esa-sceva/satcom-chunk-collection huggingface dataset:
# Object Detection in Drone Imagery via Sample Balance Strategies and Local Feature Enhancement
## 3 Methods  
Object detection from drone imagery is becoming increasingly useful in many industry scenarios. However, there are many small targets in the detection task. In addition, the variations in altitude, the object's scale, view angle, weather and illumination bring about more significant challenges than when using traditional object detection, such as when using ground-based cameras. In general, there are two technical routes, anchor-based and anchor-free. The anchor-based method can generate anchors to help achieve a high AP performance, such as Faster-RCNN [14], SSD [23], and other algorithms. However, with this method the aspect ratio needs to be artificially set, which means designing it in advance for any new datasets that have prior experience. In contrast, the anchor-free method does not require the design of anchor boxes, meaning that it has higher spatial freedom and is more suitable for target detection in UAV-based scenarios. Usually, there are two routes for anchor-free detection: Key-point based methods (e.g., Cornet [28] and CenterNet [29]) and segmentation based methods (e.g., FCOS [37], FSAF [35], Fovea [36], etc.). The keypoint-based methods enlarge the original image in order to improve accuracy based on the idea of crucial point detection, but at the same time, it increases the computing cost. On the other hand, the segmentation based method is mainly based on dense prediction to produce many false-positive samples, which thereby causes a higher recall and lower precision.  
Therefore, as Figure 1 shows, the framework of object detection from images captured by drones, which mainly consists of four parts: Feature extracting module, adaptive selection for positive and negative, and easy and hard samples, weight GANs, and classifications and regression. In this paper, we combine the idea of a keypoint-based method with the FCOS-based algorithm. Using weight-GANs, we are able to achieve a local enhancement of the feature map. Simultaneously, we design the sample selection strategies for positiveand negative samples and hard and easy samples to improve AP performance. Finally, we apply two branches to get the classification and regression results.
# Research Progress of Visual Inspection Technology of Steel Products--A Review
## 4 Detection Methods
### Statistics
#### 4.1.4 Local Binary Pattern  
The local binary pattern (LBP) is an operator that describes the local texture features of the image with rotation invariance and grayscale invariance. It is worth mentioning that the application of the LBP in classification recognition generally uses the statistical histogram of the LBP feature spectrum as the feature vector rather than the LBP feature spectrum itself. In order to improve the recognition rate, a new feature descriptor known as the adjacent evaluation completed local binary pattern (AECLBP) was proposed by [PERSON] et al. [16] for hot-rolled steel strip detection. In a recent study, an LBP operator with symbol and size, combined with a histogram and shape and distance statistic features, were developed by [PERSON] et al. [60]. On one hand, this method can solve multi-class classification problems; on the other, it also has an anti-noise ability and high classification efficiency. On the whole, the LBP method performs better than the co-occurrence matrix and filtering methods in accurately detecting the surface texture defects of steel.
# A Survey of Methods and Technologies for Congestion Estimation Based on Multisource Data Fusion
## 5 Representation of Methods Used in Data Fusion
The problem, in this case, is the division of cellular network in different cells, and that was achieved by division in so-called Voronoi cells, which partition the area into cells by observing coverage from a certain cell tower, located in the center of each cell. The authors proposed different models of combined usage of cellular and loop-based data but their problem was the actual availability of real cellular data for privacy reasons. This probably remains the largest problem in similar approaches because different countries have different restrictions for sharing even anonymized cellular data. [PERSON] et al. [89] propose an online information fusion approach for urban traffic state estimation. The approach consists of three parts of algorithms, including the evidential fusion, the data processing of loop detectors, and the data processing of GPS probe vehicles. In the application of the evidential fusion model, both loop detector data and GPS probe vehicle data are integrated so that the traffic states can be more comprehensively estimated and with more accuracy with using only one of them. The proposed approach can well balance the requirement between accuracy and real-time performance. [PERSON] et al. in [90] presented a flexible, modular, and computationally efficient software system. The system estimates multiple aspects of travel demand using Call Detail Records (CDRs) from mobile phones in conjunction with open- and crowd- sourced geospatial data, census records, and surveys. Authors used algorithms to construct O-D matrices, presenting route trip through a road network. Additionally, they presented an online, interactive visualization platform to communicate these results to researchers, policymakers, and the public. The system flexibility is tested on multiple cities around the globe.
