esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Risk Identification
#### 2.2.2 Stochastic Uncertainty Assessment Using Monte Carlo Simulations  
Metropolis and [PERSON] first introduced the Monte Carlo method [42]. Monte Carlo methods are numerical methods for solving mathematical problems by modeling random variables and statistically evaluating their characteristics.  
In general, the Monte Carlo method involves the following 4 steps [43]:  
1. The generation of a static model (process functions);
2. Defining input parameters through probability distribution functions;
3. The generation of random variables from the set of distribution of input parameters;
4. Analysis of the results obtained.  
Establishing the input data for a Monte Carlo simulation is influenced by numerous criteria and can be approached in various ways. Consequently, extensive market analyses and future demand forecasts are utilized to project metal prices. To assess production costs, industry standards and evaluations from comparable mining operations (of the same deposit type, employing similar technology and equipment, etc.) are referenced. Geological uncertainty is assessed by examining the deterministically developed geological model, the density of exploration activities, permissible assessment errors (according to various standards), and additional factors.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Geological Uncertainty** & **Market Uncertainty** & **Costs Uncertainty** & **Recovery Uncertainty** \\ \hline Geometry of orebody & Price of product & Mining costs & Mining recovery \\ Units of product—grade & Exchange rate & Processing costs & Processing recovery \\ Specific gravity & & Selling costs & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sources of uncertainty affecting the economic value of the block.  
This aligns with the introduction, where numerous authors have proposed different approaches to address different types of uncertainties, including geology, metal prices, production costs, and recovery factors.  
After establishing the input parameters, a number of simulations are conducted to generate various geological models and combinations of parameters, resulting in a comprehensive collection of input scenarios. The number of simulations remains unlimited and is primarily determined by the analyst's resources. An increase in the number of simulations clearly correlates with enhanced model reliability.  
A student license for the @Risk software (version 8.2.2) [44] was used to conduct Monte Carlo simulations.
## 2 Materials and Methods
### MALDI-TOF MS  
#### 2.2.1 Validation of Yeast Identification by MALDI-TOF MS Biotyper  
Biological material from a freshly-grown single colony was used in parallel for identification at species level (i) by sequencing 265 rDNA using NL1-NL4 primers for amplification [37] and (ii) by MALDI-TOF MS Biotyper. For MALDI-TOF MS analysis, a fresh colony was spotted onto an MSP 96 target polished steel BC (Bruker, Karlsruhe, Germany) and allowed to dry at room temperature. The spot of each colony was overlaid with 1 \(\upmu\)L 70% formic acid and dried at room temperature. All the samples were overlaid with \(\upalpha\)-cyano-4-hydroxycinamic acid (HCCA) (1 \(\upmu\)L) matrix (Bruker, Germany) for crystallization. MALDI-TOF MS analysis was performed on a MicroflexTM LT/SH MALDI-MS System (Bruker Daltonics, Bremen, Germany) using Flex Control (Version 3.1), MTB Compass (Version 3.1) (Bruker Daltonics, Bremen, Germany), and MALDI-BiotyperTM application (Bruker Daltonics, Bremen, Germany), which allows the similarity of the mass profile of an unknown microorganism to be calculated with the mass profiles in a database. To calibrate the mass spectral data generated by the instrument, the Bruker bacterial test standard (BTS) (Bruker, Germany) was added to each plate as a control. The identification of microbiological samples by this method relies on the acquisition of mass fingerprints and subsequent comparison of the data with the Biotyper database. The spectra were analyzed in an \(m/z\) of 2 to 20 kDa [38]. Results of the pattern-matching process were expressed as proposed by the manufacturer, with scores ranging from 0 to 3. Scores >2.3 indicated highly probable species identification, score values between 1.7 and 2.0 generally indicated relationships at genus level, and a score <1.7 indicated that the identification was not reliable [39; 40].
