esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Transmission Loss Simulation  
In underwater acoustic problems, SSPs are mainly used for sound field simulation, where the inversion accuracy of the former determines the accuracy of the latter. Thus, sound field simulation results can directly verify whether the inversed SSPs are effective.  
Figure 5 shows the transmission loss results from the sound field simulations using the inversed profiles combined with the Krakenc model. The SSP inversion depth is 1000 m. Considering that the inversion errors tend to be stable when the inversion depth is greater than 600 m, inversed profiles with depths of 1000 m were extrapolated to 4000 m in the experiment to reflect the sound field structure more completely. In the sound field simulation, the sound source depth is 100 m, the receiver depth is 50 m, the sound source frequency is 100 Hz, the seabed density is 1.73 g/cm\({}^{3}\), and the seabed sound speed is 1541 m/s.  
Figure 4: Inversion effect in different seasons of the two methods (unclustered vs. clustered).  
The transmission loss results show that both methods have large errors at 3.5 km, of about 10 dB, which are caused by abnormal disturbances within the inversed profiles. In terms of the transmission loss results within 25 km, both methods basically match the results obtained from the real profile and reflect the sound field structure of the first 10 convergence zones. In the 11 th convergence zone from 25 km to 40 km, the non-clustering method begins to show obvious simulation errors, of about 3 dB. These are caused by the sound field simulation errors, which in turn arise from the SSP inversion errors, which accumulate with distance. The method after acoustic stability clustering produces results that are close to the original transmission loss simulation results for depths exceeding 25 km, with an error of about 0.2 dB, reflecting the high consistency between the inversed profile and the original profile.
# YOLO-SWD--An Improved Ship Recognition Algorithm for Feature Occlusion Scenarios
## 3 Methods
### WTHead with Wavelet Convolution  
Wavelet transformation (WT) [37] is a multi-resolution analysis method capable of decomposing input images into subbands of different frequency ranges, modeling both low-frequency and high-frequency information. The low-frequency subband (LL) typically contains global contours and large-scale structures, while the high-frequency subbands (LH, HL, and HH) encapsulate finer details such as textures and edges. Wavelet convolution (WTConv) explicitly integrates wavelet decomposition and reconstruction into convolutional networks. Unlike fixed-size convolutions, WTConv partitions input features in the frequency domain, enabling the network to distinguish high-frequency edges from  
Figure 5: Structure of the DLKA module.  
low-frequency backgrounds more effectively. This enhances the detection of small targets and the extraction of intricate texture details.  
In the head section of YOLOv11, Depthwise Separable Convolution (DWConv) is conventionally used for feature processing and fusion due to its parameter efficiency and fast inference speed. To further optimize this, we replace DWConv with WTConv [38]. The structure diagram of WTConv is shown in Figure 6. The implementation involves the following steps:  
1. Wavelet decomposition: The input features undergo multi-level wavelet decomposition, producing the LL subband and three high-frequency subbands (LH, HL, and HH).
2. High-frequency processing: Convolution operations are applied to the high-frequency subbands, followed by multiplication with learnable scaling coefficients. These scaled subbands are stored alongside the LL subband.
3. Reconstruction: The stored LL and high-frequency subbands are inversely combined layer-by-layer to reconstruct features to their original resolution.
4. Residual fusion: The reconstructed wavelet features are added to the original features via residual connections, ensuring seamless integration of multi-frequency information.  
Although wavelet decomposition and reconstruction introduce additional computational steps, WTConv remains lightweight compared to traditional large-kernel convolutions or repeated stacking of complex layers. This design maintains computational efficiency while significantly improving the detection accuracy for small targets and high-detail features (e.g., ship components).
# Cross-Medium Photoacoustic Communications: Challenges, and State of the Art
###### Abstract  
The current era is notably characterized by the major advances in communication technologies. The increased connectivity has been transformative in terrestrial, space, and undersea applications. Nonetheless, the water medium imposes unique constraints on the signals that can be pursued for establishing wireless links. While numerous studies have been dedicated to tackling the challenges for underwater communication, little attention has been paid to effectively interfacing the underwater networks to remote entities. Particularly it has been conventionally assumed that a surface node will be deployed to act as a relay using acoustic links for underwater nodes and radio links for air-based communication. Yet, such an assumption could be, in fact, a hindrance in practice. The paper discusses alternative means by allowing communication across the air-water interface. Specifically, the optoacoustic effect, also referred to as photoacoustic effect, is being exploited as a means for achieving connectivity between underwater and airborne nodes. The paper provides background, discusses technical challenges, and summarizes progress. Open research problems are also highlighted.  
