esa-sceva/satcom-chunk-collection huggingface dataset:
# Location Adaptive Motion Recognition Based on Wi-Fi Feature Enhancement
## 5 Experiments and Analysis
### Experiment Settings  
Select two 9300 network interface controllers (NICs) as the transmitter and receiver of the signal and the CSI data is collected on the PicoScenes platform. The height of the equipment at the transmitting and receiving end is 1.3 m, located in the middle of the human body to realize three transmitting and six receiving points. The experimenter collected movement at different positions and orientations in the open room, office, apartment room, and corresponding aisle areas, including walking, running, etc. Ten acquisitions were carried out for different actions, each lasting for 5 min. The signal transmission frequency was set to 1000 Hz, the transmission energy was set to 30 dBm, and the subcarrier bandwidth was set to 20 MBps.  
When training the Transformer network, the appropriate parameters of the network structure are determined through parameter debugging. The average pool step is set to 4. The number of Gaussian distributions k = 10. The number of Transformer layers is set to h = 5, the input and output dimension is set to 90, and the number of heads in the multi-head attention mechanism is set to 9. The number of parallel convolutions in dynamic convolution structure is 6, and dropout is set to 0.1. There are two convolution types in the prediction layer. The size is 10, 40, the number of each type W is 128, and the dropout of this layer is set to 0.5.
# A Novel Shadow Removal Method Based upon Color Transfer and Color Tuning in UAV Imaging
###### Abstract  
Through the increasing use of unmanned aerial vehicles as remote sensing tools, shadows become evident in aerial imaging; this fact, alongside the higher spatial resolution obtained by high-resolution mounted cameras, presents a challenging issue when performing different image processing tasks related to urban areas monitoring. Accordingly, the state-of-the-art reported works can correct the shadow regions, but the heterogeneity between the corrected shadow and non-shadow areas is still evident and especially noticeable in concrete and asphalt regions. The present work introduces a local color transfer methodology to shadow removal which is based on the CIE L*a*b (Lightness, a and b) color space that considers chromatic differences in urban regions, and it is followed by a color tuning using the HSV color space. The quantitative comparison was executed by using the shadow standard deviation index (SSDI), where the proposed work provided low values that improve up to 19 units regarding other tested methods. The qualitative comparison was visually realized and proved that the proposed method enhances the color correspondence without losing texture information. Quantitative and qualitative results validate the results of color correction and texture preservation accuracy of the proposed method against other published methodologies.  
image shadow removal; color correction; shadow elimination; unmanned aerial vehicle; aerial imaging; remote sensing +
Footnote †: journal:  
Article
## 2 Proposed UWB Antenna with Quadruple Band Rejections
### Antenna Frequency Broadening  
The antenna and the ground can be equivalent to a dipole antenna [19]. Making the appropriate size cutouts on both ends of the ground plane can affect the electromagnetic coupling between the circular patch and the ground plane, greatly enhancing the impedance bandwidth [20]. W4 and L4 of the ground corner cut are analyzed in Figure 2. When W4 and L4 are larger, the low-frequency performance of the antenna is better. When W4 = 13 mm, the VSWR of the antenna at 9 GHz exceeds 2, and the UWB function cannot be realized. When L4 = 11.5 mm, the VSWR of the antenna oscillates in the high-frequency band. The distance between the radiating patch and the ground plane is marked as g, which is an essential factor affecting the impedance bandwidth of the antenna. Figure 3 shows the frequency characteristic curve of the VSWR of the antenna as a function of the parameter g. When g is changed from 0 mm to 2 mm, the bandwidth of the antenna displays a widening trend. When g = 0 mm, the VSWR of the antenna cannot meet the requirements of UWB. When g = 1 mm and g = 2 mm, the antennas are well matched in the UWB band. This result only corresponds to the numerical change of g, while the other size parameters of the antenna remain unchanged.  
Figure 1: Configuration of the proposed antenna.
# Residual Transformer YOLO for Detecting Multi-Scale Crowded Pedestrian
## 2 Related Work  
YOLOv7 [14] is the most representative model in the YOLO series [14; 15; 16; 17; 18; 19] at present, and many subsequent versions of YOLO have also drawn inspiration from its structure. Within the range of 5 FPS to 160 FPS, YOLOv7 outperforms most known object detectors in terms of both speed and accuracy. Its main structure is divided into Backbone, Neck network, and YOLO detection head, as shown in Figure 1.  