# Enhancement of Question Answering System Accuracy via Transfer Learning and BERT
## 5 Experimental Results
### Entity Link Experiment  
Table 4 shows the experimental results of entity reference recognition, and P, R, and F values are used as evaluation indicators. The results suggest that the model can achieve accurate outcomes. Traditional NER tasks need to distinguish different types of entities and determine the boundary positions at both ends of the entities. In contrast, entity mention recognition sub-tasks only need to identify the locations mentioned by the entities and do not need to distinguish the types, and simple questions that usually only contain a single entity mention, which reduces the difficulty of this sub-task. In the entity disambiguation subtask training set, the ratio of positive and negative cases was 1:5, and all candidate entities were selected for prediction in the verification set and test set. The sub-task is prone to over-fitting in the training process, so fewer training rounds and lower learning rates are selected. The evaluation indexes were Acc@N. The formula is shown as follows. For the evaluation set of Q questions, each question predicts the set of \(N\) candidate answers, \(C_{i}\) that contains the correct answer \(A_{i}\) is recorded as 1, otherwise 0. Each question is summed and then averaged. The average precision is obtained. In this paper, we merely focus on the mean accuracy for \(N=1,2,3\).  
\[\text{Accuracy@N}\,=\frac{1}{|Q|}\sum_{i=1}^{|Q|}\delta(C_{i},A_{i}) \tag{21}\]  
Table 5 presents the experimental results of solid disambiguation on the test set. Among them, the introduction of relational features in the Acc@1 index increased by about 10%. This is because the benchmark model that only uses questions and entity names as input has less available information. At the same time, the relationship features contain topological information of topic entities in the knowledge graph and their associated relationship content, thus enhancing the matching degree between topic entities and questions, effectively improving entity disambiguation. Moreover, the feature extraction capability of CNN has also brought improvement. By reserving the top three candidate entities, a high accuracy rate can be acquired, which is helpful in reducing the size of the candidate relation set for relationship prediction and enhancing the efficiency of the question answering system. To sum up, entity linking can accurately identify the entities mentions and accurately and link effectively to the entities in the knowledge graph.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Models** & **F1** \\ \hline BiLSTM-CRF & 90.28 \\ BERT-BiLSTM-CRF & 96.90 \\ Ours & 98.66 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Named Entity Recognition.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Models** & **Accuracy@1** & **Accuracy@2** & **Accuracy@3** \\ \hline Siamese BiLSTM & 87.85 & 92.58 & 94.59 \\ Siamese CNN & 88.04 & 92.68 & 94.88 \\ BERT-CNN & 89.14 & 93.19 & 95.05 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Entity disambiguation.
## 4 Experiment
### Threshold Estimation and Scenario-Based Experimental Results
#### 4.2.1 Threshold Estimation
Figure 6b illustrates the graph of intersection similarities for similar videos in the BNM environment. In the intersection algorithm-based similarity comparison, if two frames to be compared are completely matched, the resulting value is 1, and if not matched completely, the value is zero. The selection of experimental data and the experiment process are the same as in Figure 4. As a result, the similar frame threshold determined by the intersection algorithm is 0.48 or more. Figure 6c illustrates the graph of Bhattacharyya similarities for similar videos within the BNM environment. In the Bhattacharyya distance-based comparison, if two frames to be compared are matched completely, the resulting value is zero, whereas the less similar they are, the closer the distance value is to 1. The selection of the experimental data and experiment process are the same as in Figure 6a. As a result, the similar frame threshold determined by the Bhattacharyya algorithm is 0.52 or less. Seven other environments were also implemented in the same way. Table 5 shows the thresholds in eight different environments. Figure 6: Threshold for each algorithm. In Sections 4.2.2 and 4.2.3, scenarios based on the above thresholds are presented. In the experiments, similar videos were identified according to whether the investigator's reference video was original or manipulated.
