esa-sceva/satcom-chunk-collection huggingface dataset:
# Non-Ionizing Radiation Measurements for Trajectory Radars
## 4 Results and Discussion
### Analysis and Discussion
#### 4.3.2 Measurements and Limits  
Comparing the results of measured power density with the ICNIRP's [3] and Anatel's [4] limits (Table 1 or the last column of Table 4), the values for all measurement points are below the limit for occupational public (50 W/m\({}^{2}\)). However, for general population power density limit (10 W/m\({}^{2}\)), the measured value at P01 is above the limit. It evidences that the general population cannot have access to the P01 area. In addition, as stated by regulations, the occupational public needs to be warned about the non-ionizing radiation risk at point P01 and the permanence time at this area needs to be controlled when the radar is in operation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Point**} & **Predicted Power Density** & **Measured Power Density** & **Anatel / ICNIRP Limits** \\  & **(W/m\({}^{2}\))** & **(W/m\({}^{2}\))** & **(W/m\({}^{2}\))** \\ \hline P01 & 270.92 & 27.66 & 10 */50 ** \\ P02 & 1.31 & 1.29 & 10 */50 ** \\ P03 & 2.53 & 0.09 & 10 */50 ** \\ P04 & 0.49 & 0.42 & 10 */50 ** \\ \hline \hline \end{tabular}  
* General limit/** Occupational Limit.  
\end{table}
Table 4: Predicted and Measured Power Density.
# DOA Estimation of GNSS Signals Based on Deconvolved Conventional Beamforming
## 3 The Proposed Method
### The Implementation Process of the Method  
The implementation process of this method is shown in Figure 7.  
(1) The received array signal is processed by the RF front-end module. After low-noise amplification, filtering, down-conversion, and sampling, the RF signal converts to the baseband signal, \(\mathbf{x}(t)\).  
(2) Generate the local replica signal, \(s_{L}(t)\), for de-spreading the array signal.  
(3) Compute the correlation value vector, \(\mathbf{r}(\varepsilon)\), by multiplying the replica with the array signal.  
Figure 5: Super-directional beam pattern: (**a**) the comparison of CB and DCB; (**b**) the comparison of DCB under different numbers of array elements.  
Figure 6: Spatial spectrum estimation results with different DOA estimation methods (the circles represent the true DOA parameters): (**a**) estimation results under the condition of only a single direct signal; (**b**) estimation results under the condition of one direct signal and one multipath signal.  
(4) Generate the array beam pattern, \(Y(\theta)\), by employing the procedure outlined in Equation (8), through which the angle, \(\theta\), is scanned from \(-90\) degrees to \(90\) degrees.  
(5) Generate the point spread function, \(Y_{p}(X)\).  
(6) Estimate the DOA parameters using the deconvolved conventional beamforming method.
## 1 Introduction
### Emergency Braking System  
AEBSs are among the best collision-avoidance systems you can have in your car, according to numerous studies from Europe and other nations. The Insurance Institute for Highway Safety and the Highway Loss Data Institute conducted one of the most recent studies in April 2019 and discovered a 50% decrease in front-to-near collisions and a 56% reduction in front-to-near road accidents with injuries for vehicles equipped with the forward warning and AEBS [4].  
Modern technology commonly employs lidar, cameras, and radar to find obstacles in emergency braking situations systems. The faster the speed of the vehicle, the higher the chance that the AEBS will be able to stop it in time to avoid a collision. An essential part of automotive safety technology is an automated braking system [5]. It is an advanced technology that is intended to either prevent potential collisions or slow down a moving vehicle before hitting another pedestrian, or some other impediment or car. These systems use a combination of sensors, such as ultrasonic, video, infrared, or radar, to scan the area in front of the car for potential obstacles, and if any one of the obstacles is found, brake control is used to avoid a collision [6].  
