esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Computational Experiments
### Impact of Reward Setting  
In this section, the impact of the parameter setting of the reward of Q-learning is examined from numerical experiments. AGVs receive higher rewards when choosing actions that follow the path determined by A* and lower rewards when choosing actions that conflict with other AGVs. In addition, other unique reward values are obtained when AGVs choose to move in the direction of their destination or stop (see Equation (12)). When each of these unique reward settings was changed, it was checked how the performance of the proposed method changed. The layout in the experimental environment is shown in Figure 2, where 7 AGVs are given an initial node and a target node, and the transfer period until all AGVs reach the target node is measured. The results are plotted as the average of 10 experiments in the same environment.  
Figure 19 shows the transition of the transfer period when each reward parameter is changed. The results show that each reward value can be too low or too high, resulting in poor performance. Therefore, the reward value was selected to reduce the transfer period, as shown in Equation (12).  
Figure 17: Computation time with respect to the number of nodes \(N\).  
Figure 18: Computation time with respect to the number of AGVs \(A\).
## 2 Materials and Methods  
This prospective, randomized, double-blind, split-mouth clinical study was conducted at Van Yuzuncu Yil University, Faculty of Dentistry, Oral and Maxillofacial Surgery Clinic between October 2022 to May 2023. The study was conducted in accordance with the current version of the Declaration of Helsinki. The study was approved by Van Yuzuncu Yil University Faculty of Medicine Clinical Research Ethics Committee (decision number: 07 dated 1 September 2022) and complies with the CONSORT guidelines. During the study period, 553 patients were admitted for extraction of impacted wisdom teeth. Of these, 98 patients had bilateral impacted wisdom teeth, and 45 of them met the inclusion criteria for the study. Among the volunteers, those who met the inclusion criteria were informed about the study, and signed informed consent was obtained. Medical and dental history was taken at the first clinic visit. The presence or absence of bilateral impacted third molars was assessed by clinical and radiographic examination. Demographic information, facial and mouth opening measurements, pain scores, and group numbers were recorded on the anamnesis forms.  
Inclusion criteria were as follows: 18 to 40 years of age, no systemic disease, bilateral and similar (vertical or mesioangular) position, asymptomatic, and partial mucosa- and bone-retained lower impacted third molars.  
Exclusion criteria were as follows: smokers, pregnant or breastfeeding women, those not attending follow-up visits, those allergic to the materials or drugs used in the study, those with alve colitis, and those taking additional or different drugs to the study drugs.  
Randomization in the trial was achieved using the envelope method. From the envelopes given to the patients by the support staff, the patients chose which side to operate on first and which group to use first. Only the sessional staff knew the numbers on the envelopes. To ensure blinding of the study authors, the suturing phase, i.e., closure, including L-PRF placement, was performed by an independent experienced surgeon outside the study. Surgeries were performed on Mondays, Tuesdays, and Wednesdays to allow for postoperative day-2 controls. Second operations were performed at least 4 weeks later.  
Primary outcomes of the study were facial measurements, mouth opening measurements, and visual analog scale (VAS) scores. Secondary outcomes included alveolitis, infection, and the effects of flap release for primary closure. Facial measurements for swelling assessment (the reference points for the facial measurements used in the swelling score were the angulus-lateral canthus, angulus-lateral nasal corner, and tragus-pogonion distances; a single value was obtained by averaging these 3 distances in the swelling score) and mouth opening measurements to assess trismus were performed preoperatively (on postoperative day 2 and on postoperative day 7) with the patients sitting upright in the unit. Pain was assessed according to VAS, and patients rated their postoperative pain at 6, 12, 18, and 24 h and on days 2, 3, 4, 5, 6, and 7.
# Managing Emergency Situations in VANET Through Heterogeneous Technologies Cooperation
## 3 Architecture and Communication Protocol
### Protocol Communications
#### 3.2.2 Main Protocol Messages  
On the basis of WSMP protocol, in this work, broadcast and geocast message policies are introduced to improve cooperation between vehicles and to enhance drivers safety. These messages are locally and globally distributed depending on their goals. In particular, when a danger is rising it is important to disseminate information directly to the closest vehicles to notice them about the event. In this way, preventive actions may be done by drivers and vehicles in a shorter time. If the severity of event is high then it is possible to involve other layers to process the event. The warning dissemination into two separate steps allows to avoid indiscriminate flooding on the network reducing packets number and saving resources.
## 2 Materials and Methods
### Smart Agriculture and Quality 4.0 Concepts  
The foundation of the SA paradigm rests upon the advancement of innovative technologies such as the Internet of Things, cloud computing, machine vision and data analytics [23; 24]. Acknowledging the dynamic nature of the agricultural sector, SA emerges as a requisite for maintaining competitiveness amidst uncertainty [25]. Its overarching objective is to foster agricultural models characterized by flexibility in product offerings and services, facilitated by seamless communication among stakeholders and agricultural facilities throughout the agricultural production and processing cycle [26].  
