarchically in an end-to-end manner, utilizing a Swin Transformer [37] as the backbone network. It incorporates Shallow Feature Extraction (SFE) and Multi-scale Feature Fusion (MFF) techniques to extract diverse global feature information from pedestrians across multiple scales. The structure is depicted in Figure 2.  
Figure 1: The overall framework of MFFNet.  
Figure 2: Structure of the multi-scale feature fusion global branch.  
1. Shallow Feature Extraction (SFE). As indicated in [41], convolutional layers excel at image feature extraction, thereby producing a more robust feature representation. The shallow feature extraction module introduced in this paper is depicted in Figure 3.  
Initially, an image of a pedestrian is denoted as \(I\in R^{H\times W\times 3}\) (H represents height, W represents width, and 3 represents the number of channels in the image), is processed through two convolution layers with a convolution kernel size of \(2\times 2\) and stride of 2 for the initial feature extraction, resulting in shallow feature expressions. Subsequently, the number of feature channels is mapped to C via a fully connected layer, and it is fed to the Swin Transformer Block [37] for deep feature extraction. Following this, deep and shallow features are fused to strengthen the integration of local and global feature representations. Finally, the combined features are introduced into the MFF to derive a distinctive feature representation.
2. Multi-scale Feature Fusion (MFF). The backbone network employs a layer-by-layer abstraction approach to extract the target features, progressively expanding the field of view of the feature map through convolution and downsampling algorithms. Incorporating the concept of a feature pyramid [42], we introduce a multi-scale fusion module to enhance feature representations at various levels, capturing both local fine-grained features and global feature information of pedestrians. Additionally, this module is compatible with different scales of the Swin Transformer models.  
As depicted in Figure 2, within the MFF module, we independently extract features from the outputs of the different stages in the backbone network. Because the feature maps from the different stages possess distinct sizes, we apply bilinear interpolation to upsample the extracted features sequentially. Subsequently, the original features, features generated by the SFE, and upsampled features are merged using the Add operation. This fusion of multi-level features with distinct perceptual domains enables the capturing of diverse semantic information. Finally, the fused features are fed into a fully connected classifier for the ultimate output.
# Fingerprints of the Automotive Radar Scattering of Passenger Cars and Vans
## 3 Results and Discussion  
### Contribution of Underbody Detections to the Overall Scattering Characteristics of Vehicles  
As a first step, the contribution of underbody detections to the backscattering of vehicles was investigated using microwave absorbers, as illustrated in Figure 3. Figure 6 illustrates the accumulated radar detections during ten measurement runs across the two-dimensional projection of the bounding box in a false-color representation. The detection density was estimated using two-dimensional histogram smoothing [19]. Without any absorbers (Setup 1), the detections were distributed over the entire bounding box of the vehicle, and the highest detection density was located at the rear bumper. When absorbers were placed on the rear window (Setup 2), no major change in the detection distribution was noticeable. The detection density in the region of the rear window was slightly lower compared to the previous measurement. Upon placing additional absorbers between the road surface and the bumper (Setup 3), the amount of detections in the front part of the vehicle decreased significantly. A group of detections was measured at the left side mirror at \(x\,=\,3.3\,\mathrm{m}\). There is still an accumulation of detections at \(x\,=\,1.5\,\mathrm{m}\), but with a lower detection density. When the exhaust trims were also covered by the absorbers (Setup 4), this accumulation of detections was significantly reduced. We conclude that some detections are caused by reflections within the exhaust system. We speculate that the remaining detections at \(x\,=\,1.5\,\mathrm{m}\) might result from residual reflections of the absorbers on the rear window.  
Figure 7 illustrates the arithmetic mean number of target detections per measurement cycle around fixed distances \(R\) between ego-car and target car. The absorbers on the rear window (Setup 2) had almost no influence on this relationship (red solid curve and yellow dashed curve). It seems that the reflections from the C-pillar and the roof have little influence on the scattering characteristics. By additionally obstructing the propagation path underneath the vehicle (Setup 3), the number of detections was significantly reduced across all distances (green dash-dotted curve). This proves that some of the target detections were caused by multipath propagation via the road surface. When the tailpipes were also covered (Setup 4), a further reduction in target detections was observed (blue dotted curve). The number of detections was greater than two only for distances less than \(R\,=\,40\,\mathrm{m}\), including a significant number of detections from the side mirror. In summary, this shows that the underbody has a noticeable influence on the scattering characteristics of vehicles.  
