esa-sceva/satcom-chunk-collection huggingface dataset:
# Integration and Field Evaluation of an IoV System for Enhancing Road Safety
## 1 Introduction
Another important aspect of C-ITS is communication, with the recent focus being in the development of Vehicle Ad-hoc Networks (VANET), an adaptation of Mobile Adhoc Networks (MANET) for ITS. Several challenges are associated with VANET, such as pattern mobility, reply RSU and high movement speed among others, while various approaches have been developed in order to enhance VANET's ability and capacity, which is well documented in [22]. The ODOS2020 project introduces an integrated IoV system comprising several On-the-Road Units (ORUs) for collecting data from sensors embedded seamlessly on the road, calculating various passing vehicle movement parameters, such as their movement direction and speed, while sending information to a Road-Side Unit (RSB) for further analysis. The end users are informed via a dedicated Human-Machine Interface (HMI) that communicates with the RSB warns the end user using visible and audible stimuli. The main novelty of the developed system lies in its versatility in monitoring various types of vehicles, such as cars and motorcycles and in effectively warning their drivers regarding potentially dangerous road behavior, despite the type, age and communication systems of their vehicle. At the same time, it is scalable, as the system's core architecture can be expanded straightforwardly, and is easy to install. The present work focuses on the ODOS2020 system integration and evaluation in real use case scenarios. The overall system architecture and the characteristics of each one of its modules are presented in Section 2. The Human-Machine Interface used for communication with the end users is presented in Section 3. Dedicated pilot tests were performed in one of the biggest motorways in Athens, Greece, testing the entire system's performance on various real use case scenarios. The field evaluation plan, alongside the corresponding results, including technical performance and user acceptance results, is presented in Sections 4 and 5, respectively. Section 6 includes the discussion regarding the test results and presents challenges and the authors' future plans regarding further expansion and improvement of the developed system, while Section 7 summarizes the most important points of the presented work.
# Inter-Data Center RDMA: Challenges, Status, and Future Directions
## 4 Existing Representative Works
### LoWAR  
Unlike any of these jobs mentioned above, LoWAR [51] is designed for lossy networks and focuses on addressing the performance challenges of RDMA in lossy WANs. LoWAR inserts a Forward Error Correction (FEC) shim layer into the RDMA protocol stack. This layer contains both TX and RX paths with encoding and decoding controllers.  
Figure 7 describes the design diagram of LoWAR. The sender encodes data packets into coding blocks and generates repair packets. When packets are sent, a repair header extension is used to negotiate FEC parameters between the sender and receiver. On the receiving side, the RX path decodes the incoming data packets and uses the repair packets to recover lost data. The decoding controller distinguishes between data and repair packets and stores the data packets in a decoding buffer. If packets are out of order, they are held in a reorder buffer. Once the bitmap confirms that the repair is complete, the packets are  
Figure 6: Design diagram of BiCC (adapted from [50], Figure 5).  
delivered to the upper RDMA stack in the correct order, ensuring reliable data transmission even in the presence of packet loss.  
The authors implement LoWAR prototype on FPGAs and set up the experiment in a lossy 10 GE WAN environment with different RTTs of 40 ms and 80 ms and various packet loss rates ranging from 0.001% to 0.1%. The results show that LoWAR significantly improved RDMA throughput. In the 40 ms RTT and 0.001-0.01% loss rate scenario, the throughput was 2.05-5.01 times that of CX-5. In the 80 ms RTT, the improvement was even more pronounced, reaching 11.55-19.07 times.  
However, the transmission frequency of repair packets, determined by real-time feedback such as the RTT and packet loss rate, cannot withstand bursty continuous packet losses. To address such bursts, the transmission frequency of repair packets must be increased, resulting in significant additional bandwidth overhead. Concurrently, LoWAR's packet loss detection relies on bitmaps, and the generation of repair packets and loss packet recovery depends on the computing and storage resources of the hardware. In high-bandwidth scenarios, the resource consumption is exceedingly high. Thus, LoWAR is better suited for environments experiencing significant packet loss and limited bandwidth.