###### Abstract  
In the proposed work, the MATLAB program was used to model and simulate the performance of the investigated two-stage adsorption chiller with and without heat recovery using an activated carbon/methanol pair. The simulated model results were then validated by the experimental results conducted by Millennium Industries. The model was based on 10 th order differential equations; six of them were used to predict bed, evaporator and condenser temperatures while the other four equations were used to calculate the adsorption isotherm and adsorption kinetics. The detailed validation is stated in the next paragraphs; for example, it clearly notes that the simulation model results for the two-stage air cooled chiller are well compared with the experimental data in terms of cooling capacity (6.7 kW for the model compared with 6.14 kW from the experimental results at the same conditions). The Coefficient of Performance (COP) predicted by this simulation was 0.4, which is very close to that given by the Carnot cycle working at the same operating conditions. The model optimized the switching time, adsorption/desorption time and heat recovery time to maximize both cooling capacity and COP. The model optimized the adsorption/desorption cycle time (300 to 400 s), switching cycle time (50 s) and heat recovery cycle time (30 s). The temporal history of bed, evaporator and condenser temperatures is provided by this model for both heat recovery and without heat recovery chiller operation modes. The importance of this study is that it will be used as a basis for future series production.  
adsorption chiller; two-stage; activated carbon/methanol; modeling; simulation; heat recovery +
Footnote †: journal: Applied Sciences
## 4 Results and Discussion
### Automatic Defect Recognition  
After the 2 nd de-noising was performed, the roundness metric was utilized for automated defect detection. The area and perimeter of each defect were calculated, and the roundness metric equation is as follows [45]:  
\[\text{metric}=\frac{4\pi\times\text{area}}{\text{perimeter}^{2}} \tag{11}\]  
The roundness metric means that the closer to 1, the closer to a circular shape, and it is calculated as the ratio of the object's perimeter to its area. The threshold value set in this study was 0.75, and defects were recognized based on the threshold value. In addition, if an object was lower than the threshold and it was not recognized as a defect, it was set so that it could be determined by 'EdgeDetection'. Figure 13 shows the automated defect detection images of the induction thermography performed in this study and the amplitude and phase images acquired using the optical infrared thermography in [24].  
A total of 10 defects were detected due to the threshold. All defects in row 1 failed to be detected, and this was because the thermal profile in row 1 in Figure 9 had a relatively small change rate. In addition, it can be confirmed that the effect of [PERSON]'s heating generated on the edge of the STS304 specimen was detected with 'EdgeDetection'.  
Finally, after the 2 nd de-noising was performed, the root-mean-square error (RMSE) was calculated to compare the detectability with the LIT technique, and the equation is as follows [46]:  
Figure 12: Binary image with morphology operation applied: (**a**) 5 mm/s, (**b**) 10 mm/s, (**c**) 15 mm/s, and (**d**) 20 mm/s.  
Figure 13: Automatic defect detection of binarized images using boundary tracking algorithm, (**a**) object recognition and edge detection of 5 mm/s, (**b**) amplitude image, and (**c**) phase image.  
\[\text{RMSE}(\theta_{1},\theta_{2})=\sqrt{\frac{\sum_{i=1}^{n}\left(\theta_{1,i}- \theta_{2,i}\right)^{2}}{n}} \tag{12}\]  
where \(\theta_{1}\) is real value, \(\theta_{2}\) is estimated value, and n is number of areas. The roundness metric is a dimensionless unit, and the RMSE was calculated as a percentage. Table 5 shows the RMSE values compared with the results of the LIT study conducted in [24].  
