esa-sceva/satcom-chunk-collection huggingface dataset:
## 10 Return-to-Play Protocol  
Most stingers are mild, lasting a few seconds to minutes, and do not interfere with participating in the next game or practice [7, 8, 36, 37]. The decision to clear athletes to return to play is a controversial issue that relies on expert opinions, as there are no available definitive studies [4, 5, 6, 7, 9, 10, 12, 33]. The current expert consensus takes the presence and duration of symptoms, number of stinger episodes, and imaging findings to either give a temporary or permanent withdrawal from contact sports [9, 10]. According to the modified Delphi study by [PERSON] et al., if an athlete experienced the first episode of a stinger and the symptoms fully resolved in less than five minutes (no strength, sensory, and range of motion limitations are seen), the majority (84.5%) of the participants agreed the athlete can return to play the same day without a need for MRI or further imaging [10]. Athletes with more than one episode of stingers or symptoms lasting more than 5 min have a relative contraindication to immediately returning to play. They need to undergo imaging and serial examinations to rule out any underlying cervical abnormalities [9, 10]. The most critical parameters to consider are pain- and weakness-free full range of motion in the neck and shoulder joints [4, 5, 6, 7, 9, 10]. Sustaining a Grade IIIA or IIIB injury is an absolute contraindication to return to play, and further treatment is recommended (Figure 5).  
The return-to-play algorithm is a great decision-making tool, but it is not without limitations. As [PERSON] et al. [10] pointed out, the algorithm is based primarily on expert opinions, making it suboptimal. Certain decision pathways are vulnerable to subjectivity in clinical assessment, leaning on players' reports rather than an objective assessment of neurological symptom resolution. Furthermore, the standardized algorithm may not adequately account for inter-individual differences in recovery. Thus, the authors of this study lean toward performing an MRI for all occurrences of stingers regardless of symptom duration. This algorithm would benefit from prospective studies to validate its use and define its limitations further.
# Photon Counting Statistics of a Microwave Cavity Coupled with Double Quantum Dots
###### Abstract  
The statistical properties of photon emission counting, especially the waiting time distributions (WTDs) and large deviation statistics, of a cavity coupled with the system of double quantum dots (DQDs) driven by an external microwave field were investigated with the particle-number-resolved master equation. The results show that the decay rate of the WTDs of the cavity for short and long time limits can be effectively tuned by the driving external field Rabi frequency, the frequency of the cavity photon, and the detuning between the microwave driving frequency and the energy-splitting of the DQDs. The photon emission energy current will flow from the thermal reservoir to the system of the DQDs when the average photon number of the cavity in a steady state is larger than that of the thermal reservoir; otherwise, the photon emission energy current will flow in the opposite direction. This also demonstrates that the effect of the DQDs can be replaced a thermal reservoir when the rate difference of a photon absorbed and emitted by DQDs is larger than zero; otherwise, it is irreplaceable. The results deepen our understanding of the statistical properties of photon emission counting. It has a promising application in the construction of nanostructured devices of photon emission on demand and of optoelectronic devices.  
double quantum dots; waiting time distribution; large deviation statistics Article
## 4 Discussion
Omnicam intra-oral scanner uses a triangulation method while the E2 3 shape laboratory scanner uses a confocal method which is considered to be more accurate [25]. In our study we used only the single stage strategy (scanning only with the scan abutments) which should minimize the effect of the scanning on the results compared tothe two-stage strategy (scanning without the scan abutments and conducting another scan with the scan abutments) which is less accurate as described by [PERSON] et al [21]. [PERSON] et al [26]. scanned a single unit scan abutment and removed 5%, 10% and 15% from the abutment and measured the influence on the linear and angular discrepancy. A 15% scan body image deficiency generated larger discrepancies compared to 5% and 10%. In our study for both intra-oral and laboratory scanning we obtained close to 100% of the scan abutment image. It stands to reason that spatial characteristic measurements may vary depending on some factors:  
1. Geometry (cylindar versus square)
2. Material (PEEK versus titanium)
3. Scan abutment (one-piece versus two-piece)  
The study hypothesis was rejected because there was a statistical difference between the laboratory scanner and the intra-oral scanner in relation to the implant spatial position. Our study limitations are in vitro design, only one type of analog, only one intra-oral scanner (Omnicam), and that the intra-oral scanner (triangular) and laboratory scanner (confocal) are based on different scanning technologies. There is a true need for more in vitro and in vivo studies which will examine important features such as: ability to transfer the implant position with scan abutments, different implant positions and angulations, different intra-oral scanning technologies and partially dentate and completely edentulous conditions.
