esa-sceva/satcom-chunk-collection huggingface dataset:
# Resource Allocation and Offloading Strategy for UAV-Assisted LEO Satellite Edge Computing
## 5 Performance Evaluation
### Simulation Environment and Parameters
The figure illustrates that the best performance of the algorithm is achieved when the soft update factor is \(\tau=0.005\). Figure 5 shows the algorithm's performance under a varying batch size. It uses small batch learning to increase the speed of model training to reduce the cost per iteration. Small batches converge faster for training compared to extensive data collection. Still, they can lead to poor performance as the data stored in the buffer is initially over-utilized [44], reducing the importance of the data at a later stage. Large batches of data cause the network to update too slowly and may also perform poorly. The behaviour of the algorithm on a variety of sets is depicted in the graph, from which it is clear that the algorithm achieves better results when the batch size = 256. In addition, it can be found that when the batch size is 64 and 128, the system loss is higher, and the curve fluctuates more, making it difficult for the algorithm to converge quickly and reducing its performance. Figure 4: Training process with different Soft Update Rate settings. Figure 3: Training process with different Learning Rate settings. Figure 6 represents the performance of the algorithm under different discount factors. The actor network will make actions that give the critic network a high rating, and the rating calculation will use the discount factor. To reflect the continuity of the decision, the actor-network is expected to consider the reward and the next prize when choosing action \(a_{t}\). Too small a discount factor prevents the critic network from anticipating the future in time and affects the algorithm's performance. Conversely, when the discount factor is too significant, it may reduce the critic network's prediction accuracy. In the figure, the method performs better when the discount factor = 0.99.
## 3 Results and Discussion
### Evaluation of the Microstructures  
The first micrographs of the samples were taken with a light microscope. Figure 10 illustrates the differences in microstructure between the cold-rolled and PBF sheets. The images were taken of the same surface as the hole profiles. Thus, the PBF sheet shows one melted layer, instead of a cross-section of multiple layers.  
The cold-rolled sheet seen in Figure 10a has a typical austenitic stainless steel microstructure with a homogenous grain size and structure. No pores or other defects could be seen. The PBF sheet shows a much more irregular grain size pattern, but it still seems to be solid material with only few small pores. However, there are some circular patterns that can be seen as black lines in the PBF micrographs (examples circled in red in Figure 10b). After comparing them to the laser hatch pattern used in the production of the PBF sheet, it can be said that these are likely either the start or end points  
Figure 9: Measured hole tapers in degrees for all the drilled holes.  
of laser lines. To find out what these dark lines look like at higher magnification, a scanning electron microscope was employed to analyze the topography of the surface. In Figure 11, a topographic image of a similar occurrence of a semicircle-like pattern can be seen.  
From Figure 11, it can clearly be seen that the black lines in the PBF material are shallow recesses in the material. It can also be seen that they do not follow the grain boundaries. They are only about 10 \(\upmu\)m deep and 10 \(\upmu\)m wide and exist near the bottom and top surfaces of the material, and at the end and start points of the laser lines used in the hatch pattern when the sheet was produced. Overall, there seem to be too few of these defects, and they are not large enough to have a significant effect on the engraving process.  
Figure 11: Scanning electron microscope (SEM) image showing the topography of an area with a suspected defect in the PBF sheet.  
Figure 10: Magnified images of the samples showing the microstructure; (**a**) Cold-rolled steel sheet; (**b**) PBF produced sheet. Areas of interest circled in red.
## 4 Simulation Results  
### Simulation Setup  
To simulate the modulation of image data into OFDM signals, MATLAB R2024b was employed. In the simulation, various modulation schemes of OFDM communication, including \(M_{1}\)-PSK (\(M_{1}=2,\ 4,\ 8\)) and \(M_{2}\)-QAM (\(M_{2}=16,\ 32,\ 64\)), were utilized to validate the proposed CDM. The simulations were conducted in an SNR range from 0 dB to 15 dB. A detailed configuration of the dataset is presented in Table 1.  