It can be seen that the defect detectability is slightly inferior to that of the optical technique. The electromagnetic technique has an advantage in that the SNR is relatively significantly improved after filtering is applied because there is little noise due to the uniformity of the heat source. Therefore, when image processing is performed in real time rather than in the form of an image, the electromagnetic technique can be considered suitable.
# Underlay Loosely Coupled Model for Public Safety Networks Based on Device-to-Device Communication
## 6 Simulation Results
### Control Traffic Simulation  
Simulations were carried out to measure the traffic generated by network control messages exchanged between UEs and the Application Server by keeping the same parameters used in the simulation experiments presented in Table 1. Figure 24 illustrates the traffic generated by notification messages sent by UEs to the server. The resulting outbound traffic increases rapidly and non-linearly with the progression of active UEs. However, the consumed bandwidth from the server side remains relatively very low (less than 45 kb/s for 70 UEs) compared with the data rates offered by cellular technologies (LTE and 5G), which are generally measured in tens or even hundreds of Mb/s. At the same time, we notice slight repetitive peaks in the curves, which are due to the notification messages sent by the server when a new user is added. The inbound traffic supported by the server is generated by update messages sent from UEs, as illustrated in Figure 25. We note that this traffic is almost proportional to the number of UEs, and it remains relatively very low (less than 1.3 kb/s for 70 UEs); therefore, we can conclude that it will be easily supported by the server.
# Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate
## 3 Satellite Measurement of Precipitation
### Scientific and Technological Challenges
#### 3.2.6. Observing Extremes
In particular, the study of atmospheric rivers (AR, [245; 246; 247]) as a major water vapor source will benefit from satellite observations [248], also in view of quantifying the role of ARs in fueling extreme events.
## 1 Introduction
They also investigated the contribution of each parameter to \(TWL\) using a full two-level factorial design method and found that the erosion-corrosion interaction had the greatest effect on \(TWL\). [PERSON] et al. [34] measured the erosion-corrosion rate on carbon steel pipe elbows phenomena caused by seawater with a sand loading ranging from 0 to 9 g/L. Their experimental results clearly indicated that the erosion-corrosion rate varies linearly with the flow velocity and sand loading for both laminar and turbulent flows. They also proposed a linear equation that could help to predict the erosion-corrosion rate. [PERSON] et al. [35] developed a semi-empirical model for corrosion dominated synergy of non-passivating erosion-corrosion of carbon steel using a slurry pot. The aim of this study was to yield an expression for erosion-enhanced corrosion which reveals the oxide-free surface. The developed model also accommodates the enhanced corrosion due to surface deformation caused by erodent particles. A number of studies [36; 37; 38; 39; 40] have aimed to develop empirical and mechanistic models and equations for different environmental conditions which could help in material loss prediction or material selection. However, a model which can predict the weight loss due to pure erosion (\(E_{o}\)) and \(TWL\) caused by impingement of aqueous slurries has yet to be developed. The main thrust of this study is to propose an empirical regression model that relates the \(E_{o}\) and \(TWL\) with the flow velocity and sand loading along with the fundamental investigation of the erosion-corrosion of mild steel in salty slurry (containing silica sand) using a submerged impingement jet apparatus. The influence of sand loading and flow velocity on weight loss caused by erosion-corrosion, pure erosion, and erosion-corrosion interaction has been investigated. Although the empirical model was not developed under field conditions, it will provide the means for a better understanding of the effects of sand loading and velocity on the erosion-corrosion of mild steel. The pure erosion study was carried out by chemically inhibiting the corrosion by the removal of dissolved oxygen. The surface profile of the specimen after the test was analyzed using Scanning Electron Microscopy (SEM).
# A Modeling Method of Agents and SOA in Advanced Avionics System Based on AADL
## 3 Agents and SOA Modeling Method Based on AADL
### Modeling Method for Agents  
Compared with traditional software, the agent can perceive the environment, solve problems by reasoning and respond to the external environment through actions. The working state of the agents will change autonomously according to the external input, leading to possible changes to the characteristics of the agents, such as software reliability, execution time and other attributes. Recent studies show that the component attributes have a significant influence on the quantitative analysis of the avionics system; as a result, the following section will stress the working state of the agents to describe and model the agents better.  