photoacoustic; optoacoustic; air-to-underwater communication; cross-medium communication; underwater communication +
Footnote †: journal: Sensors
## 4 Results  
### Datasets and Processing  
The validation experiments utilized three datasets: the Massachusetts road dataset [34], the Deep Globe road extraction sub-challenge dataset [35], and the GF-2 dataset [36]. Within the three datasets, the Massachusetts road dataset consists of 1171 images. The size of each image is 1500 \(\times\) 1500 pixels, and its resolution is 1.2m. The Deep Globe dataset comprises 1113 satellite images accompanied by corresponding masks delineating road labels. The dimension of the raw images is 1024 \(\times\) 1024 \(\times\) 3 pixels, and the resolution is 0.5 m. The GF-2 road dataset contains 200 images, obtained from GF-2 satellite, with a size of 1500 \(\times\) 1500 pixels. Each of the three datasets is divided into three parts: training set, validation set, and test set. The details are described in Table 2.  
To maximize the effective utilization of the constrained training set, we incorporate geometric transformation techniques, encompassing random clipping as well as horizontal and vertical flip transformations. In the context of the trainable road datasets, the predominant contributor to the loss value stems from the misclassification of road pixels as background pixels. This phenomenon arises due to the inherent disparity in quantity between background and road pixels within the HR remote sensing images. Therefore, while optimization endeavors may lead to loss reduction, the fine-tuned semantic segmentation networks are susceptible to classifying uncertain pixels as background rather than road. To mitigate this challenge, we address the matter by a rebalancing strategy, where we adjust the distribution of road and non-road samples to counteract the issue.
# A Combined High and Low Cycle Fatigue Life Prediction Model for Wind Turbine Blades
## 4 Model Validation and Comparison
### Modeling Error Assessment  
To visually compare the discrepancy between the predicted and experimental life, the relative error function, defined in Equation (21) [40; 41], was introduced. To accurately evaluate the reliability of the predicted life, a box plot of the model's prediction error is presented in Figure 7.  
\[M_{\text{error}}=\log_{10}\Bigl{(}N_{f}\Bigr{)}-\log_{10}\bigl{(}N_{p}\bigr{)} \tag{25}\]  
where \(N_{f}\) indicates predicted life, and \(N_{p}\) indicates experimental life.  
Figure 7 displays the prediction results and error analysis for the four models. It is evident that while [PERSON]'s rule offers a simpler computational approach, it assumes that damage accumulates linearly with respect to the number of cycles, meaning each cycle contributes equally to the total damage, regardless of the loading sequence. This simplification fails to account for load interaction effects, where alternating H-L or L-H stress sequences induce additional damage through mechanisms such as residual stress redistribution and microcrack formation. In H-L or L-H fatigue scenarios, these interactions accelerate damage accumulation, which [PERSON]'s rule overlooks, resulting in an underestimation of cumulative damage and an overestimation of fatigue life. The M-H model, while addressing some load sequence effects, tends to underestimate fatigue life, exhibiting lower mean values and greater dispersion in the predictions. Although the T-K model incorporates a fatigue damage accumulation method to mitigate the limitations of [PERSON]'s rule under  
\begin{table}
\begin{tabular}{c c c c} \hline
**LCF Level** & \(\mathbf{\omega}\) &
\begin{tabular}{c} **Experimental** \\ **Condition** \\ \end{tabular} & **Sample** \\ \hline
80\% & 3\% & CCF–80–3\% & 3 \\
80\% & 5\% & CCF–80–5\% & 4 \\
75\% & 3\% & CCF–75–3\% & 3 \\ \hline \end{tabular}
\end{table}
Table 1: Experimental condition parameters in CCF.  
Figure 6: Comparison between predicted life and experimental life.  
CCF loading, it still shows larger prediction errors. In contrast, the model proposed here provides more accurate fatigue life predictions with tighter discretization and a lower mean value, due to its focus on load sequence effects, load interactions, and coupled damage mechanisms. These results demonstrate that the proposed model outperforms the other models in predicting CCF life.