Figure 6: Spatial detection distributions for the four measurement setups using microwave absorbers.
# Cooperative Jamming-Based Physical-Layer Group Secret and Private Key Generation
## 4 Performance Analysis  
This section is dedicated to assessing the upper and lower bounds on the SK/PK capacity. The lower bounds are derived based on the proposed group key generation scheme in the previous section, while the upper bounds are derived by the corresponding formulated enhanced DMS models.
## 5 Experimental Results
### Performance Index  
Since key character detection is a critical process in the proposed scheme, it is important to evaluate whether the proposed scheme can locate and recognize key characters effectively in document images. Here, some measurements are selected for performance evaluation. First, IoU (intersection over union) [12] is adopted as a performance index to evaluate whether the proposed scheme can locate key characters correctly. The definition of IoU [10] is described as follows:  
\[\text{IoU}=\frac{\Omega^{T}\,\cap\,\Omega^{p}}{\Omega^{T}\,\cup\,\Omega^{p}} \tag{4}\]  
where \(\Omega^{T}\), \(\Omega^{p}\), \(\cap\), and \(\cup\) represent the exact bounding box, the predicted bounding box, intersection operator, and union operator, respectively. As shown in Equation (4), IoU means the amount of overlap between the predicted and ground truth bounding box. It is expected that the higher the IoU is, the better the performance will be. In fact, IoU is usually used when computing mAP [1, 28] for performance evaluation of object detection.  
Next, we explain the performance indices of recognizing key characters used in the experiment. The recall and precision rates are widely used to measure the performance of shot change detection [37, 38]. We utilize them to evaluate the performance of the proposed scheme. The precision rate is the ratio of correct detections to the total number of detected key characters. The definition of precision is as follows:  
\[\text{Precision}=\frac{N_{C}}{N_{C}+N_{F}} \tag{5}\]  
Figure 8: Examples of data augmentation: (**a**) possible font (**b**) morphological dilation, (**c**) noise adding, (**d**) smoothing, (**e**) different background, and (**f**) mixed.  
where \(N_{C}\) and \(N_{F}\) are the numbers of correct detections and false alarms, respectively, and \((N_{C}+N_{F})\) is the total number of detected key characters. Theoretically, if a detection scheme achieves high precision rates, its performance is considered good. Here, average precision (AP) is also exploited. Furthermore, since there are some key characters that should be detected in document images, mAP [1,28] is also utilized to evaluate whether a scheme can simultaneously detect these key characters successfully. It is expected that the higher the mAP is, the better the performance will be.
###### Abstract  
This paper presents a novel closed-loop control strategy that can be applied to quadrotor trajectory tracking to improve the control accuracy and stability. Inspired by intelligent creatures, the composite control strategy combines the open-loop mode and closed-loop control to overcome some disadvantages of oscillation and overshoot under feedback control mechanisms. The open-loop mode was realized by the quadrotor maneuverability model, which is proposed to build the bridge between the flight control commands and the desired acceleration. Then, a real-time control and acquisition platform with high-precision positioning, low-delay communication, and asynchronous distribution was built to collect the UAV's real-time flight trajectory data for training the maneuverability model and to control the quadrotors asynchronously. Based on the platform and the trained model, the composite control mode on the closed-loop of the Proportional-Integral-Derivative (PID) control and the open-loop of the Long Short-Term Memory (LSTM) maneuverability model was implemented and verified. The experimental results show that the open-loop control strategy has a better advantage in the unity of time and space over the existing techniques for improving the UAV dynamic obstacle avoidance capability.  