In essence, SA and fruit production and processing should encapsulate several key Quality 4.0 domains, as outlined by [PERSON] et al. [27]. These components include the following: (1) monitoring of strawberry condition in near real-time feedback from interconnected edge devices accompanied with growing volumes of data; (2) analyses offering descriptive, diagnostic, predictive, and prescriptive capabilities, (3) logistics encompassing mobile applications, platforms, web clients, browsers, and applications for robotics and machinery; and (4) control overseeing autonomous and interconnected processes, including electronic submission of compliance/defect reports and automation of compliance work flows. The objective is to support company endeavors in enhancing strawberry production and processing processes, machinery utilization, and employee operability through the digitalization of strawberry classification and defect detection.  
Hence, within the framework of SA, emerging technologies become both cost-effective and readily available to a wider spectrum of agricultural enterprises, presenting an unprecedented opportunity to address persistent quality issues and embrace innovative solutions. Guided by these proposed domains, the primary objective of this paper is to present the development of a mobile solution for fruit classification and defect detection. This solution is anchored in the aggregation of extensive big data, driving advancements in smart agriculture and strawberry production and processing.
## 2 Materials and Methods  
### Study Design and Examined Smartphones  
This study assesses the accuracy of different single- and multi-frequency GNSS receivers integrated into smartphones under a forest canopy. The experiment examined the accuracy achievable in a static reception status. The priority was the practical evaluation of the internal GNSS receivers' capabilities under a forest canopy.  
A total of 10 smartphones with integrated GNSS modules and one geodetic receiver were used in the tests. The smartphones were selected according to different criteria. In order to obtain a comparison, older models and newer models, as well as models with single and dual-frequency receivers and models that are used in practice ('outdoor/forest mobile phones'), were combined. Table 1 shows an overview of the smartphones and GNSS receivers used in the empirical experiment. It includes the different manufacturers, models, model release years, and the used chips' hardware (construction) year, from 2015 to 2019. Additional information is provided about the used Android versions and the different API levels from 23 to 29, with all smartphones updated to the highest possible version.  
Four out of ten smartphones provide dual-frequency reception (Xiaomi Mi 8, Mi 8 Pro, and Mi 10) with their internal chips, and one smartphone provides triple-frequency reception (Huawei P40). It was often reported from practice that exactly the same smartphone types often lead to very different results. Therefore, a comparison was also made between identical models (Samsung Xcover 4s) and smartphones with the same GNSS chip (Xiaomi Mi 8 and Mi 8 pro).  
Figure 2 shows an overview of the different types of smartphones used and their ability to use the different GNSS constellations and frequencies, as well as the possibility to use double-frequency or triple-frequency reception.
## 4 Experiment Results and Discussion
### Experiment Results
#### 4.2.3 Experiment 3
(**a**) & (**b**) Tuesday data; (**c**) & (**d**) Wednesday data; (**e**) & (**f**) Thursday data. In summary, the memory profiler results of SFSDT model are pointed in Table 5. In NSL-KDD dataset, SFSDT model spent 63 s for time executed and used 145 MiB memory to generate and find the best features. Besides, in ISCX dataset, this proposed model spent averages 120.83 s and 573.33 MiB for time executed and memory used, respectively. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Memory Used** & **Time Executed** \\ \hline NSL-KDD & 145 & 63 \\ Saturday & 480 & 60 \\ Sunday & 700 & 240 \\ Monday & 600 & 120 \\ Tuesday & 650 & 190 \\ Wednesday & 500 & 55 \\ Thursday & 510 & 60 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The memory profiles of SFSDT model two IDS datasets. Figure 20: Memory profile results of SFSDT model to select the best feature subset on ISCX dataset. (**a**) Saturday data; (**b**) Sunday data; (**c**) Monday data; (**d**) Tuesday data; (**e**) Wednesday data; (**f**) Thursday data.