The Backbone is the initial feature extraction network of YOLOv7. When a \(640\times 640\) image is input into the Backbone, a set of extracted feature points is obtained in the form of feature maps. Efficient Aggregation Networks (ELAN) [14] will allow the deep network to efficiently extract features and converge reasonably by controlling the shortest and longest paths, as shown in Figure 2a, and finally, Backbone will output feature maps at different scales: \(80\times 80\), \(40\times 40\), and \(20\times 20\).  
The Neck is the enhanced feature extraction network of YOLOv7. Three feature maps obtained from Backbone are fused by up-sampling in Neck. Subsequently, the fused features are down-sampled, where the Extending Efficient Aggregation Networks (E-ELAN) extends the channel and cardinality by introducing group convolution based on ELAN and it is shown in Figure 2b, which applies the same parameter group and channel multiplier to all blocks of the computational layer, and the number of channels remains the same as ELAN, so the utilization of parameters and computational efficiency improves further. Neck finally outputs three enhanced effective feature maps at sizes of 80 \(\times\) 80, 40 \(\times\) 40, and 20 \(\times\) 20.  
The YOLO Head is the classifier and regressor in YOLOv7. It includes RepConv [20] reparameterization to fuse training multi-branch weights and enhance detection speed [21].  
Figure 1: YOLOv7 network structure.  
Figure 2: The ELAN and E-ELAN structures.  
The three feature maps obtained from Neck have width, height, and a number of channels, effectively forming a set of all feature points. YOLO Head assesses whether objects are present at the feature points corresponding to the anchor box and then outputs the class and location information of these objects.  
YOLOv7 possesses a deep network architecture, and it performs excellently in most application scenarios. However, as the network depth increases, it may not extract features from occluded or densely packed objects adequately, potentially leading to the loss of fine-grained details. These issues are also reflected in the context of crowded pedestrian detection.
Sensors_**2014**, _14_, 4364-4383; doi:10.3390/s140304364  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**A Wireless Fatigue Monitoring System Utilizing a Bio-Inspired Tree Ring Data Tracking Technique**  
**[PERSON] 1, [PERSON] 2, [PERSON] 3, [PERSON] 2,* and [PERSON] 1,2**  
1 School of Civil Engineering, Harbin Institute of Technology, Harbin 150090, China;  
E-Mails: [EMAIL_ADDRESS] (S.B.); [EMAIL_ADDRESS] (J.O.)  
2 School of Civil Engineering, Dalian University of Technology, Dalian 116024, China;  
E-Mail: [EMAIL_ADDRESS]  
3 School of Software Technology, Dalian University of Technology, Dalian 116024, China;  
E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel./Fax: +86-411-8470-9716.  
_Received: 22 November 2013; in revised form: 20 February 2014 / Accepted: 20 February 2014 / Published: 5 March 2014_  
**Abstract:** Fatigue, a hot scientific research topic for centuries, can trigger sudden failure of critical structures such as aircraft and railway systems, resulting in enormous casualties as well as economic losses. The fatigue life of certain structures is intrinsically random and few monitoring techniques are capable of tracking the full life-cycle fatigue damage. In this paper, a novel _in-situ_ wireless real-time fatigue monitoring system using a bio-inspired tree ring data tracking technique is proposed. The general framework, methodology, and verification of this intelligent system are discussed in details. The rain-flow counting (RFC) method is adopted as the core algorithm which quantifies fatigue damages, and Digital Signal Processing (DSP) is introduced as the core module for data collection and analysis. Laboratory test results based on strain gauges and polyvinylidene fluoride (PVDF) sensors have shown that the developed intelligent system can provide a reliable quick feedback and early warning of fatigue failure. With the merits of low cost, high accuracy and great reliability, the developed wireless fatigue sensing system can be further applied to mechanical engineering, civil infrastructures, transportation systems, aerospace engineering, _etc_.  
