esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Biological Control and the Three Ps: Predation, Parasitism, and Pathogens  
As said, the European Directives and the National Action Plans led to the development of different guides for IPM based on culture. Because of pesticide concerns, a special emphasis has been put on the potential benefits of biological control. Natural enemies can be used to regulate and reduce insect pest population levels [79]. The main objective is to reduce pest populations in the stages where the pests are more susceptible to be predated or parasitised [80].  
Without attempting to provide an exhaustive list of possibilities, these organisms can be divided into three different categories:  
1. Predators: These organisms feed directly on pests, thereby reducing their populations.
2. Parasitoids: Parasitoids are organisms that lay their eggs inside or on the bodies of pests. The emerging larvae feed on the pest, eventually killing it.
3. Pathogens: These are microorganisms, such as bacteria, fungi, or viruses, which infect and cause diseases in pests. By spreading among pest populations, they significantly reduce their numbers.  
The essence of biological control is to promote the ecosystem services provided by the natural enemies that are present in each agroecosystem. However, the olive agroecosystem is often a complex landscape where the arthropod community of natural enemies varies according to the environmental conditions, land uses, and agricultural practices. For instance, soil management practices related to the agricultural intensification of the olive groves in Mediterranean countries has greatly affected biodiversity [81]. Among other facts, arable fields or specialization in few crops simplifies the landscape and reduces the possibility of having a great number of species [82]. This, joined with the use of herbicides to destroy natural vegetation, which often creates a microclimate and retains humidity, eliminates natural niches for the arthropod community. Thus, a lower abundance of pest species is related to landscape complexity in olive groves due to its effect on promoting different biocontrol organisms and the general biodiversity of arthropods [83; 84; 85]. Olive groves with lower cover crop and no heterogeneous surrounding habitats decrease the probability of promoting the biological control of _B. oleae_[86]. So, preserving habitats for natural enemies--protecting them from perturbations derived from crop cultivation, crop harvesting, phytosanitary applications, or weather variations--will guarantee the presence of the local arthropod species overwintering near the crop fields, preventing pest outbreaks during early spring [87]. Indeed, habitat management often implies the manipulation of the plant composition, increasing its diversity in order to increase the abundance of natural enemies by providing food sources (such as nectar or pollen), mating sites, and shelter, and as a consequence, regulating insect pest populations [79]. A table listing some potential biological control organisms in olive crops is shown in Supplementary Material S1 and is commented on below.
# Predicting Shot Accuracy in Badminton Using Quiet Eye Metrics and Neural Networks
## 4 Methodology
### Neural Network Model
#### 4.6.2 Model Training  
The dataset was split into training (70%), validation (15%), and test (15%) sets. We applied the Adam optimizer with a learning rate set at 0.001, using binary cross-entropy as the loss function. The model was trained for 100 epochs with early stopping to prevent overfitting.
# Thermal Comfort Applied in Hospital Environments: A Literature Review
## 2 Methods
### Research Questions (RQs)  
The main goal of this study was to verify and summarize the studies that apply thermal comfort in hospital environments. In order to achieve this goal, 4 research questions (RQs) are proposed:  
1. According to [PERSON] et al. [23] and [PERSON] et al. [3], thermal comfort is required in indoor environments because it directly affects people's perception, in terms of health/wellbeing and productivity. On the other hand, [PERSON] et al. [24] claim that optimizing the energy used in buildings, whether for heating or for cooling, is a reality today, because there is a need for energy saving. Based on these premises, RQ1 is formulated:
2. **RQ1: Considering studies on thermal comfort in hospital environments, what are the main aspects that are taken into account: health/wellbeing, productivity or energy saving?**
3. According to [PERSON] and [PERSON] [25], the PMV model does not consider the adaptive actions that people undertake in indoor environments in order to maintain their comfort, leading PMV to underestimate or overestimate the real thermal sensation felt by people in buildings. Based on this premise, RQ2 is formulated:
4. **RQ2: Considering studies on thermal comfort in hospital environments, which relate PMV and real thermal sensation, studies indicate that PMV predicted well, underestimates or overestimates the real thermal sensation?**
