esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
In Figure 5(a),b, it can be seen that SVR has not performed as poorly as KNN, but has failed to produce acceptable predictions for the yield in 2014 (the year with the highest yield in the data). Overall, RF has had the most reliable performance in predicting chickpea yield even in the presence of yield fluctuations due to rainy years and droughts. The use of machine learning techniques for predicting crop yields can present new opportunities for decision makers to increase food security. Especially in areas where crop yields are affected by uncertain conditions, like rainfed agriculture. In rainfed agriculture, due to the fact that the plant is not irrigated by the farmer and the plant receives its required moisture from the environment, and these environmental conditions are variable. Use of these influential factors and machine learning techniques for predicting crop yields provides useful information to government decision makers, warehousing and farmers. Figure 6: Results of the prediction models. Remote sensing data are comprehensive, accurate and varied. However, they are only available in developed countries. The needs of these countries determine the priority of the time and place of collection of this data. Also due to the fact that the collection of this information is done from different satellites and different sensors. There are different scales, units and time periods, which prolongs the process of refining and collecting data. Meteorological data obtained from synoptic stations have less lost information and are easier to access for less developed countries because they are collected locally. This data also needs to be refined. Access to them in the studied area was difficult. Also, some sensors were not present in all stations. Considering two different time scenarios for data collection as well as combining data for months shows the impact of some indicators is greater in certain seasons. For example, in the RF method for the OC time scenario, the most important criteria were surface moisture, minimum air humidity, and subsurface humidity in March, January, and January, respectively. For SVR and KNN methods, the most important indicators were ET and GPP, which were MA (average March and April) and April, respectively. The application of machine learning methods using the collected data showed that the random forest method had the least errors for the OC scenario. In summary, defining different time scenarios for data collection enables the researcher to make more accurate estimates of future rainfed agricultural yield by collecting specific data at specific times.
## 2 Materials and Methods
### Battery
#### 2.4.3 Lead-Acid Batteries: [PERSON] et al.'s Weighted Ah-Throughput Model  
The weighted Ah-throughput model presented by [PERSON] et al. [30] assumes that operating conditions are typically more severe than those used in standard tests of cycling and float lifetime.  
This model uses weighting factors for the charge throughput over the battery life to model the lost capacity due to the different aging mechanisms. These weights depend on the \(DOD\), the current rate, the acid stratification, and the time since the last full charging.  
Battery voltage at each time step was calculated depending on if the battery was \(I_{bat}(t)>0\) (charging), Equation (7), or \(I_{bat}(t)<0\) (discharging), Equation (8), using the Shepherd model [49]:  
\[V_{b}(t)=V_{0}-gDOD(t)+\rho_{c}(t)\left(\frac{I_{bat}(t)}{C_{N}}\right)+\rho_{ c}(t)M_{c}\bigg{(}\frac{I_{bat}(t)}{C_{N}}\bigg{)}\bigg{(}\frac{SOC(t)}{C_{c}- SOC(t)}\bigg{)} \tag{7}\]  
\[V_{b(t)}=V_{0}-gDOD(t)+\rho_{d}(t)\left(\frac{I_{bat}(t)}{C_{N}}\right)+\rho_{ d}(t)M_{d}\bigg{(}\frac{I_{bat}(t)}{C_{N}}\bigg{)}\bigg{(}\frac{DOD(t)}{C_{d}(t)- DOD(t)}\bigg{)} \tag{8}\]  
where \(V_{0}\) (V) is the open-circuit equilibrium cell voltage at the fully charged state, \(g\) (V) is an electrolyte proportionality constant, \(\rho_{c}(t)\) and \(\rho_{d}(t)\) (\(\Omega\)Ah) represent the aggregated internal resistance during charge or discharge, and \(C_{c}\) and \(C_{d}(t)\) represent the normalized capacity of the battery during charge or discharge.  
This model estimates the capacity loss by corrosion, \(\Delta C^{c}(t)\) and the capacity loss by cycling (degradation), \(\Delta C^{d}(t)\). Each hour, the remaining battery capacity, \(C_{rem}(t)\), could be estimated as the normalised initial battery capacity \(C_{d}(0)\) minus the capacity loss by corrosion and degradation. The end of the battery life was considered to be when \(C_{d}(t)=0.8\)\(C_{N}\).  
\[C_{rem}(t)=C_{d}(0)-\Delta C^{c}(t)-\Delta C^{d}(t) \tag{9}\]  
Figure 4: Lead-acid battery: cycles to failure vs. depth of discharge (DOD) [48].  