# A Novel Searching Method Using Reinforcement Learning Scheme for Multi-UAVs in Unknown Environments
## 2 Materials and Methods  
In this paper, for a specialized sea area \(E\) which is built using two-dimensional coordinates, there are unknown targets \(\{Target_{i},i=1,2,\ldots,N_{t}\}\), unknown no-fly zones \(\{Mence_{i},i=1,2,\ldots,N_{m}\}\), and known homogenous UAVs \(\{V_{i},i=1,2,\ldots,N_{v}\}\), where \(N_{t}\), \(N_{m}\), and \(N_{v}\) are the numbers of targets, no-fly zones, and UAVs, respectively. Airborne UAVs enter the sea area where the mission needs to be carried out. After that, each UAV uses its own onboard sensor to search the unknown targets independently. It is expected that multiple UAVs can find as many targets as possible through cooperative search in the shortest time with the least cost. In this research, we assumed that all UAVs could communicate through a relay station to ensure that the communication between the UAVs was normal. Below, we provide detailed descriptions of our environmental model, our collision avoidance strategy, the aerial vehicle model, and our objective.
###### Abstract  
A medical device will emit electromagnetic radiation to its surrounding environment either actively or passively. However, clinicians are unaware as to whether the ambient electromagnetic radiation will exceed the human body's endurance capacity. In this paper, the mathematical model of electromagnetic parameters devoted to Specific Absorption Rate (SAR) testing of medical devices was established using a Debye Model. Body liquids featuring dielectric properties including the conductivity and permittivity of tissues at various body parts were simulated on the basis of results derived from the model. A simplified anthropomorphic phantom for the SAR test was founded on the basis of geometric parameters by following the principles of resemblance and consistent conductivity. A full-band electromagnetic mathematical model of brain, muscle, heart, lungs, stomach, and kidneys was set up. Electromagnetic radiation levels of a wearable Electrocardiograph monitoring device were measured and found that the maximum electric field intensity was up to 30 V/m, and the electromagnetic radiation SAR value was 0.96 W/kg, which were equivalent to the electromagnetic radiation exposure of the occupational group. The results established that electromagnetic radiation of certain medical devices exceeded the allowed values specified by the World Health Organization (WHO). Therefore, further studies within the field of medicine are required to decide whether additional evaluation measures should be required.  
electromagnetic exposure; anthropomorphic phantom; specific absorption rate; medical device; Debye Model
# FPGA Implementation of a Deep Learning Acceleration Core Architecture for Image Target Detection
## 2 Proposed Method
### Computational Scheduling Kernel Pipeline Design
#### 2.2.3 Partial Loading and Partial Calculation of Data within Layers  
Due to the limited on-chip BRAM storage space, the data and parameters cannot be simultaneously loaded into the data storage area. If the data is accessed directly from DRAM, it will greatly slow down the operation efficiency of the acceleration core. Considering that the output of the convolutional layer and the fully connected layer is independent, the acceleration core designed in this paper adopts a partial loading and partial computation strategy, splitting the independent data and only computing the loaded data in each computational kernel inference, then storing the output data in the DRAM. The output data is then stored in the DRAM at the corresponding location to reassemble the output into a complete computational layer. On-chip storage includes the parameter buffers, the data loading buffers, and the data storage buffers, which must be treated separately. If the storage space is too large, it will take up too many BRAM resources on the FPGA, affecting the potential of the acceleration core to scale parallelism for more computationally intensive models; if the storage space is too small, the on-chip BRAM will not be able to load the input data required at a time. This  
Figure 9: Array_partition constraint effect. situation would increase the bandwidth requirements of the acceleration core and increase unnecessary parameter loading time. Considering that the input tensor size of the FC17 layer in the TinyYolo network is 1 \(\times\) 1 \(\times\) 50,176, which requires the most significant data load buffer with a depth of 6272 at 128 bit width, this paper sets the data loading buffer bits in depth to 2048 \(\times\) 4. In addition, the data storage buffer is 128 bits in width and 8192 bits in depth, and the parameter storage is 128 bits in width and 2048 bits in depth. This paper shows the partial loading strategy for each layer of the TinyYolo network inference in Table 1.  
