esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Material and Methods
### Statistical Analysis Procedures  
Statistical analysis was conducted by using BIAS (Epsilon Verlag, Darmstadt, Germany). After testing the data of each group for normal distribution using the Kolmogorov-Smirnov test, it was found that the data of group 3 were not normally distributed and, therefore, non-parametric tests were used. To test for group differences per measurement condition, the Kruskal-Wallis test was used, while to compare measurement conditions per group, the Friedman test was used. For the following multiple pair comparison, the Conover-Iman test or Wilcoxon matched pairs test were used accordingly. All _p_-values were subsequently subjected to a Bonferroni-Holm correction. The significance level was set at 5%.
# YOLO-SAR: An Enhanced Multi-Scale Ship Detection Method in Low-Light Environments
## 4 Results
### Data Visualization Analysis  
To visually assess YOLO-SAR's detection performance, representative images from the Lowship dataset have been selected for analysis. YOLO-SAR has been benchmarked against YOLOv3-tiny, YOLOv5, YOLOv9t, YOLOv10n, and YOLO11n. The detection comparison is shown in Figure 11. Heatmaps are also used to highlight the feature-map regions to which each algorithm attended. A blue-to-red gradient heatmap indicates attention levels: deep red denotes highest attention, while deep blue denotes lowest. The heatmap results are presented in Figure 12.  
From the detection results in Figure 11, the improved YOLO-SAR framework successfully identifies all ship targets within the images. The YOLO-SAR framework reduces missed detections by enhancing long-range relationship modeling and achieves precise recognition of occluded ships through the adaptive weighting of ship feature sets. YOLOv9t and YOLOv10n fail to detect small ships in the second image, mainly due to the loss of target features under low-illumination conditions. In the third image with occluded ships, all algorithms except for YOLO-SAR exhibit poor performance when suffering with occlusions. This phenomenon is evidenced by redundant detection boxes and missed targets. This deficiency is primarily due to multi-level feature conflicts that arise during occlusion. These findings underscore that the enhanced YOLO-SAR framework demonstrates exceptional  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Precision/\%** & **Recall/\%** & **mAP@50/\%** & **mAP@50-90/\%** & **Params/M** & **GFLOPS/G** \\ \hline Baseline (YOLO11n) & 90.3 & 84.8 & 91.1 & 59.2 & 2.5 & 6.4 \\ +SCINet & 91.7 & 86.4 & 91.9 & 59.4 & 3.2 & 10.5 \\ +RFAConv & 91.8 & 84.7 & 91.7 & 60.8 & 2.6 & 6.7 \\ +ASFF & 91.5 & 86.1 & 92.2 & 60.6 & 4.2 & 9.8 \\ +SCINet +RFAConv & 92.1 & 86.6 & 92.6 & 60.8 & 3.2 & 10.7 \\ +SCINet +ASFF & 92.3 & 87.1 & 93.1 & 61.1 & 8.5 & 17.8 \\ +SCINet +RFAConv +ASFF & 92.9 & 88.6 & 93.8 & 61.6 & 8.5 & 18.1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The ablation experiment results for the Lowship dataset.  
detection performance in complex environments. The heatmaps in Figure 12 more clearly illustrate YOLO-SAR's advantages over other algorithms. With a wider receptive field, the YOLO-SAR framework can accurately locate ship targets and reduce missed detections. Furthermore, its adaptive spatial feature fusion mechanism effectively facilitates interaction between high-level semantics and low-level contours, thereby significantly improving overall detection accuracy.  
Figure 11: The detection comparison: (**a**) ship image with normal lighting and ground-truth labels; (**b**) low-illumination image; (**c**) YOLO-SAR; (**d**) YOLOv3-tiny; (**e**) YOLOv5; (**f**) YOLOv10n; (**g**) YOLO11n.  
Figure 12: Heatmap comparison results: (**a**) ship image with normal lighting and ground-truth labels; (**b**) low-illumination image; (**c**) YOLO-SAR; (**d**) YOLOv3-tiny; (**e**) YOLOv5; (**f**) YOLOv10n; (**g**) YOLO11n.