trajectory tracking; open-closed-loop control; real-time platform; maneuverability model Article
# The Effect of Different Socket Types on Implant Therapy While Using Flapless Ridge Preservation
## 3 Results
### Mandibular Implants
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Socket Classification**} \\ \cline{3-6}  & & **I** & **II** & **III** & **IV** & \(p\)**-Value** \\  & & **(_n = 6_)** & **(_n = 14_)** & **(_n = 16_)** & **(_n = 3_)** & \\ \hline Age at dental implant (years) & 54.0 (10.4) & 50.3 (10.7) & 52.9 (6.6) & 56.2 (11.0) & 0.688 \\ Age at extraction (years) & 53.8 (10.4) & 50.0 (10.7) & 52.5 (6.6) & 55.5 (11.4) & 0.712 \\ Sex & Female & 3 (50.0\%) & 4 (28.6\%) & 9 (56.3\%) & 0 & 0.229 \\  & Male & 3 (50.0\%) & 10 (71.4\%) & 7 (43.8\%) & 3 (100.0\%) & \\ Smoking & Non-smoker & 4 (66.7\%) & 9 (64.3\%) & 13 (81.3\%) & 2 (66.7\%) & 0.688 \\  & Former smoker & 2 (33.3\%) & 5 (35.7\%) & 3 (18.8\%) & 1 (33.3\%) & \\  & Healthy & 2 (33.3\%) & 8 (57.1\%) & 7 (43.8\%) & 1 (33.3\%) & 0.068 \\ Systemic diseases & Diabetes mellitus & 0 & 0 & 1 (6.3\%) & 2 (66.7\%) & \\  & Osteoporosis & 1 (16.7\%) & 0 & 0 & 0 & \\  & Other diseases & 3 (50.0\%) & 6 (42.9\%) & 8 (50.0\%) & 0 & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Association of the baseline conditions and socket classification for mandibular implants with gapless ridge preservation. and extraction aetiology (\(p\) = 0.010). Class II sockets required significantly more bone re-grafting than Class I sockets (92.9% vs. 33.3%, \(p\) = 0.005). Method A was used for almost all Class I-III sockets (100%, 92.9%, 87.5%, respectively), but all Class IV sockets (\(n\) = 3) underwent method B1. In terms of extraction aetiology, periodontitis was involved in 28.6%, 56.3%, and 100% of Class II, III, and IV sockets, respectively, while none of the six Class I sockets had a periodontitis extraction aetiology. No significant association of socket classification with implant site (UM/non-UM), bone density, primary stability, implant survival, the period from extraction to stage I, the period from stage I to final prosthesis placement, and the total treatment time was observed.
## 1 Introduction  
Unmanned aerial vehicles (UAVs), commonly known as drones, are gaining a considerable amount of attention in the field of structural health monitoring (SHM). The visual inspection method with image processing is a common concept when using drones to find cracks, rusts, or other damage types that can be visually identified. Due to the nature of the aforementioned method, damages is usually identified when it has been noticeably progressed. For this reason, a new concept is required to allow drones to identify damage at an earlier stage, or detect internal damage such as thickness loss due to corrosion. To date, various authors have investigated structural health monitoring using the electromechanical impedance (EMI) method, including monitoring of drones. However, there are only a few studies available where drones are utilized to monitor structures which are focused on image processing [1; 2; 3]. [PERSON] et al. performed an investigation on remote sensing capabilities of a commercialized UAV (Parrot AR 2.0) for crack detection from various distances [1]. An algorithm was developed in MATLAB for image post-processing where a field test was conducted on a pedestrian bridge in order to evaluate the performance of the UAV. [PERSON] et al. introduced an approach involving a combination of hat transform and HSV thresholding technique for detecting cracks using UAV for a real time SHM [2]. Field testing was conducted in the outdoor environment at -Vel Tech University in India to evaluate the performance for the proposed study, during which factors such as wind and random image noises resulted in a few erroneous results. [PERSON] et al. used an octocopter (a UAV with eight blades weighing 2.5 kg) for a building inspection where photos were taken at high speed and frequency [3]. During the study, more than 12,000 photos were taken over the four day period for crack inspection of the target structure.  
In this work, a new concept of converging a drone with a vibration-based non-destructive evaluation (NDE) method is proposed. Such a concept will allow one to potentially identify damage at an earlier stage, reducing the overall maintenance cost of a structure. With the advancement in measurement technologies, the equipment used for conducting NDE methods are becoming smaller, lighter, and cheaper. Thus, with the new concept of equipping a drone with a vibration-based NDE method, possibilities are created for new research areas in the field of remote sensing-based structural health monitoring.