Capacity loss by degradation \(\Delta C^{d}(t)\) was calculated counting the weighted number of cycles, with the impact of the SOC, the discharge current, and the acid stratification. At the same time, the capacity loss by corrosion was estimated, which is proportional to the effective corrosion layer thickness, which grows during the lifetime of the battery depending on the corrosion voltage of the positive electrode and temperature. This is a complex model with many equations; further details can be found in [30].
## 6 Limitations and Future Work  
While the proposed FDRL system demonstrates significant improvements in O-RAN slicing and resource allocation, certain limitations should be acknowledged. These limitations highlight potential areas for future research and enhancements.  
The efficiency of MEC resource allocation is highly dependent on the initial configuration parameters, such as slice definitions, total resources reserved per slice, and the load balancing mechanism. While the system can dynamically adapt through deep reinforcement learning, the initial parameterization plays a crucial role in achieving optimal allocation. In highly dynamic scenarios where MEC resource availability fluctuates unpredictably, performance could be further improved by integrating additional adaptive mechanisms.  
Although the FL-based approach enhances privacy and scalability, it introduces additional processing overhead due to periodic aggregation rounds. One key factor is the added latency introduced by TLS encryption, which secures communications between clients and the aggregation server. While the impact of this latency remains manageable in the current setup, real-time applications with strict latency constraints, such as URLLCs services, might require further optimizations, such as compression techniques or differential updates for reducing model exchange overhead.  
The effectiveness of the FL framework is contingent upon active participation from a diverse set of clients. Insufficient client involvement can result in a global model that lacks generalizability due to limited data representation. To address this, it is crucial to implement robust incentive mechanisms that encourage clients to contribute their local models during aggregation rounds. Future efforts should explore adaptive incentive strategies, such as dynamic aggregation weight adjustments and customized pricing models, to enhance client engagement and ensure the development of a comprehensive global model.  
While FL significantly reduces bandwidth usage compared to centralized training by transmitting model parameters instead of raw data, its application to DRL in the O-RAN presents additional challenges beyond computational costs. Although local training introduces computational overhead, modern AI hardware has mitigated this constraint in many scenarios. However, the real challenges in the O-RAN arise from energy efficiency, synchronization, and real-time latency requirements, which can impact large-scale FL deployments. Future optimizations should consider lightweight RL models and hybrid training strategies that offload intensive computations to more capable network elements.  
These limitations serve as potential avenues for future research, focusing on improving the efficiency, adaptability, and scalability of FDRL-based resource allocation in O-RAN environments.
## 5 Conclusions  
The need for renewable and clean energy has opened the way for designing highly efficient solar absorbers. One such solar absorber is designed with an I-shaped metamaterial resonator backed by SiO\({}_{2}\) and GaAs substrates. The I-shaped metamaterial design is also compared with the thin-wire metamaterial design to obtain the optimized metamaterial design. The I-shaped metamaterial has the highest absorption, at 96%, in the 500 nm to 3000 nm wavelength range. It is also showing a peak absorption of 99.95% in the infrared range. The I-shaped optimized design is also investigated for E-field response, which clearly shows that for higher wavelengths absorption is higher because of the GaAs substrate. The optimization of the structure is also investigated for SiO\({}_{2}\) and GaAs thicknesses. The effect of GaAs thickness is minimal, and it is visible at higher wavelengths. A perfectly designed solar absorber can be used for solar thermal energy conversion applications. Further improvement in the absorption can be achieved in the future by changing the materials and applying the other metamaterial component in the design.  
conceptualization conceptualization, M.A. and A.A.; methodology, K.A.; software, M.A. and A.A.; validation, M.A.; formal analysis, A.A. and K.A.; investigation, M.A. and A.A.; resources, M.A. and A.A.; writing--original draft preparation, all authors; writing--review and editing, all authors. All authors have read and agreed to the published version of the manuscript.  
The authors extend their appreciation to the Deputyship for Research and Innovation, Ministry of Education in Saudi Arabia, for funding this research work through the project number 223202.  