## 3 Results and Discussion
Future research could build on these insights by incorporating dynamic models that assess the interaction between grade, vehicle type, and road conditions to enhance the predictive accuracy and generalizability of these findings [28; 29]. The findings of this study have significant implications for road/transportation and automotive engineering. For road/transportation engineering, the accident risk density map provides a practical tool for identifying high-risk areas and implementing safety measures, like speed breakers, drainage systems, and anti-icing technologies. For automotive engineering, the identified accident patterns offer insights for developing advanced \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Road** & **Accident** & **Percentage** & **Accident** & **Percentage** & **Accident by Type of** & **Percentage** & **Percentage** \\
**Closs** & **Occurrence** & **(\%)** & **Occurrence** & **(\%)** & **Occurrence** & **(\%)** \\ \hline B-E & Single-vehicle collision & 40 & Multiple-vehicle collision & 25 & Rollover & 21 \\ \hline B-F & Rear-end collision & 29 & Sideswipe collision & 24 & Side- impact collision & 23 \\ \hline C-D & Side-impact collision & 38 & Pedestrian collision & 22 & Head-on collision & 19 \\ \hline C-E & Sideswipe collision & 32 & Run-off-the-road & 25 & Pedestrian collision & 24 \\ \hline C-F & Single-vehicle collision & 34 & Side-impact collision & 21 & Pedestrian collision & 19 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._driver-assistance systems (ADASs) tailored to conditions such as icy or high-slope roads. These findings can contribute to improving road safety during adverse winter conditions. Regarding limitations, this study focuses on a specific geographic region and winter months, which may limit generalizability. Additionally, only three algorithms were analyzed, and further exploration with advanced models could yield more robust results. Future research could address these limitations by expanding the dataset to other regions and seasons, incorporating more machine learning algorithms and utilizing real-time road and weather data. Such efforts would enhance the applicability of the findings and contribute to the broader fields of transportation and automotive engineering.
###### Abstract  
Jitter is becoming an important factor in high-speed serial link and integrated circuits (ICs). Generating controllable jitter plays a crucial role in simulating the test environment of high-data links, evaluating the performance of IC, preventing jitter in high-speed serial link, and even testing the synchronous trigger circuit. In this paper, a digital synthesis for jitter generation and a logical combination method for selecting jitter on the rising edge or falling edge of a data pattern are presented. Precisely controllable jitter is generated by digital synthesis, including sinusoidal period jitter, rectangular period jitter, duty cycle distortion (DCD) jitter, and adjustable random jitter. Additionally, the validity and accuracy of the proposed method were demonstrated by hardware experiments, where the jitter frequency had an accuracy of \(\pm\)30 ppm and the jitter amplitude had a step of 2 ps.  
high-speed serial link; integrated circuits (ICs); digital synthesis; logical combination; jitter generation +
Footnote †: journal: Electronics Electronics
# A Systematic Overview of Recent Methods for Non-Contact Chronic Wound Analysis
## 8 Discussion
Furthermore, the wounds itself can be very complex where tissues permeate each other in such a way that even a seasoned medical expert could have trouble in separating them precisely. All those problems make this topic non trivial and therefore very interesting to research. Similarly, to the wound segmentation problem, wound tissue classification is basically about determining appropriate visual features typically calculated over an area and then applying machine learning. Used machine learning methods include almost all of them, from LDA [11], and PCA [46], to methods like k-means [52], random forest [11], and SVM [40] which is a sort of a pinnacle of the classical machine learning technique. Recently deep neural networks have been the focus of the research in this area as well [45]. Wound tissue classification is reliant on successful wound segmentation phase because it enables the tissue classifier to focus solely on the wound area and distinguish details and separate tissues only typically found on wounds. Wound segmentation, on the other hand, requires that the system detects and separates the wound as a whole. This enables researchers to develop a more profound and complex classifier which potentially would not give meaningful results or would run too slow if run for example on the whole input image. Wound evaluation can be based on multiple sources of information as in [107], but is usually mostly based on wound dimensions (depth, circumference, area, and volume) and amount of presence of certain tissue types. Therefore, more precise measurements will lead to more precise wound evaluation, which means that the wound measurement should be relied on 3D cameras as opposed to regular 2D cameras that have inherent error when imaging curved surfaces [103]. Furthermore, applying new machine learning techniques developed for 3D data input could result in more precise wound segmentation and tissue classification.
