esa-sceva/satcom-chunk-collection huggingface dataset:
# RHS-YOLOv8: A Lightweight Underwater Small Object Detection Algorithm Based on Improved YOLOv8
## 2 Methodology
### Hybrid Bridge Feature Pyramid Network Fusion Block  
Due to the small size and weak features of underwater small objects, they are prone to miss detection when performing the detection task; thus, the characterization ability of the detection model needs to be improved. Feature fusion is a common method to improve the model's characterization ability, where high-level features contain rich texture features and deep-level features contain more semantic features. To effectively utilize these two types of features, we designed a module called the HBFPN (Hybrid Bridge Feature Pyramid Network) for fusing current features, high-level features, and deep features. The structure of the HBFPN fusion block is shown in Figure 4.  
Letting the high-level feature be \(F_{high}\), the deep-level feature be \(F_{deep}\), and the current-level feature be \(F_{current}\), first, for the processing of high-level features, we adopt the ADown module (the downsampling module used by YOLOv9) [37] for downsampling. The ADown module effectively reduces the resolution of the high-level feature map through a series of well-designed convolution and pooling operations, while preserving the rich texture features. This process can be expressed as Equation (8).  
\[F^{\prime}_{high}=\text{ADown}(F_{high}) \tag{8}\]  
where \(F^{\prime}_{high}\) denotes the high-level features after downsampling. The ADown module reduces the complexity of the model by decreasing the number of parameters, which helps to improve the efficiency of the model operation, especially in resource-constrained environments.  
Then, for the deep features, we employ the DySample module [38] for upsampling. The DySample module utilizes advanced interpolation algorithms and convolution operations  
Figure 4: The structure of the HBFPN fusion block.  
to increase the resolution of the deep feature map so that it has the same size as the current-layer feature, facilitating subsequent fusion. The upsampling process can be expressed as Equation (9).  
\[F^{\prime}_{deep}=\text{DySample}(F_{deep}) \tag{9}\]  
where \(F^{\prime}_{deep}\) denotes the deep layer feature after upsampling. Finally, in the feature fusion stage, we fuse the high-level feature \(F^{\prime}_{high}\) after downsampling, the deep-level feature \(F^{\prime}_{deep}\) after upsampling, and the feature \(F_{current}\) of the current layer. This fusion makes full use of the feature information of different layers and improves the model's representational ability. The fusion operation \(F_{fused}\) can be expressed as Equation (10).  
\[F_{fused}=\text{Concat}(F^{\prime}_{high},F^{\prime}_{deep},F_{current}) \tag{10}\]  
The neck part of YOLOv8 is usually used to fuse feature figures of different scales. We integrate the HBFPN into the neck part of YOLOv8 and replace the original Concat operation. This design is particularly suitable for the detection tasks of underwater small objects and diverse target sizes, where the multi-scale fusion mechanism effectively solves the problem that small objects are easily overlooked.
## 2 Materials and Methods
### Measurement
#### 2.1.4 Force and Moment  
Force and moment measurements were performed with the tire test device at the University of Wisconsin-Milwaukee. As shown in Figure 2 it consisted of a welded steel frame and an aluminum fork to hold a bicycle wheel in a desired orientation on top of a small treadmill of flat-top chain. It had a two-degrees of freedom pivot, implemented with an automobile universal joint, far (1.3 m) forward of the bicycle tire so that slight variations in vertical or horizontal position produced negligible variations in orientation angle. The forward pivot was implemented with needle-bearings so that any friction in the bearings or seals generated a negligible lateral force at the contact patch.  
Figure 1: Capturing mountain bike tire tread profile with carpenter’s contour gauge.  
This device allowed for sweeping slip and camber angles while measuring the lateral force, \(F_{y}\), and vertical moment, \(M_{z}\), generated in the contact patch. It used a force sensor to maintain the lateral location of the contact patch and a second force sensor acting on a lever arm of known length to prevent rotation of the fork that held the bicycle wheel about its steering axis.  
In order to allow for the inevitable flexibility of the test frame and the bicycle wheel, the slip orientation of the bicycle rim was measured with a pair of laser position sensors mounted rigidly to the support platen for the flat-top chain near each end of the contact patch. Similarly, the camber orientation of the rim was measured with an accelerometer on the fork.  
Slip angle was altered by pivoting the treadmill about a vertical axis under the center of the contact patch. Camber angle was altered by tilting the fixture about the longitudinal axis of the universal joint, which passed through the contact patch.  