The data will be made available at the corresponding author's reasonable request.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Design**} & **Visible and Infrared Region** & **Cost (Unit Cell)** \\  & **from 500 nm to 3000 nm** & **Cost (Unit Cell)** \\ \hline I-shape metamaterial design & 96 & Low \\ Thin wire metamaterial design & 95 & Low \\ [32] & 90 & Moderate \\ [33] & 90 & Moderate \\ [34] & 86.5 & Low \\ [35] & 93 & High \\ [36] & 93.1 & Low \\ [37] & 90 & High \\ [38] & 95 & Low \\ [39] & 80 & High \\ [40] & 71.1 & High \\ [21] & 70 & High \\ [41] & 84 & Low \\ [42] & 93.7 & Moderate \\ [43] & 92 & High \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of solar absorber designs with the proposed double substrate design of I-shape metamaterial and thin metamaterial.
# Insulators and Defect Detection Based on the Improved Focal Loss Function
## 3 Experiment and Dataset
### Evaluation Metrics  
The experimental results were evaluated using precision (\(P\)), recall (\(R\)), and mean average precision (\(mAP\)). Accuracy rates were used to measure the classification effect between different categories of the overall model, recall rates were used to measure the overall effect of the model across different categories, and mean accuracy was used to measure the overall accuracy of the model. The formula is as follows:  
\[P=\frac{TP}{TP+FP} \tag{6}\]  
\[R=\frac{TP}{TP+FN} \tag{7}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Total** & **Train** & **Test** \\ \hline insulator & 3409 & 2665 & 744 \\ defect & 1600 & 1254 & 346 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dataset label distribution table.  
Figure 5: Data set analysis. (**a**) Number of insulators and defect labels. (**b**) Label visualization. (**c**) The center point of the label distribution. (**d**) Distribution of label aspect ratios.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Model** & **Number** \\ \hline CPU & Intel(R) Core(TM) i7-10870H & 1 \\ Memory & Crucial 16 G DDR4 & 2 \\ Graphics & GeForce RTX 3080 10 G & 1 \\ Hard Disk & Western Digital SN570 1 T & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental environment configuration.  
\[mAP=\frac{\int_{0}^{1}P(R)dR}{M} \tag{8}\]  
Among them, true positives (_TP_) is the number of positive samples correctly identified as positive samples, false positives (_FP_) is the number of negative samples incorrectly identified as positive samples, true negatives (_TN_) is the number of negative samples correctly identified as negative samples, and false negatives (_FN_) is the number of positive samples incorrectly identified as negative samples. \(M\) is the number of target categories.
# A Study on the Analysis of the Effects of Passenger Ship Abandonment Training Using VR
## 4 Results of Video and VR Passenger Ship Abandonment Training  
### Participants' Education Satisfaction  
A total of 60 participants took part in the experiment, with 30 participants using video training (Group 1) and 30 participants using VR training (Group 2). The positions of the participants comprised 38 third mates, 4 second mates, 1 chief mate, 15 third engineers, and 2 second engineers; 88% of the total participants were third mates or third engineers. As for the age group composition, 48.33% of the participants were in their 20 s, 48.33% were in their 30 s, and 3.33% were in their 40s.  
Figure 4: Video and VR training concept and VR equipment (HTC VIVE Pro wireless).  
Figure 3: VR contents: (**a**) announce, (**b**) escape from cabin to muster station, (**c**) evacuation system, (**d**) operation of life-raft.  
Figure 2: Video contents: (**a**) alarm sound, (**b**) escape from cabin to muster station, (**c**) evacuation station, (**d**) evacuation status.  
To examine the education satisfaction of the participants, nine questions were assessed using a 5-point Likert scale: (1) difficulty in use, (2) understanding of the content, (3) understanding of the procedures, (4) understanding of the roles, (5) realism, (6) efficiency of education, (7) effectiveness of education, (8) positive response to use of VR system for training, and (9) opinion on extending VR training courses in the maritime industry. As a result, the education satisfaction using the video-based training method scored 3.79 points, while the VR-based training method scored 4.32 points; that is, the seafarers participating in the study demonstrated a higher level of satisfaction with the VR-based training methods.  
However, in terms of the difficulty of use, the score of the video-based training method was 4.17 points, whereas the score of the VR-based training method was 4.33 points, indicating more difficulty in use of the VR-based training method. Moreover, in terms of the extension of training using the relevant equipment, the video-based training method scored 3.80, whereas the VR-based training method scored 4.57 points. This confirms that most of the participants expressed a strong desire for an extension of training using the VR-based training method.  
