esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Transient Analysis
#### 4.2.1 Load Step Transient  
Model with PID Controllers  
The load step transient study was repeated five times for 1600 and 1100 engine speeds by limiting the amount of power (from 1 to 5 kW) provided as electrical assistance to the compressor, as shown in Figure 22.  
It is clear that the maximum power limit set for the 1100 rpm case exceeds the model requirement, based on the current PID tuning, for achieving the fastest possible response.  
The results in Figure 23 reveal that an electrical assistance of 1 kW can reduce the transient response time of the engine by 70% while higher levels of power can lead to a reduction of more than 85%. As shown in Figure 23a, power levels of more than 3 kW have almost a negligible effect on the response time of the engine. It is obvious by looking at Figure 23b that the improvement in response time is due to the rapid increase of the turbocharger's shaft speed. The fact that the initial shaft speed is higher, as a result of the smaller turbine, also contributes to a fast transient change. Moreover, Figure 23c shows that the electrical assistance provided to the turbocharger pushes the operation of the compressor towards the right side of the mass flow/pressure ratio map, which leads to a more efficient operation of the compressor.  
Figure 23: _Cont._  
Figure 22: Limits and level of power provided to the compressor during tip-in transient: (**a**) 1600 rpm; (**b**) 1100 rpm.  
On the other hand, the transient improvement at very low engine speeds is of higher magnitude, as shown in Figure 24a,b. This happens due to the very low pre-turbine pressures occurring at 1100 rpm. Moreover, as illustrated in Figure 24c, the electrical assistance provided to the turbocharger pushes the operation of the compressor towards the surge line of the map.  
Figure 23: Load tip-in results at 1600 rpm engine speed: (**a**) BMEP response time; (**b**) turbocharger speed response time; (**c**) compressor performance.  
Figure 24: Load tip-in results at 1100 rpm engine speed; the two peaks highlighted in the red circle are a result of the mass flow rate fluctuation when the throttle valve opens: (**a**) BMEP response time; (**b**) turbocharger speed response time; (**c**) compressor performance.  
Open-Loop Study (No PID Controllers)  
The open-loop study was performed for investigating the hardware capabilities without any effects from the tuning of the PID controllers. Figure 25 reveals the importance of the initial level of power, as well as the average power provided to the compressor. Cases with the maximum amount of power provided during the first section benefit from the fastest response and highest BMEP levels. On the other hand, Figure 25b shows that the power distribution is equally important as the average power provided to the compressor for a fast transient response time.
## 4 Results
### Performance Analysis Based on Classification Algorithms  
In the second experiment, five different classification algorithms (i.e GBT, DT, RF, NB, and DL) are employed as a single classifier. The model competence is computed based on precision, recall, accuracy, and f-measure. Among the five single classifier models, GBT demonstrated the highest accuracy of 92.98% and 93.19% for the Orange and Cell2 Cell dataset, respectively, while the DT classifier shows the lowest results and achieves 85.40% and 87.04% for both the Orange and Cell2 Cell dataset. Figure 3a,b illustrate the results of the classifiers evaluated in this research.
## 2 Materials and Methods
### Methods  
#### 2.7.1 Data Processing Summary  
Survey grade LiDAR products were used as inputs to georeference canopy and solar radiation models. The ERDAS Spatial Modeler (ERDAS, Atlanta, GA, USA, part of Hexagon Group) was used to derive leaf area index (_LAI_) data from the multispectral image, and Boise Center Aerospace Laboratory (BCAL) Vegetation and Intensity tools developed in ENVI (Harris Geospatial, Boulder, CO, USA) were used to derive canopy metrics from the LiDAR point-cloud data that were subsequently gridded to a 30 m resolution raster format from raster data scales less than 30 m.  
The Klamath Mixed Conifer (KMC) forest-type was selected as the focal plant community to delineate the study area. KMC is a California Wildlife Habitat Relationship System(CWHRS) attribute \"crosswalked\" in the Classification and Assessment with LANDSAT of Visible Ecological Groupings (CALVEG) data layer [29]. All LiDAR point-cloud and imagery data were resampled to 30 m resolution using bilinear or nearest neighbor methods for discrete and continuous data and then clipped to the KMC vegetation type within the analysis extent and elevation boundary in ArcGIS (ESRI, Redlands, CA, USA). This step established the study area, yielding 144 data points for each independent variable. The raster resolution and the corresponding centroids provided plot size and location coordinates for the field analysis.  
The pre-processing of survey-grade data products required to analyze the study area and select plots for biodiversity sampling is shown in a diagram (Figure 3).  