The vertical load borne by the tire was generated simply by the weight of the device's frame. Additional mass could be added above the fork as desired. For this study, the applied normal load was set to 418 N, which equated to front tire normal load of a 95 kg rider sitting on an 11 kg bicycle with 40% front and 60% rear weight distribution.  
The lateral force generated in the contact patch was transmitted through the bicycle wheel to the fork, and thus to the test device frame. From the frame, the force was transmitted to both the lateral force sensor and the universal joint. A simple static summing of the moments about a vertical axis through the universal joint provided a relationship between the lateral force generated in the contact patch and the lateral force measured by the sensor. The mounting point on the frame for the lateral force sensor was positioned on the same axis through the center of the contact patch as the universal joint so that changes in camber angle had no effect on the lateral force sensor geometry.  
Figure 2: Tire force and moment measuring device at the University of Wisconsin–Milwaukee.
# Evaluation of the Occurrence of Phthalates in Plastic Materials Used in Food Packaging
## 3 Results and Discussion
### Quantification of Phthalates in Plastic-Based Food Packaging  
The HS-SPME/GC-qMS method was applied to investigate the occurrence of phthalates in 14 types of plastic-based food packaging acquired in a local market. The RT and mass spectra comparison confirmed the occurrence of individual phthalates. Table 4 shows the phthalates concentration found in 14 plastic packages used in the food industry and determined by HS-SPME/GC-MS after method validation. DIBP was the most prominent phthalate found in all samples, with a frequency of occurrence (FO) of 100%, and its concentration ranged from 3.61 to 10.6 \(\upmu\)g/L. DOP was the second most dominant phthalate detected in food packages (FO = 64%) at concentrations ranging from 1.03 to 2.83 \(\upmu\)g/L. BDE was detected in two samples, but its concentration was lower than the LOQ. BBP was detected only in one sample, and its concentration did not exceed 1.42 \(\upmu\)g/L. Therefore, the \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**RT (min)** & **Compounds** & **Spiked Level (\(\upmu\)g/L)** & **\% REC \(\pm\) SD** & **Intra-Day (\%)** & **Inter-Day (\%)** \\ \hline \multirow{4}{*}{13.17} & \multirow{4}{*}{DIBP} & 0.5 & 90.20 \(\pm\) 1.3 & 3.5 & 7.5 \\  & & 30 & 91.60 \(\pm\) 0.9 & 1.9 & 5.8 \\  & & 60 & 104.9 \(\pm\) 1.8 & 0.9 & 3.3 \\ \hline \multirow{4}{*}{22.55} & \multirow{4}{*}{BBP} & 0.5 & 107.9 \(\pm\) 2.0 & 5.9 & 8.5 \\  & & 30 & 111.3 \(\pm\) 1.9 & 1.3 & 6.7 \\  & & 60 & 105.7 \(\pm\) 3.2 & 0.9 & 1.7 \\ \hline \multirow{4}{*}{26.49} & \multirow{4}{*}{BDE} & 1 & 98.30 \(\pm\) 0.8 & 7.9 & 12.7 \\  & & 30 & 91.30 \(\pm\) 1.4 & 3.7 & 10.6 \\  & & 60 & 94.00 \(\pm\) 1.7 & 0.6 & 3.5 \\ \hline \multirow{4}{*}{27.77} & \multirow{4}{*}{DOP} & 1 & 109.2 \(\pm\) 4.3 & 1.5 & 8.2 \\  & & 30 & 99.20 \(\pm\) 1.6 & 1.4 & 3.1 \\ \cline{1-1}  & & 60 & 95.50 \(\pm\) 0.8 & 0.7 & 2.2 \\ \hline \hline \end{tabular} RT—retention time; % REC—recovery percentage; SD—standard deviation;* validation obtained for sample 3.  
\end{table} 
Table 2: Recovery and precision of phthalates in food packaging, using sample 3, at three spiked levels *.
## 4 Modeling of AV-Mixed Traffic Information Services
### Evaluation of the Importance of Service Operations  
To evaluate the importance of each operation of the derived services, a survey was conducted with 22 traffic experts. First, to evaluate the importance of each operation in terms of three scales (urgency, necessity, and applicability), relative superiority was evaluated for each scale. Moreover, the importance of each operation was evaluated using a Likert scale of five points for each category. Below is the equation for calculating the evaluation score for the importance of each operation.  