# Feature Fusion Graph Consecutive-Attention Network for Skeleton-Based Tennis Action Recognition
## 6 Experiments and Results
#### Computational Efficiency  
The proposed FFSCAN had only 1.33 M parameters and required 2.746 GFLOPs per \(200\times 17\) sequence. On the GPU (RTX 4090) it achieved 0.96 ms latency, which corresponds to \(\approx\)1041\(FPS\) and allowed for smooth real-time inference. Compared to the baseline ST-GCN, we had \(\approx\)22.65% fewer FLOPs and a \(\approx\)45.5% shorter processing time, with incomparably higher accuracy. The obtained computational efficiency results are presented in Tables 10 and 11.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Node** & **Layer** & **Configuration** & **FLOPS [G]** & **Part** \\ \hline \multicolumn{4}{l}{Stem} \\ \hline
1 & Conv2d \(1\times 1\) & 3\(\rightarrow\) 64 & 0.75 & 0.03\% \\ \hline \multicolumn{4}{l}{Blocks 1–3 (64\(\rightarrow\)64)} \\ \hline
2 & 3\(\times\) GSA & 64 & 49.06 & 1.8\% \\
3 & 3\(\times\) ACAM & 64 & 6.16 & 0.2\% \\
4 & 3\(\times\) TCN & 64 & 0.11 & \(\ll\)0.1\% \\ \hline \multicolumn{4}{l}{Blocks 4–5 (64\(\rightarrow\)128)} \\ \hline
5 & 2\(\times\) GSA & 128 & 130.94 & 4.8\% \\
6 & 2\(\times\) ACAM & 128 & 16.37 & 0.6\% \\
7 & 2\(\times\) TCN & 128 & 0.15 & \(\ll\)0.1\% \\ \hline \multicolumn{4}{l}{Blocks 6–7 (128\(\rightarrow\)256)} \\ \hline
8 & 2\(\times\) GSA & 256 & 524.38 & 19.1\% \\
9 & 2\(\times\) ACAM & 256 & 65.31 & 2.4\% \\
10 & 2\(\times\) TCN & 256 & 0.31 & \(\ll\)0.1\% \\ \hline \multicolumn{4}{l}{GAP + FC} \\ \hline
11 & Global AvgPool & — & — & — \\
12 & FC 256\(\rightarrow\)4 & — & 0.006 & \(\ll\)0.1\% \\ \hline \multicolumn{4}{l}{Sum} & 2746 G & 100\% \\ \hline \end{tabular}
\end{table}
Table 10: Comparison of inference complexity and speed for the baseline models (ST-GCN) and the proposed FFSCAN. The time measurement was performed on RTX 4090 and a single-core Ryzen 7950X with the sequence \(T=200\), \(V=17\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Dataset** & **Stroke** & **Mean** & **Max** & **Min** & \(\pm\)**SD** \\ \hline \multirow{4}{*}{THETIS} & Forehand & 90.22\% & 95.39\% & 79.31\% & 3.64\% \\ \cline{2-6}  & Backhand & 90.90\% & 96.59\% & 84.27\% & 3.31\% \\ \cline{2-6}  & Volley forehand & 91.47\% & 96.28\% & 89.71\% & 3.69\% \\ \cline{2-6}  & Volley backhand & 91.71\% & 92.12\% & 79.83\% & 4.67\% \\ \hline \multirow{4}{*}{Tennis-Mocap} & Forehand & 94.27\% & 97.72\% & 90.91\% & 3.40\% \\ \cline{2-6}  & Backhand & 93.51\% & 96.82\% & 86.97\% & 4.28\% \\ \cline{2-6}  & Volley forehand & 92.08\% & 97.67\% & 89.72\% & 3.77\% \\ \cline{2-6}  & Volley backhand & 91.46\% & 95.48\% & 83.37\% & 4.77\% \\ \hline \multirow{4}{*}{3 DTennisDS} & Forehand & 91.09\% & 96.76\% & 89.31\% & 3.68\% \\ \cline{2-6}  & Backhand & 92.63\% & 96.30\% & 89.91\% & 3.80\% \\ \cline{1-1} \cline{2-6}  & Volley forehand & 92.39\% & 96.26\% & 90.31\% & 2.68\% \\ \cline{1-1} \cline{2-6}  & Volley backhand & 93.77\% & 95.92\% & 88.33\% & 3.87\% \\ \hline \end{tabular}
\end{table}
Table 9: Obtained F1-score results for individual strokes based on the proposed model.
# A Three-Dimensional Electrochemical Process for the Removal of Carbamazepine
## 3 Results and Discussion
### 2D Electrochemical Process Operational Conditions
#### 3.1.4 Effect of pH on CBZ Removal  
The influence of the initial pH on the CBZ removal was studied for different pH values: 3, 5, 7, and 9. The pH value was adjusted with HCl and NaOH solutions. The CBZ working solution had a pH of 5. The results shown in Figure 4 indicated that the CBZ removal was much faster at pH 3 than that for the other studied pH values. Nevertheless, for these pH values, the removal rate was very similar, around 90% after 5 min.  
These results were in accordance with those obtained by [PERSON] et al. [47], who studied the effect of pH on the removal of CBZ in a photocatalytic process and found that at low pH values, the degradation of CBZ was higher than that observed at pH values close to neutrality. The authors hypothesized that the CBZ CONH\({}_{2}\) group was more susceptible to degradation at acidic pH.  