Each 30 m field plot references a pixel of gridded LiDAR data with the same resolution. All diversity values resulting from plot data are numerically ordinal and assumed to be spatially continuous. As ArcGIS transforms the geographic co-ordinate system (GCS) of layers to match the map's reference GCS, all gridded raster and vector data used in the analysis were transformed to match the survey grade bare earth DEM provided in the post-acquisition LiDAR products. The WorldView-2 imagery was also transformed to these co-ordinate systems [30] using the ArcGIS transformation tools.  
Forest dynamics (disturbance types and succession) are spatio-temporal phenomena that were not the direct focus of the study. There is a time-lag of five years between the LiDAR and multispectral data acquisition and the final field sampling of biodiversity plots. For this reason, sites evidencing plot-scale canopy disturbance were excluded from the study to exclude remotely sensed data that did not align with the observed vegetation height.  
Figure 3: Data processing steps for the analysis extent and study area.
# CFD-DEM Simulation of the Transport of Manganese Nodules in a Vertical Pipe
## 5 Results and Discussion
### Effect of Initial Mixture Velocity
#### 5.3.2 Average Parameters  
As seen in Figure 14, the cases featured by different initial mixture velocities have similar average seawater velocity distribution patterns. The maxima of the average liquid and solid velocities arise at the pipe axis, while near the pipe wall, the two velocities reach their minimum values, respectively. Due to the effect of the wall shear, the maximum average liquid velocity is even higher than the initial mixture velocity, and the average liquid velocity near the wall decreases sharply to its minimum. Near the wall, a significant influence of the viscous force results in low average liquid velocity. Meanwhile, at the cross-sectional center, the inertia force promotes the increase in the average liquid velocity. In fact, the kinetic behaviors of the particles are less affected by the wall shear effect. This can be attributed to the low frequency of tangential friction between particles and the wall. However, direct collision between a particle and the wall results in a considerable decrease of the kinetic energy of the particle. Consequently, the average solid velocity decreases continuously in the radial position.  
Figure 14: Effects of initial mixture velocity on average phase velocities (\(C_{p,0}=5\%\), \(d_{s}=15.0\) mm).  
Figure 13: Instantaneous particle velocity distributions at different initial mixture velocities (\(C_{p,0}=5\%\), \(d_{s}=15.0\) mm).  
It is evidenced in Figure 15 that the average slip velocity decreases with increasing initial mixture velocity. The drag force depends on the drag coefficient and the liquid velocity. With increasing initial mixture velocity, liquid velocity plays a more important role than the drag coefficient, resulting in the rise of the drag force and the decrease of slip velocity. It is inferable that high initial mixture velocity is beneficial to the conveying of large particles. However, more energy input is necessitated.  
It is evidenced in Figure 16 that the initial mixture velocity has a slight effect on average SVF distributions in the pipe. The increase of the initial mixture velocity results in a decrease of the average SVF. For the three cases, average SVFs reach their minimum value around \(r/R=0.65\) and then rise near the wall. This can be attributed to the fact that particles around \(r/R=0.65\) prefer to be impacted toward the axis or wall of the pipe. With increasing initial mixture velocity, at given initial SVF, the number of particles released at the inlet increases. However, increasing initial mixture velocity improves the mixture conveying performance, manifesting in the decrease of slip velocity and average SVF. This implies that the ratio of the local SVF to the initial SVF is smaller for larger initial mixture velocities.  
Figure 16: Effects of initial mixture velocity on average SVF (C\({}_{v,0}\) = 5%, \(d_{s}\) = 15.0 mm).  
Figure 15: Effects of initial mixture velocity on average slip velocity (C\({}_{v,0}\) = 5%, \(d_{s}\) = 15.0 mm).