**Keywords:** fatigue; structural health monitoring (SHM); rain-flow counting method; data tracking of tree rings; Digital Signal Processing (DSP); wireless sensor; PVDF
# Research Progress on Flexible Oxide-Based Thin Film Transistors
###### Abstract  
Oxide semiconductors have drawn much attention in recent years due to their outstanding electrical performance, such as relatively high carrier mobility, good uniformity, low process temperature, optical transparency, low cost and especially flexibility. Flexible oxide-based thin film transistors (TFTs) are one of the hottest research topics for next-generation displays, radiofrequency identification (RFID) tags, sensors, and integrated circuits in the wearable field. The carrier transport mechanism of oxide semiconductor materials and typical device configurations of TFTs are firstly described in this invited review. Then, we describe the research progress on flexible oxide-based TFTs, including representative TFTs fabricated on different kinds of flexible substrates, the mechanical stress effect on TFTs and optimized methods to reduce this effect. Finally, an outlook for the future development of oxide-based TFTs is given.  
oxide semiconductor; oxide-based thin film transistor (TFT); flexible device; mechanical stress; display +
Footnote †: journal: Applied Sciences
## 2 Methodology
### Vehicle Path Feature Extraction
#### 2.4.2 Trajectory Point-Labeling Algorithm Based on Regular Polygons  
Trajectory point-labeling is a crucial step in converting continuous GPS coordinate sequences into discrete node sequences. Traditional nearest-neighbor matching methods are prone to mismatches, particularly in areas with dense intersections. The regular polygon coverage method proposed in this study effectively addresses this issue.  
Figure 5: Road network visualization results after adding original and supplementary OD points.  
The core concept of the algorithm is to construct a regular polygon coverage area centered on each node (intersection or OD point), representing the node's \"influence range.\" When a trajectory point falls within a regular polygon, the vehicle is considered to have passed through the corresponding node. The specific implementation steps are as follows:  
1. Regular polygon construction: Using the makePolygon2() function, a regular hexadecagon (with an adjustable number of sides) is generated for each node. The circumscribed circle radius of the regular polygon is a key parameter requiring optimization based on road network characteristics.
2. Trajectory point assignment determination: Using MATLAB's inpolygon() function to determine whether each trajectory point lies within a regular polygon. This function, based on the ray casting principle, offers high computational efficiency and accuracy.
3. Path compression and organization: Merging consecutive trajectory points at the same node, removing trajectory points on road segments (marked as '00'), ultimately obtaining the sequence of nodes traversed by the vehicle.
## 3 Lidar Theory
### Motion Effect on Lidar Measurements  
On a moving platform such as a buoy or ship, the lidar is subjected to motion in six DOF: rotational motion (roll \(\beta\), pitch \(\varphi\), and yaw \(\psi\)) around the \(x\), \(y\), and \(z\) axes and translational motion (surge, sway, and heave) along the \(x\), \(y\), and \(z\) axes, respectively. This motion affects the observed \(v_{r}\) values and the velocity vector resulting from retrieval (\(\vec{a}\)). If the lidar experiences rotational motion in one DOF, such as rotation around the \(y\) axis, the  
Figure 3: Coordinate system and angles for lidars. The laser beam is emitted at a fixed elevation angle (\(\phi\)) and at an azimuth angle (a). The half–opening angle (\(\theta\)) equals \(90^{\circ}-\phi\). The LOS velocity (\(v_{r}\)) at height \(h\) is linked to the three-dimensional wind velocity vector and the corresponding angles.  
beam orientation along the \(x\)-axis is altered by \(\varphi\) (Figure 4a); therefore, the observed LOS velocity changes from \(u\sin(\theta)\) to \(u\sin(\theta+\varphi)\).  
As the standard wind vector retrieval uses the beam projection based on \(\theta\) instead of \(\theta+\varphi\), the retrieved wind vector will be erroneous.  
Further, if the lidar experiences translational motion in one DOF, such as along the \(x\) axis (\(u_{t}\)), the observed LOS velocity changes from \(u\sin(\theta)\) to \(u\sin(\theta)\,+\,u_{t}\sin(\theta)\). Consequently, \(\hat{u}\) changes (see Figure 4b). In a combination of rotational and translational motion (Figure 4c), both \(u\) and \(u_{t}\) are projected onto the rotated beam, changing \(u\sin(\theta)\) to \(u\sin(\theta+\varphi)\,+\,u_{t}\sin(\theta+\varphi)\).  