In the training of the CDM, a U-Net with an input size of \(32\times 32\times 3\) was utilized for the learning process of the reverse process. The hyperparameter \(\beta_{t}\) of the variance schedule was set from \(10^{-4}\) to 0.035. The interpolation parameter \(v_{t}\) was set to \(v_{t}=\sqrt{\left(1-\sqrt{\hat{x}_{t}}\right)/\sqrt{\hat{x}_{t}}}\). The training was conducted with 50 diffusion steps, a learning rate of \(10^{-4}\), and a dropout rate of 0.1. In Equation (28), the weight \(\lambda\) for \(\mathcal{L}_{\text{VLB}}\) in the learning of \(\Sigma_{\theta}(x_{t},y,t)\) was set to \(10^{-3}\) to prevent \(\mathcal{L}_{\text{VLB}}\) from overshadowing \(\mathcal{L}_{\text{simple}}\). A summary of the simulation parameters is shown in Table 2. The simulation was run on a desktop with AMD PRO 5975 WX, with memory of 32 GB DDR4, and NVIDIA GeForce RTX 4090 GPU.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Experimental Parameters** & **Values** \\ \hline Image dataset & CIFAR-10 (\(32\times 32\times 3\)) \\ Training/test dataset & 50,000/10,000 images \\ Signal point (\(N_{DFT}\)) & 256 \\ Modulation scheme & \(M_{1}\)-PSK (\(M_{1}=2,\ 4,\ 8\))\& \(M_{2}\)-QAM (\(M_{2}=16,\ 32,\ 64\)) \\ SNR range & 0 dB-15 dB \\ Multipath taps (\(M\)) & 4 \\ Channel estimation & Least square (LS) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental setup for generating an OFDM-based received dataset.
# Beam Training for Millimeter-Wave Communication Based on Tabu Table Enhanced Rosenbrock Algorithm
## 6 Experimental and Result Evaluations
### Neighborhood Structure  
In TS, neighborhood structure and size are key factors for a local search algorithm to get significant performance. The larger the neighborhood is, the higher the probability of successful search, the more energy will be consumed. We compare the search performance of different neighborhood sizes (i.e., radius of the neighborhood \(R\)) with the method of neighborhood structure as defined in Section 5. As shown in Figure 5, based on the 100 independent realizations, we have plotted the cumulative distribution function (CDF) diagram of searched values at different neighborhood radius \(R\) in order to make the performance comparison more clearly. We get the best search performance when \(R=2\). Moreover, the analysis shows that the complexity of \(R=2\) is lower than \(R=3\). So, in this paper, the neighborhood radius is set to 2.
# A Novel Approach against Sun Glare to Enhance Driver Safety
###### Abstract
Driving patterns are observed in paper [8], too, where gaze patterns of drivers were identified on different road turns and traffic lights. It was observed that, while taking left or right turns and waiting in traffic light, the focus of drivers considerably changes. As of yet there are not many solutions to the glare problem. The manual sun visor is still widely used but has major drawbacks. First, it must be manually operated by the driver. The fact that the driver must operate the sun visor combined with the blindness caused by the sun can lead to an accident. As an alternative, a sun visor operated by a servo motor [9] that, based on a light sensor, decides whether to open or close the flap can be used. Second, the whole windshield is not covered by classic sun visors. During CES 2020 [10], Bosch Virtual Visor featured a transparent LCD screen paired with a small in-cabin camera used to track the sun shining on the driver's face. The system employs artificial intelligence to locate facial features (including eyes, mouth, and nose) in order to track shadows as they move across the driver's face. A patented algorithm is then used to pinpoint where the driver's eyes are and selectively block and unblock (darken) sections of the virtual visor in real time to prevent blindness. The key benefit is that 90% of the visor always remains transparent, so the driver can still see out far more effectively than they otherwise would with a conventional fabric-covered visor. The drawback of this prototype is that the fabric-covered visor is just replaced and not fully removed. An anti-glare prototype was proposed by [11], where the sunlight was reproduced using a projector emitting bright white light and webcams were used to track the eyes of the driver. The study shows promising results, being limited by the existing technology at the time when it was conducted. Therefore, the prototype could be tested only in a laboratory environment, hence, it was unusable in a real car test scenario. The purpose of the current paper is to present an anti-glare system suitable for vehicles that aims to use the existing elements in the vehicle in order to increase the traffic safety. Actual technologies like eye tracking systems, HUD, and light intensity sensors are used. The only sensor that is added is a sun tracking device. The tradeoff is that if the sun tracking sensor is added, the conventional sun visors can be removed, therefore, balancing the cost of production. The paper is based on the ideas presented in [12], focusing on the practical side of the idea, touching implementation details, and showing simulation results for the case in which the glare is caused by the direct beams of the sun. Furthermore, only the sun rays that are coming through the windshield are taken into consideration, but the presented principles can be applied for side windows as well. All the results were obtained using MATLAB simulations since the realization of a real-life prototype, usable in a car, was unattainable due to cost limitations.