# Revealing Accessibility of Cryptic Protein Binding Sites within the Functional Collagen Fibril
## 3 Collagen Interactions and the Fibril Surface
### Fibril Surface Identity  
The [PERSON] laboratory reported the identity of the solvent-exposed surface of the collagen I fibril based on fitting the X-ray fiber diffraction model of the collagen I microfibril from rat tail tendon [53] into the profile of the collagen I fibril that is observed by scanning electron microscopy (SEM) and AFM [96; 97; 98]. In the microscopy images, the collagen fibril follows a corrugated pattern with up-ward and downward slopes, the peak of which [PERSON] et al. suggests can be explained by the C-terminal telopeptide; furthermore they suggest that the C-telopeptide is exposed on the surface with the N-terminus buried within the fibril [98]. This would place segments D5 and D4 on the exterior of the fibril. Segment D5 contains a (GPO)\({}_{5}\) domain adjacent to the C-telopeptide. This imino-acid rich GPO repeat is expected to form the most stable triple helix domain, and may have a protective role to less stable sequences within the fibril core [54]. However, this surface leaves buried and inaccessible high affinity recognition sequences for collagen-binding integrins and the single interaction site for platelet receptor VWF, which is shared with DDRs and SPARC. [PERSON] and [PERSON] have proposed an alternative orientation with the opposite face exposed that would make these sites more accessible [58]. This raises an interesting question: if critical interaction domains are buried within the collagen fibril interior, how do biological processes that depend on these interactions occur? In order gain a perspective on the residue-specific exposure of identified binding motifs within the collagen fibril, we used computational tools to build a collagen fibril model and calculate SASA from the fibril surface of each residue in our model.
# Optimization Design of RV Reducer Crankshaft Bearing
###### Abstract  
The crankshaft bearing is the key component of a rotate vector (RV) reducer. However, owing to the harsh working load and restricted available space, the bearing often suffers from fatigue failure. Therefore, this study proposes a novel optimization method for RV reducer crankshaft bearings. A nonlinear constraint optimization model for the design of the bearing considering the crowned roller profile is formulated and is solved by using a crow search algorithm. The goal of the optimization is to maximize the fatigue life of the bearing. The design variables corresponding to the bearing geometry and crowned roller profile are considered. The load working conditions of the bearing and structure of the RV reducer are analyzed. Various constraints, including geometry, lubrication, strength of the bearing, and structure of the RV reducer, are established. Through the optimization design, the optimum crowned roller profile suitable for the working load of the bearing is obtained, and the stress concentration between the roller and raceway is eliminated. Taking the crankshaft bearing of RV-20E and RV-110E type reducers as examples, the bearings were optimized by the proposed method. After optimization, the bearing life of the RV-20E type reducer is increased by 196%, and the bearing life of the RV-110E type reducer is increased by 168%.  
RV reducer; crankshaft bearing; fatigue life; bearing design; bearing optimization
# Microwave SINIS Detectors
## 5 Fabrication Technology of SINIS
### Direct Deposition Technology  
Shadow evaporation is relatively simple, and allows the depositing of the SINIS structure in one vacuum cycle with one e-beam lithography. However, this method is limited in the dimensions of junctions and in the choice of materials. Besides this, magnetron sputtering is much more widely used, and is the main technology for industrial fabrication. In our earlier work [37], instead of shadow evaporation, we succeeded in fabrication using separate lithography, with a Ti absorber and with magnetron sputtering. The Al technology includes one more step of etching the native aluminum oxide before the fabrication of the tunnel barrier.  