# Linear Parameter-Varying Model Predictive Control of AUV for Docking Scenarios
## Appendix A  
In this appendix, the AUV nonlinear dynamics model and related subsystems are presented. The model consists of reference frames definition, vehicle rigid-body dynamics and actuators' models. The parameters for the AUV model that are not defined in this appendix are reported in Appendix B.  
The AUV dynamics are defined by a set of nonlinear equations with respect to two coordinate reference frames termed the earth-fixed (inertial) and body-fixed frames. The inertial reference frame is used to represent vehicle pose (position and orientation). The body-fixed reference frame is used for velocities (linear and angular) and has its origin coincident with a center of buoyancy that moves/rotates together with the AUV. The velocities in the body-fixed frame are \(\
u=[u,v,w,p,q,r]^{T}\) with \(\
u_{1}=[u,v,w]^{T}\) linear surge, sway and heave velocity and \(\
u_{2}=[p,\ q,r]^{T}\) angular roll, pitch and yaw speed. The position of the AUV in the inertial reference frame is \(\eta=[x,\ y,\ z,\ \phi,\ \theta,\ \psi]^{T}\) with \(\eta_{1}=[x,\ y,\ z]^{T}\) linear position and \(\eta_{2}=[\phi,\ \theta,\ \psi]^{T}\) angular orientation.  
\[\dot{\eta}_{1}=J_{1}(\eta_{2})\
u_{1} \tag{1}\]  
Figure 13: “Naminow-D” docking technology demonstrator.  
\[J_{1}(\eta_{2})=\left[\begin{array}{ccc}c(\psi)c(\theta)&-s(\psi)c(\Phi)+c(\psi)s (\theta)s(\Phi)&s(\psi)s(\Phi)+c(\psi)c(\Phi)s(\theta)\\ s(\psi)c(\theta)&c(\psi)c(\Phi)+s(\Phi)s(\psi)s(\theta)&-c(\psi)s(\Phi)+s( \theta)s(\psi)c(\Phi)\\ -s(\theta)&c(\theta)s(\Phi)&c(\theta)c(\Phi)\\ \end{array}\right] \tag{21}\]  
\[\dot{\eta_{2}}=J_{2}(\eta_{2})v_{2} \tag{22}\]  
\[J_{2}(\eta_{2})=\left[\begin{array}{ccc}1&t(\theta)s(\Phi)&t(\theta)c(\Phi) \\ 0&c(\Phi)&-s(\Phi)\\ 0&s(\Phi)/c(\theta)&c(\Phi)/c(\theta)\\ \end{array}\right] \tag{23}\]  
where \(s(\alpha)=\sin(\alpha)\), \(c(\alpha)=\cos(\alpha)\) and \(t(\alpha)=\tan(\alpha)\). Equations (20)-(23) can be written in compact form such that \(\dot{\eta}=J(\eta_{2})v\) with \(J(\eta)=diag(J_{1}(\eta_{2}),J_{2}(\eta_{2}))\).
## 2 Numerical Simulation and Device Design
### Device Structure and Fabrication  
According to the simulation results above, we designed and fabricated the IMC laser via the identical active layer approach with a simple and cost-effective conventional laser manufacturing process [34] (Figure 4a,b). The device structure was grown on an n-type InP substrate via metalorganic chemical vapor deposition (MOCVD). The MQWs were sandwiched between 50-nm-thick graded-index separate-confinement heterojunction (GRIN-SCH) InGaAlAs layers, which consisted of six InGaAlAs wells with 1.2% compressive strain (\(\lambda_{\text{PL}}=1.28\)\(\upmu\)m, PL is photoluminescence). Figure 4c shows the \(\lambda\)/4 shifted grating in both DFB sections with the same grating pitch, as defined by e-beam lithography on a  
Figure 3: (**a**) Simulated small-signal modulated response of the IMC laser with different semiconductor optical amplifier (SOA) lengths. (**b**) The dependence of the photon–photon resonance (PPR) frequency on the SOA length. The SOA length was varied from 220 to 600 \(\upmu\)m, and the distributed feedback (DFB) length was fixed at 220 \(\upmu\)m. I\({}_{\text{DFB1}}\) was fixed at 100 mA, while I\({}_{\text{SOA}}\) and I\({}_{\text{DFB2}}\) were adjusted to ensure that the device would work under the mutual injection-locking (MIL) state with a similar suitable side-mode suppression ratio (SMSR).  