# Performance Evaluation of Mobile RPL-Based IoT Networks under Hello Flood Attack
## 6 Performances Analysis
### The Reference Point Group Mobility (RPGM)
#### 6.2.2 End-to-End Delay  
In Figure 8, which illustrates the E2 ED, the \(x\)-axis represents the number of nodes ranging from 0 to 60, while the \(y\)-axis measures the E2 ED in milliseconds (ms) from 0 to 4.5 ms. The graph depicts eight distinct lines representing different combinations of mobile and attack nodes in simulations conducted in a mobile environment using the RPGM mobility model, with and without the Hello Flood attack. Here, mobile nodes denote those  
Figure 7: PDR without and with attack using RPGM mobility model.  
in the mobile environment without the attack, while attacker nodes represent malicious nodes within the same environment. Notably, there is a consistent trend of increased E2 ED as the number of nodes rises across all scenarios. For example, configurations with no attack nodes and 10% mobile nodes have the lowest E2 ED. On the other hand, configurations with more mobile and attack nodes have the highest E2 ED. As the percentage of mobile and attack nodes increases, E2 ED tends to escalate, with the highest values likely associated with scenarios featuring a significant proportion of both mobile and attack nodes in the network. It is also observed that as the proportion of mobile nodes increases, E2 ED similarly rises.  
The combination of the RPGM model and the Hello Flood attack contributes to increasing the E2 ED within a network environment through several interconnected mechanisms. Firstly, the structured movements of RPGM nodes cause network areas to become crowded, especially when nodes move together in groups. This causes packet collisions and retransmissions, which slow down packet delivery and raise E2 ED. This congestion is exacerbated by the Hello Flood attack, which floods the network with excessive traffic, saturating communication channels, and causing interference that further delays packet delivery. Secondly, the movement patterns defined by the RPGM introduce instability in routing paths, leading to suboptimal routing decisions and increased packet loss, further contributing to elevated E2 ED. The Hello Flood attack exacerbates routing instability by overwhelming routing protocols and disrupting packet delivery, adding to the delay. Thirdly, the increased resource consumption within RPGM groups, as nodes contend for communication resources, leads to bottlenecks and queuing delays, further degrading network performance and contributing to increase E2 ED. The Hello Flood attack intensifies resource consumption by flooding the network with excessive traffic, straining network resources even further. Lastly, the RPGM's mechanisms for handling node movement at boundaries introduce additional delays in packet transmission, which, when exploited by the Hello Flood attack, further interfere with packet delivery, ultimately increasing E2 ED.
## 3 SAgic-IoT Platform Description
### Layered IoT Architecture
#### 3.2.1 Gathering Layer  
The gathering layer collects data relevant to physical environmental parameters from a given area of interest from all the sources on farmland and sensor nodes on the fields (such as air temperature and humidity, soil temperature and moisture, and pH, among others). It also captures images through the cameras distributed in the monitoring region.  
In the last decade, several efforts have developed and defined low-power D2D standards and solutions [85; 86; 87], generating many promising radio technologies for D2D connectivity for local and wide area networks.  