## 3 Results
### Validation and Inter-Comparison of MODIS-Based Metrics
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Vegetation Index** & **Method** & \(\mathbf{r}\) & \(\mathbf{R^{2}}\) & **RMSE (Days)** & **Bias (Days)** \\ \hline \multirow{4}{*}{sPRI\({}_{1}\)*} & **Gaussian** & **0.83** & **0.74** & **17.5** & \(\mathbf{-16.8}\) \\  & **Double Sigmoidal** & **0.84** & **0.80** & **9.8** & \(\mathbf{-8.7}\) \\  & FFT & 0.33 & 0.53 & 23.9 & \(-\)23.1 \\  & Savitzky–Golay & 0.57 & 0.36 & 22.6 & \(-\)21.4 \\ \hline \multirow{4}{*}{NDVI\({}_{\text{maiac}}\)*} & Gaussian & 0.63 & 0.66 & 11.2 & \(-\)9.7 \\  & Double Sigmoidal & 0.64 & 0.60 & 7.9 & \(-\)2.3 \\  & **FFT** & **0.90** & **0.79** & **17.2** & \(\mathbf{-16.5}\) \\  & **Savitzky–Golay** & **0.77** & **0.73** & **14.8** & \(\mathbf{-14.0}\) \\ \hline \multirow{4}{*}{NDVI\({}_{\text{prod}}\)**} & Gaussian & 0.37 & 0.21 & 17.3 & 12.6 \\  & Double Sigmoidal & 0.66 & 0.44 & 18.0 & 8.6 \\  & FFT & 0.10 & 0.00 & 31.6 & \(-\)1.2 \\  & Savitzky–Golay & 0.11 & 0.09 & 45.2 & \(-\)22.5 \\ \hline \multirow{4}{*}{PAR **} & Gaussian & 0.15 & 0.01 & 26.5 & \(-\)24.7 \\  & Double Sigmoidal & \(-\)0.04 & 0.00 & 11.3 & 3.23 \\ \cline{1-1}  & FFT & \(-\)0.08 & 0.04 & 39.8 & \(-\)36.9 \\ \cline{1-1}  & Savitzky–Golay & \(-\)0.32 & 0.13 & 32.3 & \(-\)30.15 \\ \hline \hline \end{tabular}
* Sample size = 12; ** Sample size = 13.  
\end{table} 
Table 3: Statistics of correlation between the start of the growing season (SGS) derived from ground measured NEE and SGS derived from MODIS VI time series and PAR. \(R^{2}\): Coefficient of determination; \(r\): rho from Spearman Rank’s correlation; RMSE: Root mean squared error. \(R^{2}\) > 0.7 are highlighted in bold. Insignificant relationships are marked in grey (\(p\)-value > 0.05).
# Online Optimization of Pickup and Delivery Problem Considering Feasibility
## 2 Pickup and Delivery Problem
### Constraints
#### 2.2.5 Constraints on Agent Fuel  
When agents are implemented by using electric vehicles and drones, we need to consider fuel constraints.  
We introduce the continuous variable \(q_{l}(t)\in\mathcal{R}\) as a variable representing the fuel of the agent \(l\) at time \(t\). Let \(q_{0}\) denote the agent's maximum fuel. Suppose that the initial fuel is given by \(q_{l}(0)=q_{0}\). The fuel \(q_{l}(t)\) at time \(t\) is defined by  
\[q_{l}(t)=\begin{cases}q_{0}&\text{if the agent $l$ is located at the vertex in $\mathcal{S}$,}\\ q_{l}(t-1)-1&\text{otherwise,}\end{cases} \tag{13}\]  
which implies that the fuel is fully charged at the store vertex and decreases by one at the vertex, except for the store vertex.  
The constraint on the fuel of the agent \(l\) is given by  
\[q_{l}(t)\geq c,\quad t\in\{\tau_{i},\ldots,\tau_{i}+T_{f}-1\} \tag{14}\] \[q_{l}(\tau_{i}+T_{f})\geq c_{f}, \tag{15}\]  
where \(c,c_{f}\geq 0\) are given constants. According to the policy of model predictive control, the terminal constraint may be different with other constraints. This difference can be realized via a method for choosing \(c\) and \(c_{f}\).
