esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Evaluation of Surface Quality, Material Utilization, and Efficiency
### Evaluation of Surface Quality
#### 3.1.1 Identifying Predominant Factors Affecting the Response and Determining Their Limits  
The predominant factors affecting the response in each regression model have been discussed above. Their upper and lower limits that are of interest in this study are given in Table 1. These factors are coded as \(-1.68\), \(-1\), \(0\), \(+1\), and \(+1.68\). Specifically, the ranges of the three deposition parameters are determined based on preliminary experiments, which allow good bead appearance with little sputter and no visible defects. It should be pointed out the bead width is not a constant value along the build direction due to the presence of the stair-stepping effect, as shown in Figure 3d. To address this issue, the average bead width is used, which is defined as the ratio of the cross-section area to the height of the produced wall.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Symbol**} & \multirow{2}{*}{**Factor**} & \multirow{2}{*}{**Unit**} & \multicolumn{5}{c}{**Level**} \\ \cline{4-7}  & & \(\mathbf{-1.68}\) & \(\mathbf{-1}\) & \(\mathbf{0}\) & \(\mathbf{1}\) & \(\mathbf{1.68}\) \\ \hline \multicolumn{7}{c}{**Regression model 1 (Response: bead width)**} \\ \hline \(W_{FR}\) & Wire-feed rate & m/min & 3.4 & 3.7 & 4.3 & 4.8 & 5.1 \\ \(T_{S}\) & Travel speed & m/min & 0.35 & 0.4 & 0.48 & 0.55 & 0.6 \\ \(W_{V}\) & Welding voltage & V & 16.6 & 17.3 & 18.4 & 19.5 & 20.2 \\ \hline \multicolumn{7}{c}{**Regression model 2 (Response: surface roughness)**} \\ \hline \(S_{S}\) & Spindle speed & rpm & 1000 & 2400 & 4500 & 6600 & 8000 \\ \(T_{FR}\) & Tool-feed rate & mm/s & 1 & 1.8 & 3 & 4.2 & 5 \\ \(C_{D}\) & Cutting depth & mm & 1 & 1.4 & 2 & 2.6 & 3 \\ \hline \end{tabular}
\end{table}
Table 1: Coding for factor and level.  
Figure 4: Relation between surface roughness and the key process parameters.
# Underwater Object Detection Method Based on Improved Faster RCNN
## 2 Related Work  
### Development of Underwater Object Detection Technology  
With the rapid development of deep learning methods, it is widely used in various fields, e.g., [8; 9; 10; 11; 12; 13; 14]. The successful cases aptly illustrate that deep learning methods can be fully applied to detect marine organisms and their growth environments. The deep learning-based object detection methods are generally divided into two main technical routes: one-stage object detection algorithms and two-stage object detection algorithms. Regarding the one-stage object detection methods, [PERSON] et al. invented the one-stage object detection algorithm YOLO in 2015 [15], which excels in the speed of detecting images. [PERSON] proposed the SSD algorithm [16], which is based on the principle of detecting objects at different scales by setting different detection branches in the network. Subsequently, through the continuous efforts, YOLOV2 [13], YOLOV3 [17], YOLOV4 [18] and other algorithms of the YOLO series appeared one after another. Regarding the two-stage object detection algorithm, in 2014 [PERSON] firstly proposed the RCNN algorithm [19], which has good performance in detection accuracy but runs very slowly due to too many complex calculations. In the same year, [PERSON] invented the SPPNet [20] algorithm with spatial pyramidal pooling structure, which is 20 times faster than RCNN with little change in the accuracy. [PERSON] introduced the Fast RCNN [21] algorithm with substantial improvement in the speed of training and testing in 2015. [PERSON] invented the Faster RCNN [22] algorithm, and [PERSON] also used the faster R-CNN and deep transfer learning to realize the detection of K-complexes in EEG waveform images [23], which are more accurate, faster, and very close to real-time performance.  
On the other hand, [PERSON] proposed an unsupervised color correction method for underwater image enhancement [24], which optimized the problems of low contrast and color distortion in the underwater images. [PERSON] [25] proposes a robust and unsupervised deep learning algorithm to automatically detect fish, thereby reducing the burden of manual annotation. [PERSON] [26] uses the LSTM (Long Short-Term Memory)neural network for water quality classification, which improves the detection accuracy and real-time performance of the water quality monitoring system. [PERSON] [27] proposed a method for detecting abnormal behavior of underwater fish by combining deep learning object detection, fish tracking and DTW (Dynamic Time Warping).
