esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Sensitivity Analysis  
According to Figure 7a and Table 6, machine vision technology is the most adopted and impactful technology in the proposed case study. Figure 9 thus analyzes how much demand and defect rate variations affect objective functions, comparing the proposed original model (O) and the model where machine vision technology is not a possible option to select (NO-MVI). Specifically, the bar plot in Figure 9a ranks the input variables based on their influence on the loss (objective function degradation), while the contour plot in Figure 9b illustrates how the variables' values affect performance degradation.  
According to Figure 9b, removing machine vision led to a measurable increase in the objective function, with relative cost increases ranging from 20.5% to 62.2% depending on the values assumed by demand and defect rate parameters. More specifically, the plot revealed a non-monotonic relationship, with elevated loss occurring in two distinct regions: one at low demand and low defect probability and another at higher demand levels with moderate defect probability. In addition, the feature importance analysis in Figure 9a indicated that variations in demand had a greater influence on cost outcomes than variations in defect probability.  
On the contrary, Figure 10 illustrates the results of the Sensitivity Analysis when the proposed original model (O) is compared with the one where the number of controls cannot be adjusted based on the situation (MCN).  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**ID**} & **Objective Function Delta** & **Computational Time Delta** \\  & **[\%]** & **[\%]** \\ \hline O vs. NO-MVI & +42.6 & \(-\)26.6 \\ O vs. NO-HAI & +1.5 & \(-\)27.1 \\ O vs. NO-HI & +0.7 & \(-\)23.8 \\ O vs. MCN & +0.01\% & +8.0\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of the Ablation Analysis.  
According to Figure 10b, the increase in cost associated with forced full inspection ranged from 0.012% to 0.512%, with a mean increase of 0.164% and a median of 0.132%. These results indicate a consistent, albeit moderate, benefit from allowing the model to determine the number of items to inspect. The maximum observed cost increase, 0.512%, occurred in a case with a defect probability multiplier of 0.93 and a demand multiplier of 1.26. This represents a high-demand condition with a moderately elevated quality risk, where excessive inspection activity leads to cost inefficiencies. In general, Figure 10b reveals that scenarios with higher demand multipliers tend to yield higher cost deltas when full inspection is enforced. Moreover, the relationship with defect probability is more nuanced, as reported in Figure 10a, suggesting nonlinear effects and potential interactions between the two factors.
## 5 Simulation Results
### Obstacle Avoidance  
The first version of this experiment is performed using 23 agents and two cases of a single obstacle avoidance are performed. The first case is a small obstacle as shown in Figure 6. This part of the experiment depicts how the swarm goes around the obstacle in order to avoid it. In VRep simulation, the small obstacle is represented by a bunch of trees. This simulation shows how the obstacle is avoided by the swarm's drones flying around the trees. This is possible because the obstacle is relatively small and the agents are able to tolerate the obstacle between the attraction forces. The second version uses a big obstacle as depicted in Figure 7. In the second case, the agents avoid the obstacle by taking a side path. The big obstacle is represented by a building in VRep-simulation, where the swarm of drones navigates describing a side path to the building. This avoiding obstacle strategy occurs because there is not enough space between the attraction forces to allow broad obstacles to stay in between the agents.  
Figure 6: Simulation of robots avoiding small obstacle.  
Figure 7: Simulation of robots avoiding large obstacle.
## 4 Discussion  
### Effect of Total Precipitation and Temperature on PC Concentration  
The hourly reanalysis data collected from the official website of ERA5 were collated into daily mean meteorological data. Meteorological data for the retrieval image day and the preceding seven days were obtained, as shown in Figure 9.  
Combined with the PC concentration retrieval results, it can be found that the PC concentration is affected by temperature to a certain extent, as temperature affects the activity of algal cell reproduction and metabolism. When the temperature dropped to  
Figure 8: Retrieval map of PC concentration in Qinzhou Bay on 2 December 2017.  
Figure 9: Daily mean sea surface temperature and total precipitation.  
20 \({}^{\circ}\)C, the undetected area of PC concentration increased significantly. But in general, the correlation between PC concentration and temperature is not significant. This can potentially be attributed to continuous rainy weather, causing water bodies with high cyanobacterial abundance from inland areas to converge with the river and flow into Qinzhou Bay. This phenomenon is especially obvious on October 13. Before the outbreak of _P. glo_ red tide, the surface PC concentrations in the bay are relatively low. The influence of rainfall relieves the phosphorus limitation for cyanobacterial growth in the inner bay, leading to a rapid increase in PC concentration within the inner bay. As the water flows towards the neck of the bay and the outer bay, PC concentrations gradually decreased. This resulted in a bimodal pattern in PC concentration statistics (Figure 7a). Over time, due to the constraints of low temperatures and the competition succession of _P. glo_, the undetected area of PC concentration continued to expand in the outbreak area of _P. glo_. So, low temperatures lead to a decrease in PC concentration, while precipitation causes an increase in PC concentration.