As seen from Table 1, the parameter buffers, the data loading buffers, and the data storage buffers were not overflowed during the inference process of the whole TinyYolo network by the partial loading and partial computing strategy, and the BRAM resource consumption of the whole acceleration core is kept within a reasonable range. In order to make the access bandwidth of data meet the arithmetic power requirement of the CNN algorithm after being accelerated, the access method of data must be specially designed, for which we propose three methods: data streaming, BRAM expansion, and partial calculation, which can significantly improve the matching requirement of data and arithmetic power so that the CNN algorithm can execute almost at total capacity. Traditional methods have yet to be studied in this area.
## 6 Experiments
### What-If Scenario
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{2}{c}{**Objective Function**} & \multicolumn{2}{c}{**Generation**} & \multirow{2}{*}{**Sample Efficiency**} & \multicolumn{2}{c}{**Infeasible Allocation**} \\ \cline{2-2} \cline{5-7}  & **Avg** & **Std** & & & **Avg** & **Std** \\ \hline PPO & 3.9320 & 0.1915 & - & - & 36.27 (46.82) & 16.67 & 6.42 \\ DQN & 1.6071 & 0.3228 & - & - & 128.5 (25.37) & 293.13 & 52.55 \\ GA & 3.4371 & 0.1398 & 33.5 & 11.0383 & 4288 & 398.87 & 11.04 \\ PSO & 4.1917 & 0.0927 & 46.0667 & 4.4793 & 1843 & 340.47 & 36.15 \\ QPSO & 4.3677 & 0.0159 & 37.0 & 10.017 & 1480 & 64.43 & 19.67 \\ MPSO & 4.3771 & 0.0123 & 37.16 & 8.3463 & 7432 & 66.40 & 18.53 \\ GWO & 4.0910 & 0.2003 & 48.2 & 3.4780 & 1446 & 230.33 & 32.38 \\ GA-ECT & 2.2287 & 0.4854 & 36.6633 & 10.2166 & 4693 & 199.97 & 30.69 \\ PSO-ECT & 3.9613 & 0.2053 & 49.33 & 0.9222 & 1973.20 & 33.90 & 14.16 \\ QPSO-ECT & 4.1593 & 0.1512 & 45.7333 & 4.1848 & 1829 & 0.0 & 0.0 \\ MPSO-ECT & 4.2681 & 0.0313 & 47.3333 & 7.4664 & 1493 & 0.0 & 0.0 \\ GWO-ECT & 4.1297 & 0.0727 & 48.9667 & 1.16078 & 1469 & 0.7 & 0.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of the average best solutions and the sample efficiency for the chosen algorithms in the what-if scenario. the what-if experiment. On the other hand, the training of CI algorithms does not create a knowledge base that these algorithms can exploit when the scenario changes remarkably. However, the cold restart technique was effective in re-optimizing the allocation of service instances.
# Research on Large Divergence Angle Laser Ranging System +
## 5 Results  
Inter-satellite distance measurement is a primary focus in applications such as formation flying of small satellites and satellite constellations. Since both the ranging platform and the target are in high-speed motion, it is challenging to achieve high-precision tracking and aiming, which makes reliable distance measurement difficult. To address this issue, this paper proposes a technical solution that simultaneously increases the laser divergence angle of the laser rangefinder and the field of view (FOV) of the receiving optical system. The design of the Er-glass laser's beam-shrinking emission optical system and the wide-FOV receiving optical system is detailed in the paper, achieving a laser divergence angle of 26 mrad and a receiving FOV of 30 mrad. Field tests of the ranging prototype demonstrate that the rangefinder can stably measure the distance to a signal tower target at 709 m with an aiming axis deviation of 0.6\({}^{\circ}\), and under this test condition, a ranging accuracy of \(\pm\)0.5 m can also be achieved. This demonstrates that increasing both the laser rangefinder's beam divergence angle and receiver field of view enables precise target ranging without requiring full optical alignment. Field testing confirms that the design of the ranging system reduces the requirement for aiming precision of the target and can reliably measure distances in scenarios where both the ranging platform and the target are in high-speed motion.  