# Traceability of Surgical Instruments: A Systematic Review
## 2 Traceability Surgical System Architecture Overview
### Layer 3  
The third layer, the service, represents the bridge between the traceability system and end users. It ensures an optimal user experience by providing intuitive interface features for data visualization in an understandable-friendly manner and generating meaningful reports. Reports can include information on instrument usage, patient numbers, maintenance history, sterilization data, etc. It is closely linked to the data management layer to ensure transparent communication and real-time updates display. Several parameters can be displayed for effective decision-making:  
1. The number of uses [21] indicates the total number of times an instrument has been used until a fault is observed. This parameter helps predict the lifespan of each instrument based on the number of uses.
2. Identification Accuracy [15]: Evaluates accuracy by reporting the system's reliability in correctly identifying instruments.
3. The usage rate [21] and activity rate [27] calculate the ratio between the number of uses and the number of sterilizations of an instrument. This parameter makes it possible to understand the efficiency of the use of each instrument.  
\[usage\ rate=\frac{number\ of\ uses}{number\ of\ sterilizations} \tag{1}\]
4. Usage Percentage [18]: Calculates the usage percentage of an instrument based on the total number of operations in which it has been recorded. This parameter is crucial in the process of rationalizing surgical platforms.  
\[Usage\ Percentage=\frac{number\ of\ operations\ including\ the\ instruments\ used}{total\ operations}\times 100 \tag{2}\]
# An Optimization Framework for Data Collection in Software Defined Vehicular Networks
## 3 Materials and Methods  
### Overview of the Data Collection Mechanism  
There exist data collection mechanisms for the centralized and distributed architectures of SDVNs as reviewed in the literature. In a centralized architecture, data is first broadcast to the neighbors, and then the collected data will be sent to the controller by all nodes, whereas in a distributed architecture, only the all-nodes broadcasting data step takes place. However, there is no data collection mechanism for the hybrid SDVN architecture to the best of our knowledge. In this paper, we optimize the data collection of the hybrid architecture of an SDVN.  
Note that for the proposed data collection for a hybrid SDVN architecture, an **agent** is a node that collects data from neighbors and unicasts the collected data along with the node's own data to the centralized controller. A **broadcasting node** is a node that broadcasts its own data to its neighbors.  
The collected data can be either vehicle status or vehicular sensor measurements. The vehicle status is a collection of data comprising the Basic Safety Message (BSM), such as current time, position (latitude, longitude, elevation), transmission, speed, heading, steering wheel angle, acceleration, brake system state, and vehicle size [39]. In addition, vehicles can be employed with sensors such as Red Green Blue (RGB) cameras, depth cameras, Light Detection and Ranging (LIDAR) sensors, proximity sensors, Radio Detection and Ranging (RADAR) sensors, etc.  
In subsequent data-gathering cycles of the proposed data collection framework for hybrid architecture, first, the broadcasting nodes will broadcast their data (status and/or sensor measurements) with the set of one-hop neighbors' node ID for the broadcasting node (\(S_{i}\)), while the agent nodes will broadcast only metadata to their immediate neighbors. After broadcasting, the agent nodes need to send the collected data received from their neighbors, added with the agent node's own data to the centralized server as the second step. Data is sent to the controller in order to make network decisions such as routing optimization, network management, load balancing, etc., based on the knowledge of the global network data. The data-collection process using the proposed optimization framework is described in detail in Section 3.3.2.
###### Abstract  
This article examines the positioning features of polycrystalline, monocrystalline, and amorphous silicon modules relative to the focus points of concentrator photovoltaic modules under real meteorological conditions using a dual tracking system. The performance of the photovoltaic modules mounted on a dual-axis tracking system was regarded as a function of module orientation where the modules were moved step by step up to a point where their inclination differed by 30\({}^{\circ}\) compared to the ideal focus point position of the reference concentrator photovoltaic module. The inclination difference relative to the ideal focus point position was determined by the perfect perpendicularity to the rays of the sun. Technology-specific results show the accuracy of a sun tracking photovoltaic system that is required to keep the loss in power yield below a defined level. The loss in power yield, determined as a function of the measurement results, also showed that the performance insensitivity thresholds of the monocrystalline, polycrystalline, and amorphous silicon modules depended on the direction of the alignment changes. The performance deviations showed clear azimuth dependence. Changing the tilt of the modules towards north and south showed little changes in results, but inclination changes towards northwest, southwest, southeast, and northeast produced results diverging more markedly from each other. These results may make the planning of solar tracking sensor investments easier and help with the estimate calculations of the total investment and operational costs and their return concerning monocrystalline, polycrystalline, and amorphous silicon photovoltaic systems. The results also provide guidance for the tracking error values of the solar tracking sensor.  