## Materials and Methods
### Remotely Sensed Data
#### 2.2.1 UAS Imagery Collection and Processing
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **ID** & **Subsystem** & **Definition** \\ \hline Subtidal Haline & 2100 & Subtidal Haline & the substrate is continuously submerged [by tidal water and]... ocean-derived salts [at least] 0.5\% during the period of average annual low flow. \\ \hline Intertidal Sand & 2253 & Intertidal Haline & unconsolidated particles smaller than stones [constitute at least 25\% aerial cover and] are predominantly sand. Particle size ranges from 0.00625 mm to 2.0 mm in diameter. 1 \\ \hline Emergent & 2260 & Intertidal Haline & characterized by erect, rooted, herbaceous \\ Wetland & 2260 & Intertidal Haline & The species include true shrubs, young \\  & & & trees, and trees or shrubs that are small or \\  & & & & stunted because of environment. 2 \\ \hline Supratidal Sand & 2323 & Supratidal Haline & 1 \\ \hline Scrub-Shrub & & & includes areas dominated by woody \\  & & & vegetation less than 6 m (20 feet) tall. \\  & & & The species include true shrubs, young \\  & & & trees, and trees or shrubs that are small or \\  & & & stunted because of environment. 2 \\ \hline Upland Sand & 6123 & Supratidal & 1 \\  & & & Upland \\ \hline Herbaceous & 6131 & Supratidal & herbaceous upland habitat that is \\  & & & Upland \\ \hline Scrub-Shrub & & & \\ Upland & 6140 & Supratidal & 2 \\ \hline Forested Upland & 6150 & Supratidal & characterized by woody vegetation that is \\  & & & Upland \\ \hline Subsystem & & Definition \\ \hline Intertidal Haline & & the substrate is exposed and flooded by tides; includes the associated \\  & & splash zone;... ocean-derived salts measure [at least] 0.5\% during the \\  & & & period of average annual low flow. \\ \hline Supratidal Haline & & nontidal wetlands containing at least 0.5\% ocean- derived salts at some \\  & & & point during a year of average rainfall. \\ \hline Supratidal & & any coastal upland area above the highest spring tide mark that is \\  & & periodically over-washed, covered, or soaked with seawater during storm \\  & & & events to an extent that affects habitat structure or function. \\ \hline \hline \end{tabular}  
* 1 and 2 signify equal substrates/vegetation types in different subsystems.  
\end{table} 
Table 1: Nine Habitat Classes from National Estuarine Research Reserve System Classification Scheme (NERRSCS) used to map cover types for the Rachel Carson Reserve. The NERRSCS follows the hierarchy System > Subsystem > Class > Subclass. All subsystems used in this project fall in the overarching Estuarine system type. Upland in this study area is defined as 0.9 m above the Mean Lower Low Water (MLLW) tidal datum, defined as the average minimum water level across all tidal days [35].
## 5 Conclusions  
We aimed to address the lack of knowledge on the relationship between the mechanisms of urban expansion and PM\({}_{2.5}\) pollutants from a multi-scale and macro perspective. This paper, based on data on land use, population density, socioeconomic indicators, and environmental monitoring stations, revealed the influence of the coupled driving mechanism of spatial-temporal urban expansion indices on PM\({}_{2.5}\) pollutants. The main conclusions are as follows:  
1. From 2010, 2015, and 2020, the concentration of PM\({}_{2.5}\) pollutants decreased by 26.15 \(\upmu\)g/m\({}^{3}\) in the \"2 + 26\" cities of BTH. Among them, the trend in the spatial characteristics of PM\({}_{2.5}\) pollutants decreased from the central-northern part of the country to the surrounding areas.
2. The Moran I Index showed that 0.36, 0.52, and 0.63 of urban construction land were located in \"hot-spot areas\" with respect to the city area in 2010, 2015, and 2020, respectively.
3. There were significant spatial heterogeneity characteristics in the urban expansion index. In addition to the fractal dimension, the values of the other urban spatial morphology factors all increased with urban expansion. The influencing factors of meteorology were relatively less affected by urban expansion.