# A Geographical Heuristic Routing Protocol for VANETs
## 5 Performance Evaluation
### Evaluation of the Forwarding Phase in GHR
_“Together”_ means that to apply the test, it is not needed to differentiate the use of Tabu in the forwarding techniques. scenario. The higher delay is because of the longer paths. In addition, since the amount of possible next forwarding nodes decreases due to the list of prohibited nodes (i.e., Tabu list), the carry and forwarding procedure is used more often, increasing the average delay of packets. Finally, the % of idle time sensed by a node decreases when our Tabu routing is enabled. In Figure 4d, it can be seen that as the vehicle density increases, the difference between using or not using Tabu in the percentage of idle time increases, as well, reaching 2% in the scenario with 250 vehicles. There are two reasons for the decrease of the percentage of idle time; first, the higher number of hops needed by Tabu to reach the destination; second, it is important to notice that the use of a Tabu list is overhead to be carried by each packet until reaching the destination. This means more bandwidth utilization for a packet transmission at each hop in longer paths than when Tabu is disabled. Nevertheless, the slightly higher delay and lower idle time are not so important compared to the noticeable lower losses achieved when the Tabu list is used. Thus, this trade-off clearly shows benefits in favor of the Tabu list. Regarding the behavior of the forwarding techniques, as can be seen from Figure 4a, in low vehicle density, the forwarding inspired in simulated annealing (\(\alpha=3\)) has a slightly and statistically significant improvement (around 2%), compared to the other three approaches. Indeed, these three forwarding approaches, i.e., best node, random and first legal, behave similarly according to our statistical analysis (see \(p\)-values \(>0.05\) for packet losses and 100 vehicles in Table 4). For the medium density, there is not a statistically-significant difference among the forwarding techniques, neither when the Tabu is used, nor when this option is disabled (see second row in Table 3). In the high density scenario, the behavior is different and depends on if Tabu routing is being used or not. When Tabu is not used, it is clear from Figure 4a that the selection of the best neighbor gets the lowest percentage of packet losses. On the other hand, a complete random selection or the selection of the first legal neighbor as the next forwarding node has the worst % of packet losses. The reason lies in the high number of hops that these two forwarding techniques use to reach the destination. When Tabu routing is used in the high density scenario, the behavior of the routing techniques changes completely. The degree of randomness given by the SA forwarding (\(T=1\), \(\alpha=3\) in Figure 4a) gets the best results. The advantage of this approach is based on not always selecting the best node, which could avoid
###### Abstract  
Modern safety-critical systems depend heavily on communication networks while operating in increasingly polluted electromagnetic environments. Forward Error Correction codes are increasingly being used in safety-critical applications; however, vulnerabilities can still be caused by undetected corrupted data. Within this paper, the effectiveness of primitive Reed-Solomon Codes under single-frequency electromagnetic disturbances is assessed. Additionally, the impact of various parameters including the message length, the Reed-Solomon Codes' symbol size, and the amplitude of the induced voltages are also investigated. Simulations show that, at harmonics and some certain ratios of the bit-rate frequency, the susceptibility of Reed-Solomon Codes relative to this type of disturbance increases substantially. In worse-case scenarios, the rate of undetected corrupted data at these ratios could increase to values above 80%. It is shown that the main reason that Reed-Solomon Codes fail to detect such errors is due to the repetitive nature of code words' symbols, as well as a special relation among the symbol size, the channel's bit-rate, and the disturbance frequency. Accordingly, this paper proposes to add an extra inversion layer to the communication protocol to enhance the resiliency of these codes against single-frequency electromagnetic disturbances. Finally, it is shown that the proposed layer substantially mitigates the ratio of undetected corrupted data under the considered electromagnetic environment. By using the proposed approach, the rate of undetected corrupted data at the frequencies of concern decreased to values near 0%.  
communication networks; electromagnetic disturbances; Reed-Solomon codes; forward error correction; vulnerability; false negatives; resilience +
Footnote †: journal: _electronics_