solar energy; solar module; sun-tracking; sun-tracking sensor; azimuth angle; tilt angle +
Footnote †: journal: Accepted: 14 August 2019; Published: 17 August 2019
# GPU Parallel Implementation for Real-Time Feature Extraction of Hyperspectral Images
## 2 Hyperspectral Image Feature Extraction Based on INAPC
```
1 INPUT: hyperspectral image \(X\)
21: reshape the raw data to matrix X [row*col, band]
3 band: band numbers; row, col: spatial dimensions
42: centralization matrix:
5 for k = 1: band
6\(\overline{X}_{k}\);
7\(T\ =\ X_{k}-\overline{X}_{k}\);
8 end
93: covariance matrix: \(S\ =\frac{1}{\text{row*col-1}}T^{T}T\);
104: noise estimation:
11 fort = 0: row
12 for i = 1: col
13 for j = 0: band
14\(\Delta_{1}[i+t*col,j]\ =\ X[i+t*col,\ j]-X[i+t*(col-1),\ j]\)
15 end
16 end
17 end
18 fort t = 0: row - 1
19 for i = 0: col
20 for 0 = 1: band
21\(\Delta_{2}[i+t*col,\ j]\ =\ X[i+t*col,j]-X[i+(t+1)*col,j]\)
22 end
23 end
24 end
25 final: N = \((\Delta_{1}+\ \Delta_{2})/2\);
265: for k = 1: band
27\(\overline{N}_{k}\);
28\(\overline{T}_{N}\ =\ N-\overline{N}_{k}\);
29 end
30: C\({}_{N}\ =\ \frac{1}{\text{row*col-1}}T_{N}\);
317: apply 1 st SVD to C\({}_{N}\), and get \(U_{N}\),\(\sum_{N}\), \(V_{N}^{T}\);
328: noise whitening matrix \(W\ =\ U_{N}-\frac{1}{\sqrt{\sum_{N}}}\);
339: adjusted observation matrix: \(C_{D}\ =\ W\text{SW}^{T}\);
340: apply 2 nd SVD to C\({}_{D}\), and get \(U_{D}\), \(\sum_{D}\), \(V_{D}^{T}\);
351: choose the rank value k based on choosing rule;
356: cut off the SVD results \(U_{k}\) based on k;
363: \(P\ =\ WU_{k}\);
374: **OUTPUT: feature extraction result \(Y\ =\ P^{T}\mathbf{X}\)**
```  
**Algorithm 1**Improved noise adaptive principal component (INAFC) algorithm.
## 6 Results  
It may be worth mentioning that the R software packages were employed in this paper in order to obtain accurate and robust results. In addition, the coefficients of the parameters (factors) have been calculated for all the proposed models, and the measures of Residual Mean Squared Error (RMSE) and the coefficient of determination (R Square) have been computed and chosen as performance criteria in order to compare the fitting between the proposed models.  
Prior to analyzing the regression analysis for predicting DC power in SPVSs, it is essential to highlight the significance of the input parameters that influence the model's predictions. The eight key predictor variables used in the analysis represent crucial environmental and meteorological factors that directly affect the performance of SPVSs. These variables are as follows:  
* Irradiance_AVG (X1): The average of solar irradiance is a fundamental input for predicting DC power as it directly correlates with the amount of solar energy available for conversion.
* Temperature_AVG (X2): The average ambient temperature affects both the efficiency of the PV panels and the electrical output. Higher temperatures can reduce the efficiency of solar cells.
* Air_pressure_relative1 (X3): The relative air pressure at the installation site potentially influences energy conversion efficiency through its interaction with weather patterns.
* Air_pressure_absolute1 (X4): The absolute air pressure can similarly impact the operational conditions of the system.
* Humidity_relative1 (X5): Relative Humidity affects panel performance by influencing air density and potentially causing condensation or dirt accumulation on the panels.
* Humidity_absolute1 (X6): Absolute Humidity related to the moisture content in the air can affect system components and environmental interactions.
* Wind Speed (X7): The Wind Speed helps cool the SPVS panels, potentially improving efficiency, but can also introduce mechanical stress.
* Wind Direction (X8): The Wind Direction may indirectly influence cooling or the accumulation of debris on the panels, thus affecting performance.  