4. Different levels of activity require specific environmental conditions for people, in order to achieve thermal comfort. Thus, it is important to define the target group of the research [26]. In addition to this factor, the type of environment in which people are inserted in the hospitals usually has its own standardized environmental requirements, determined by the type of activity to be performed [21]. Based on these premises, RQ3 is formulated:
5. **RQ3: Considering studies on thermal comfort in hospital environments, what was the most evaluated group and which area within the hospital was most evaluated?**
6. The hospital environment is complex, since it can change from waiting rooms to operating rooms and intensive care units (ICUs), which demand different requirements for environmental parameters due to the type of activity/care. The concern with planning the environment must go far beyond simply offering thermal comfort to its occupants [27]. Thus, the hospital environment should be prepared to offer a good indoor environmental quality (IEQ). Based on these premises, RQ4 is formulated:
7. **RQ4: What other parameters of indoor environmental quality (IEQ) were evaluated, in addition to thermal comfort?**
## 3 SD-QGrid Routing Platform and Algorithms
### SD-QGrid Offline Q-Learning Training Process
#### 3.3.2 Aggregation Unit of SDN CN  
After each RSU uses its analysis unit to generate Tables 4 and 5 (if available) for recording the average number of nodes and the average number of outgoing nodes for each connected road segment in period \(t\), respectively, it will periodically forward these tables and the vehicle trajectory in Table 3 to the SDN CN, which stores the vehicle trajectory in its trajectory database. As the SDN CN collects these tables sent by all RSUs in period \(t\), it will adopt its aggregation unit to aggregate the average number of nodes in Table 6 and the average number of outgoing nodes in Table 7 from tables forwarded by all RSUs belonging to each grid. Hence, the SDN CN is able to further calculate the directionality of each grid using the aggregated vehicle information, and count it in a certain period of time within the experimental date range as follows: It determines the total number of outgoing nodes, i.e., \(n_{i}\), \(i=\{0,\ 1,\ 2,\ \ldots,\ 7\}\), that each grid moves to the neighbor grid in eight directions in the date range, which is then divided by the total number of days in the date range to calculate the average directionality of individual grids in this period for each day in the date range. It finally records the statistical data in the average grid directionality table, as shown in Table 7.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Grid Index** & **Average Number of Nodes \(H_{t}^{*}\)** & **Time Period \(t\)** \\ \hline \((0,2)\) & 314 & 2021-10-13-10-12 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Average number of nodes of \(Grid_{s}\) in period \(t\).  
Figure 6: The offline Q-learning training process of SD-QGrid.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Current Grid Index** & **Neighbor Grid** & **The Start** & **The End** & **Average Number of** & **Time Period \(t\)** \\
**Index** & **Intersection** & **Intersection** & **Coordinate (x, y) of** & **Coordinate (x, y) of** & **Outgoing Nodes** & **Time Period \(t\)** \\  & & **Road Segment \(i\)** & **Road Segment \(i\)** & **Road Segment \(i\)** & **Road Segment \(i\)** \\ \hline \((0,2)\) & \((0,3)\) & \((x_{s},y_{s})\) & \((x_{s},y_{s})\) & \(110\) & 2021-10-13–10-12 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Average number of outgoing nodes of road segment \(i\) in period \(t\), if road segment \(i\) crosses two grids.
# Use of Machine Learning Methods for Indoor Temperature Forecasting
## 2 Methodology and Materials
### Selection of Predictive Models  
In this study, a set of AI-based algorithms and a gray box model were compared to identify the most suitable model to predict the indoor temperature of the room. Furthermore, these models were evaluated according to their forecast accuracy and their performance. A detailed description of the adopted models will be presented below.
# An Intuitionistic Fuzzy-Rough Set-Based Classification for Anomaly Detection
## 7 Conclusions, Limitations, and Lines for Future Work  
### Conclusions  
In this article, a hybrid algorithm consisting of both rough set and fuzzy set theoretic approaches is presented for the detection of anomaly. The algorithm is a classification-based algorithm which uses rough set and intuitionistic fuzzy set to deal with uncertainty in the dataset. The obtained rules can be expressed using intuitionistic fuzzy sets. The algorithm generates certain rules from lower approximation space, possible rules from upper approximation space, and boundary rules from boundary regions. Each attribute contributing in a rule can be expressed in terms of its membership value and non-membership value.  
Figure 16: Comparative analysis of execution time of MCAD [30] and IFRSCAD.  
Figure 15: Execution time of IFRSCAD for different data sizes (\(m\)).  