## 3 Construction Methodology of the Traffic Accident Knowledge Graph
### Construction of the Traffic Accident Knowledge Graph
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Field Name** & **Description and Example** \\ \hline Entity & Accident.case\_id & Unique identifier for the accident case; e.g., 21900517 \\ Relationship & REPORTED\_IN\_YEAR & Relation linking an Accident to its reporting year; e.g., 21900517 \\ Entity & DBYear.year & REPORTED\_IN\_YEAR 2019 \\ Relationship & HAS\_JURISDICTION & Year when the accident was reported; e.g., 2019 \\ Entity & Jurisdiction.value & Connects Accident to jurisdiction; e.g., 21900517 \\ Relationship & HAS\_SPECIAL\_CONDITION & HAS\_JURISDICTION Los Angeles \\ Entity & SpecialCondition.condition & Links Accident to special road condition; e.g., 21900517 \\ Relationship & HAS\_PRIMARY\_ROAD & Description of special condition; e.g., Vista Point or Rest Area \\ Entity & PrimaryRoad.name & Links Accident to primary road; e.g., 21900517 \\ Relationship & HAS\_DISTANCE & \multicolumn{1}{c}{} \\ Entity & Distance.value & Associates Accident with distance off road; e.g., 21900517 \\ Relationship & HAS\_DIRECTION & HAS\_DISTANCE 0.5 \\ Entity & Distance.value & Distance from reference point (miles); e.g., 0.5 \\ Relationship & HAS\_DIRECTION & Links Accident to traffic direction; e.g., 21900517 \\ Entity & Direction.value & HASS\_DIRECTION Northbound \\ Relationship & HAS\_WEATHER1 & Traffic direction at accident location; e.g., Northbound \\ Entity & \multicolumn{1}{c}{} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Description of key entities and relations in the Traffic Accident Knowledge Graph. Figure 3: Schematic diagram of the Traffic Accident Knowledge Graph construction workflow. This study employs the Neo4j graph database as the storage and visualization platform for the knowledge graph. Leveraging Neo4j's native graph storage engine and ACID-compliant transactions, the system can efficiently manage tens of thousands of entities and relations. Neo4j supports the Cypher query language, enabling flexible execution of path retrieval, graph algorithms, and neighborhood analysis, and provides comprehensive visualization tools for intuitively displaying the complex semantic associations among traffic accident entities, thereby facilitating subsequent knowledge retrieval and decision support. As shown in Figure 4, only a subset of the constructed knowledge graph is displayed due to the volume of data. The California Traffic Accident Knowledge Graph (TAKG) comprises 33,755 entity nodes and 115 relation types, encompassing over 120 categories of accident-related elements--such as accident events, participants, victims, and environmental factors. For downstream embedding, the training, validation, and test sets contain 460,005, 57,495, and 57,500 triples, respectively, thus fully reflecting the multi-source heterogeneity and spatiotemporal dynamics of real-world traffic accident data.
## 4 The Connectionist Model and Its Components
### The Connectionist Structure  
Figure 5 shows a connectionist model related to the example sentence of Figure 1. Each item \(g_{i}\) of the sentence (words and punctuation marks) is represented by an individual unit \(u_{i}\) (localist model). The number of units is then prefixed for a given input sentence, whereas the number of connections and their weights are determined by the optimization algorithm.  
The basic type of relationship in a sentence concerns a totally joined \(n\)-gram, (\(g_{i}\),..., \(g_{j}\)) [17] with arcs \(W_{i,j}\), which is modeled by a connection between units \(u_{i}\),..., \(u_{j}\), with strength \(w_{k,h}\). An example of this relationship is \(W_{14,16}\) in Figure 5. Note that such \(n\)-grams in general do not correspond to clauses or other grammatical concepts that could be labeled: a connection could be virtually established on any subsequence. The optimization procedure limits the number of connections. Another type of relationship concerns a partially joined \(n\)-gram. An example of this relationship is \(W_{1,12}\) in Figure 5, involving \(g_{1}\), \(g_{2}\), \(g_{11}\), and \(g_{12}\) only.  
For a network with \(N\) nodes, the \(k\)-th output, \(k=1\),.., \(N\), is the following:  
\[out(u_{k})=\frac{1}{p_{k}}\sum_{h}(w_{k,h})^{2} \tag{6}\]  
where \(w_{k,h}\) are the weights corresponding to the \(p_{k}\) connections related to the node \(u_{k}\).
# Prototype System for Supporting Medical Diagnosis Based on Voice Interviewing
## 1 Introduction
### Methodology  
The schematic that represents the workflow of an interactive medical interview system, which is designed for accurate and adaptive diagnosis, is in Figure 1. The User interacts with the system primarily through voice input, with an option to provide text-based clarification when needed. The input is processed in the Recognition and Vector Extraction module, where the system converts speech to text and extracts diagnostic vectors based on the symptoms described by the user. These vectors are stored in the Disease Vector Database, which serves as the core for diagnosing conditions and guiding further patient interactions.  
In developing our system, we used Monte Carlo Cross Validation (MCCV25) [5; 6] to estimate the effectiveness of the models. In other words, we consider the average efficiency from 25 random splits of the data and their classification.  
A Machine Learning Model for Quality Estimation assesses the reliability and accuracy of the extracted vectors, providing feedback to refine the diagnostic process. The system then initiates an Iterative Conversation with the User, asking follow-up questions to enhance data quality and completeness. Finally, the List of Possible Diseases module filters and presents potential diagnoses to the user, based on the processed information, thus enabling a comprehensive and user-centric diagnostic experience.  
This structured approach highlights the system's ability to integrate patient input, iterative refinement, and machine learning for a robust diagnostic process.  
