esa-sceva/satcom-chunk-collection huggingface dataset:
# Environment-Aware Adaptive Reinforcement Learning-Based Routing for Vehicular Ad Hoc Networks
## 5 Simulation Results and Analysis
### Simulation Results and Analysis
#### 5.2.2 Impact of Data Interval  
In this simulation, the source node attempted to send packets at different rates to further observe the routing performance.  
Figure 15 illustrates the packet delivery rates at different transmission rates. As the message transmission rate increased, the number of generated packets within a certain period also rose. With the passage of simulation time, the increasing number of packets burdened the network. EARR performed exceptionally well at various transmission rates; the protocol evaluated the effective bandwidth and could adapt to network environment changes in real time.  
Figure 16 demonstrates the throughput at different transmission rates. As the transmission rate increased, the throughput also grew correspondingly. The EARR maintained a  
consistent linear growth trend at various transmission rates, thereby indicating its ability to adapt to different network congestion levels. The throughput of the EARR protocol was more than double that of the GPSR and CAEB. Vehicles could adapt to varying mobility, interference, and network states. Due to the low packet loss rate in the EARR, the EARR showed significant performance improvements in throughput.  
Figure 17 demonstrates the average hop count at different transmission rates. Similarly, to ensure high PDR and throughput, the routing hop count increases. Therefore, the average hop count in EARR is higher than that in other protocols. Analyzing the routing performance over simulation time reveals that high PDR and throughput are maintained only when the destination is nearby. Consequently, GPSR consistently maintains a very low hop count throughout the simulation, while the proposed protocol experiences a higher average hop count, leading to increased latency due to the additional hops. Considering results from Figure 18, the normalized delay of EARR is generally similar to that of GPSR and CAEB. Only when the transmission rate reaches 120, significant fluctuations occur in EARR. This phenomenon is a result of ensuring high PDR in high-load communication scenarios.  
Figure 19 illustrates the overhead ratio at different transmission rates. The EARR protocol demonstrated remarkable adaptability to varying network traffic loads, thereby consistently maintaining an extremely low overhead ratio. In contrast, the GPSR exhibited stable overhead ratios due to its higher packet-loss rate, thereby making it challenging for the network to experience high-load situations. The CAEB relied on edge nodes to forward packets, thereby keeping these nodes consistently busy and resulting in an increasing overhead ratio over time.
## 6 Discussion
### Limitation of Agro-Meteorological Indicators  
The proposed crop yield forecasting system is based on two groups of data. One group contains predictors related to the crop growth conditions described by agro-meteorological indicators. The other group consists of satellite-derived vegetation indices describing the instantaneous physical state of crops affected by the growing conditions. The fusion of both groups of predictors improves the quality of crop yield forecasts because they mutually diminish their deficiencies/limitations. In this respect, the agrometeorological data (either originating from climatological reanalysis or from interpolation of synoptic observations), have a resolution of a few kilometers, which may be sufficient to describe air temperature, insolation, or average precipitation across a flat terrain. However, some extreme weather events such as hailistorms, heavy rain, strong winds, or frosts will not be captured by a coarse agro-meteorological data. Therefore, it is necessary to use satellite-based predictors, which characterize the vegetation state induced by these adverse weather events. At the same time, satellite vegetation indices have their own limitations. Firstly, in the case of medium resolution imagery pixels contain a mixture of different crop types. Secondly, the saturation of NDVI above a certain biomass level [48] can hamper sensitivity to higher crop yields. Thus, the synergy between agro-meteorological and satellite predictors positively impacts crop yield modeling.
## 1 Introduction  
Aroma is determined by the concentration and composition of various volatiles. Tea aroma characteristics affect its quality, consumer preference, and commercial value. Based on the degrees of fermentation, tea can be classified into three large categories: non-fermented green tea, semi-fermented oolong tea, and fully fermented black tea [1, 2]. Tea making starts with fresh tea leaves as the raw material, which has a grassy odor. During tea processing the volatile profiles are altered and eventually form the characteristic aroma [3, 4, 5, 6]. Volatiles retained in the made teas are either derived from fresh tea leaves or newly synthesized during postharvest tea making. Here, for clarity, the volatiles existed in the preharvest tea leaves are named as endogenous volatiles (EVs) of fresh tea leaves. Accordingly, the non-endogenous volatiles (NEVs) refer to those that are not detected from the fresh tea leaves but present in made teas.  