Figure 2: Simulated small-signal modulation response of the IMC laser with different (**a**) I\({}_{\text{DFB1}}\), (**b**) I\({}_{\text{DFB2}}\), and (**c**) I\({}_{\text{SOA}}\) values. Inset: schematic diagram of the variation of the detuning frequency as I\({}_{\text{DFB2}}\) varied.  
35-nm-thick grating layer upon GRIN-SCH. In the final MOCVD growth step, a p-InP cladding layer and InGaAs contact layer were grown on the whole wafer. A double-trench inverted-trapezoidal-shaped ridge of 3.5-\(\upmu\)m width was formed by wet etching (Figure 4d). Additionally, the 20-\(\upmu\)m gap between each adjacent section was formed by etching the InGaAs contact layer to provide electrical isolation. To reduce the parasitic capacitance, polyimide material was spun to fill the trenches for planarization. After the sample chip was cleaved, both facets were deposited with AR dielectric coatings. The optical feedback from each facet's reflection was significantly reduced by the AR coatings, which helped alleviate the influence of optical feedback on the working condition of the IMC laser to ensure the single-mode stability of the DFB sections.
## 2 Background and Related Work
### DShot Protocol and ESC Communication
#### 2.1.2 Timing Characteristics and Constraints  
DShot's digital encoding provides theoretical timing precision of \(\pm\)0.83 us at DShot1200, representing a significant improvement over PWM's \(\pm\)20 us jitter. Achieving this precision requires maintaining the bit timing tolerance within \(\pm\)5% for reliable decoding, frame update rates between 1-8 kHz depending on application requirements, and ESC response latency typically spanning 1-3 frame periods.
# Research on a Multi-Type Barcode Defect Detection Model Based on Machine Vision
## 2 Background Literature
The model maintained high accuracy and real-time performance on the ArTe-Lab 1D dataset, enhancing methodological versatility and engineering utility. Nonetheless, existing solutions remain constrained to niche datasets and scenarios, hindering adaptation to diverse real-world demands. Faster R-CNN has been extensively adopted for barcode detection. [PERSON] et al. [22] enhanced their Region Proposal Network (RPN) via oriented anchors, improving robustness to distorted barcodes and localization precision. [PERSON] et al. [23] optimized fully connected layers in Fast-RCNN and introduced quadrilateral vertex-based bounding box regression, further increasing detection accuracy and adaptability. In lightweight research, [PERSON] et al. [24] proposed an efficient CNN for industrial surface defect inspection, reducing computation while preserving accuracy. Their subsequent lightweight deep CNN algorithm for SDD achieved superior speed and precision with minimal parameters, demonstrating strong generalization [25]. [PERSON] et al. [26] developed L-Net, a computation-optimized CNN for edge devices, contributing theoretical insights to lightweight learning. Despite promising results, these approaches struggle with model transferability and dataset scope limitations. In summary, while substantial progress has been made, persistent challenges remain, including small-scale datasets predominantly covering single barcode types, insufficient model generalizability across diverse scenarios, and significant technical barriers in practical defect inspection. Consequently, lightweight, efficient, and universal solutions are imperative. To address these needs, we introduce YS-LiBARNet, which diverges from prior dual-branch/two-stage frameworks through the following:  
(1) A hierarchical attention fusion module that dynamically learns--rather than statically combines--the relevance of ResNet-derived local textures and ViT-based global contexts;  
(2) An end-to-end lightweight pipeline (YOLOv8n \(\rightarrow\) coordinate mapping \(\rightarrow\) ROI cropping) sharing unified data flows between localization and defect recognition;  
(3) C2f lightweighting and cross-stage parameter sharing, reducing total parameters by \(\approx\)40% versus contemporaries (e.g., Hybrid-DC, DefT).