## 2 Experimental Setup and Proposed Method
### Economic Indicators
#### 2.3.2 Net Present Value  
NPV is an important metric used to determine whether a project is profitable. NPV is the discounted value of all future cash flows (positive and negative) over the entire life of an investment. NPV usually considers the interest rate equal to the inflation rate; therefore, the true value of money is considered in each operating year. Also, factors such as opportunity cost, inflation, and risk are all factored into the NPV to determine the overall present value of the project. Thus, the NPV method converts future payments to present values and makes them comparable. The most important advantage of NPV is that it considers the time value of money and converts future cash flows to present value. Cash flows need to be discounted because of the concept of the time value of money. To calculate the NPV correctly, the discount rate must be determined correctly. In most cases, the discount rate can be expressed as the company's weighted average cost of capital and includes the company's interest rate, loan payments, and dividend payments. A positive NPV indicates that projected earnings exceed expected costs, and the investment will likely be profitable [60]. On the other hand, a negative NPV indicates that the investment is likely to result in a loss. NPV can be expressed mathematically by Equation (10).  
\[NPV=\sum_{t=0}^{n}\frac{R_{t}}{(1+r)^{t}}, \tag{10}\]  
where \(R_{t}\) is the net cash inflow-outflow during a single period \(t\), \(t\) is the number of periods, and \(r\) is the discount rate.
## 6 Results and Analysis
### Ablation Experiment
#### 6.3.2 Comparison among Various Attention Mechanisms  
To further validate the advantages of our proposed attention mechanism Res-EMA, we incorporated attention mechanisms such as Residual Squeeze-and-Excitation (Res-SE) and Residual Coordinate Attention (Res-CoA) [11, 14] into our model in a similar manner. Additionally, we include a baseline module without any attention mechanisms, denoted as the residual block. The positions of the Res-CoA and Res-SE modules within the network are depicted in Figure 9.  
The training was conducted for 50 epochs using Jacquard dataset in a consistent environment. After each epoch, we evaluated the grasping performance on approximately 5499 previously unseen images. To ensure fairness, all models were trained using identical network architectures. The experimental results are presented in Figure 10. Comparing the performance of the residual block, Res-CoA, Res-SE, and our Res-EMA modules, it is evident that only our Res-EMA module achieves a peak accuracy of 96.5%, signifying its substantial advantage. The Res-EMA module demonstrates its superior capability in focusing on grasp-related features. This module significantly enhances the detector's ability to accurately identify the optimal grasping region.  
In Figure 11, the first column displays the grasping detection results with our Res-EMA module, the second column shows the results with Res-CoA module, the third column presents the results with the Res-SE module, and the last column illustrates the detection results of the baseline module with residual blocks without any attention mechanism.  
Figure 8: The Res-EMA module ablation study. (**a**) The results with the Res-EMA module. (**b**) The results without the Res-EMA module.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Test 1** & **Test 2** & **Test 3** & **Test 4** \\ \hline FFPM & \(\times\) & ✓ & \(\times\) & ✓ \\ Res-EMA & \(\times\) & \(\times\) & ✓ & ✓ \\ Accuracy (\%) & 94.4 & 95.5 & 95.6 & 96.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The results of the ablation study.  
One can observe that the grasping produced by adding the Res-EMA module is more accurate and facilitates easier object grasping by the robot. While the other attention modules also contribute to effective grasping, their precision is comparatively lower. Among them, the model with only residual blocks performs the worst. In the last column of the fourth row in the Figure 11, representing a small clamp, the detected grasp configuration is invalid. The reason is that this model fails to extract effective grasp-related features, while the model with the Res-EMA module efficiently extracts spatial location features to ensure precise grasp positions. Moreover, extracting features at multiple scales benefits the robot in grasping objects of different sizes.  
Figure 10: Experimental results of various attention mechanisms.  
Figure 9: Different attention mechanisms.