By incorporating these diverse environmental inputs, regression models aim to provide accurate predictions of DC power output. Understanding the individual and combined influence of these parameters allows for more efficient management, optimization, and control of SPVSs, ensuring higher performance under varying conditions. The modelsleverage the interplay between these factors to predict outcomes, offering critical insights into how weather fluctuations and system dynamics impact power generation.
## 3 Experimental Verification
### Extraction of Arrival Time  
After the group velocity measurements, the arrival time of the damage-scattered signals was also necessitated for the ellipse methods. The received signals were first processed through a low-pass filter with a frequency of 1 MHz, which eliminated the high-frequency noise from the environment. Figure 5 shows typical waveforms of the signals received from the sensing path AB (A is an actuator and B is a sensor) in the intact and the current (with a small crack) states, respectively. The S0-dominated Lamb wave signal was more obvious, while the damage-scattered signals submerged in multimode and boundary reflection signals could not be directly recognized. Then, signal processing methods were applied to obtain the linear and nonlinear features of the damage-scattered signals.  
For processing the linear features, the received signals in the two states were normalized according to the respective maximum value, and then subtraction of the two received signals was performed to obtain the damage-scattered signal, as shown in Figure 6. The damage-scattered signal was further subjected to Hilbert transform to acquire an envelope curve, and the time corresponding to the maximum peak (red dot) of the envelope curve was taken as the arrival time of the damage-scattered signal.  
Figure 4: Comparison of experimental data (\(\blacksquare\) and \(\blacktriangle\)) and calculated dispersion curves for a 2 mm thick plate (red dashed line shows S0 mode and blue dashed line shows A0 mode).  
For processing the nonlinear features, short time Fourier transform (STFT) was applied to transform the received signals from the time domain into the time-frequency domain. Spectrogram of a received signal from the sensing path AB in the current state is shown in Figure 7, including amplitude profiles at the respective fundamental and double frequencies for both the intact and the current states. It was found that the amplitude profile at the fundamental frequency in the current state (red dashed line) was essentially the same with that in the intact state (blue line), while the amplitude profile at the double frequency in the current state (black dashed line) was higher and more obvious than that in the intact state. Due to interaction with the small crack, the amplitude profile at the double frequency had an extra hump in the current state, considered as the second harmonics of S0 mode. Thus, the time corresponding to the maximum peak (black dot) of the amplitude profile was regarded as the arrival time of the crack-induced S0 mode. The arrival time was also related to the length of the window function in the STFT as well as the absolute time between the excitation and the reception signals.  
Figure 5: Typical waveforms of signals received from the sensing path AB in the intact (blue line) and the current (red dashed line) state, respectively.  
Figure 6: The damage-scattered signal with envelope curve received from the sensing path AB.
# Measuring the Centrality of DNS Infrastructure in the Wild
## 4 Methodology
### Client-Side DNS Infrastructure Measurement
#### 4.2.2 Resolver Pool Discovery  
One evidence of the centrality of the client-side DNS infrastructure is the prevalence of resolver pool, which implies that a large number of FDNS will be dependent on a small number of iRDNS. To clarify the relationship between FDNS and iRDNS and uncover hidden resolver pools, previous researchers have relied on large-scale measurement platforms, such as RIPE [2] or PlanetLab [24], or specific DNS software implementation mechanisms, i.e., the _CNAME_ chain [12; 14; 20]. While the former might be costly, the latter yield unsatisfactory performance, as we have already proved. We propose a novel and lightweight resolver pool discovery approach based on NS chain reflecting. The core idea of our method lies in that we turn some featured resolvers into distributed probes by carefully designing the domain resolution process, as illustrated in Figure 5. These featured resolvers interact with the authoritative name server with the recursion desired (RD) flag like a client. The main steps of our approach are as follows:  
**Step 1: Collecting featured resolver list.** We conducted Internet-wide scanning to collect the featured resolvers from the DNS log files of our ADNS generated. Filtering resolvers that send queries with RD flag from the logs, we obtain 1561 IPs and then verify the validity of these IPs. Finally, we compile 1500 featured resolvers distributed in 18 countries or regions that can be used in our measurements.  
**Step 2: Setting up custom name server.** We deployed the authoritative name server of our registered domain as a bridge name server by configuring a special _NS_ record to make it possible for targeted public DNS service IP receiving queries with RD flag. These _NS_ records delegate a couple of a subdomain to targeted public DNS service IP. In addition, to make sure that the resolution process is completed successfully, we also delegate one _NS_ record of that subdomain to the real name server, which provides the answer record by modified BIND9. Similar to the Internet-wide measurement setup, we choose _TXT_ records and encapsulate resolver IP in responses.  