## 3 Results
### Comparison with Other Mainstream Detection Models  
This study is based on architectural improvements to the YOLO v11n series. Considering the widespread application and strong performance of YOLOv5, YOLOv7, YOLOv8, and YOLOv10 in agricultural tasks, these versions were selected as the primary comparison models. To provide a comprehensive and fair evaluation of detection performance under comparable network scale and architectural conditions, the corresponding n-series and tiny-series models were included as baseline comparisons.  
To assess the performance benefits of the proposed SugarRow-YOLO model, it was systematically compared against several mainstream models. The comparative results are summarized in Table 3. The experimental findings reveal that SugarRow-YOLO provides substantial improvements in precision, recall, average precision, and computational efficiency. Notably, it achieves a mAP\({}_{50.95}\) of 69.2%, markedly surpassing other benchmark models. This highlights its enhanced suitability for challenging agricultural object detection tasks. Furthermore, the recall rates of SugarRow-YOLO are comparable to those of YOLOv5n and YOLOv7-tiny, while reducing computational complexity by nearly 50% compared to YOLOv7-tiny. In addition, SugarRow-YOLO achieves higher accuracy and mean Average Precision (mAP) than YOLOv5n. Moreover, although YOLOv8n performs well in terms of precision and mAP\({}_{50.95}\), crop row detection tasks place greater emphasis on recall. A high recall is more beneficial for ensuring the complete detection of all  
Figure 8: Comparison of detection results between YOLOv11n and SugarRow-YOLO on the test set: yellow bounding boxes denote missed detections, red bounding boxes represent false detections, and green bounding boxes highlight crop targets exhibiting significant morphological variations. (**a**) Missing lines, broken lines and inter-row speeds stress; (**b**) row closure.  
true targets, thereby laying a solid foundation for the subsequent continuous modeling of crop row structures. Therefore, considering the trade-off between recall and efficiency, SugarRow-YOLO demonstrates greater practical value compared to YOLOv8n. Compared to YOLOv11n, SugarRow-YOLO achieves improvements of 2.0% in precision, 1.9% in recall, 1.6% in mAP\({}_{50}\), and 4.7% in mAP\({}_{50-95}\), while maintaining comparable computational complexity. SugarRow-YOLO achieves an inference speed of 53.6 FPS while maintaining a moderate computational complexity and significantly improving detection accuracy. Although its FPS is slightly lower compared to some lightweight models such as YOLOv7-tiny and YOLOv8n, considering the overall performance improvement and well-controlled computational cost, SugarRow-YOLO still demonstrates potential for practical applications.
## 4 Data and Results
### Improvement of Digitalization Benefit in Time Performance
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Digitalization Improvement (Technology Utilization) for Purpose(s)**} \\ \hline
**No** & **Purpose (Ranking\#)** & **Tech \& Describe** & **No** & **Purpose (Ranking\#)** & **Tech \& Describ** \\ \hline \multirow{4}{*}{1} & & 1. 3D modeling & & & \\  & & 2. VR & & & 1. 3D modeling \\  & & 3. 3D Laser scanning & & & 2. VR \\  & Site Layout Planning (3) & 4. Oblique photography & & 3. AR \\  & & 5. GIS & 9 & & 3. AR \\  & & 6. 4D SCHEDULING & Construction Quality & 4. Solid Model (include 3D printing) \\  & & 7. 4D Assembling & & Management (I) & 5. 3D Laser scanning \\  & & & & 6. Oblique photography \\ \hline \multirow{4}{*}{2} & & 1. 3D Laser scanning & & & \\  & & 2. Oblique photography & & & 6. LoT \\  & & 3. GIS & & & 9. Collaboration platform \\  & Engineering (4) & 4. 4D SCHEDULING & & & \\  & 5. Lofting robot & & & & \\  & & 6. 5D BIM & 10 & Special Structure Design (15) & 1. 3D modeling \\ \hline \multirow{4}{*}{3} & & 1. 3D Laser scanning & & & 1. 3D modeling \\  & Construction Schedule & 2. Oblique photography & & & 2. VR \\  & Management (7) & 3. 4D SCHEDULING & & & 3. 3D Laser scanning \\  & & 4. 4D Assembling & & & 4. 