Although the technology for automated braking systems varies depending on the automaker, all technologies start with sensory input. The system that uses radar, lidar, or cameras, to check whether anything is in front of the car, varies from manufacturer to manufacturer. For instance, the system analyses the likelihood of a collision based on the traffic in front of the vehicle. If an object is found, the system keeps measuring the sensor data directly. The AEBS measures the distance between both the vehicle and the object moving in front of the vehicle and calculates their relative speeds. If the system concludes that the vehicle's speed is greater than the speed of the identified item in front of the car, it can automatically apply the brakes to prevent a potential collision. An automated braking system can also communicate with a car's GPS and use its database of traffic signs and other data to apply the brakes quickly if the driver does not [7].  
The electronic control unit (ECU) on your car gives the AEBS access to additional data about it. The AEBS can evaluate whether the speed at which you are traveling has the potential to result in a collision by factoring in your vehicle's speed and measuring  
Figure 1: Overview of the AEB process [3].  
the distance to the object detected in front of the vehicle. When an impending collision is identified, the AEBS will examine the braking systems. The AEBS will not step in if you have already applied the brakes and are slowing down sufficiently to avoid the crash. However, if you have not applied the brakes or have not done so with enough force because of the approaching obstacle or vehicle, the AEBS will take control and apply the brakes for you [8].
## 1 Introduction
A great deal of progress has also been made in numerical techniques to get reliable results with low computational cost [23]. Therefore, the CFD is now widely applied by choosing proper numerical methods in the environmental field to deal with such complex systems. Incorporating fluid dynamics in modeling of chlorine fate in cooling systems could help to configure in-situ electro-chlorination systems. Several recent studies have focused on CFD simulation coupled with disinfection models. Such works aim to increase disinfection efficiency and reduce disinfectant dosing and byproduct formation [24; 25; 26; 27] and are mostly focused on the impact of flow patterns on disinfectant behavior. Computational fluid dynamics considering chemical reactions in water supply systems has remained in its early stage due to challenges associated with validating and accurately defining unsteady flow rates and multi-phase flow conditions [24]. Nevertheless, CFD is considered an effective tool for modeling chlorine distribution through a system and its use is of interest [28]. In this study, we evaluate in-situ electro-chlorination in a pilot-scale cooling system using a CFD code including the chlorine decay kinetics in predicting the behavior of generated chlorine. Specifically, a cylindrical electrode was installed inside the water pipe to generate chlorine. The flow pattern of the cooling water through the pipe was modeled using the CFD code as well as the chlorine concentration of the cooling water flowing along the pipe. The chlorine concentration measured experimentally from the pilot-scale cooling system was then compared with simulation results. We believe that our study is the first attempt to accurately simulate chlorine behavior in a cooling system using a model integrating chlorine decay kinetics and CFD.
###### Abstract
The emergence of YOLOv5 algorithms in 2020 has catalyzed extensive research on construction worker safety monitoring. [PERSON] et al. [14] augmented YOLOv5 with a super-resolution reconstruction module to preprocess low-quality images prior to detection head processing, demonstrating measurable performance improvements in subsequent experiments. In parallel, [PERSON] et al. [15] established a helmet detection system leveraging YOLOv5 variants, reporting 95.8% mAP with YOLOv5x and 70.4 FPS with YOLOv5n in controlled trials. Architectural modifications have been systematically explored: [PERSON] et al. [16] extended YOLOv5's architecture by adding a fourth detection scale for small-object recognition, while integrating attention mechanisms into the backbone network. Complemented by specialized data augmentation and transfer learning strategies, their framework achieved substantial accuracy gains in helmet usage detection. Addressing environmental complexity, [PERSON] et al. [17] designed YOLOv5-GBCW through backbone reconstruction and Coordinate Attention (CA) module integration, validating its efficacy across diverse hazardous environments, including construction sites and underground mines. Computational efficiency improvements have received equal attention: [PERSON] et al. [18] optimized YOLOv5s by substituting its backbone network and implementing the Convolutional Block Attention Module (CBAM), yielding a 42% reduction in computational load and a 40% decrease in model size without compromising detection accuracy. For comprehensive behavior analysis, [PERSON] et al. [19] developed an enhanced YOLOv5 variant capable of recognizing 12 distinct unsafe behaviors under adverse conditions, including low illumination and partial occlusion, significantly advancing safety monitoring in complex industrial settings. Specialized applications have further demonstrated the algorithm's adaptability: [PERSON] et al. [20] incorporated Swin Transformer architectures with attention mechanisms to address small-target detection challenges in large-scale hydraulic projects, markedly improving recognition accuracy for densely distributed safety helmets. This paper summarizes the limitations of existing models, as shown in Table 1.  