## 4 Space-Based Crop Mapping
### Our Approach
Once cloud masks had been built and applied, several vegetation-related indexes were extracted from multispectral data, such as NDVI, tasseled cap, LAI, and others, in order to capture as many nuances as possible in the spectral behaviour of each vegetated species. No registration was needed at a later stage between images and masks/DEMS as the result of fusing images appeared to match, with sufficient accuracy, the other geospatial data used at later stages. The following step consisted of filling in the gaps in the time series left as a consequence of cloud mapping. This was accomplished by estimating missing values with an interpolation approach based on observed series in past sequences; the current series with gaps was compared with past sequences in the geographical neighbourhood. The most similar local series from the past was detected and the value corresponding to the gap was used in place of the missing value. Once the series were complete, unsupervised clustering was applied to them, in the form of \(k\)-means classification applied to the time series arranged in vectorial form. This resulted in a partition of the land areas into homogeneous regions. An urban mask obtained by classification of the multispectral dataset [30] and a ready-to-use water mask also derived from satellite data [31] allowed us to remove most of the non-vegetated areas. This was the unsupervised part of the procedure, followed by a step including human supervision. Boundaries between homogeneous areas obtained through the previously mentioned unsupervised clustering were automatically extracted and visually compared with true-color satellite images in order to check their consistency. Where discontinuities apparent in true-colour satellite images did not match the above mentioned boundaries, we assumed insufficient separability of classes. These latter cases were tackled by downloading additional multispectral data at higher resolution (LANDSAT or, where available, Sentinel-2 data) to increase spectral (and temporal) separability, and clustering was repeated on the enriched dataset. This turned out to be decisive in some cases, but not many overall as only a dozen dubious boundaries had to be treated with additional data download. A further double-check step was added, consisting of an extensive search for georeferenced ground pictures published on the web. Pictures of crops were selected among those whose time stamps reported the same year/growth season as the analyzed satellite data. Where a region generated by clustering contained pictures with different crop species, such region was treated as a case of insufficient separability, and tackled using a similar approach to the one described above for inconsistent region boundaries. Only few cases of inconsistence were however observed, and the vast majority (around 98%) of checks resulted in confirmation of the first results. A final, sample check was carried out on the result to confirm that no contrary evidence emerged on homogeneity of the extracted regions. Figure 3 shows a sample result on Costa Rica.
## 4 Consequences and Discussion
### Discussion  
CMGBO, which was designed in this paper, uses as many swarms as the objective number and lets each swarm focus on optimizing one objective. These swarms work cooperatively and communicate through an external hared archive. CMGBO benefits from the following three aspects when solving MOPs:  
1. As each swarm focuses on optimizing one objective, it can use conventional or any other improved GBO to solve a single-objective problem. Importantly, the difficulty of fitness assignment can be avoided;
2. As an external shared archive is used to store the non-dominated solutions found by different swarms, and because the shared-archive information is used to guide the seed update, the algorithm can use the whole search information to approximate the whole PF quickly;
3. As \(\varepsilon\)-dominance is performed on the archived solutions in the update process, the algorithm is able to avoid local PFs. This is helpful for MOPs with multimodal objective functions or with complicated Pareto sets. The performance of the proposed CMGBO was tested on different sets of MOPs with various objective functions, PFs and Pareto sets. CMGBO was compared with some state-of-the-art and modern MOEAs and MOPSOs, based on IGD and HV. The experimental results show that CMGBO not only generally outperforms the compared algorithms on ZDT problems, but it also generally performs remarkably better than all the other algorithms on DTLZ problems. When dealing with UF problems with complicated Pareto sets, CMGBO is also one of the most promising algorithms. The experimental results support the good performance of CMGBO on these metrics. Furthermore, the benefit of the shared archive used in CMGBO was investigated to demonstrate both the benefit of \(\varepsilon\)-dominance in bringing in diversity to avoid local PFs and the benefit of the archived information in guiding the particles to approximate the PF quickly. Lastly, the results indicate that the MPMO technique may contribute to reducing the search complexity for each swarm, and a small population size is efficient enough to obtain good performance. The CMGBO algorithm proposed in this paper is an instantiation of the MPMO technique by using CMGBO. However, as it is a new and general technique using multiple populations to tackle multiple objectives in MOPs, there are still many interesting issues worth further studying in MPMO. Future research work will include the following aspects:  
1. Applying other algorithms and their improved variants to realize MPMO for the further evaluation of the performance of the multiple-population algorithms in solving MOP. Nevertheless, the information-sharing and population communication strategy should be redesigned when new optimization algorithms are used. For example, we can let individuals perform crossover among different populations for GA information sharing and communication;
2. Using the MPMO-based multi-objective algorithms to solve MOPs with many objectives and to solve those problems in dynamic, noisy and uncertain environments;