**Equation. Importance of Operation (_operImp_)**  
\(operImp_{i}=\sum\limits_{j}(urgWeight_{ij}\times urgEvalScore_{ij}+necessWeight_{ij} \times necessEvalScore_{ij}+applicWeight_{ij}\times applicEvalScore_{ij})\)  
where,  
\(urgWeight_{ij}\): _the urgency weight assigned to the \(i^{th}\) operation by the \(j^{th}\) expert_  
\(urgEvalScore_{ij}\): _the urgency Likert score assigned to the \(i^{th}\) operation by the \(j^{th}\) expert_  
\(necessWeight_{ij}\): _the necessity weight assigned to the \(i^{th}\) operation by the \(j^{th}\) expert_  
\(necessEvalScore_{ij}\): _the necessity Likert score assigned to the \(i^{th}\) operation by the \(j^{th}\) expert_  
\(applicWeight_{ij}\): _the applicability weight assigned to the \(i^{th}\) operation by the \(j^{th}\) expert_  
\(applicaEvalScore_{ij}\): _the applicability Likert score assigned to the \(i^{th}\) operation by the \(j^{th}\)expert_  
\(i=1,2,\cdots,Number\ of\ operations\)  
\(j=1,\ 2,\ \ldots,\ Number\ of\ experts\)  
Figure 5: Operations for each service and relationships between services.  
Figure 6 describes the results of the evaluation proceeded by experts. The four operations that received the lowest operation importance are getTrafficSignal(), getTrafficIncident(), getTrafficFacility(), and getTrafficFlow(). These operations correspond to the primitive types of AV-mixed Traffic Information defined in Section 3. The five operations that received the lowest importance are getTrafficFlowStatistics(), getTrafficFactorExtraction(), getTrafficIncidentStatistics(), getTrafficFlowIndexPrediction(), getTrafficRelationshipbetweenFactors(). The information provided by these operations is the \"analyzed\" traffic information, which is not provided by conventional traffic information centers. Therefore, it is assumed that unfamiliarity with this information and uncertainty regarding the reliability of the analysis results contributed to this outcome.
# Investigating Master-Slave Architecture for Underwater Wireless Sensor Network
## 4 System Model
### The Energy Propagation Model  
The attenuation of the acoustic channel in UWSNs over depth \(d\) is explained by the following formula [41]:  
\[10*logA(d,f)=k.10*logd+d.10*loga(f) \tag{3}\]  
In the above equation, the right side occupies two terms, i.e., the spreading loss and absorption loss, respectively, where \(k\) is a constant whose values are 1, 1.5, and 2, and constitutes the geometry of propagation. Varying the values of \(k\) affects the geometry accordingly. In shallow water, the propagation geometry is cylindrical if the value of \(k=1\), spherical in deep water if \(k=2\). Similarly, for \(k=1.5\), the geometry of the propagation is practically spread, where \(\alpha(f)\) represents the absorption coefficient.  
The following expression can be used to find the underwater noise.  
\[N(f)=N_{t}(f)+N_{s}(f)+N_{w}(f)+N_{th}(f) \tag{4}\]  
where \(N_{t}(f)\), \(N_{s}(f)\), \(N_{w}(f)\) and \(N_{th}(f)\) represent the turbulence noise, shipping noise, wave noise, and thermal noise, respectively.
# Grouting Process Simulation Based on 3D Fracture Network Considering Fluid-Structure Interaction
## 2 Methodology
### Fluid-Structure Interaction Model
#### 2.2.4 Boundary Conditions  
1. Inlet boundary conditions: according to the data of grouting pressure measured by grouting recorder and taking the mean value of grouting pressure during grouting period, pressure inlet is set at the boundary of grouting borehole interval. The corresponding grout VOF at the inlet is set to 1.
2. Outlet boundary conditions: the pressure outlet is set at the end boundary of the fracture, and the pressure satisfies the second boundary condition.
3. Initial conditions: assuming that there is no groundwater during grouting, the fractures are filled with air before grouting, and the initial air VOF in the fracture is set to 1.
4. Displacement boundary conditions: the bottom boundary of the computational domain is the \(z\)-axis constraint, the lateral boundaries are the \(x\)- and \(y\)-axis constraints.