An agent can be defined as  
\[A=(X_{A},\ Y_{A},\ S_{A},\ U_{A}), \tag{1}\]  
where \(X_{A}\) is the input set and \(Y_{A}\) is the output set of the agent;  
\[S_{A}=\{s_{A1},\ s_{A2},\ s_{A3},\ \ldots,s_{An}\}, \tag{2}\]  
where \(S_{A}\) is the working state set of agent, \(s_{An}\) denotes nth working state.  
\[U_{A}=\left\{u_{A12},\ u_{A13},\ u_{A31},\ \ldots,u_{Aij}\right\}, \tag{3}\]  
where \(U_{A}\) is the set of trigger conditions of the working state transition; \(u_{Aij}\) denotes the trigger condition for transitioning from the ith working state to the jth working state.  
In the process of modeling the agent, the appropriate components need to be selected to describe the agent first. The agent in the avionics system is the software with specific functions, so the modeling method of software is used to describe the agent. Therefore, the agent is represented by a process component, which receives input or output through interfaces on the process component. The input elements in set \(X_{A}\) and output elements in set \(Y_{A}\) can be events, data or messages. Next, due to the multiple working states of an agent, the process component cannot describe its properties in different states, so modes are used  
Figure 3: Avionics architecture with agents and SOA.  
to represent the working states in set \(S_{A}\) at the component level. In different modes, the properties of the process component can describe the detailed characteristics of the agent through different attribute values. In the trigger condition of mode transition, specified events, data or messages, which are in set \(U_{A}\), can be set according to the characteristics of the agent to trigger the transition of the working mode of the agent. In Figure 4, the following three states of agent p1 are set through the mode component: mode1, mode 2 and mode 3, and three conditions, trigger1, trigger2 and trigger3, are set to trigger the transition of the agent state.
## 2 Literature Review
### Statistical Fitting Model  
Due to the mature development of statistical fitting modeling methods, advanced surface-roughness prediction methods have been developed. Experimental designs such as Taguchi method [24], Box-Behnken design [25], and central composite design [26] are commonly used, combined with RSM for modeling and subsequent analysis and prediction tasks. In the field of workpiece grinding surface-roughness prediction, statistical fitting modeling techniques have been widely used and have shown some progress in enhancing surface quality and optimizing process parameters.  
[PERSON] et al. [27] used the Taguchi-based multi-objective grey relational analysis method to optimize the turning process parameters of Inconel 718 alloy and simultaneously minimize surface roughness, tool wear, and machining time. However, the experimental data sample size was very small, so the model lacks widespread reference value. [PERSON] et al. [25] first combined laser-assisted machining and pure fast tool servo for the machining of glass-ceramic optical free-form surfaces, designed experiments using Box-Behnken design, and established a regression model of surface roughness using RSM. Nevertheless, the applicability and accuracy of laser-assisted machining are inferior to that of robot grinding, and the impact of experimental time factors and data sample size on modeling has not been taken into account. [PERSON] et al. [28] modeled and optimized the grinding process of hard and brittle materials using Box-Behnken design, multivariate nonlinear regression, response surface method, and Monte Carlo simulation in order to optimize the grinding parameters of a newly designed grinding wheel. [PERSON] et al. [29] utilize nondestructive testing, empirical models, and polynomial fitting to predict surface roughness and select the appropriate grinding parameters. By combining the response surface method with other techniques, they were able to improve the process's surface quality. However, these techniques are limited to a single material or processing method, and the modeling results they produced can only be used as empirical references--they are not easily transferable to the robotic grinding of small-batch workpieces.
## 4 Cluster-Based Distributed Compressed Sensing for QoS Routing
### Distributed QoS Routing  
Each node distributively selects the optimal next hop with the objective of minimizing energy consumption and satisfying QoS requirements in delay and reliability.  