## 2 Theoretical Background
### Seismic Performance Assessment
#### 2.2.2 Fragility Formulation  
The seismically exciting structure is subjected to damage to such an extent as during ground shaking; the seismic analysis can be carried out through fragility analysis. With a lot of computational efforts, the fragility curve shows a relationship between the probability of exceeding taken limits of EDPs and the exceedance of damage [28]. Mathematically the fragility function can be expressed as follows:  
\[P(C|IM)=\varphi\left[\frac{ln\left(\frac{IM}{\theta}\right)}{\beta}\right] \tag{5}\]  
where at \(IM=x\), \(P\) denotes the GM probability which motivates the structural collapse, \(\varphi(\ )\) is the function of standard normal cumulative distribution, \(\theta\) is the median and \(\beta\) is the standard deviation of the IM and lastly, \(C\) is the damage state [8]. The fragility parameter followed in this study is the maximum likelihood estimation (MLE) [8,29], where the determination of the parameters can be made by the following:  
\[Likelihood=\left(\prod_{i=1}^{m}P(C|IM)\right)(1-P(C|IM_{max}))^{n-m} \tag{6}\]  
\[\{\hat{\theta},\hat{\beta}\}=a\underset{\theta,\beta}{arg\max}\sum_{i=1}^{m} \{\ln P(C|IM)\}+(n-m)\ln(1-P(C|IM)) \tag{7}\]  
where the number of IM intensities are notified by m, \(p=1\) (when the case exceeds the limit state (LS)) or \(p=0\) (not exceed the LS) and \(q=1-p\). The fragility curve is exposed from elastic to the collapse level for a set of GM by IDA. Besides, the higher threshold value of the IM is determined by adopting the high confidence of low probability of failure (HCLPF) methodology. HCLPF is the identification of that GM, where the 5% failure confidence remains at 95%.
## 6 Challenges and Future Research Directions
### Future Research Directions  
To address these challenges, several future research directions can be explored:  
_Advanced Precoding Techniques:_ Developing more sophisticated precoding techniques, such as those that can adapt to dynamic channel conditions, will be crucial in improving the performance of SWIPT in mMIMO-NOMA systems. _Hybrid PS Schemes:_ Exploring hybrid power splitting schemes that can dynamically adjust the PS ratio based on real-time channel conditions and user requirements could enhance the efficiency of SWIPT systems. _Machine-Learning Integration:_ The integration of machine-learning techniques to optimize resource allocation and manage interference in SWIPT-enabled mMIMO-NOMA systems could provide significant performance gains. _Experimental Validation:_ Conducting real-world experiments and field trials to validate the theoretical models and algorithms developed for SWIPT in mMIMO-NOMA systems will be essential to assess their practicality and effectiveness. _Energy Harvesting Technologies:_ Investigating new energy harvesting technologies and materials that can operate efficiently in a broader range of conditions could improve the overall viability of SWIPT in practical systems. Furthermore, one can explore the possibilities of interplay with emerging technologies like intelligent reflecting surface (IRS), cell-free MIMO, and many more.
# Evaluation of the Impact of Fines on the Performance of Sub-Base Materials
## 3 Test Results and Discussion
### Engineering Characteristics of Soil Aggregate Mixtures  
The gradation curves of the coarse aggregate samples are shown in Figure 1. The effective grain size (\(D_{10}\)) of these samples is 0.17 mm, while the median grain size (\(D_{50}\)) is in the range of 5.0-7.0 mm. The gravel content (fraction coarser than US #4) ranges between 50 and 57%, whereas the sand content varies from 41 to 48%. The percentage of fines (size lesser than Sieve No. 200) present in the samples is only 2%. Grain sizes with a reference to 60%, 30%, and 10% fines (\(D_{60}\), \(D_{30}\) and \(D_{10}\), respectively) were calculated from the GSD curves. The coefficients of uniformity and curvature vary from 33 to 55 and from 1.0 to 1.12, respectively. Both samples were classified as well-graded gravels (GW), according to the Unified Soil Classification System (USCS), ASTM D-2487 whereas the inherent fines present in the samples were non-plastic. These samples were mixed with non-medium and high-plasticity fines to prepare the composites. The gradation curves of the aggregate samples used in the study are shown in Figure 1.  
Laboratory tests were performed on these composite samples and the results are summarized in Table 2. All the samples belong to gradings B or C as per the AASHTO gradation. The blended samples were classified as per the Unified Soil Classification System (USCS). With the addition of medium-plasticity fines up to 15% and high-plasticity fines up to 5%, the sample's classification changes from GW-GM to GW-GC (well-graded gravel with clay). Details of the classification tests for all composite samples prepared as per AASHTO gradings B or C are summarized in Table 2.  
Composite samples prepared as per the AASHTO gradation 'B' have an average specific gravity of 2.65, a flakiness index (FI) of 34, an elongation index (EI) value of 42, and a Los Angeles abrasion (LAAV) value of 19, whereas for composite samples prepared as per the AASHTO gradation 'C', these values were 2.66, 16, 39, and 42, respectively.  