3. Applying the new MPMO-based multi-objective algorithms to real-world problems.
# A Video-Based Fire Detection Using Deep Learning Models
## 1 Introduction
In addition, the areas of SRoFs, including both flame and smoke, are calculated and their temporal changes are reported to interpret the dynamic fire behavior with the final fire decision. Experiments show that the proposed long-term video-based method can successfully improve the fire detection accuracy compared with the still image-based or short-term video-based method by reducing both the false detections and the misdetections. The method discriminates fires from fire-like video sequences especially well. For example, chimney smoke, sunset, and clouds often induce errors in conventional computer vision-based fire detection. Also, the method reflects the temporal behavior of real fire situations well by providing area information of SRoFs. Therefore, our key contributions can be summarized as follows:  
1. We propose a deep learning-based fire detection method that avoids the time-consuming efforts to explore hand-crafted features. Because it automatically generates a set of useful features after training, it is sufficient to construct the proper deep learning model and to gather a sufficient amount of training data. Therefore, we have constructed a large fire dataset which contains diverse still images and video clips, including the data from well-known public datasets. Not only is the dataset used for the training and testing of our experiment, but it also could be an asset for future computer vision-based fire detection research. 2. Our deep learning-based method emulates a human process of fire detection called DTA, in that SRoFs are detected in one scene and the temporal behaviors are continuously monitored and accumulated to finally decide whether it is a fire or not. In the method, Faster R-CNN is used to detect SRoFs against non-fire objects with their spatial features, and LSTM temporally accumulates the summarized spatial features by using the weighted Global Average Pooling (GAP), where the weight is given by the confidence score of a bounding box. The initial decision is made in a short period, and the final decision is made by the majority voting of the series of decisions in a long period. 3. The proposed method has been experimentally proven to provide excellent fire detection accuracy by reducing the false detections and misdetections. Also, it successfully interprets the temporal SRoF behavior, which may reduce false dispatch of firemen. The remainder of this paper is organized as follows. The related work is introduced in Section 2, the details of our proposed method are given in Section 3, and the experimental results and discussions are presented in Section 4. Finally, several concluding remarks are given in Section 5.
# A Comprehensive Survey on Knowledge-Defined Networking
## 2 Overview of Knowledge-Defined Networking (KDN)
### KDN vs. SDN vs. Conventional Network  
Traditional networking is the oldest approach to networking and involves manual configuration and management of devices. This method of networking has been prevalent since the beginning of networking, and it still prevails in modern communication networks.  
SDN is a more recent approach that separates the control plane from the data plane and enables greater flexibility in network design. The control plane is moved to a centralized location, and network administrators use software applications to manage the network. This paradigm allows network administrators to manage networks more easily and quickly due to increased flexibility and programmability.  
KDN creates a self-learning, self-optimizing, and self-healing network by integrating AI and ML technology. Data analysis is a tool used by KDN systems to automatically improve network performance, adapt to shifting network circumstances, and spot and resolve potential network issues before they become bigger difficulties.
## 4 Results and Analysis
### PLES Conflict Measurement  
To quantify spatial conflicts, a modeling approach based on the PLES framework was employed [34; 35]. Landscape ecology indices were measured to evaluate spatial conflicts in Zengcheng District for 2010, 2015, and 2020, with the results summarized in Table 5.  
The PLES conflict index revealed a temporal distribution pattern of weak conflict > weaker conflict > medium conflict > stronger conflict > strongest conflict. Overall, the intensity of spatial conflicts has increased over time but remains within a manageable range. The proportion of stable and controllable units decreased slightly but still accounted for over 80%. Moderate conflicts rose between 2010 and 2015 but showed a decline from 2015 to 2020. However, the share of severe conflict units steadily increased, from 0.89% in 2010 to 2.15% in 2020, indicating growing pressure on the spatial functions of production, living, and ecology in certain areas.  
Over the past decade, the spatial distribution of conflict levels in PLES of Zengcheng District has undergone significant changes (Figure 7). In 2010, the majority of the area experienced weak conflicts, with moderate conflicts concentrated in the southern and central regions and almost no strong conflict zones observed. By 2015, weak and moderate conflict areas expanded, particularly in the southern and central regions of the district, mirroring the growth of living spaces.  
By 2020, weak and moderate conflict areas continued to spread, and the extent of strong conflict zones increased, primarily concentrated in the southern region. This area includes a 60 km\({}^{2}\) national-level economic and technological development zone, where production spaces are significantly constrained by the expansion of living spaces.  
Figure 6: Photo of Zengcheng District in 1989. Note: Sourced from Baidu and other online platforms.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Average**} & \multicolumn{6}{c}{**Spatial Proportion of Each Level/\%**} \\ \cline{2-7}  & & **Weak Conflict** & **Weaker Conflict** & **Medium Conflict** & **Stronger Conflict** & **Strongest Conflict** & **Total** \\  & & **0.0 \(\leq\) E \(<\) 0.2** & **0.2 \(\leq\) E \(<\) 0.4** & **0.4 \(\leq\) E \(<\) 0.6** & **0.6 \(\leq\) E \(<\) 0.8** & **0.8 \(\leq\) E \(<\) 1** & **0 \(\leq\) E \(<\) 1** \\ \hline
2010 & 0.1722 & 58.92 & 30.48 & 5.08 & 4.63 & 0.89 & 100 \\
2015 & 0.3243 & 35.81 & 44.32 & 12.25 & 6.16 & 1.46 & 100 \\
2020 & 0.1859 & 57.78 & 29.40 & 4.70 & 5.97 & 2.15 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Statistical results of the spatial conflict index of PLES in Zengcheng District, 2010-2020.