# Experimental Validation of a GNSS Receiver Antenna Absolute Field Calibration System
## 3 Results and Discussion  
According on the experimental validation methodology detailed in Section 2.3, and the receiver antenna calibration campaigns conducted in May and June of 2023, dual-frequency calibration results, i.e., PCC models (PCO and PCVs), for G01 and G02 were estimated for all six individually calibrated GNSS receiver antennas (Table 2). The final PCC model of antennas that have been calibrated multiple times (see Table 2) was determined according to the methodology presented in [PERSON] et al. (1975) and [PERSON] (1982), and is based on a conversion to a common datum and mean PCO vector. Since four antennas of the Trimble Zephyr 2 Geodetic type (LMMT internal labels Z1, Z2, Z3, and Z4) have very similar phase patterns, to maintain simplicity, here only results for the Z1 (TRM57971.00 NONE, S/N: 30739001) antenna are presented (Figure 9). Furthermore, Figures 10 and 11 present calibration results of antennas LEIAX1202 GG NONE (I/L: LG) and TPSPG_A1 NONE (I/L: TP).  
Figure 10: LEIAX1202 GG NONE antenna (S/N: 08190116, I/L: LG) LMMT individual calibration results (PCO vector and PCVs) for GPS L1 (G01) and GPS L2 (G02).  
Figure 9: TRM57971.00 NONE antenna (S/N: 30739001, I/L: Z1) LMMT individual calibration results (PCO vector and PCVs) for GPS L1 (G01) and GPS L2 (G02).  
Calibration results, i.e., PCC models, of the three main antenna types included in the validation show dominant elevation-dependent variations (PCV values). However, for antennas LEIAX1202 GG NONE (Figure 10) and TPSPG_A1 NONE (Figure 11), substantial azimuth-dependent variations, for both G01 and G02 frequencies, are noticeable. This PCC's azimuth dependency is particularly pronounced for the TPSPG_A1 NONE antenna, primarily attributed to the antenna's square-shaped design and lack of a ground plane (Figure 5c). The TRM57971.00 NONE antenna (S/N: 30739001, I/L: Z1) exhibits a significantly more stable phase pattern, as to be expected given its classification as a reference-station-grade antenna and its superior quality among the three analyzed. Moreover, a difference in the PCO Up component between G01 and G02 PCC models of 7.8 mm is notable and interesting (Figure 9).
# A Cost-Aware Framework for QoS-Based and Energy-Efficient Scheduling in Cloud-Fog Computing
###### Abstract
As such, existing strategies do not optimize the energy efficiency performance based on the QoS obligations of fog jobs, resulting in the metric of measuring client satisfaction formulating schedules that lack a joint energy- and QoS-optimized performance.
## 2 Materials and Methods
### Method Validation
#### 2.6.1 Accuracy and Precision  
Accuracy was defined as the percentage of closeness of the measured concentration to the nominal concentration under the same conditions. As the analyte is also an endogenous molecule, the QCs for accuracy and precision were prepared using a surrogate matrix [19]. QCs were made up by spiking the surrogate matrix with known amounts of AA (0.50 \(\mathrm{\SIUnitSymbolMicro g}\)/mL, 1.00 \(\mathrm{\SIUnitSymbolMicro g}\)/mL, and 5.00 \(\mathrm{\SIUnitSymbolMicro g}\)/mL), followed by processing and analysis anticipated for the study samples. The accuracy was presented as percent recovery and calculated using the following formula [19]:  
\[Accuracy\ (\%)=\frac{Measured\ concentration}{Nominal\ concentration}\times 100 \tag{1}\]  
Precision was defined as the closeness of agreement between a series of measurements obtained from multiple sampling of the same homogeneous sample under prescribed conditions. It was assessed at two levels: intra-day (repeatability) and inter-day precision and expressed as a percent relative standard deviation (%RSD) [19].  
Intra- and inter-day precision were determined both using surrogate matrix QCs, and authentic matrix QCs (unspiked, spiked with 0.50 \(\mathrm{\SIUnitSymbolMicro g}\)/mL, 1 \(\mathrm{\SIUnitSymbolMicro g}\)/mL, and 5 \(\mathrm{\SIUnitSymbolMicro g}\)/mL AA), with five replicates each. Inter-day precision was evaluated by analyzing each QC over three consequent days [19].