Building upon preceding research advancements, this study presents an enhanced YOLOv5s-based detection framework incorporating three strategic enhancements to the baseline architecture. The optimized framework achieves enhanced detection accuracy, while maintaining real-time processing capabilities, thereby enabling effective identification of construction workers' unsafe behaviors in operational construction environments.
## 3 System Model and Problem Formulations  
In this section, a proposed system model is defined and proposed as an optimization problem formulation. 5G has diversified features such as higher speed, massive connectivity, and very high reliable service, and to meet these requirements, 5G introduces new technologies such as SDN, NFV, network slicing, MIMO, millimeter band, and Heterogeneous Network (HetNet). Due to the use of millimeter waves, a higher data rate can be achieved. Still, it has a smaller coverage area, and there is a need for a more significant number of millimeter wave BTS, resulting in higher costs. In highly dense areas, it is advantageous to deploy the millimeter band, but if there is an uneven distribution of population,the coverage becomes an issue, and the millimeter wave will not be cost-effective; there is a need to use mid-band frequencies to have broader coverage. In this paper, a mixed cell architecture of macro-RRU using sub-band 3.5 GHZ and micro-RRU using 28 GHZ is implemented to provide the required data rate and ensure full coverage. Figure 1 shows the proposed mixed cell structure, where RRU\({}_{\text{m}}\) are placed in dense areas, and RRU\({}_{\text{M}}\) are placed in sparsely populated areas. We calculated the link budget for 3.5 GHz and 28 GHz, considering different losses encountered while propagating the 5G signal. We then computed the Maximum Allowable Path Loss (MAPL) and the corresponding maximum radius for coverage, maintaining QoS parameters for uplink and downlink. Capacity dimensioning was performed based on the demographic distribution and users' required data rate of at least 50 Mbps. The capacity and coverage dimensioning was carried out in 5G greenfield and 5G brownfield, i.e., using existing 4G infrastructure. The minimum number of RRUs after coverage and capacity dimensioning are fixed, and their best placement is optimized using metaheuristic algorithms. The best-optimized location of RRUs among them is selected, maintaining the requirement of both an average data rate of 50 Mbps and coverage of at least 98%. If the criteria are unmet due to coverage holes, the RRUs are added iteratively. The comparative analysis of 5G greenfield and brownfield is performed, and mixed deployment of both FR1 and FR2 is implemented if it is best result-oriented. Carrier Aggregation (CA) is carried out to minimize the number of RRUs without degrading QoS. Figure 2 shows the proposed framework of 5G network RRU placement optimization, demonstrating the thoroughness of our approach. Table 1 shows notations and corresponding descriptions. Based on the third generation partnership project (3 GPP TR-38.901) specifications [47], we carry out link budget calculations for both the frequencies FR1 (3.5 GHz) and FR2 (28 GHz), respectively.
# Blockchain-Driven Intelligent Scheme for IoT-Based Public Safety System beyond 5G Networks
### Applications of Blockchain in 6G Network
#### 1.1.2 Smart Manufacturing  
In the real-world scenario, massive amounts of data are exchanged between the devices for large-scale manufacturing. Therefore, machines have the interaction capability to cooperate via the Industrial Internet in the manufacturing industry. In order to improve the performance of the entire manufacturing process, machines autonomously make local decisions [13]; however, there is a requirement for always-on connectivity, a higher data rate, more reliability, and ultra-low latency in the manufacturing industry. The 6G mobile communication techniques are developed to target the aforementioned manufacturing industrial services; however, there are security issues in the manufacturing industry where a malicious user could tamper with the manufacturing data. Therefore, blockchain-based networks are integrated into manufacturing systems that offer cybersecurity guarantees via smart contracts, hash functions, digital signatures, and immutable digital ledgers to tackle security and privacy issues. Recently, [PERSON] et al. [14] incorporated digital twins with decentralized permissioned blockchain networks to build better mutual trust and data flow in manufacturing processes. They proposed a ManuChain prototype to realize lower- and upper-level intelligence for crowd self-organize and holistic optimization. Their proposed scheme aided a new value for transforming and upgrading manufacturing systems. It also enhanced the efficiency of the manufacturing process.  