Conceptualization, S.Y.; methodology, J.J.; validation, J.J. and Y.F.; editing, J.J.; visualization, J.J. and S.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 3 Results and Discussion  
### Characterization of Cattle Sera by ELISA Method  
A cattle serum panel consisting of 83 positive samples and 50 samples from healthy animals was characterized by the ELISA method to determine the titers of specific antibodies and confirm the preliminary diagnosis. All sera with an antibody titer higher than the antibody titer in a standard weakly positive cutoff serum were considered positive. Quantitative values of ELISA and ICA for 83 tested positive cattle sera are presented in Table 11. Although these levels are not strictly proportional (since in ICA reagents interact in the kinetic mode, and the overall level of immunoglobulins affects the result), the groups of samples with low signals intersect well.  
Because the main problem of serodiagnostics by the ICA method is poor staining for weakly positive sera, three sera with the lowest antibody titers exceeding the threshold level were selected from the panel. The results of ELISA testing of these sera, together with the cutoff serum and negative control, are presented in Figure 1. These weakly positive sera were used as control bioassays to compare the sensitivities of the three ICA systems.  
Optimization of the Concentration of GNP-(Protein G) Conjugate and LPS B. Abortus in the Immunochromotographic Serodiagnostics of Cattle Brucella  
The dependence of the staining of the analytical zone of the test strip on the concentration of the GNP-(protein G) conjugate was studied to choose the optimal concentrations of reagents used in the final configuration of the test system. For this purpose, the GNP-(protein G) conjugate was tested at five dilutions having D\({}_{520}\) values 1, 2, 5, 10, and 20 and at two antigen concentrations, namely, 0.16 and 0.32 mg/mL. The obtained data (Figure 2) demonstrate that with the growth of the GNP-(protein G) conjugate concentration the color intensity of the analytical zone increases, but nonspecific staining of the working membrane also increases. When the conjugate is concentrated to D\({}_{520}\) = 20, incomplete removal from the conjugate pad is also observed, which may be associated with partial aggregation of the conjugate. As a result of the above, a working dilution of the GNP-(protein G) conjugate corresponding to D\({}_{520}\) =10 was chosen for the final configuration of the test system.  
Figure 1: Enzyme-linked immunosorbent assay (ELISA) detection of antibodies against LPS of _Brucella abortus_ in cattle serum.  
Figure 2: Testing a serum sample by gold nanoparticles (GNP)-based immunohistochemical system with varied D\({}_{520}\) of the GNP-(protein G) conjugate and varied antigen concentrations.
# Making a Case for Hybrid GFRP-Steel Reinforcement System in Concrete Beams: An Overview
## 2 Background
### Fibre Reinforced Polymers (FRP) as Alternative Reinforcement in RC Beams
It is generally observed that the durability mechanism of GFRP is largely based on resin hydrolysis, fibre degradation, and interfacial bonding behaviour after exposure to hygrothermal conditions and fatigue regimes. This clearly highlight the importance of materials design for GFRP to using materials that can withstand such environmental degradative actions by choice and design of resin mixes. Other works [68, 69, 70, 71] suggested that using nano-composite and E-glasses advantages the durability of GFRP in harsh environments. Additionally, some studies [72, 73] recommended carbon/glass fibre-reinforced polymer hybrid (HFRP) in critical applications such as oil wells and bridges to address these durability issues. However, while there may be concerns about the limitations of GFRPs on their long-term durability, there are pieces of evidence presented [74, 75, 76] that has highlighted GFRP advantages over steel rebars in tackling durability concerns in RC structures. Though GFRP only shows half the compression strength of steel, it is not a disadvantage since reinforcements are meant to take tensile loads, and for that reason, its functionality as a reinforcing element is enabled by its proven tensile strength [77]. However, to achieve similar elastic-plastic behaviour of steel RC systems, some studies [78, 79, 80] experimented with combinations of different types of FRP reinforcements. Although such combinations gave good results in laboratory tests and in computer simulations, the high initial cost restricts the use of non-GFRP composites in commercial applications [81]. The ratio of post-yield deformation to yield deformation characteristics favours the steel to govern the ductility in RC structures [82]. A series of investigations have identified that ductile response of steel can effectively be initiated in concrete beams with hybrid GFRP-steel reinforcements [83, 84, 85, 86, 87], therefore achieving enhanced ductility compared to GFRP-only RC beams. Current design standards, such as those of the American Concrete Institute [88] allow hybrid GFRP-steel solutions in large-scale concrete works. Though there  
Figure 4: Young’s Modulus vs. Density (Generated by CES EduPack, 2015). is convincing evidence on the workability of hybrid GFRP-steel RCs, the research works are moderately limited. There is still a need to understand more about their structural response and how they could be optimised. This paper reviews the literature on hybrid GFRP-steel reinforcement systems and provides an outlook for future research.