**Step 3: Sending queries to featured resolvers.** We sent lookups to featured resolvers obtained in step 1 and then collected the resolution data. Due to the load-balancing nature of public DNS and ISP DNS, we improve our coverage by repeating step 3 several times.  
As shown in Figure 5, we send a resolution request (Q1) from the vantage point to featured resolvers. Then, featured resolvers send a resolution request (Q2) to the bridging name server we have deployed. By utilizing the customized response (R1), we redirect the featured resolvers to the target DNS (Q3). Subsequently, the target DNS initiates a  
Figure 4: Client-side DNS infrastructure measurement architecture.  
resolution request (Q4) to the real authoritative name server. Ultimately, at the vantage point, we receive a response record (R4) embedded with the target DNS resolver pool IP.
# Enhancement of Subcooled Flow Boiling Heat Transfer with High Porosity Sintered Fiber Metal
## 1 Introduction
[24] conducted experiments to prove the surface influence on the flow boiling heat transfer, using the same surface in three different cases, a fresh machined surface, the same aged channel surface, and the same surface after polishing. The results showed a reduction in the wall superheat temperature at the same heat flux, with the third case having the best performance followed by the first case. They attributed the heat flux enhancement to a lack of oxidation on the fresh machined surface and the proper cleaning of the surface. [PERSON] et al. [3] proposed a porous honeycomb plate under subcooled flow boiling to increase the critical heat flux, achieving an improvement ratio of around 2.4 times using porous media in comparison with the plain surface with water as a working fluid. The heat flux in subcooled flow boiling is directly related to the inlet subcooling, the mass flux, system pressure, the bubble behavior, and the surface conditions [25; 26]. In general, researchers have reported CHF enhancement from 30% to 200% for pool boiling and from 20% to 100% for the case of flow boiling. [PERSON] et al. [27] reviewed the literature and compared the works related to flow boiling and pool boiling, concluding that the nucleation site density was higher for the porous media than for the plain surfaces. These nucleation sites promote bubble formation and fast heat dissipation. However, there exists a hydrodynamic limit that needs to be considered regarding the design of porous media as the surface. The present study is focused on the heat flux enhancement using high-porosity sintered fibers attached on the surface as a passive method, and the results are compared with the bare surface. The mass flux, porous height, and inlet subcooling temperature are considered as a parameter to affect the heat flux. The bubble formation was recorded and analyzed with a high-speed camera.
# Curve-Localizability-SVM Active Localization Research for Mobile Robots in Outdoor Environments
## 3 Curve-Localizability-SVM Active Localization
### Curve-Localizability-SVM Path Planning
In order to obtain the path with the highest localizability, we need to solve the following objective function:  
\[D=\{(x_{1},y_{1}),(x_{2},y_{2}),...(x_{m},y_{m})\},y_{i}\in\{+1,-1\}.\]6  
Considering that the robot pose estimated by the state of the particles is constantly changing during the localization process, the starting point of the planning path will change according to the state of global localization or relocation. Under this premise, conventional path planning goals such as the shortest path length or shortest navigation time cannot be applied. In order to facilitate the comparison of our algorithm and other active localization strategies, we set the path length as a constant, obstacles and other accessibility factors are considered as regular constraints, so that the planned path contents the following conditions:  
\[D=\{(x_{1},y_{1}),(x_{2},y_{2}),...(x_{m},y_{m})\},y_{i}\in\{+1,-1\}.\]7  
\[D=\{(x_{1},y_{1}),(x_{2},y_{2}),...(x_{m},y_{m})\},y_{i}\in\{+1,-1\}.\]8  
\[s.t. \begin{cases}g_{i}\in S_{pass},\;\forall g_{i}\in P_{l}\\ D(P)=const\end{cases}  \tag{16}\]  
where \(d(g_{i},g_{i+1})\) is the Euclidean distance between the surface points corresponding to the grids \(g_{i}\) and \(g_{i+1}\), \(S_{pass}\) is the accessible area of the map calculated by the model of robots and OctoMap offline [24], and \(D(P)\) is the total length of the generated path. In the constraint space, we specify the total length of the path as a constant value and ensure that all points on the path are within the area that the robot can pass freely. Based on the above objective function and constraint conditions, we can generate a path with the highest localizability for the robot in outdoor environments.