Suppose a cluster head needs to forward a video frame to the sink. We define the set of selectable neighbors that are closer to the sink than itself and on the same side of the track of the event with the sink by \(F_{i}\). The next hop node \(v_{j}\) is selected from \(F_{i}\) according to the following rules:  
\[\text{Given }: v_{i},v_{j}\in F_{i},c\in C_{i,j},r_{i,j}^{c}\in R\] \[\text{Find }: v_{j}^{*},c^{*}\] \[\text{Minimize }:\ E\left(\frac{L}{p_{i,j}^{c}},d\left(v_{i},v_{j} \right)\right), \tag{18}\]  
subject to:  
\[\frac{L}{p_{i,j}^{c}\cdot r_{i,j}^{c}}<T_{i,j}, \tag{19}\] \[p_{i,j}^{c}\geq P_{i,j}. \tag{20}\]  
The local optimal next hop \(v_{j}^{*}\) is the node that results in the minimum energy consumption under local delay and local reliability constraints. A channel transmission rate, i.e., \(r_{i,j}^{c}\), is chosen from a set of channel transmission rates \(R\) belonging to \(\left\{R_{1},R_{2},...,R_{n}\right\}\).  
The objective of (18) is to minimize energy consumption for transmitting a packet of \(L\) bits data with channel transmission rate over a distance of \(d\left(v_{i},v_{j}\right)\). Constraint (19) is the local delay requirement. Constraint (20) is the local reliability requirement.
# Multi-Media and Multi-Band Based Adaptation Layer Techniques for Underwater Sensor Networks
###### Abstract  
In the last few decades, underwater communication systems have been widely used for the development of navy, military, business, and safety applications, etc. However, in underwater communication systems, there are several challenging issues, such as limitations in bandwidth, propagation delay, 3D topology, media access control, routing, resource utilization, and power constraints. Underwater communication systems work under severe channel conditions such as ambient noise, frequency selectivity, multi-path and Doppler shifts. In order to collect and transmit the data in effective ways, multi-media/multi-band-based adaptation layer technology is proposed in this paper. The underwater communication scenario comprises of Unmanned Underwater Vehicles (UUVs), Surface gateways, sensor nodes, etc. The transmission of data starts from sensor nodes to surface gateway in a hierarchical manner through multiple channels. In order to provide strong and reliable communication underwater, the adaptation layer uses a multi-band/multi-media approach for transferring data. Hence, in this paper, existing techniques for splitting the band such as Orthogonal Frequency-Division Multiple Access (OFDMA), Frequency-Division Multiple Access (FDMA), or Orthogonal Frequency-Division Multiplexing (OFDM) are used for splitting the frequency band, and the medium selection mechanism is proposed to carry the signal through different media such as Acoustic, Visible Light Communication (VLC), and Infrared (IR) signals in underwater. For the channel selection mechanism, two phases are involved: 1. Finding the distance of near and far nodes using Manhattan method, and 2. Medium selection and data transferring algorithm for choosing different media.  
multi-band; multi-media; adaptation layer; underwater communication; visible light communication (VLC); infrared (IR); acoustic +
Footnote †: journal: Applied
# A Novel Energy-Efficient Reservation System for Edge Computing in 6G Vehicular Ad Hoc Network
## 3 Methodology
### Propagation Models for Simulation
#### 3.15.3 Nakagami Fading Model  
The Nakagami model essentially a fading model that accounts for multi-path fading-induced signal intensity variations. Because the model is not well suited for simulations as a route loss model, it can be combined with other loss models to improve the performance [29]. It has a gamma distribution and a continuous probability distribution for various settings.  
\[p(x;m,\ \omega)=\frac{2m^{m}}{\Gamma(m)\omega^{m}}\ x^{2m-1}\ e^{-\frac{m}{ \omega}x^{2}} \tag{10}\]  
The fading depth and average power received are denoted by the \(m\) and \(\omega\) variables, respectively. Updating the gamma distributions yields the probability density function (pdf) at a given distance. As with a stochastic model, the message continues to follow a gamma distribution with average power fading and intensity with amplitude \(x\geq 0\). When \(m=1\), the Nakagami model transforms into the Rayleigh model.