4. The compactness index, the industrial structure, and temperature were the main urban expansion factors affecting PM\({}_{2.5}\) pollutants in the \"2 + 26\" cities of BTH. Among them, the compactness index and the centrality ratio, as the most important interaction combinations, explained 0.92 of PM\({}_{2.5}\) pollutants. Urban expansion, human activities, and meteorological conditions were important factors in the variation of PM\({}_{2.5}\) pollutants. At the same time, remote sensing data images as well as Geo-Detector models had strong geospatial analysis capabilities. Our findings will help achieve the environmental eco-city goal of promoting the improvement of the urban atmospheric environment.  
Conceptualization, S.Y.; methodology, S.Y.; software, S.Y.; validation, P.W.; writing--original draft preparation, S.Y. and P.W.; review and editing, S.Y. and Y.H.; visualization, S.Y. and Y.S.; funding acquisition, Q.C. and P.W. All authors have read and agreed to the published version of the manuscript. This research was supported by the Science and Technology Research Project of Jiangxi Provincial Education Department, China, Grant NO. GJJ2204003.  
Not applicable.  
The datasets presented in this study can be found here: [[https://landscan.ornl.gov/](https://landscan.ornl.gov/)]([https://landscan.ornl.gov/](https://landscan.ornl.gov/)); accessed on 17 September 2023; www.resdc.cn; accessed on 17 September 2023; [[https://www.stats.gov.cn/sj/ndsj/](https://www.stats.gov.cn/sj/ndsj/)]([https://www.stats.gov.cn/sj/ndsj/](https://www.stats.gov.cn/sj/ndsj/)); accessed on 17 September 2023; [[https://air.cnemc.cn:18007/](https://air.cnemc.cn:18007/)]([https://air.cnemc.cn:18007/](https://air.cnemc.cn:18007/)); accessed on 17 September 2023.  
The authors declare no conflict of interest.
## 3 System Configuration  
The system configuration of a six-mode coherent transmission system is illustrated in Figure 12, with a \(2^{23}-1\) pseudorandom binary sequence (PRBS) sequence modulated on every mode over a 30-km FMF. The OSNR setting block indicates that the additive white Gaussian noise (AWGN) noise is added after the FMF transmission, while the optical filtering block specifies that a Gaussian filter with a 33-GHz bandwidth is used to suppress the out-of-band noise before the coherent detection.  
Figure 11: A scalable architecture of spatial multiplexing MIMO decoders for field-programmable gate arrays (FPGAs) [39].  
Figure 12: Configuration of a six-mode coherent transmission system [29].  
After mode de-multiplexer (Mod DEMUX), parallel signals are launched into the receiver, and are converted to baseband in the optical front end by internally mixing with the local oscillator (LO) at a linewidth of 100 kHz. The electrical signal in-phase and quadrature (I/Q) components are then re-sampled at two samples per symbol by the analog-to-digital converter (ADC). After the MIMO processing, a BER is estimated for each mode [40].
# Unmanned Vehicles' Placement Optimisation for Internet of Things and Internet of Unmanned Vehicles
## 6 Proposed Algorithm: Communication Optimisation Integration
### Position Optimisation Algorithm
The implemented code was profiled to evaluate the potential improvement of its performance. For example, only the call of built-in MATLAB \"distance\" function for geodesic distance increased with an algorithm execution time over 53 s. After implementing our own geodesic distance computing function based on the Haversine Equation [80], the elapsed time decreased from more than 140 s to less than 16 s.  
The values of \(a_{j}\) and \(b_{j}\) in Equation (28) are equal to 1. The initial placement for all the following scenarios is depicted in Figure 11, whereas the target wireless sensor network is WSN4. We introduce the performance indicator \(\sigma\), which represents the ratio between the sum of all subunitary weights of the edges of the path and the total number of edges of the path of the communication graph (\(\sigma=\frac{s}{n_{m}+1}\), where \(s\) is computed based on Equation (30). When a successful link is established between the gateway and served WSN, \(s\to n_{m}+1\) and, thus, \(\sigma\to 1\). The significance of very low values of \(\sigma\) and \(\sum_{i=0}^{n_{m}}c_{i}=n_{m}+1\) would reside in the fact that more close UVs than necessary are acting as relays, rising interference issues and inefficient placement or UV choice, which is not the case for the evaluated scenarios. \[s=\sum_{i=0}^{n_{m}}\frac{c_{i}PL_{CN[i],CN[i+1]}}{LB} \tag{30}\]  
where \(CN[0]\) represents the gateway, \(CN[n_{m}+1]\) represents the served WSN and  
\[c_{i}=\left\{ \begin{array}{cc}1&PL_{CN[i],CN[i+1]}\leq LB\\ 0&PL_{CN[i],CN[i+1]}>LB\end{array} \right. \tag{31}\]  
Further, we denoted the distance between gateway and WSN (\(d_{WSN-GW}\)) by \(d\). The number of necessary and sufficient UVs (\(n_{UAV}\), \(n_{USV}\)) and virtual nodes, \(n_{m}\), for each scenario was determined based on the simulations, that is, we determined the minimum values for which the communication link for gateway-served WSN can be ensured.