Recently, blockchain has been investigated and integrated with mobile edge computing (MEC) to overcome the computational workload. It reduces time complexity and significantly improves system performance. In [15], the authors presented a smart manufacturing system by integrating MEC with blockchain technology. They also proposed a swarm-intelligence-based optimization model with numerous tasks that enhance the processing time. Blockchain offers device-level information transmission and manufacturing service transactions in their proposed approach. Then, [PERSON] et al. [16] proposed asmart manufacturing scheme using distributed ledger technology that supports on-demand production. They introduced a blockchain-embedded smart manufacturing system that enhances manufacturing efficiency. They investigated latency and limited throughput issues in the blockchain network. Therefore, they proposed a deep Q-network that maximizes the user's net profit. Their results show that their proposed framework is highly scalable. It offers consistency, availability, and privacy by incorporating off-chain storage, ledger isolation, and encryption mechanism.
## 2 Related Work  
### Commercial Products  
Several MARs have already been commercialized, mainly in the US and Western countries. The most commercially successful product is Obi [8], a 6-DOF robotic arm developed in the United States. This product uses a bowl divided into four compartments as a tray, and whenever the user presses a button interface, the bowl rotates so that the arm can take another food. Once a food acquisition command is given, the robot arm scoops the food with a spoon along predefined paths. Afterward, it brings the food to the user's mouth, the location of which is taught by the caregiver. However, despite these well-made products, only limited types of food can be served. Another US product, Meal Buddy [9], serves food using a 3-DOF robot arm with three independent bowls and spoons. Mealtime Partner [10] is also a US product, consisting of three bowls on the rotational plate and 2-DOF eating utensils. This product is placed right in front of the user's mouth and provides food to the user through a very simple movement of the spoon. Neater Eater [11], developed in the UK, consists of a rotating dish and a 3-DOF robotic arm and acquires food with a spoon. This product is characterized by a tablet interface that allows for the easy setting of various detailed robot functions. Bestic [12], from Sweden, consists of one rotational bowl and a 4-DOF robotic arm. It assists with simple meals using a spoon based on preset parameters.  
MARs developed in the Western world, mainly in the US, do not consider how to handle food such as rice or soup placed on a tray with separate compartments because it was developed to suit the food culture of putting food on a plate and eating it with a spoon or fork. On the other hand, the cases developed in Asia are slightly different. My Spoon [13], developed in Japan, consists of a rectangular tray divided into four compartments and a 5-DOF robot arm. It can handle various types of food using a fork and a spoon simultaneously as tongs. CareMeal [14; 15], from Korea, includes a tray with multiple compartments and two robotic arms. Each arm picks up food with tongs and serves it to the user's mouth with a spoon. These two products were developed to fit the Asian diet and have one thing in common: they use tools that can act as tongs or chopsticks to handle rice or vegetable side dishes. Although no device has commercialized at present, several studies have been aimed to create meal assistance robots that use chopsticks [16; 17; 18; 19]. Using chopsticks as tableware is not only more emotionally satisfying in Asian communities but also has the advantage of better handling Asian food.  
The commercialized MARs introduced above have common limitations. First, because they move along predefined paths, they cannot actively acquire food by recognizing the location of the actual food. In addition, since most are focused on foods that are easy to consume with a spoon, it is difficult to serve a variety of food. Finally, users that struggle with physical manipulation may have difficulty using buttons or joystic-type user interfaces.