In addition, an attribute can contribute in both the certain rules as well as the possible rules. Therefore, each rule obtained by the algorithm is expressed using intuitionistic fuzzy set. The algorithm is named IFRSCAD. The proposed algorithm's performance is demonstrated by experimental analysis, and using the datasets KDDCUP'99 [52] and Kitsune [53], the algorithm extract anomalies with the accuracy of 96.99% and 91.989%, respectively. The comparative analysis shows that the proposed algorithm outperforms a couple of well-known classification-based algorithms.  
Finally, the proposed algorithm's time-complexity is found to be less dependent on dimension of the dataset and, rather, more on the size of the datasets. However, the detection rate depends more on dimensions, as evident from the obtained results. The proposed algorithm's time-complexity is compared with a clustering-based algorithm MCAD [30], and under the assumption of constant dimension, the algorithm is found to be more efficient than MCAD [30].
# MIDOM--A DICOM-Based Medical Image Communication System
## 4 Evaluation
### Latency Reduction
#### 4.3.4 CT  
When putting all of the benchmarks performed in this paper in perspective, it cannot be avoided to state that this benchmark yielded the worst results for latency reduction. As can be observed in Figure 9, the significant difference between CBPC2 and CBPC compression methods can be observed for Wi-Fi and 3G, with the latter being significant due to relatively low throughput, which makes every percentage point essential to spare. CBPC2 compression method yields at most 47% reduction in latency for the 3G environment, 49% for the LTE environment, and 52% for the Wi-Fi environment. For the CBPC compression method, every environment yields up to 50% reduction in latency. When comparing reductions with ZIP compression, both CBPC2 and CBPC do not achieve significant reductions, especially for the 3G network with ZIP-reducing latency to 55%, CBPC to 50%, and CBPC2 to 47% of the raw image transfer latency, meaning that CBPC2  
Figure 6: Latency reduction for Angio benchmark.  
and CBPC are only 8 and 5 percentage points better than ZIP compression method. The margin between ZIP and CBPC2 compression methods is at least 18 percentage points for other benchmarks. Here, we again observe cases in which our compression methods do not achieve at least 50% of reduction in latency (CBPC2 and CBPC in Wi-FI environment, CBPC in LTE environment), which, in the context of other benchmarks, was observed only for Angio benchmark (CBPC in Wi-Fi environment).
## 4 Summary and Future Directions
### Identifying Spatial Variations within a Pasture  
Optimizing the production and quality of forage requires consideration of spatial components of pastures [39]. Plant species diversity, functional composition, and forage production vary by landscape positions [39]. Hence, pastures will be divided into multiple zones based on landscape positions, and location and distance from shade/water/supplements. These multiple zones within a pasture will be monitored over time using high-resolution satellite imagery and/or images taken from small unmanned aerial systems (UAS). Plant species composition, biomass yield, LAI, soil types, soil profile moisture, microbial community, water quality, and greenhouse gas emissions (e.g., soil fluxes of nitrous oxide and CO\({}_{2}\)) will be sampled at monthly intervals during the active growing season from these multiple zones.  
Re-distribution of macronutrients (e.g., N, P, K, S, Ca, and Mg) in exceta tends to be non-uniform in grazed pastures, and can result in high concentrations within localized areas near lounging places, supplement feeders, shade, and water troughs, thereby affecting pasture heterogeneity and the environment [40; 41; 42; 43; 44]. Thus, macronutrient distributions within a pasture will be monitored comprehensively to maximize the efficiency of pastures.
# Robust Quadrotor Control through Reinforcement Learning with Disturbance Compensation
## 3 Disturbance Observation and Control Strategy  
Figure 2 depicts a block diagram of the quadrotor control with the DCRL framework. The proposed DCRL strategy enhances the RL control policy with external disturbance observer and compensator to strengthen the system robustness. The compensation algorithm estimates the external forces and adjusts the input command for the RL controller. The RL controller then changes the motor thrusts accordingly. In Figure 2, the observer takes the attitude \(\mathbf{q}_{f}\) and acceleration \(\mathbf{a}_{f}\) as input, and outputs the estimated external disturbance. \(\mathbf{F}_{ext},\hat{\mathbf{F}}_{ext}\) are the external disturbance and the external disturbance estimated by the observer. The disturbance compensator calculates the \(\mathbf{q}_{comp}\) from quadrotor attitude \(\mathbf{q}_{f}\) and \(\hat{\mathbf{F}}_{ext}\). The original RL actor was trained to hover at the original point by receiving the state \(s\) which contains the position, velocity, attitude and angular velocity of the quadrotor, and output four motors thrust follows the policy \(\alpha(s)\). In DCRL, to make the quadrotor hover at the reference position \(\mathbf{x}_{ref}\), the original point can be shifted with an off-set of reference command and as the input of position \(\mathbf{x}_{dev}\) to the RL actor. The DCRL generates thrust command with the sum of RL actor output and compensation force.  