Considering the typical pH range of domestic wastewater (around 7,) the use of pH 3 would imply a pH adjustment, which would increase the operational costs and would not be adequate for the discharge of the effluents in the aquatic environment. Therefore, pH 7 was considered the best choice for the remaining experiments. The low sensitivity of CBZ removal to the variation of pH in the range between 3 and 9 could be explained by its presence in the neutral form in this pH range, as shown in Figure 5.  
Figure 3: Effect of NaCl concentration on the removal of CBZ from aqueous solutions (operational conditions: j = 6.67 mA/cm\({}^{2}\); interelectrode distance = 3.5 cm; CBZi = 10 mg/L; pH = 5; reaction time = 60 min; cathode = STS; anode = MMO).  
The spectral analysis confirmed that even after 60 min of solution preparation, there was no evidence of CBZ spectrum changes (changes in the chemical species) in the pH range studied that could interfere in the CBZ quantification and consequently in the degradation observed in the electrochemical experiments (Supplementary Figure S1).
## 3 Factors Influencing Volatile Composition
### Postharvest Handling
#### Storage Atmosphere  
Lowering O\({}_{2}\) and raising CO\({}_{2}\) can maintain the quality of many fresh fruits for extended periods. However, exposure of fresh product to O\({}_{2}\) levels below their tolerance level can increase anaerobic respiration and lead to the development of off-flavor. Storage of fruit under controlled atmosphere (CA) conditions can reduce the capacity of several fruit to produce ethylene and alter production of aroma volatiles [109]. Storage of peach fruit cv \"Rich Lady\" for 15 days, under 3% O\({}_{2}\) + 10% CO\({}_{2}\) at 2 \({}^{\circ}\)C, improved juiciness, sweetness, perception of flavor, emission of aroma volatile compounds and sensory acceptance in comparison with fruit stored in cold air [110]. Decreasing O\({}_{2}\) levels and increasing CO\({}_{2}\) levels of \"Titania\" blackcurrants retarded the capacity of 3-week stored fruit to synthesize terpen. Differential changes among the various groups of terpen were more important, where terpene alcohols reached a peak in 6-week air-stored fruit, and storing berries under a high CO\({}_{2}\) level (18 kPa) and/or decreasing O\({}_{2}\) level (2 kPa) resulted in lower biosynthesis of these alcohols compared to control fruit. CA storage conditions led to a transitory reduction in the emission of alcohols but a recovery was recorded with prolonged storage. Non-terpene esters differed greatly in storage, in particular the ester ethyl butanoate. Air-stored fruit at both sampling dates synthesized significantly higher amounts of esters than freshly harvested fruit but a significant decline was observed for branched butyl substances (2-methyl butyl butanoate) after 6 weeks storage [111].  
Use of packaging and edible coatings can create a modified atmosphere (MA) with reduced O\({}_{2}\) and elevated CO\({}_{2}\) levels, similar to that of CA. Use of edible coatings affects flavor and the level of volatile flavor compounds in citrus, apple and mango fruit [112; 113; 114]. The coating barrier probably induced anaerobic respiration and the synthesis of ethanol and acetaldehyde, and entrapped volatiles, including ethanol and acetaldehyde [115]. The effects of different edible coatings on mango fruit showed that mango \"carnauba\" was effective in retarding fruit ripening, retaining fruit firmness, and improving fruit quality attributes including levels of fatty acids and aroma volatiles. Semperfresh and _A. vera_ gel (1:1 or 100%) slightly delayed fruit ripening but reduced fruit aroma volatile development. _A. vera_ gel coating did not exceed the commercial mango \"carnauba\" and Semperfresh in retarding fruit ripening and improving aroma volatile biosynthesis [113].
# Clustering-Based Data Dissemination Protocol Using the Path Similarity for Autonomous Vehicles
## 3 Proposed Method  
In this section, we discuss the path-based clustering protocol proposed in this paper.