4D Assembling \\  & & 5. Lofting robot & & & 5. Lofting robot \\  & & & & & 6. IoT \\ \hline \multirow{4}{*}{4} & & 1. 3D modeling & & & 1. VR \\  & & 2. VR & & & 2. AR \\  & & 3. AR & & & 3. Solid Model (include 3D printing) \\  & & 5. 3D Laser scanning & & & 4. 4D SCHEDULING \\  & & 6. Oblique photography & & & 5. 4D Assembling \\ \hline \multirow{4}{*}{5} & & 1. 3D modeling & & & 1. 3D modeling \\  & & 2. Solid Model (include 3D printing) & & & 2. 3D Laser scanning \\  & & 2. Solid Model (include 3D printing) & & & 3. GIS \\  & & & & 4. IoT \\ \hline \multirow{4}{*}{6} & & 1. 3D modeling & & & 1. 3D modeling \\  & & 2. VR & & & 2. 3D Laser scanning \\  & & 2. Solid Model (include 3D printing) & & & 3. GIS \\  & & & & 4. IoT \\ \hline \multirow{4}{*}{7} & & 1. 3D modeling & & & 1. 3D modeling \\  & & 2. Collaboration platform & & & 2. 3D Laser scanning \\  & & 3. 3D Laser scanning & & & 3. GIS \\  & & 4. Oblique photography & & & 4. IoT \\ \hline \multirow{4}{*}{8} & & 1. 3D modeling & & & 1. 3D modeling \\  & & 2. Collaboration platform & & & 2. 3D Laser scanning \\  & & 3. 3D modeling & & & 4. 3. GIS \\  & & 4. Oblique photography & & & 4. 3. GIS \\  & & 6. 4D Assembling & & & 4. 3. GIS \\  & & 7. Lofting robot & & & 4. 3. GIS \\  & & 8. IoT & & & \\  & 9. Collaboration platform & & & & \\  & 10. AI & & & & \\ \hline \multirow{4}{*}{8} & & 1. IoT & & & \\  & 2. CDF & 18 & Cost Estimation (15) & 1. 5D BIM \\  & & 3. 3D modeling & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Digitalization improvement (technology utilization) for particular purpose(s).
# Survey on Data Hiding Based on Block Truncation Coding
## 3 Survey of Related Works
### Non-Reversible Data Hiding  
#### 3.1.1 Direct Bitmap Substitution (DBS)  
[PERSON] and [PERSON] [25] proposed an AMBTC-based DH that directly partitions the blocks of an image into smooth blocks and complex blocks and then substitubes the bitmaps of the smooth blocks with secret bits. The threshold \(T\) is used to divide the image into smooth and non-smooth blocks. The \(T\) is a means of determining the embedding capacity (EC) and the quality of the cover image. That is, if \(T\) is raised, the EC increases in proportion to it, and on the contrary, the quality of the cover image deteriorates. That is, the merit of it  
Figure 4: Classification of DH based on BTC/AMBTC.  
Figure 3: An example of AMBTC; (**a**) original block; (**b**) a bitmap; (**c**) reconstructed block.  
is that the quality of the stego image (note that the cover image hides the secret bit) can be controlled by adjusting the threshold \(T(=|\mu_{1}-\mu_{0}|)\).  
[PERSON] and [PERSON] [29] introduced a method of inserting data into a bitmap of a smooth block and proposed a method to reduce distortion of the image by adjusting the two quantization levels through recomputation, but the recomputation requires the original image. [PERSON] and [PERSON] [30] subdivided less complex blocks and highly complex blocks. In 2016, [PERSON] et al. [31] introduced an AMBTC-based DH using a 2-bit plane and four quantization levels. The merit of this method is the high payload, and the demerit is the reduced compression ratio. This study, after further extension by [PERSON] et al., provides improved image quality and higher DH capacity while reducing the compression ratio in half. Existing studies based on BTC used DBS together with their methods. The reason seems to lie in the improvement of the data hiding capacity and the improvement of the image quality.  
**Example 2**.: _Here, we explain how to embed 16-bits into the trio \((\mu_{0},\mu_{1},BM)\) = (146, 149, 00110101101000) by using DBS (see Figure 3). Let us assume that the secret data \(d\) = [1 0 0 1 0 0 1 0 1 1 1 1 0 0 0 0] are used here, and the threshold value \(T\) is 5. If the difference between the two quantization levels \(\delta=|149-146|=3<T\) is less than 5 (predefined threshold value), it corresponds to a smooth block. Therefore, DBS need to be performed. As a result, the updated trio is trio \((\mu_{0},\mu_{1},BM)\) = (146, 149, 100100101110000), where underlined pixels are updated._