One of the key challenges is determining the best data-transmission networking technology based on: transmission time, range, network size, and so on [88; 89; 90]. Table 1 summarizes some of the results of these past studies.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Bluetooth** & **ZigBee** & **Wi-Fi Halow** & **Ultra-Wideband** \\ \hline Frequency & 2.4 GHz & 868 MHz, 915 MHz and & 900 MHz & 3.1–10.6 GHz \\ Max. signal rate & 1,2,3 Mbps & 10–100 m & Up to 347 Mbps & 53.3–480 Mbps \\ Coverage & 10 m & 10–100 m & Up to 1 km & 300 m \\ Max. no of cell nodes & 8 & \textgreater{}65,000 & Up to 6000 devices & 8 \\ Nominal TX power & 0–10 dBm & (\textgreater{}25) - dBm & \textgreater{}17 dB & \textgreater{}41.3 dBm/MHz \\ Security & E0 encryption algorithm & 128-bit AES & & AES \\ Number of R.F. channels & 79 & 1/10; 16 & 26 channels & 1–15 \\ Channel bandwidth & 1 MHz & 0.3/0.6 MHz; 2 MHZ & 2 MHz and 16 MHz & 500 MHz–7.5 GHz \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of wireless technologies for inter-node communication.  
Figure 2: SAgric-IoT five-layered architecture.
## 4 Implementation and Discussion of Results
### Selecting the ARIMA Parameters
#### 4.3.2 Prediction of Future Confirmed, Death, and Recovered Cases  
The number of new confirmed, recovered, and death cases are determined by the disease transmission force and the investment in disease prevention resources, according to assumptions (A1)-(A3). The time-series data give existing information on COVID-19 disease development and investment resource management. Such information can be used to analyze new confirmed, recovered, and mortality cases that have occurred recently. The ARIMA model was used to fit COVID-19 cases from 1 April 2020 to 31 May 2021, based on the assumptions and by applying the features set utilizing the current available time-series data. We report a 14-day forecast, 30-day forecast, and 60-day forecast of the COVID-19 pandemic for each of the three cases (see Figures 11-13). The orange line extending until June 2021 represents the actual daily confirmed, recovered, and mortality cases and is followed by the forecasts for 3 periods: 0-2 weeks (yellow line), 2 weeks-1 month (black line), and 1-2 months (green line).  
Figure 11 indicates that the number of daily new confirmed cases (based on 60-day ahead forecasts) fluctuates approximately between 1400 and 1700. It is seen that the number of daily new confirmed cases maintains some fluctuation without any clear sign of a downtrend over a forecasted period of the first month. From the second month,  
Figure 10: A scatter plot of the death cases by the ARIMA on the testing dataset.  
Figure 9: A scatter plot of the recovered cases by ARIMA on the testing dataset.  
a downward trend appears to be forming. However, the forecasting period is not large enough for a definitive conclusion about the trend.  
Figure 12 indicates that the number of daily new recovered cases (based on 60-day ahead forecasts) resides in the approximate range between 1000 and 1800. The forecast curve shows an increasing trend during the first 2 weeks and then takes a dive at the start of the next period (black part of the curve) before resuming the increasing trend again after. However, the growth starts declining in the final phase of the 60 days.  
A forecast on the mortality cases reveals a wavy trend, increasing during the first 2 weeks, then decreasing until the end of the 1-month forecasting and finally showing another wave as shown in Figure 13.  
Figure 11: Prediction over a 60-day forecast for the confirmed cases using the ARIMA model.  
Figure 12: Prediction over a 60-day forecast for the recovered cases using the ARIMA model.