# Cooperative UAV Scheme for Enhancing Video Transmission and Global Network Energy Efficiency
###### Abstract  
Collaboration between multiple Unmanned Aerial Vehicles (UAVs) to set up a Flying Ad Hoc Network (FANET) is a growing trend since future applications claim for more autonomous and rapid deployable systems. The user experience on watching videos transmitted over FANETs should always be satisfactory even under influence of topology changes caused by the energy consumption of UAVs. In addition, the FANET must keep the UAVs cooperating as much as possible during a mission. However, one of the main challenges in FANET is how to mitigate the impact of limited energy resources of UAVs on the FANET operation in order to monitor the environment for a long period of time. In this sense, UAV replacement is required in order to avoid the premature death of nodes, network disconnections, route failures, void areas, and low-quality video transmissions. In addition, decision-making must take into account energy consumption associated with UAV movements, since they are generally quite energy-intensive. This article proposes a cooperative UAV scheme for enhancing video transmission and global energy efficiency called VOEI. The main goal of VOEI is to maintain the video with QoE support while supporting the nodes with a good connectivity quality level and flying for a long period of time. Based on an Software Defined Network (SDN) paradigm, the VOEI assumes the existence of a centrailized controller node to compute reliable and energy-efficiency routes, as well as detects the appropriate moment for UAV replacement by considering global FANET context information to provide energy-efficiency operations. Based on simulation results, we conclude that VOEI can effectively mitigate the energy challenges of FANET, since it provides energy-efficiency operations, avoiding network death, route failure, and void area, as well as network partitioning compared to state-of-the-art algorithm. In addition, VOEI delivers videos with suitable Quality of Experience (QoE) to end-users at any time, which is not achieved by the state-of-the-art algorithm.  
energy-aware; energy-efficiency; UAV replacement; UAV coordination 20 October 2018; Accepted: 22 November 2018; Published: 27 November 2018
###### Abstract  
Corn harvesting mode has gradually changed from ear harvesting to direct grain harvesting. In view of the problems of high moisture content in corn harvesting in China, such as the rates of broken grains (BGR) and uncleared grains (UGR) being too high, a new single longitudinal axial threshing cylinder was designed, which mainly included a cylinder spindle, a spiral feeding inlet, a T-type rasp bar, a separating straight rod, and a spiral extracting rod. Firstly, the three states of grain during the threshing process and the key influence factors of threshing and force analysis of corn ears in the threshing device were analyzed, then the structure of the threshing cylinder was designed, and its parameters were determined by theoretical analysis results. The arrangement mode of threshing elements adopted a combination of a T-type rasp bar, a separating straight rod, and a spiral extracting rod with a 6-head spiral pattern and an arrangement step of 250 mm. Secondly, the arrangement step of threshing elements was determined by discrete element method solution (DEM) simulation; the result showed that the average movement velocity was 55.04 m/s and the threshing time was 6-8 s. Finally, a multiple factors experiment of the threshing device was carried out, and the result showed that the order of the effect factors of the BGR and UGR was cylinder rotational speed \(>\) concave clearance \(>\) feed amount. When cylinder rotational speed was 309.17 r/min, concave clearance was 35.48 mm, and the feed amount was 6.13 kg/s. The verification experiment result showed that the BGR and UGR were 1.24% and 1.33%, respectively, which meet standard requirements. The research results could provide a reference for the design of a high moisture content grain threshing device and combine harvester.  
agricultural machinery; corn; high moisture content; low damage threshing; working parameter
### Modelling Testing and Qualification Ability in Preliminary Design Trade-Off Studies  
Test activities are performed, from early concept development to detailed design [28]. In the space domain, qualification activities are test activities performed to demonstrate that a product meets the specified requirements, such as those regarding performance, safety, legislation, quality or reliability [29]. Outside the space domain, similar purposes can be attributed to the verification, validation and testing (VVT) [30]. Verification most often aims to test the fulfilment of requirements, while validation activities test the fulfilment of stakeholders' needs. The ISO standard 15288 draws a clear parallel between VVT activities and qualification [31]. Data from testing is utilized to improve a product design, however, acquiring data from test activities can be expensive in terms of cost and schedule delays. According to [PERSON] and [PERSON] [32], these activities make up to 40% to 60% of the total cost of a product development project; however, they are still not actively considered in early stages of a product architecture design. Modelling the duration and cost of qualification activities in relation to a product design can support the design of product architectures with affordable test and qualification phases.  
Previous research on modelling and simulation of product development activities (in terms of duration and costs) has been conducted using a discrete-event simulation approach [33], in which 'cost drivers' are used. In literature, drivers are often used for describing the causes that affect the output of a system. The term is widely implemented when referring to the factors that cause a change in a cost, in this case, cost drivers (e.g., [34]). However, at present there is no systematic way of modelling how design alternatives impact test and qualification activities (e.g., [35]).