# A Complete Pipeline for Heart Rate Extraction from Infant ECGs
## 2 Materials and Results
### The HR Pipeline  
#### 2.3.1 Raw Heart Rate Calculation  
Once a set of R-peaks is detected, the heart rate can be calculated as described by Equation (1). This is an instantaneous heart rate calculation reflecting beat-to-beat changes. For some research questions, an average heart rate (collected over a few beats) will be preferred and will likely reduce the impact of noise in the calculation. Here, only instantaneous heart rate was considered. Even with good R-peak-detection methods, a peak can be missed or a non-R-peak can be incorrectly labelled. This leads to an erroneous heart rate measurement, which can be detected using filtering.
# KubeHound: Detecting Microservices' Security Smells in Kubernetes Deployments
## 4 Detecting Security Smells in Kubernetes-Based Microservices
### Non-Encrypted Data Exposure  
We consider a possible instance of the _Non-Encrypted Data Exposure_ smell, which occurs when data-at-rest encryption is not enabled in data stores. We therefore introduce the following analysis to detect one such smell instance.  
**Data-at-Rest Encryption Not Enabled in DBMSs**. This technique enables the detection of an instance of the _Non-Encrypted Data Exposure_ smell by statically analysing the configuration files of database management systems (DBMSs). The analysis aims at determining whether encryption-at-rest of data is enabled in a DBMS by inspecting its Kubernetes configuration files, Dockerfiles, or custom configuration files. Such a static analysis can be implemented by suitably configuring the static analysis featured by Checkov [15] to detect whether data encryption is enforced by the configuration files of the most widely used DBMSs (e.g., MongoDB, MySQL, PostgreSQL, or Redis). This analysis could be extended to work with other DBMSs not yet supported by Checkov.
## 1 Introduction
Nevertheless, the atmospheric acoustic scientists mainly focus on sound propagation problems with larger scales or sizes [11; 12; 13]. It is true that the governing equations of most CAA based methods can solve both wind turbine ANGeneration and ANPropagation problems, but the heavy simulation cost often limits the computational domain into small size. It is clear that more efficient methods with less computational effort are needed. To satisfy this requirement, the flow passing wind turbine, the ANSource and the ANPropagation through long path are naturally combined together in this study. The engineering ANSource model for wind turbine [14; 15; 16] is built into the above mentioned AD flow solver. Such that the relative inflow velocity and AOA of each blade section are obtained from the AD-CFD simulation and then fed into the noise source model of wind turbine. Afterwards, the relative sound pressure level (RSPL) from wind turbines is simulated over large distances from the turbine positions to the desired receivers. The wind turbine ANSource are the key inputs to the propagation solver [11; 12]. The wind turbine wake flow, computed by the AD-CFD method, is considered as the medium of ANPropagation. The complex flow environment is found as an important factor for long-path wind turbine ANPropagation. In the previous study of [PERSON] [17] and [PERSON] [18], the influences of atmospheric conditions on ANPropagation were investigated. Both studies showed that there is large noise variation under different wind profile and atmospheric stability conditions. More recently, this parabolic equation (PE) method is used by [PERSON] et al. [19; 20] for wind turbine noise applications. It was clearly shown that the wind turbine ANPropagation has different refraction characteristics under various wake conditions. The present work focuses on an integrated method for modeling of flow, ANSource and ANPropagation, and the detailed phenomenon of ANPropagation through multi-wind turbine wakes are studied. This paper is organized following the sequences: In Section 2, the numerical method for wind turbine flow simulation, ANGeneration and ANPropagation modeling are described; Section 3 provides results of various validation cases first and more results from the integrated flow-noise model afterwards; conclusions are provided in the last section.