Figure 2: The structures of quadrotor control. (**a**) Block diagram of the quadrotor control structure with reinforcement learning control and external disturbance compensator. (**b**) The neural network in the reinforcement learning (RL) actor.  
The RL controller in the DCRL was trained to recover and hover at the original point under an ideal simulation environment, while the external disturbances were not considered in RL control policy training. Several reasons exist for not adding external disturbances in RL training. First, the RL controller sometimes has superior performance to traditional methods if the simulation environment is highly similar to a real-world controller model. However, such a model has numerous uncertainties and is highly difficult to reproduce in a simulator. In this study, the sensor noise was one of the uncertain factors because each inertial measurement unit (IMU) sensor on the flight computer had different physical characteristics. Second, the sensor noise does not follow the assumption of the MDP in RL theory; thus, the final performance of the trained policy cannot be guaranteed to be suitable. Finally, the aforementioned traditional external disturbance estimation methods have been demonstrated to be effective. Therefore, we focused on eliminating known disturbances by using an RL controller with a traditional observation method for achieving a superior positioning performance in this study.
## 4 Model Prediction Results and Analysis
### Ga-Gbrt Model
As mentioned above, the number of iterations needs to be terminated early to achieve a higher accuracy, which conforms to the previous analysis. The default parameters recommended by SCKIT are \(M=100\), \(D=3\), \(V=0.1\), and the default loss function is least squares. The iteration error of the model of the default parameters on the training set and the test set is shown in Figure 7a, and the optimized model is presented in Figure 7b. As revealed by the figure, the unoptimized model is in the test set. The convergence effect is unsatisfactory, the MSE is 0.0097, and the optimized model produces the same effect as the training set. The MSE reaches 0.0076. Therefore, after the optimization by the genetic algorithm, the generalization performance of the algorithm on the training data set is improved when compared to the default parameter GBRT. The parameters obtained by the training are factored into the GBRT model to obtain the GBRT prediction machine, and the surface roughness of the test group data is predicted, as indicated by Figure 8. The error will increase with the rise of the feed, as the rising feed will increase the vibration of the machine tool, which will not only cause the surface roughness to increase, but also result in the instability of an experimental error. In order to reduce this error, the average value has been measured for three times in the experiment. It also provides the major means to reduce the error source when the surface roughness is measured in literatures [5; 6; 7].
# Deep Convolutional Neural Network-Based Approaches for Face Recognition
## 4 Methodology and Experiments
### Comparison with the State-of-the-Art Models
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**References** & **Model** & **Datasets** &
\begin{tabular}{c} **Recognition** \\ **Accuracy** \\ \end{tabular} & **Mean** & **Variance** & **Time** \\ \hline [PERSON] et al. (2016)[40] & Hybrid ConvNet-RBM & LFW & 93.83\% & 93.80 & 0.03 & Not available \\ [PERSON] et al. (2017)[42] & DeepFace based on DNN using & LWF & 97.38\% & 97.32 & 0.03 & Not available \\  & VGGNet & LFW & 97.45\% & 97.33 & 0.02 & Not available \\ [PERSON] et al. (2014)[53] & DeepID2 & LFW & 99.15\% & 99.12 & 0.03 & Not available \\ [PERSON] et al. (2017)[59] & BQA method based on CNN & YTF & 99.01\% & 99.00 & 0.01 & Not available \\ [PERSON] et al. (2017)[42] & DeepFace based on DNN using & YTF & 97.38\% & 97.32 & 0.03 & Not available \\  & VGGNet & YTF & 98.98\% & 98.92 & 0.03 & Not available \\ [PERSON] et al. (2015)[49] & Deep CNN & YTF & 98.98\% & 98.92 & 0.03 & Not available \\ [PERSON] et al. (2014)[13] & DeepFace system & YTF & 97.35\% & 97.32 & 0.03 & Not available \\ [PERSON] et