Thus, if the lidar is impacted by motion, Equation (3) changes to  
\[\vec{v}_{r}=\mathbf{RN}(\vec{u}+\vec{u}_{T}), \tag{5}\]  
with the translational motion vector \(\vec{u}_{T}\) and the rotation matrix \(\mathbf{R}\), constructed from the rotation matrices \(\mathbf{R}_{x}\), \(\mathbf{R}_{y}\), and \(\mathbf{R}_{z}\) corresponding to roll (\(\beta\)), pitch (\(\varphi\)), and yaw (\(\psi\)) of the inertial coordinate system:  
\[R_{x}=\begin{pmatrix}1&0&0\\ 0&\cos\beta&\sin\beta\\ 0&-\sin\beta&\cos\beta\end{pmatrix},\quad R_{y}=\begin{pmatrix}\cos\varphi&0&- \sin\varphi\\ 0&1&0\\ \sin\varphi&0&\cos\varphi\end{pmatrix},\quad R_{z}=\begin{pmatrix}\cos\psi& \sin\psi&0\\ -\sin\psi&\cos\psi&0\\ 0&0&1\end{pmatrix}. \tag{6}\]
# Optimization of Slotted Steel Plate Shear Walls Based on Adaptive Genetic Algorithm
## 3 Optimization Analysis of SSPSWs
### Optimization Results  
For bilateral-joined SSPSW configurations (SPSW-TB12, SPSW-TF, SPSW-TB36, and SPSW-TB48) with varying frame aspect ratios, the optimization objective minimizes displacement at non-elastic dissipated energy (ALLPD), attaining target values of 8.55 mm, 9.13 mm, 10.23 mm, and 11.71 mm, respectively. Optimized parameters in Table 2 reveal minimal slotted plate width variation across frame aspect ratios. Ductility maximization necessitates full-frame utilization of slotted plates, with inter-slot column aspect ratios (l/b) exceeding critical thresholds to enhance deformability. However, increasing frame aspect ratios inversely reduces structural stiffness, constraining optimized plate widths within defined bounds. Consequently, SSPSW implementations in high-aspect-ratio frames require amplified stiffness contributions from slotted plates to compensate for inherent flexibility. For SSPSW models with varying plate thicknesses (SPSW-TT8, SPSWTT10, SPSW-TF, and SPSW-TT14), optimization yields target displacements of 9.02 mm, 8.99 mm, 9.12 mm, and 8.97 mm, respectively. Table 2 summarizes optimized parameters, revealing a decreasing trend in inter-slot column width (b) with increasing plate thickness (t). Analysis demonstrates that thicker plates satisfy stiffness constraints more readily under equivalent requirements, inherently reducing slit configuration stiffness. Enhanced ductility correlates with higher inter-slot column aspect ratios (l/b) and lower width-to-thickness ratios (b/t), as evidenced by parametric optimization results. This inverse relationship between plate thickness and ductility underscores a critical design trade-off: while thicker plates improve stiffness by 12-15%, they reduce ductility capacity by 18-22%. Consequently, SSPSW design prioritizes thinner steel plates (t \(\leq\) 12 mm) to balance stiffness demands with ductility performance requirements. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Beam Length** & **Plate Thickness** & **Frame Stiffness** & **Optimized** \\  & **(mm)** & **(mm)** & **(mm)** & **(N/m)** & **Stiffness (N/mm)** \\ \hline SPSW-TF (FF) & 2400 & 12 & 89,767 & 99,742 \\ SPSW-TB12 (FB12) & 1200 & 12 & 97,016 & 107,795 \\ SPSW-TB36 (FB36) & 3600 & 12 & 82,679 & 91,865 \\ SPSW-TB48 (FB48) & 4800 & 12 & 77,131 & 85,701 \\ SPSW-TT18 (FT8) & 2400 & 8 & 89,767 & 99,742 \\ SPSW-TT10 (FT10) & 2400 & 10 & 89,767 & 99,742 \\ SPSW-TT14 (FT14) & 2400 & 14 & 89,767 & 99,742 \\ \hline \hline \end{tabular}  
* Notes: SPSW represents a steel plate shear wall with slits, with the initial letters T and F after \"-\", indicating that the connection forms are two-sided connection and four-sided connection, respectively. The frame stiffness is defined as the ratio of the force generated after applying displacement to the displacement itself, i.e., K = F/\(\Delta\), where F is the force generated after applying displacement, and \(\Delta\) is the displacement applied to the frame.  
\end{table} 
Table 1: Optimization scheme of SPSWs.
# A Testbed for GNSS-Based Positioning and Navigation Technologies in Smart Cities:
## 6 Test Campaigns and Results
### DGNSS-Based Cooperative Positioning
#### 6.3.3 Service Profitability and Accuracy Improvement  
The actual benefit provided by the proposed collaborative framework can be observed in terms of reduction of the position Mean Squared Error (MSE) w.r.t. the standalone GNSS solution, namely profitability.  