###### Abstract  
Kwifruit, as a climacter fruit, undergoes rapid ripening and senescence after harvest, making it highly susceptible to softening, rotting, and spoilage. Therefore, monitoring the key quality parameters of kiwifruit, particularly the accurate detection of soluble solids content (SSC), is considered crucial. The performance of two spectral acquisition methods--diffuse reflectance and diffuse transmission--in detecting SSC in kiwifruit was compared. Various preprocessing methods and feature wavelength selection techniques were employed, and regression models were constructed using partial least squares (PLS) analysis. The stability and accuracy of the models were validated through an independent validation set. The results indicated that the spectral data acquired by the diffuse reflectance method, preprocessed using Savitzky-Golay smoothing and combined with competitive adaptive reweighted sampling (CARS), yielded a coefficient of determination (\(R^{2}\)) of 0.98 for the prediction set, with a root-mean-square error (RMSE) of 0.66. In contrast, the spectral data obtained by the diffuse transmission method, preprocessed using multiplicative scatter correction and combined with CARS, achieved an \(R^{2}\) of 0.95 and an RMSE of 0.93 for the prediction set. This study demonstrated that both methods were effective for detecting SSC in kiwifruit, with the diffuse reflectance method showing the greater advantage.  
k +
Footnote †: journal: applied sciences
## 2 Materials and Method
### Ghana
#### 2.2.3 Development of Antimicrobial Management Guidelines Ghana Police Hospital (GPH)  
The Health Improvement Scotland (UK) visited GPH in May 2019 to partner with a multidisciplinary team of health professionals and facilitate the AMS program using the Scottish Triad Approach: Information, Quality Improvement and Education [12]. A multidisciplinary antimicrobial stewardship committee was established that developed an action plan for the further development of AMS in GPH. This included conducting a Point Prevalence Survey (PPS) of antimicrobial prescribing and development of the identified improvements and guidelines required [13]. The Global-PPS (GPPS) study was conductedfor the first time in May 2019 for one day on all inpatients that stayed overnight and were present at 8:00 a.m on survey day. The data was collected manually from prescription charts and patient notes. The data was then entered on the GPPS web-based application. Antimicrobial utilisation was assessed against the quality indicators, including:  
* indication of antimicrobial use documented in the patient notes;
* compliance with guidelines for documented indication (where guidance was available);
* missing/undocumented guidelines;
* documentation of stop or review date for antimicrobials in the notes (and prescription chart);
* proportion of surgical prophylaxis prescribed for less than or greater than 1 day.  
The guidelines used were the seventh edition of the national STGs of Ghana.  
Tanzania: Observership and Subsequent Pharmacy Leading Delivery of AMS at Kilimanjaro Christian Medical Centre (KCMC) and within Local Community  
The core team consisted of pharmacists from Northumbria Healthcare NHS Trust (NHCT) and Kilimanjaro Christian Medical Centre (KCMC) supported by an MDT with IPC nurses, microbiologists and pathologists.  
At the outset of the KCMC-NHCT partnership, a core team of pharmacists (as well as microbiologist and medical director from KCMC) undertook a 4-week observership at NHCT, including professional shadowing and a leadership development program providing them with the skills and confidence to take the lead in the following AMS project. The programme designed is available as Supplementary Materials 4 (KCMC Visit to UK_Skeleton programme 03.5.19).  
The project adopted a multifaceted approach encompassing a wide range of interventions beginning with a robust point prevalence survey to provide antimicrobial usage data that highlighted the focus for education. Materials such as lectures, workshops and posters were developed and translated into Swahili for delivery. Dedicated workshops were organised for hospital staff, and the team added antibiotic awareness training for the first time as part of the school general education trips by hospital staff and traffic accident training for Boda boda drivers.
# Study of Urban Unmanned Aerial Vehicle Separation in Free Flight Based on Track Prediction
## 4 Case Application
### Calculation of Track Prediction Error  
#### 4.1.1 Data Source  
For this model solution, the data source selected is 7 days of urban logistics UAVs flight data in May 2021, with an average of about 26,000 tracks per day, totaling 182,511 track data. There are six delivery routes in total. The logistics distribution in this case is from a central station to three stations, each with two alternative routes. Within these data, there are approximately 15 flight operations per day for each route, covering the phases of departure climb, cruise, and descent landing.  
As previously mentioned, to achieve autonomous separation maintenance, track prediction is necessary. To improve prediction accuracy, a two-stage track prediction model is constructed based on historical operational data. The historical data are clustered and classified, yielding the following operational results.