al. (2015)[55] & DeepID2+ & YTF & 93.20\% & 93.17 & 0.03 & Not available \\ [PERSON] (2015)[58] & Global expansion ACNN & ORL & 91.67\% & 91.65 & 0.02 & 4.58 min \\ [PERSON] (2015)[58] & Global + local Expansion & ORL & 93.30\% & 93.27 & 0.03 & 5.7 min \\  & ACNN & & & & & \\ [PERSON] et al. (2017)[51] & CNN + SVM & ORL & 97.50\% & 97.47 & 0.03 & 0.46 min \\ [PERSON] et al. (2017)[43] & CNN-2 & ORL & 95.00\% & 94.97 & 0.03 & Not available \\ [PERSON] et al. (2015)[59] & Sparse representation face & FEI face & 61.31\% & 61.30 & 0.01 & Not available \\  & AlexNet + SVM & **97.50\%** & 97.47 & 0.03 & 0.125 s \\  & ResNet-50 + SVM & FEI face & **98.50\%** & 98.47 & 0.03 & 0.051 s \\  & Transfer learning (AlexNet) & **98.70\%** & 98.67 & 0.03 & 0.062 s \\ \hline  & AlexNet + SVM & **99.17\%** & 99.15 & 0.02 & 0.081 s \\  & ResNet-50 + SVM & ORL & **100\%** & 100 & 0 & 0.043 s \\ Our proposed model (2019) & Transfer learning (AlexNet) & **99.17\%** & 99.15 & 0.02 & 0.078 s \\ \hline  & AlexNet + SVM & **100\%** & 100 & 0 & 0.10 s \\  & ResNet-50 + SVM & **100\%** & 100 & 0 & 0.054 s \\ Transfer learning (AlexNet) & **100\%** & 100 & 0 & 0.075 s \\ \hline  & AlexNet + SVM & **94\%** & 93.97 & 0.03 & 0.140 s \\  & ResNet-50 + SVM & LFW & **94\%** & 93.97 & 0.03 & 0.078 s \\ Transfer learning (AlexNet) & **95.63\%** & 95.60 & 0.03 & 0.087 s \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of results with other face recognition (FR) models. our models achieved less accuracy than [PERSON] et al. [42], [PERSON] et al. [53], and [PERSON] et al. [54], as we did not apply any pre-processing method and we used the pre-trained model rather than other researchers who built their system from scratch for the FR problem. Finally, in the time complexity in the comparison, not all models set the illustration for the time. The model ResNet-50 with SVM took less time than other our models.
# Electric Vehicles: Benefits, Challenges, and Potential Solutions for Widespread Adaptation
## 4 Strategies for Overcoming Challenges
### Charging Infrastrcture  
Since electric vehicles often have a smaller driving range than conventional vehicles, their owners may be concerned that they may run out of juice before reaching their destination. Even though the range of EVs is expanding, some drivers, particularly those who need to go long distances, still find it challenging [57]. However, the consumer will be aware of the open slots if they can reserve charging times in advance. Customers can thus research alternative slots besides those already waiting in line. By answering consumers' queries and easing their worries over the charging network, good charging infrastructure will also help to reduce their \"range anxiety\" [1].  
There are several ways to effectively alleviate range anxiety, even if it makes customers unhappy and presents an economic hurdle to EV adoption.  
First, fast DC charging is a practical method for reducing the time it takes to recharge and extending the range when traveling between cities by highway. Various driving styles have various energy and recharge requirements; thus, EV infrastructure planners should consider this. Properly and dynamically building EV recharging infrastructure helps alleviate range anxiety [28].  
Second, a mathematical vehicle model that can forecast \"real road\" driving energy consumption and drivable range may be utilized to estimate accurate energy consumption and drivable range.  
Third, developing countrywide charging stations can also help alleviate range anxiety, but this cannot be done without government incentives or public-private collaboration.  
Finally, range anxiety can be decreased by using a network path selection model. For EV drivers, this model chooses the quickest and best route using an algorithm. These models, meanwhile, might be improved by judging the exit time and duration of a stop at a charging station. The driving range can be increased by employing series, parallel, and series-parallel charging arrangements with extremely efficient electric motors. To partially alleviate range anxiety, some EV manufacturers even provide complimentary rental automobiles for local trips outside the EV range [19; 42].