# Simulation of Autonomous Underwater Vehicles (AUVs) Swarm Diffusion
## 2 Materials and Methods
### The Vehicle
#### 2.2.2 Short-Range Communications  
Short-distance communication between the vehicles is based on an optical laser communication system. Although it is not the primary focus of this paper, it is useful to spend a few words to describe the general architecture. Due to the low dynamics (speed) of the drones, when they approach, it is possible that they establish UOWC (underwater optical wireless communication) contact in which they exchange different data, which will then serve to diverge and avoid collision. A 520 nm laser diode (LD) is a simply modulated NRZ-OOK (Non-Return-to-Zero On-Off Keying): According to [19], we have a data rate of 500 Mbps with a BER (bit error rate) of \(2.5\times 10^{-3}\) over 100 m well below the forward-error correction of \(3.8\times 10^{-3}\) with 7% overhead.  
From a functional point of view, the Rx-Tx chain is described in Figure 2: It starts from the signal generator that, through the \"NRZ-OOK\" unit, passes to pulse shaping and then to the AWG (Arbitrary Wave Generator), which carries the signal to the laser diode: Downstream, we will find the focusing  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Displacement** & **Length** & **Beam 1** & **Wing Span** & **Cruise Speed** & **Range 2** & **Endurance** & **Depth** \\ \hline
59 kg & 0.920 m & 0.22 m & 0.445 m & 18 km & 34 km/h & -5700 km & 168 h & 200 m \\ \hline \hline \end{tabular} \({}^{1}\) Without annular wing. 2 At cruise speed.  
\end{table}
Table 1: AUV: General characteristics and performances (estimated).  
Figure 1: Prospective cross-section of the autonomous underwater vehicles (AUV) Albacore: The interior sections and supports are visible.  
optical group (absent in the figure for clarity). The signal, revealed by the APD (avalanche photodiode), once synchronized and equalized, is sent to the key demodulation and, from there, to the navigation system of the drone.  
The signal exchanged is very essential (as seen from Figure 3 below): It contains the identification code of the drone, information regarding its electromechanical \"health\" and, last but not least, position and speed, supported by its IMU (Inertial Measurement Unit). At this point, the two drones have all the information necessary to change direction according to the protocols indicated.
# Query-Based Cascade Instance Segmentation Network for Remote Sensing Image Processing
## 4 Discussion  
Since the QCIS-Net model is larger, it converges slowly in the two datasets without transfer learning, which leads to poor results. However, the accuracy of the model after transfer learning improves significantly, and the performance of the proposed method is better than the comparison experiments. However, there are large fluctuations in the objects of \(AP_{L}\) in the experiments, which may be due to the fact that the proportion of small objects in the SAR and optical image experimental datasets is significantly higher than large objects. This may be one of the reasons for the fluctuations.  
Although our approach has achieved good results in the IS of SAR images, the dataset for the SAR images is limited, and the annotations in the original images are not detailed enough. As a result, the final training results may not always perfectly fit the instance objects. Therefore, we are considering expanding the SAR image dataset in the future  
Figure 9: Qualitative experimental results on the NWPU-VHR-10 dataset. (**a**) Shows the ground truth annotations of the objects; (**b–g**) Shows the results of the other methods, Cascade Mask-RCNN, Mask-RCNN, MS-RCNN, HTC, SC-Net, and Insta-Boost, respectively; (**h**) Shows the results of our QCIS-Net. We use green boxes to represent missed detections, yellow boxes to represent mixed detections, and false detections with blue boxes.  
and planning to annotate the dataset with more precision. In addition, initializing the object query as zero during training can lead to the relatively slow convergence of the network. Hence, we plan to use the pyramid layer to output feature maps at different levels. By utilizing instance activations from different levels of feature maps, we can guide the initialization process of the query. This allows the query to possess semantic information and instance cues from the image during initialization, thereby accelerating the model's convergence speed. The cascade architecture can give decent results for the network, but it still affects the inference speed, which is not suitable for use on mobile devices. Therefore, building real-time inference models through lightweight model techniques, such as knowledge distillation, becomes one of the priority tasks in our future work.