It accounts for the percentage of epochs in which the hybrid solution provided a better estimate (lower MSE) than the GNSS-only solution, both compared to the GFLP (self-assessed through the CPA). Figure 13 presents a qualitative view of the promising amount of profitable epochs in a sample experiments at UAB campus. Figure 14 shows the 3D profitability of the experiments belonging to each test campaign.  
Profitability was actually normalised w.r.t. to the number of epochs in which cooperation was been successfully performed. A considerable subset of experiments overcame the validation criteria (profitability higher than the 10%) both in 2D and 3D MSE. For what concerned the _profitability_, the results obtained during the BRC-BRC test campaign are comparable with the results retrieved in the TRN-DUB campaign. This assessed the performance improvement which can be provided through the proposed paradigm, by considering a single aiding agent in a pairwise cooperation.  
Pairwise cooperation was hence assessed to be effective and profitable with an accuracy improvement overcoming the 10% for more than the 25% of the test timespan. The actual mean profitability values computed over all the experiments were indeed 26.41% (Sm01) and 30.59% (Sm02) in 3D profitability campaign, and 33.82% (Sm01) and 37.53% (Sm02) for 3D profitability.  
Such preliminary findings in non-ideal network and visibility conditions for both the devices makes DGNSS-CP a promising technology for mass-market devices and receivers in urban and mild-urban scenarios.  
Figure 14: Collaborative positioning 3D profitability from pairwise cooperation of two Xiaomi Mi8 Pro running independent CPA (Collaborative Positioning Application) instances. TRN-DUB Test campaign on the left and BRC-BRC Test campaign on the right. The lower red line identifies the minimum validation criteria w.r.t. the overall test duration (upper red line).  
Figure 13: Sample pedestrian trajectory across UAB Campus showing on the left the GFLP solution in red and GNSS standalone fixes in black (BRC-BRC campaign). On the right, the East/North reference frame highlights the profitable epochs in which DGNSS-CP (Cooperative Positioning) improved the positioning solution of more than the 10% in accuracy.  
The implementation of the proposed DGNSS-CP paradigm also assessed the feasibility of a passive, pass-through node i.e., CPS integrated in the _testbed_ making it relevant to the service itself. A limitation is that the actual CP service cannot be distributed among the connected devices and despite being centralised, it does not currently implement any logic for the selection of optimal collaborative contributions (also known in multi-agent-related literature as information censoring).
# Fish Detection in Fishways for Hydropower Stations Using Bidirectional Cross-Scale Feature Fusion
## 3 Results and Analysis
### Comparison Experiments  
To verify the effectiveness of the WIoU loss function, this study analyzes the performance differences between WIoU and the shape intersection over union (SIoU), efficient intersection over union (EloU), and generalized intersection over union (GIoU) loss functions. Each of these loss functions was modified in the YOLOv8 n model without any other changes. As shown in the loss function experiment results in Table 1, SIoU and EloU both lead to a decrease in precision (P) and mean average precision ([EMAIL_ADDRESS]) of the original YOLOv8 n model. GIoU decreases the precision (P) but improves the [EMAIL_ADDRESS]. In contrast, YOLOv8 n + WIoU achieves the highest [EMAIL_ADDRESS].  
Figure 5: Sample Images from the Fish26 Dataset.  
To verify the superior detection performance of the proposed method, a comparative analysis was conducted on the Fish26 dataset. The proposed method was compared with current mainstream object detection algorithms such as the YOLO series, SSD, and Faster R-CNN. The experimental results are presented in Table 2. The results indicate that both the Faster R-CNN and SSD models exhibit lower detection accuracy and speed, making them unsuitable for fish detection tasks in underwater environments. While YOLOv5n, YOLOv6n, and YOLOv7-Tiny models benefit from having fewer model parameters and lower computational complexity, their detection accuracy is lower compared to the YOLOv8n model. In contrast, the proposed method outperforms the YOLOv8n in both recall and precision, with improved detection accuracy and speed. Specifically, the [EMAIL_ADDRESS] of the proposed method is 1.7% higher than that of the YOLOv8n model, while the model's parameter count has been reduced by 24%, and its computational load is only 6.2G. This balance of detection accuracy, speed, and lightweight design makes the proposed method better suited for complex underwater fish detection tasks. Compared to mainstream object detection models, the proposed method model demonstrates superior detection performance, as illustrated in Figure 6.