# Review of Land Surface Albedo: Variance Characteristics, Climate Effect and Management Strategy
## 3 Variance Characteristics of Surface Albedo for Essential Land Types and Scales
### Albedo Variances for Special Objects  
Surface albedos of some special objects have also been noted. Within geomorphic circles, rocks with low albedos usually present higher levels of radiative warming than those with high albedos when their temperatures are no more than the surrounding air temperature. Nevertheless, as the rock temperature exceeds that of the surrounding air, their temperature variances show little difference for both dark and light rocks [150]. The impact of flower albedo on soil microclimates was also compared to the effect for vegetation without flowers [110]. Albedos of special artificial materials have also attracted much attention. In agriculture, widely used greenhouse cladding materials (e.g., commercial polymer films) offer a potential means to increase the surface albedo of the local environment, which can subsequently affect the local thermal environment [151]. Additionally, the albedos of daily necessities have also been investigated, such as those of clothing and carpets [152].
# Privacy and Security in Digital Health Contact-Tracing: A Narrative Review
## 3 Results
### Privacy  
Digital contact-tracing apps have raised complex concerns, with the main privacy topics mentioned in the literature being risks, concerns, and protection. People are hesitant when they perceive a risk to their privacy primarily related to their sensitive health data. Privacy concerns appeared to be main factor deterring the acceptance of contact-tracing apps [16; 17]. On the other hand, the assurance of privacy protection encouraged acceptance.
# Solar Extreme and Far Ultraviolet Radiation Modeling for Aeronomic Calculations
## 2 Source Data  
### EUV Data  
It can be expected that the existing models for use in monitoring geophysical conditions can be noticeably improved due to the emergence of new data over the past 30 years. Time series of measurements obtained from satellites differ in both the spectral range, observation period, measurement duty cycle, and accuracy, calibration, and a number of other parameters.  
When developing an empirical-statistical model, it is necessary that the data used satisfy a number of requirements. First, their variations should correspond to the widest range of levels of solar activity, from minimum to maximum. The data should be obtained with the same instrument during the solar cycle or over a longer period. However, with long-term measurements, the sensitivity of the instruments can change significantly due to degradation of the sensors (see, for example, [24; 25; 26]).  
At the same time, it is difficult to distinguish long-term changes caused by solar activity in time series from the effects of degradation of measuring instruments. It should be noted that the change in sensitivity with time can be nonmonotonic, as was observed, for example, in measurements by the VUSS instrumentation onboard the CORONAS-F spacecraft [27]. Elimination of degradation effects is possible by using in-flight calibration, comparing current data with calibration measurements on rockets or with reference (gas-discharge lamps or stars) sources. Recently, it has been possible to eliminate the effects of hardware degradation with the help of improved data analysis methods [25].  
Currently, the most detailed EUV spectra in the region shorter than 105 nm are systematically observed on the TIMED (Thermosphere-Ionosphere-Mesosphere Energetics and Dynamics) spacecraft. To create the model, we used long-term observations of the solar spectra in the short-wave ultraviolet region of 26-195 nm, carried out during 2002-2021. Equipment SEE (Solar Extreme Ultraviolet Experiment) includes the spectrometer EGS (EUV Grating Spectrograph), and the device XPS-XUV Photometer System- \"System of X-ray and ultraviolet photometers \". EGS instruments (spectrograph with a diffraction grating) measure in the wavelength range of 27-194 nm with a resolution of 1 nm, and XPS instruments cover the wavelength range 0.1-27 nm and use silicon photodiodes and filters as X-ray detectors [28]. The data of observations by the SEE equipment for each day are posted on the Internet on the TIMED spacecraft website. At present, this spacecraft is, in essence, the only source of daily data on the solar spectrum in the EUV region.
# Carrier Dynamics in InGaN/GaN on the Basis of Different In Concentrations
## 1 Introduction
In previous studies, we proposed a free carrier grating decay time with different spatial periods to determine the bipolar diffusion coefficient, D = 2.1 cm\({}^{2}\)/s, with 470 ps effective carrier life for estimating the corresponding 40 cm\({}^{2}\)/Vs hole mobility and 10\({}^{18}\) cm\({}^{-3}\) density of the carrier [21]. Time-resolved-FWM (TR-FWM) and photoluminescence (PL) technologies are used to study the dynamics of the disequilibrium carrier and the competition of non-radiative and radiative recombination in different In\({}_{\text{x}}\)Ga\({}_{1-\text{x}}\)N-doped heterostructures to further describe the correlation between photoelectric transmission and structural characteristics of InGaN alloy [22]. In this study, we optically analyzed InGaN/GaN samples grown on sapphire with different In concentrations and capping layers. The grown structures were evaluated using time-integrated luminescence and TR-FWM techniques. Section 2 discusses the growth conditions and experimental procedures. Section 3 presents the X-ray diffraction (XRD), temperature dependence (PL), and TR-FWM results. Finally, Section 4 provides the conclusions drawn from the study.