## 3 Numerical Simulations
### Numerical Method and Model Validation  
#### 3.4.1 Numerical Method  
The finite element analysis software ANSYS Workbench 18.2 was used for the numerical simulation. The flow rate was set to 0.2 m\({}^{3}\)/h (with a corresponding internal heat source value of 8.5 \(\times\) 10\({}^{7}\) W/m\({}^{3}\)), and the thickness of the substrate was 1 mm. With a time step of 1 s, grid sizes of 21,120, 28,080, and 32,480 were used for the simulation. The changes in the coating temperature with time for each group of grids are shown in Figure 17. The results of the 32,480 grid simulation differed from those of the 21,120 grid simulation by a maximum of 3.31 \({}^{\circ}\)C. To ensure the accuracy of the numerical simulation, the 32,480 grid size was used for subsequent simulations.  
The changes in the coating temperature with three time steps (0.1 s, 1 s, and 10 s) are shown in Figure 18. During the coating heating process, the simulated coating temperature was lower when the time step was 10 s. Considering the time and accuracy of the simulation, 1 s was selected as the time step for subsequent simulations.  
Figure 17: Coating temperature versus time with different grid sizes.
## 2 System Model of MC-MDCSK-MCS
### Receiver  
The MC-MDCSK-MCS receiver's block diagram is shown in Figure 4. First, \(p_{r}(t)\), the received signal, is multiplied with the synchronized in-phase carriers. Then, the resultant signals are processed by matched filters and a sampler to obtain the recovered baseband signals. Received reference copies recovered from \(n\) subcarriers are collected and then averaged by the average block for noise smoothing. The obtained averaged reference signal are utilized for MCS-MDCSK demodulation. Correlation detection is carried between the averaged reference sequence and each estimated data sequence. Lastly, the resultant sequences experience the P/S block--a mode converter of a parallel mode to a serial mode--to recover the initial information packet.  
Figure 3: Block diagram of the MCS-MDCSK Modulation.
## Appendix A
If any coordinate  % (variable) is outside its upper limit Ub then a one appears in the  % corresponding column of J.  J = vo_tmp > Ub;  vo_tmp(J) = Ub(J); % Assigns the limit value to the coordinate  
vo(i,:) = vo_tmp; % Coordinate update by rows  end  
function [vs] = escala(n,NVAR,ve,vub,par)  % Function for variable scaling  %  % Input arguments:  % n (scalar) number of fireflies in each subpopulation % NVAR (scalar) number of variables  % ve (vector) optimization/design variables  % vub (vector) upper bound for design  % par (logic) parameter 1/0  % par = 1 ---> convert optimization variables to  % design variables  % par = 0 ---> convert design variables to  % optimization variables  %  %  
% Output arguments:  % vs (vector) design/optimization variables  %  
vs = zeros(size(ve));  
if par == 1  for iloop = 1:n  for jloop = 1:NVAR  vs(iloop,jloop) = ve(iloop,jloop). * vub(jloop);  end  end  elseif par == 0  for iloop = 1:n  for jloop = 1:nVAR  vs(iloop,jloop) = ve(iloop,jloop)./ vub(jloop);  end  end  else  disp('error in <escala.m> incorrectly defined argument <pars')  end  
function [m_max_est,param_max]-param_intrp(a,b,c,ma,mb,mc)  % Lagrangian interpolation  %  
% output arguments:  % m_max_est estimated average maximum  % param_max parameter value for m_max_est  %  
% Input arguments:  % a minimum parameter value  % b intermediate parameter value  % c maximum parameter value  % ma mean for a  % mb mean for b  % mc mean for c  medias = #(x) ma. * ((x-b). *(x-c))./ ((a-b). *(a-c)) +... mb.*((x-a). *(x-c))./ ((b-a). *(b-c)) +... mc.*((x-a). *(x-b))./ ((c-a). *(c-b));  X = linspace(a,c,100);  [m_max_est,I] = max(medias(X));  param_max = X(I);  end