Furthermore, in terms of the understanding of content, understanding of procedures, understanding of roles, realism, efficiency of education, effectiveness of education, and positive response for use of the VR system for training, the VR-based training method received higher scores. Figure 5 shows the results of the education satisfaction comparison between the two methods.
# Performance of a Mid-Size Net-Zero Energy Solar House
###### Abstract  
The University of Alabama at Birmingham (UAB) was one of 16 collegiate teams from around the world that participated in the U.S. Department of Energy Solar Decathlon 2017 competition. An interdisciplinary team of students from across the university was engaged in a 2-year long process to design and build a house that is powered completely by solar power. The house was equipped to run all the typical appliances of an average modern house at similar levels on a conventional power grid. The net-zero house was built and tested on the UAB campus. Considering Birmingham's weather, a safe room was built to ensure the safety of occupants during events of extreme weather, such as a tornado. A ductless HVAC unit consisting of an inverter-type 3-speed outdoor unit supplied refrigerant to four high-wall indoor units providing the primary source of space conditioning. To achieve a model of efficiency and cost effectiveness, the house incorporated a heavily insulated envelope and precise glazing protection. The roof, floor framing and walls had R-30 batt and foam insulation. With a design informed by southern vernacular language, the building is oriented to maximize solar access and to use roof planes for shading the majority of the year. Peak power generation of the panels was recorded at 9.6 kW. The home has a centralized energy management system that can provide access to energy consumption data and allow control of lighting, appliances, and plug loads remotely. Energy modeling showed that the annual electricity consumption for heating and cooling with variation in wall types were 8470 to 11,661 kWh. For the month of October, it was calculated varying from 683 to 763 kWh, with varying air changes per hour from 0 to 1.5.  
net-zero energy building (NZEB); Solar Decathlon; solar power; OpenStudio (EnergyPlus); energy conservation; system performance +
Footnote †: journal: Applied  
consumption of electricity has increased approximately 3.4% each year since 1990 [4]. Electricity consumption is predicted to increase by up to 50% in 2050 [5]. The goal of the Solar Decathlon is to not only hopefully eradicate these problems in the future, but also to create comfortable and more energy efficient homes.  
Buildings account for the largest savings potential in terms of cost and reduction of GHG emissions [6]. Furthermore, the construction industry is responsible for more than 40% of carbon emissions in Europe alone [7]. By tackling issues with energy demand and construction, the environmental impact of structures can be reduced and longevity can be increased. Team Alabama's Surviv(AL) House takes all this into account, along with adding features to make it an attractive prospect for both builders and residents. In this paper, the Surviv(AL) House's specifications are discussed and performance analyzed.
## 7 Application on Engineering Design and QoS-CSC Problem
### Experimental Design for QoS-CSC Problem  
Concerning the experimental setup for the QoS-CSC issue, this paper refers to [PERSON] [25] and [PERSON] et al. [61] and sets up experiments of different sizes. Specifically, we assume the values for the four specified QoS attributes are randomly distributed within the range of [0.6, 0.96], with corresponding weights set as \(p_{t}=0.35\), \(p_{c}=0.3\), \(p_{r}=0.2\), and \(p_{a}=0.15\). Aiming to replicate real-world decision-making processes, these weights reflect the varying priorities assigned to each attribute in practical contexts. We specify the entire quantity of subtasks associated with task \(T\) to grow from 20 to 60 in increments of 20. The MCS of each subtask grows from 50 to 150 in increments of 50. For example, T - 20 - 50 means that task \(T\) can be decomposed into 20 subtasks, and each subtask has 50 optional MCS. To evaluate the efficacy of the proposed MIARO in tackling the QoS-CSC challenge, MIARO is compared with outstanding algorithms mentioned in Section 5.3, algorithms previously applied to the QoS-CSC problem, and the most recent ARO algorithms. Among these algorithms are HHO, PSO, HTLBO [25], MWOA [29], IGWO [62], ARO, and IARO [63]. All algorithms are executed for 1000 iterations, with each case being run independently 30 times.  