# A Sustainability Analysis Based on the LCA-Energy-Carbon Emission Approach in the Building System
## 4 Results and Discussion
### LCA-Carbon Emission Analysis
#### 4.2.4 The Carbon Emission in the Building Renewal Stage  
There are three categories of upgrading strategies that focus on sustainable goals. Design strategy 1 revolves around green vegetation, including measures such as vertical landscape walls, rooftop gardens and sunken plaza gardens. Design strategy 2 emphasizes equipment upgrades, such as adding solar photovoltaic power generation devices, rainwater collection systems, heat pump technology utilization and updating the fresh air system. Design strategy 3 aims to improve the spatial performance of the building complex, involving the replacement of energy-saving walls and the use of phase change storage walls, etc.  
From the view of a renewal operation, three scenarios have been executed. The specific data and calculation processes are displayed in Table 9. In Figure 14, the change trend is clear. Updated scenario 3 discharged 2584 \(\text{CO}_{2}\), more than updated scenario 1 (1650.3 \(\text{CO}_{2}\)) and updated scenario 2 (1092.2 \(\text{tCO}_{2}\)). This update process is designed for a usage of 20 years.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Item** & **Data** & **Unit** & \
\begin{tabular}{c} **Carbon Emission** \\ **Factors** \\ \end{tabular} &
\begin{tabular}{c} **Carbon** \\ **Emission** \\ \end{tabular} & **Unit** \\ \hline \multicolumn{6}{c}{Updated Scenario 1} \\ \hline PVC & \(1.14\times 10^{4}\) & Kg & 4.79 kg\(\text{CO}_{2}\)/kg & 54.606 & \(\text{tCO}_{2}\) \\ \hline Cement & \(4.72\times 10^{5}\) & Kg & 0.07 \(\text{tCO}_{2}\)/t & 33.04 & \(\text{tCO}_{2}\) \\ \hline Water & \(9.52\times 10^{3}\) & Kg & 0.82 kg\(\text{CO}_{2}\)/m\({}^{3}\) & 7.8064 & \(\text{tCO}_{2}\) \\ \hline \multicolumn{6}{c}{Diesel fuel} & \(6.76\times 10^{6}\) & Kg & 0.23 \(\text{tCO}_{2}\)/t & 1554.8 & \(\text{tCO}_{2}\) \\ \hline \multicolumn{6}{c}{Updated Scenario 2} \\ \hline Bricks & \(5.67\times 10^{4}\) & Kg & 0.24 kg\(\text{CO}_{2}\)/kg & 13.608 & \(\text{tCO}_{2}\) \\ \hline Concrete & \(3.71\times 10^{5}\) & Kg & 0.13 kg\(\text{CO}_{2}\)/kg & 48.23 & \(\text{tCO}_{2}\) \\ \hline \multicolumn{6}{c}{Diesel fuel} & \(4.48\times 10^{6}\) & Kg & 0.23 \(\text{tCO}_{2}\)/t & 1030.4 & \(\text{tCO}_{2}\) \\ \hline \multicolumn{6}{c}{Updated Scenario 3} \\ \hline Glass & \(6.15\times 10^{4}\) & Kg & 1.4 kg\(\text{CO}_{2}\)/kg & 86.1 & \(\text{tCO}_{2}\) \\ \hline Aluminum & \(2.36\times 10^{1}\) & Kg & 15.8 \(\text{tCO}_{2}\)/t & 372.88 & \(\text{tCO}_{2}\) \\ \hline Copper & \(1.73\times 10^{1}\) & Kg & 3.73 \(\text{tCO}_{2}\)/t & 0.065 & \(\text{tCO}_{2}\) \\ \hline \multicolumn{6}{c}{Diesel fuel} & \(9.24\times 10^{6}\) & kg & 0.23 \(\text{tCO}_{2}\)/t & 2125.2 & \(\text{tCO}_{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 9: The carbon emission of the building renewal stage.  
Figure 13: The carbon emission in the building operation stage.