The quality of tea depends first and foremost, on the quality of the raw material and rational application of technological processes. EVs are affected by the tea cultivar, growing environment, and agronomic practices [7; 8; 9; 10]. It is generally assumed that EVs synthesis during tea making processes contribute significantly to the aroma characteristics of made teas; extensive efforts have been invested to characterize EVs biosynthesis pathways and related gene expressions during postharvest tea making, in the hope to find new strategies to improve the aroma quality of made teas [11; 12; 13; 14; 15; 16; 17]. In most of these studies, volatiles were measured in a relative- or semi- quantitative manner; volatiles of fresh tea leaves versus those of made teas were not quantitatively extracted and compared. Thus, it remains unclear whether these synthesized EVs really contribute to the aroma quality of made teas. In this study, we started with the same batch of fresh tea leaves (_Camellia sinensis_ cv _Tieguanyin_), and made three different types of tea (green tea, oolong tea, and black tea) according to respective standard tea processing methods (Supplementary Figure S1). Their volatiles were extracted by a solvent-assisted-flavor evaporation, then quantified by gas chromatography-flame ionization detector (GC-FID) combined with FID response factor correction. We found that compared to fresh tea leaves, the volatile profiles of made teas were dramatically altered; the majority of the EVs contents were reduced, while many NEVs accumulated simultaneously. The total volatile contents of made teas were significantly lower than those of the fresh tea leaves. By calculating the odor-activity value (OAV), the potential volatiles contributing to the aroma characteristics of each tea type were identified. Our data suggest that EVs synthesis during postharvest tea processing did not make significant contributions to the aroma quality of made tea.
# A Labelling Method for the Travelling Salesman Problem
## 1 Introduction  
The travelling salesman is defined as a network \(G\), given by \(G=(K,L)\), where \(K\) denotes the set of nodes in the network and \(L\) denotes the set of links in the network. For convenience, node \(1\) is considered as the home city. Starting from node \(1\), the salesman has to visit \(K-1\) nodes exactly once and return to the home city while minimizing the travelled distance. The nodes in the network may represent locations, cites, game parks, mountains, airports and countries, among others. Network arcs represent roads, railway lines and rivers, among others. The TSP can be classified as a symmetric TSP or non-symmetric TSP. The TSP is a combinatorial optimization problem that is very complex to solve since it changes to form new variants as technology improves. The cost-constrained travelling salesman problem (CCTSP) in Sokkappa [1], price collecting travelling salesman problem (PCTSP) in Dogan and Alkaya [2], maximum scatter travelling salesman problem (MSTSP) in [PERSON] et al. [3], vehicle routing problem (VRP) in [PERSON] et al. [4], equality generalized travelling salesman problem E-GTSP in [PERSON] et al. [5] and clustered travelling salesman problem (CTSP) in Kawasaki and Takazawa [6] are some of the variants of TSP. Many algorithms and heuristics such as the Greedy Algorithm (GA), Dynamic Programming (DP), Simulated annealing algorithm (SA), Neighborhood search algorithm (NSA), Genetic Algorithm (GA) and TABU Search algorithm (TA) are among the well-known developed algorithms used to solve the TSP.
## 4 Results and Analysis
### Results of Classifiers with Hyperparameter Optimization
#### 4.2.6 Hyperparameter Optimization of RTF
The maximum performance measures of the classifiers after hyperparameter optimization are provided in Table 15.  
From Table 15, the Rotation Forest ensemble outperformed other classifiers with an accuracy of 97.91%, sensitivity of 97.91%, specificity of 97.66%, precision of 97.92%, F-measure of 97.91%, and AUC of 0.996 using the Random Forest  
From Figure 9, the squares indicate incorrectly classified instances, and the cross marks denote correctly classified instances. The three blue squares indicate that three instances are incorrectly predicted as the presence of heart disease, and the nine red squares signify the nine instances are incorrectly predicted as the absence of heart disease. The blue cross marks indicate the instances that are correctly classified as the absence of heart disease, and the red cross marks imply the instances that are correctly classified as the presence of heart disease. The ROC plot of the Rotation Forest ensemble after optimization is shown in Figure 10.  
From Figure 10, the area under the ROC curve of the Rotation Forest ensemble on optimal components is 0.996, the same as that obtained with this ensemble on the complete set of principal components as in Figure 6a. An AUC value close to 1 indicates that the model can achieve a high true positive rate, the proportion of instances that are correctly identified as positive for the disease, while still maintaining a low false-positive rate, the proportion of individuals who are incorrectly classified as positive for the disease. The bar chart of the machine learning model's cost (incorrectly classified instances) utilizing optimal components, based on Tables 7 and 15, without and with hyperparameter optimization, is shown in Figure 11.  