Figure 1: Gradations for the sub-base samples.
## 3 Methodology
### Review and Final Filter  
As a third step, we categorized the papers based on the information that each paper offered. First, the papers were classified based on the type of environment in which the research was conducted, i.e., either in controlled environments (i.e., lab-based) or uncontrolled environments (i.e., construction site), based on the setting described in the paper. A controlled environment was defined as an enclosed space with precisely regulated environmental variables to meet operational needs; these variables could be temperature, light, pressure, and humidity [28]. Conversely, an uncontrolled environment was a representation of the \"real word\" where unpredictable outdoor conditions and variational environments were left as they affected any ongoing situation [29]. Then, the papers were classified by type of project, i.e., either horizontal, vertical, or academic research. Vertical projects include all construction structures that stretch vertically, for example, skyscrapers, towers, apartment buildings, office buildings, and other types of commercial buildings. Frequently referred to as heavy civil construction projects, horizontal projects are structures that stretch in length more than height, such as bridges, roads, highways, railroads, airfields, pipelines, and transit. Some academic studies did not relate to a specific construction project but included academic exploration through a survey, a questionnaire, university experiment, or AR implementation in lab-based environments.  
The next classification of the papers focused on the documented challenges identified in relation to AR usage. We carefully recorded all the challenges mentioned throughout the papers and examined how frequently each challenge was cited by different sources. Similarly, the next classification examined the documented benefits of utilizing AR. Using a similar approach to the previous approach, we documented all the benefits identified in the 59 papers and analyzed the number of times each benefit was mentioned.  
Once the deep review of each paper was completed, each paper was classified by type of environment and type of project. In addition, challenges and benefits reported in each paper were grouped in one table with the number of times each attribute (challenge or benefit) was mentioned. In some cases, when a challenge or a benefit was reported only once but never repeated a second time by any other paper, the attribute was removed from the table considering that there was no consensus from more than one paper. In other cases, divergence was noted when the same attribute was reported as a challenge in a paper and a benefit in another paper. In these cases, we went back to the paper referencing the attribute and tried to understand the context and the type of project in which AR was used, to try to understand the reason behind the divergence. Once all attributes were identified and those that were reported more than once were noted, the results showed patterns for which there was consensus or divergence in attributes reported in the research.
# A Microservice-Based Smart Agriculture System to Detect Animal Intrusion at the Edge
###### Abstract  
Smart agriculture stands as a promising domain for IoT-enabled technologies, with the potential to elevate crop quality, quantity, and operational efficiency. However, implementing a smart agriculture system encounters challenges such as the high latency and bandwidth consumption linked to cloud computing, Internet disconnections in rural locals, and the imperative of cost efficiency for farmers. Addressing these hurdles, this paper advocates a fog-based smart agriculture infrastructure integrating edge computing and LoRa communication. We tackle farmers' prime concern of animal intrusion by presenting a solution leveraging low-cost PIR sensors, cameras, and computer vision to detect intrusions and predict animal locations using an innovative algorithm. Our system detects intrusions pre-emptively, identifies intruders, forecasts their movements, and promptly alerts farmers. Additionally, we compare our proposed strategy with other approaches and measure their power consumptions, demonstrating significant energy savings afforded by our strategy. Experimental results highlight the effectiveness, energy efficiency, and cost-effectiveness of our system compared to state-of-the-art systems.  
smart agriculture; animal intrusion detection; LoRa; fog computing 2024 16 296 1
# Digitalization Processes in Distribution Grids: A Comprehensive Review of Strategies and Challenges
## 3 Grid Operations
### Voltage and Reactive Power Control in Operation
#### 3.6.2 Management of Reactive Power to Improve Efficiency and Reduce Losses  
Managing reactive power is crucial for operational efficiency in power systems. Study [195] introduces a coordinated volt-var controller (VVC) that employs a sensitivity matrix-based method, enhancing voltage and reactive power management. This approach leverages digital tools for sensitivity analysis and control optimization, simplifying complex calculations and improving decision making. The research emphasizes the ability of digital solutions to reduce computational complexity in traditional volt-var control, supporting real-time applications in smart distribution networks.  
Additionally, a digital approach to voltage regulation in ADNs using a distributed control system with predictive analytics to anticipate renewable outputs and load shifts that enables proactive voltage control has been established. It integrates OLTCs and a distributed algorithm for coordinating reactive power from DERs by applying the ADMM optimization method. This underscores digitalization's critical role in bolstering grid stability and efficiency, particularly in systems with high renewable energy penetration [126].