All of the technologies that were presented previously have one common feature--the tunnel junction is deposited in one vacuum cycle. This is necessary for the creation of a  
\begin{table}
\begin{tabular}{c c c} \hline \multicolumn{2}{c}{**Direct-Write Technology**} \\ \hline \multicolumn{2}{c}{**Direct-Write Trilayer Technology**} & \multicolumn{2}{c}{**Direct-Write Technology**} \\ \hline \multirow{4}{*}{Advantages} & \multirow{4}{*}{Any type of lithography} & Can be applicable for industrial processes of magnetron sputtering and plasma etching. Such process bring flexibility in the choice of metal film materials and the possibility of creating a high-quality tunnel barrier. Any type of lithography \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \hline \multicolumn{2}{c}{Disadvantages} & Three technological cycles, additional layers for passivation & Two technological cycles \\ \hline \end{tabular}
\end{table}
Table 3: _Cont._high-quality tunnel junction. With separate cycles (lithography of one layer and deposition of the first film, and then a similar procedure for the second), the quality of the tunnel junction will be significantly worse due to the fact that uncontrolled parasitic oxides form on the surface of the first film. To solve this problem, we developed a modified direct-write technology [33,38] (Table 3, right column). The idea is to use ion etching before the deposition of the second layer in the deposition chamber. In this case, the layer of aluminum oxide is removed, and it is possible to oxidize the \"clean\" aluminum layer, or make an additional thin (a few nm) aluminum layer to be oxidized in order to create a high-quality tunnel barrier. Additionally, such technology allows flexibility in the choice of metal film materials.  
Photos of the samples fabricated via different technologies are presented in Figure 8a-d.
# Fast Type-II Hartley Transform Algorithms for Short-Length Input Sequences
## 3 Fast Algorithms for Small-Size DHT-II
### Algorithm for 4-Point DHT-II  
Let us design the algorithm for the four-point DHT-II. The four-point DHT-II is expressed as follows:  
\[\mathbf{Y}_{4\times 1}\!=\mathbf{C}_{4}\mathbf{X}_{4\times 1}, \tag{11}\]  
where \(\mathbf{Y}_{4\times 1}\,=\,\begin{bmatrix}y_{0},\,y_{1},\,y_{2},\,y_{3}\end{bmatrix}^{ \text{T}}\), \(\mathbf{X}_{4\times 1}\,=\,\begin{bmatrix}x_{0},\,x_{1},\,x_{2},\,x_{3}\end{bmatrix}^{ \text{T}}\), \(\mathbf{C}_{4}\,=\,\begin{bmatrix}1&a_{4}&1&0\\ 1&0&-1&a_{4}\\ 1&-a_{4}&1&0\\ 1&0&-1&a_{4}\end{bmatrix}\) with \(a_{4}=1.4142\).  
To change the order of the columns of \(\mathbf{C}_{4}\), we define the permutation \(\pi_{4}\) in the following form: \(\pi_{4}\,=\,\begin{pmatrix}1&23&4\\ 1&32&4\end{pmatrix}\).  
After permutation of the columns of \(\mathbf{C}_{4}\) according to \(\pi_{4}\), we obtain the matrix \(\mathbf{C}_{4}^{(\mathbf{A})}\,=\,\begin{bmatrix}1&1&\\ 1&-1&0&a_{4}\\ 1&1&-a_{4}&0\\ 1&-1&0&a_{4}\end{bmatrix}\) with the permutation matrix \(\mathbf{P}_{4}\,=\,\begin{bmatrix}1&&\\ &1&\\ &1&\\ &&1\end{bmatrix}\).  
Taking into account the properties of structural matrices [5; 30], the computational procedure for the four-point DHT-II is represented by the expression  
\[\mathbf{Y}_{4\times 1}\!=\mathbf{W}_{4}^{(1)}\mathbf{D}_{4}\mathbf{W}_{4}^{(0)}\mathbf{P}_{4}\mathbf{X}_ {4\times 1}, \tag{12}\]  
where \(\mathbf{W}_{4}^{(0)}\!=\mathbf{H}_{2}\oplus\mathbf{I}_{2}\), \(\mathbf{D}_{4}\!=\!\textit{diag}\!\left(1,\,1,\,s_{0}^{(4)},\,s_{0}^{(4)}\right),s_{0}^{(4)}=a_{4}\), \(\mathbf{W}_{4}^{(1)}\!=\mathbf{H}_{2}\otimes\mathbf{I}_{2}\).  
A data flow graph of the proposed algorithm of the four-point DHT-II is presented in Figure 3. In particular, the number of multiplication operations may be reduced from 4 to 2 and the number of addition operations is decreased from 8 to 6.  
Figure 3: Data flow graph of the algorithm for the computation of four-point DHT-II.  
Figure 2: Data flow graph of the algorithm for the computation of three-point DHT-II.