The Section 1.2 provides an overview of the machine learning classifiers used in the study, their features, and default parameter settings. The primary focus is on the usage of a diverse set of algorithms to ensure robustness and adaptability across different data distributions. Each classifier [7] was selected for its unique strengths in handling medical data, ensuring comprehensive evaluation and high accuracy in predictions.
# Characteristic Aroma Compound in Cinnamon Bark Extract Using Soybean Oil and/or Water
## 3 Results and Discussion
### Changes in the OAVs and Their Contribution Rate of Volatile Compounds
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Compound** & **Odor Descriptors** & \multicolumn{2}{c}{**Odor Threshold\({}^{\mathbf{m}}\)**} & \multicolumn{2}{c}{**Water Extract**} & \multicolumn{2}{c}{**Oil/Water Extract**} & \multicolumn{1}{c}{**Reference**} \\ \cline{3-8}  & **(mg/g)** & **OAVs** & **Contribution Rates (\%)** & **OAVs** & **Contribution Rates (\%)** & & \\ \hline Aldehyde & grass, green & 5 & 27–55 & 3-5 & 2-63 & 2-21 & [24] \\ Hexanal & green, fatty & 1 & 117–149 & 14-18 & 10-33 & 8-12 & [30] \\ Furfural & almond, bread, sweet & 10,000 & \(<\)1 & \(<\)1 & \(<\)1 & \(<\)1 & [17] \\ Benzaldehyde & almond, burnt sugar & 350 & 24–32 & 3-4 & 0-10 & 0-3 & [23] \\ P-Anisaldehyde & fremel, hawhorn & 21 & 27–41 & 3-5 & 1-8 & 0-3 & [22] \\ Cinnamaldehyde & cinnamon & 385 & 111–213 & 11–26 & 5-56 & 4-12 & [22] \\ Alberne & green, cambrerous & 450 & \(<\)1 & \(<\)1 & \(<\)1 & [16] \\ D-Limenen & citrus, mitt & 10 & 19–27 & 2-3 & 16–17 & 6-14 & [4] \\ Alcohols & & & & & & & \\ Eucalyptus & minty, eucalyptus & 12 & 13–29 & 2-3 & 3-12 & 2-5 & [23] \\
1-Pentanol & rubber, phenolic & 3 & N.D. & N.D. & \(+\)19 & 2-13 & \\
1-Octen-3-cl & mushroom & 1 & 36–127 & 3-15 & 22–31 & 9-18 & [30] \\ Linalool & flower, lvender & 6 & 79–114 & 9-11 & 9-10 & 3-7 & [4] \\ Isoplegol & minty & 1000 & 0-1 & \(<\)1 & \(<\)1 & \(<\)1 & [29] \\ a-Terribel & green & 330 & 2 & \(<\)1 & \(<\)1 & \(<\)1 & [27] \\ Bornel & camphor & 140 & 7 & 1 & 1-2 & 1 & [21] \\ Benzyl alcohol & popcorn, sweet & 900 & \(<\)1 & \(<\)1 & \(<\)1 & \(<\)1 & [26] \\ Ether & Estragole & licorice, anise & 6 & 22–41 & 3-5 & 4–5 & 1–2 & [4] \\ Anethole & anissed-like & 15 & 110–394 & 13–37 & 41–50 & 16–30 & [4] \\ Ketones & & & & & & & \\
6-methyl-5-Hepten2-one & green, nutty & 50 & 2–3 & \(<\)1 & \(<\)1 & 0.85 & [28] \\ Acetophenone & floral, almond & 65 & 4–5 & 0–1 & \(<\)1 & 1.03 & [23] \\ Ester & & & & & & & \\ Isobornyl acetate & woody, camphor & 1800 & \(<\)1 & \(<\)1 & \(<\)1 & 0.98 & [18] \\ Ethyl cinnamate & honey & 16 & 8 & 1 & 0–1 & 1.05 & [20] \\ Cinmany4 acetate & floral & 150 & 4–11 & 0–1 & \(<\)1 & 1.05 & [19] \\ \hline \hline \end{tabular}  
* Each value is expressed as mean \(+\) SD (\(\eta-3\)). Means in the same row with no common superscript _different_ significantly. Older thresholds were obtained with water applied as the matrix.  
\end{table} 
Table 3: Changes in OAVs and contribution rates of aroma substance in the water extract and in the aqueous phase of the oil/water extract. Hexanal and D-limonene show \"grass, green\" and \"citrus, mint\" odors [4; 24], respectively, which are distinct from the characteristic aroma of cinnamon. Consequently, the characteristic aroma of the aqueous phase decreased in the presence of soybean oil.