Table 16 shows the mean, standard deviation, and best solution obtained for MIARO, HTLBO, HHO, IARO, ARO, MWOA, IGWO, and PSO on different sizes of the test case. In Figure 9, the algorithm's convergence curves are presented for different test case sizes. When compared to other algorithms, MIARO shows a superior convergence rate and enhanced solution accuracy. From the standard deviations, findings reveals that MIARO has greater stability and still shows better performance as the problem size increases.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & \(\mathbf{x_{1}}\) & \(\mathbf{x_{2}}\) & \(\mathbf{x_{3}}\) & \(\mathbf{x_{4}}\) & \(\mathbf{f(x)}\) \\ \hline MIARO & \(\mathbf{0.18298476}\) & \(\mathbf{2.40727556}\) & \(\mathbf{9.5818017}\) & \(\mathbf{0.18298476}\) & \(\mathbf{1.47303616}\) \\ ARO & \(0.18285781\) & \(2.40883109\) & \(9.58449592\) & \(0.1829719\) & \(1.47339317\) \\ HHO & \(0.15290745\) & \(3.00380828\) & \(9.56118504\) & \(0.18377475\) & \(1.51498918\) \\ SSA & \(0.18110689\) & \(2.4362047\) & \(9.58186914\) & \(0.18298435\) & \(1.47471301\) \\ GWO & \(0.18224077\) & \(2.41967179\) & \(9.58091834\) & \(0.18302064\) & \(1.47395785\) \\ WOA & \(0.16292322\) & \(2.76374739\) & \(9.54103365\) & \(0.18577749\) & \(1.51057734\) \\ MFO & \(0.18298464\) & \(2.40727737\) & \(9.5818017\) & \(0.18298476\) & \(1.47303627\) \\ SCA & \(0.17728408\) & \(2.56334035\) & \(9.67744656\) & \(0.18313148\) & \(1.50123532\) \\ PSO & \(0.18298476\) & \(2.40727556\) & \(9.5818017\) & \(0.18298476\) & \(1.47303626\) \\ \hline \hline \end{tabular}
\end{table}
Table 15: Best values and corresponding solutions for welded beam.
# Effect of Topography Truncation on Experimental Simulation of Flow over Complex Terrain
## 4 Turbulence Characteristics  
### Turbulence Intensity  
Turbulence intensity is given by the standard deviation of streamwise wind velocity normalized by mean velocity. The turbulence intensity profiles of the measurement locations, shown in Figure 7, intensely change in the region near the ground. Location A1 in the windward region shows higher turbulence intensities at the lower part of the profile for WTS, which indicates that flow separation induced by topographic truncation enhanced turbulence production in the region near the model edge to a greater extent, leading to inaccurate experimental results. Combined with the analysis of the mean velocity in Section 3.1, the study noted that flow separation in the case of WTS generated a similar profile in mean velocity but a different profile in turbulence intensity with OT. This means that flow separation induced by terrain truncation at the region near the terrain model edge is a difficult issue in turbulence simulation. Moreover, the lower level of turbulence intensity in cases with transition sections indicates that implementing transition sections in the experiment can effectively avoid flow separation, but there is no avoidance of the speed-up effects.  
In the leeward region, for locations A2 and A3, there is at least one case with transition sections consistent with OT. Compared to WTS with lower turbulence intensity, both WTC and RTC obtained similar results with OT at location A2. As the cutting plane of location A2 is shown in Figure 5, the artificial cliff of WTS induced upwind flow separation, which was surmounted in the WTC and RTC cases. For location A3, RTC is the only case that obtained results approaching OT, which may be attributed to the fact that the ramp transition section took the place of the mountain peak in the front with producing flow separation. Coincidentally, Mcauliffe and Larose [26] used ramps to generate flow separation instead of the original topography with a mountain peak, and they also obtained acceptable results.  
Figure 7: Profiles of turbulence intensity at measurement locations: (A1) location A1; (A2) location A2; (A3) location A3; (A4) location A4; (A5) location A5.  
For the valley region, although the mean velocity profiles of the two measurement locations showed similar trends, the turbulence profiles were quite different at the elevations in the lowest 300 m above the ground. Compared with the case of OT, the other three show higher turbulence levels with decreasing height, and the largest discrepancy is found at the lower height, which is almost twice that of the OT. It seems that the increase in turbulence level caused by topographic truncation is inevitable regardless of implementation with transition sections when measurement locations are in the valley region.