Figure 10: Roc plot of the rotation forest ensemble after optimization. Figure 9: Classifier errors plot of rotation forest ensemble. From Figure 11, the cost of all machine learning models has decreased with hyperparameter optimization. Specifically, the AdaBoost M1 model achieved a remarkable drop in cost from 73 to 15 instances, followed by the SVM classifier from 88 to 39 instances. The best-performed ensemble Rotation Forest has yielded a reduction in cost from 23 to 12 instances.
# PET/CT Radiomics in Lung Cancer: An Overview
## 2 Methodology
### Feature Extraction
#### 2.4.3 Deep Learning  
Deep Learning is a relatively new data-driven paradigm for image analysis [39]. In this model, the feature computation is no longer defined a priori and hard-coded as in the hand-crafted methods but is learned from the data. This scheme is typically implemented by combining suitable computational blocks (_layers_) to form more complex structures (_convolutional neural networks_--CNN). Certain types of such blocks contain a number of free parameters, the values of which are determined via some training procedures. These consist of presenting the network with large sets of pre-classified (labelled) ROIs, through which the network adjusts ('learns') the values of the free parameters.  
Deep Learning has recently shown great potential for computer-assisted diagnosis [38; 40] and for prediction of response to therapy [41] in patients with lung cancer. A potential drawback of Deep Learning, however, is that the resulting features are not as easy to interpret as the hand-designed ones, nor readily linkable to clinically relevant image findings [42]. The paradox, then, is that, even if the results are good, we don't know why; as a consequence, it is hard to investigate the methods for possible sources of bias and/or mistakes (for a discussion on the perils of excessively complex algorithms, see also ([43], Ch. 6)).
# Evaluation of English-Slovak Neural and Statistical Machine Translation
## 1 Introduction
#### Research Objectives  
This research aims to compare the outputs of a phrase-based statistical MT system with the outputs of a neural MT system. The comparison will be linguistically motivated and conducted based on automatic translation quality metrics (BLEU_1, BLEU_2, BLEU_3, and BLEU_4). We will also distinguish between two MT systems in both their SMT and NMT versions (Google Translate--GT_SMT and GT_NMT--and the European Commission's MT tools--mt@ec and eTranslation). The aim is comprised of two partial objectives.  
The first objective deals with Google Translate (GT); i.e., based on BLEU_n metrics, we compare the quality of statistical machine translations and neural machine translations of journalistic texts from English into Slovak.  
The second objective is similar to the first, but it deals with the European Commission's MT tool; i.e., based on BLEU_n metrics, we compare the quality of statistical machine translations and neural machine translations of journalistic texts from English into Slovak.  
We assume that the machine translation system based on neural networks will achieve a better quality (accuracy) than the statistical machine translation system, regardless of whether it is an MT system trained on general texts (Google Translate) or trained on domain-specific parallel texts (the European Commission's MT tool).  
The structure of the paper is as follows: We provide a brief review of related work in the field of MT focused on SMT and NMT (Section 2). Then, we describe the applied research methodology and dataset (Section 3). The Section 4 provides research results based on an automatic MT evaluation and a residual analysis (Section 4). The Section 5 offers a discussion of the results, and the Section 6 summarizes the contributions and limitations of the presented research.
# Power System State Estimation Approach Considering Transmission Line Temperature
## 3 Augmented Jacobian Etc-Se Approach
### Solving Approach for Augmented Jacobian Etc-Se  
As aforementioned, the AJ-ETC-SE is an overdetermined equation set. With the augmented Jacobian matrix \(\mathbf{H}\), the AJ-ETC-SE can be solved by the Newton method, as shown in Figure 2. Thus, the detailed solving procedures of the proposed AJ-ETC-SE approach can be listed as,  
1. [label=(0),ref=]
2. Obtain system topology and network parameters.
3. Input the measured electrical and meteorological data.
4. Initialize the voltage phasors, line temperature, and corresponding parameters.
5. Form the admittance matrix, and calculate the augmented Jacobian matrix \(\mathbf{H}_{mag}\) according to (19)-(32).
6. Calculate the correction vector \(\Delta\mathbf{x}_{ang}\) by (36) and update the state vector \(\mathbf{x}_{ang}\) by (18).
7. If \(\|\Delta\mathbf{x}_{mag}\|_{\infty}\!\!\leq\!\!\xi\), output the state vector \(\mathbf{x}_{aug}\), otherwise, return to step (4).