# The Design and Implementation of a Phased Antenna Array System for LEO Satellite Communications
## 6 Performance Evaluation
### Limitations of the Proposed System  
To evaluate the performance of the proposed antenna array in terms of Operating Frequency, Bandwidth, VSWR, and Return Loss, we can compare the data from Table 2, which was compiled to present these parameters for antennas developed in 2007 [41], 2009 [42] and 2020 [43], suitable for satellite communications at frequencies of 10 GHz, 5.6 GHz, and 1.8 GHz, respectively. Thus, one of the identified limitations of our antenna system results in the smallest measured value of bandwidth (BW), recorded at 87 MHz (24%), indicating a  
reduced capacity of our antenna system to cover a wider range of frequencies or to support multiple channels simultaneously compared to previously studied antennas.  
Several other factors could be acknowledged as potential constraints for our proposed system. Among them, antennas operating at 436.5 MHz may exhibit restricted performance in signal propagation over extended distances due to inherent wave propagation characteristics at this frequency. Moreover, they may demonstrate heightened vulnerability to interference and spectral congestion attributed to the pervasive utilization of the radio spectrum and nearby electronic devices. Furthermore, compatibility challenges might arise with existing equipment or infrastructure, typically optimized for higher frequencies prevalent in 5G networks.  
Nevertheless, the VSWR value of the proposed system is the lowest among those obtained for any of the other antenna systems presented in Table 2, indicating a high efficiency of the antenna array across a wide frequency range. This capability can be considered a significant advantage of the system. Maintaining a low level of VSWR over a broad frequency spectrum is crucial for the optimal performance of the four-element, three-quarter wavelength antenna array in various usage scenarios.
# Microbial Colony Detection Based on Deep Learning
## 4 Experiment
### Analysis and Evaluation
The other examined microorganisms, however, frequently cluster or overlap, especially in low-dilution samples where more colonies are visible on the plate surface. Additionally, larger colonies frequently have fuzzier edges and less contrast with the agar substrate, which results in less accurate mAP detection. The IoU between the true and predicted bounding boxes is taken into account by the mAP score when evaluating the detection quality. It quantifies the extent of overlap between the ground truth and detected regions, indicating how well the model identifies microbe locations accurately. In addition to experimenting with the two-stage object detection algorithm, we supplement it with the latest one-stage object detection algorithm, YOLOv8, utilizing YOLOv8x as our pre-trained model. The primary training parameters are detailed in Table 3. We configure the \"patience\" parameter to 50, signifying that training would halt if no significant improvement is observed after 50 epochs. Consequently, in practice, the model training concluded at the 129 th epoch. The results of the model training are presented in Table 3 and Figure 8. The most outstanding model achieved a mAP of 76.7%, a remarkable 17.5% improvement over the best-performing model (Cascade R-CNN with HRNet) on the AGAR dataset. These results underscore the model's exceptional performance on the AGAR dataset. Figure 6: Five microbial colonies’ average precision results for Faster R-CNN, Cascade R-CNN, Mask R-CNN, and Cascade Mask R-CNN with ResNet50, HRNet, and Swin Transformer backbone using AGAR dataset. Figure 8: The results of YOLOv8x using AGAR dataset. (**a**) The total loss of training (including box_loss, obj_loss, and cls_loss). (**b**) The AP50 curve of YOLOv8x. (**c**) The mAP curve of YOLOv8x. Figure 7: The PR curve results of the nine models, where the recall and precision metrics are the parameters of the bounding box. The curves with different colors indicate the PR curves for different IoUs ([0.50:0.05:0.95]). \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Training Settings**} & \multicolumn{2}{c}{**Results**} \\ \hline epochs & 300 & _S. aureus_ & 0.849 \\ batch & 4 & _B. subtilis_ & 0.727 \\ iou & 0.7 & _P. aeruginosa_ & 0.696 \\ lr0 & 0.00001 & _E. coli_ & 0.698 \\ lrf & 0.0001 & _C. albicans_ & 0.866 \\ momentum & 0.9 & AP50 & 0.925 \\ weight decay & 0.05 & mAP & 0.767 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Training parameters and results of experiment on the YOLOv8x model using the AGAR dataset.