# MQC-MB: Multiphoton Quantum Communication Using Multiple-Beam Concept in Free Space Optical Channel
## 4 The Implementation of MQC-MB
### Multiple Beam Quantum Communication Phase
#### 4.2.1 Step 1: Transmission of Quantum Signal  
After the laser beam strikes the polarizer in the initial phase, the beam is then assembled by a mirror and a beam combiner. Next, the beam is directed towards the half-wave plate to change the polarization of the beam using the secret polarization angle \(\theta\). After the beam passes through the fine pointing assembly, it is then directed to the transmitter array.
## 2 Importance of Thermal Condition Monitoring
### Wind Turbine Rotatory System  
Challenging environmental factors combined with high turbulent operating conditions place considerable pressure on wind turbines and result in the failure rate of components. It is vital to strictly observe the wind turbine operations. Proper uses of condition monitoring to detect the early stage events minimize the thermal impacts and fatigue development [31]. Temperature is a critical and readily measured indicator for the health status of wind turbine components. Since wind turbines deal with moving parts, the concern for condition monitoring should be with the generator temperature for real-time measurements. Other variables that have a close relationship with the generator compartment temperature should be included especially in tropical areas where the temperatures may escalate beyond the threshold levels [32; 33].  
The real-time online temperature measuring variables emphasize the importance of monitoring thermal status in wind turbines. Power generated from wind turbines bears significant influence on the temperature of the generator. When the output power is high, the generator stator current becomes very large, resulting in the generator temperature increasing. The impact affects the insulation cooling of the generator [19; 34]. The condition demands critical attention because the temperature of the generator cooling air (GAT) impacts the stator cooling. It then affects the generator stator winding temperature (GT) when cooling is ineffective, affecting the components of rotatory compartment therein. Ambient temperature has a high impact on the local temperature when the wind turbine experiences changes in the short term (for example, from a day to the night) and in the longer term (weeks to months). There is also development due to passing weather fronts in the systems and seasons. The ambient temperature must be considered explicitly during the design and installation of the system/plant. Moreover, changes due to the fluctuating weather heads create significant turbulence on the turbine aerodynamics and further develop friction loss [34]. The Nacelle temperature has a close relationship with the generator temperature and cannot be overlooked [34]. In particular, the electrical/electronic components located within the Nacelle could experience extremely high-temperature variations leading to inconsistent operational stresses or thermal fatigue. In order to maintain acceptable temperature levels inside the Nacelle and to efficiently control the thermal impact, it is necessary to have a real-time online condition monitoring system to send information promptly, hence our design concept.
## 3 Results and Discussion  
### Sampling  
Ten samples in total were collected from three different sampling sites. Five samples were from Killary harbour and were labeled KHN18, KHM19, KHJ19, KHS19 and KHO19, collected in the months of November 2018, March 2018, June 2019 and September and October 2019. Three samples were obtained from Mulroy bay and were labeled MBF19, MBMay19 and MBJuly19, as they were sampled in the months of February, May and July 2019. Two were obtained from Ardgorom, samples AJ19 and AJuly19, obtained in the months of June and July 2019. Most of the by-products were seed mussels, except KHN18, which were broken shells. Samples labeled MBJuly19, KHS19 and KHO19 were undersized mussel by-products containing byssus and broken shells.
## 4 Overall Design and System Analysis
### System Implementation
#### 4.3.1 System Framework Design  
For cooperative positioning, the position calculation can be performed on underwater vehicles, as well as in the surface control center. If the calculation is carried out on underwater vehicles, additional hardware and software requirements are needed, such as the high-precision clock and dedicated calculation resources. Meanwhile, base stations will consume more energy due to the need for continuous signal transmission, which also exacerbates challenges in mitigating interference. In order to improve the system feasibility for all kinds of underwater platforms, reduce base station energy consumption, and acoustic interference, we put the position calculation system in the surface control center.  
Basically, the \(U^{2}CPNT\) system combines long baseline positioning and underwater acoustic communication principles by deploying five or more floating/submersible buoys or unmanned boat base stations, as shown in Figure 3, which is a minimum system. Thesestations, with known absolute positions obtained through the RTK Beidou/GPS systems for clock synchronization, receive periodic sound signals from acoustic beacons on underwater submersibles. By analyzing time variances and geographic data from base stations, the system accurately determines submersible positions and transmits this information to the surface/shore command center via wireless or mobile networks.  
For large-area CPNT application, communication and networking are essential functions to ensure reliable information transmission. Acoustic beacons transmit submersible status data to buoy base stations, which relay the data to the command center. After calculating, the surface/shore-based command center interface shows the position, trajectory, depth, and status of underwater submersibles. The shore-based control center can also send control commands to UUVs. The system framework is shown Figure 4.