esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Non-Covalent Interactions
### CNT-Based Nanocomposites  
CNTs possess extraordinary properties including a high aspect ratio and electrical conductivity, which make them excellent candidates for a variety of applications such as sensors, storage, for example. However, the resulting nanocomposites can suffer from the natural tendency of nanotubes to agglomerate due to strong Van der Waals forces, which results in the poor dispersion of CNTs into the polymeric matrix. In situ polymerization associated with ultrasonication is one way to overcome this drawback. SWCNTs have been successfully dispersed into an aromatic polyimide matrix through the in situ polymerization of the monomers 1,3-bis(3-aminophenoxy) benzene (APB) and 2,2-bis(3,4-anhydrodicarboxyphenyl) hexafluoro- propane (6 FDA) under ultrasonication [39]. The authors reasoned that the cavitation phenomenon overcame the agglomeration of SWCNTs, and that the decrease in Brownian motion was due to the high viscosity of the solution. The choice of initiator for polymerization also controls CNT dispersion. [PERSON] et al. [40] selected a porphyrin-based zinc complex initiator to carry out the ring-opening polymerization of lactic in the presence of CNTs in tetrahydrofuran, while also serving as mediator to bind CNTs to the resulting polylactic acid polymer. They reported that strong \(\pi\)-\(\pi\) stacking interactions were formed between the porphyrin molecules and CNTs, resulting in CNTs embodied in the matrix through non-covalent binding (observed using FTIR, UV-Vis, and fluorescence spectroscopy).  
Although in situ polymerization is widely used to create carbon-based nanocomposites based on the adsorption of carbon fillers in the matrix, other techniques such as melt-mixing can be considered. [PERSON] et al. [41] reported the uniform wrapping of multi-walled carbon nanotubes (MWCNTs) within a polyethylene-based matrix. First, MWCNTs were functionalized by a copolymer dispersant synthesized from the monomers of phenyl methacrylate and glycidyl methacrylate, followed by grafting with amine. The optimal weight ratio of the MWCNTs to the polymer dispersant was found to be 1:0.75. Then, they cured the wrapped CNTs and the resulting powder was extruded with polyethylene (PE). They demonstrated the efficiency of the polymer dispersant in preventing the agglomeration of CNTs by reducing the Van der Waals interactions. The CNT/PE nanocomposites showed low surface resistivity, leading to high conductivity, making them good potential electromagnetic and electrostatic shields.
## 2 Materials and Methods
### Neighborhood Search and Feature Extraction
#### 2.3.2 Geometric Feature Extraction of 3D Points
Therefore, a relative elevation was defined in this study to describe the elevation information of each object, calculated as follows: \[f_{z}(p_{i})=\frac{z_{i}-z_{\text{min}}}{z_{\text{max}}-z_{\text{min}}},\] (5) where \(z_{i}\) is point \(p_{i}\)'s elevation; Zmax and Zmin are the maximum and minimum elevation values of all points in the neighborhood space.
## 2 Literature Review
### Prognostics and Health Management (PHM)
In fault diagnostics, the most used models of artificial networks are the feed-forward neural network (FFNN) configurations, in which information moves in only one direction [37; 38; 39; 40]. In the literature, the use of FFNNs is proposed for gears and bearings [41; 42; 43; 44; 45], engines [46], and other types of mechanical components. Recently, the literature has also investigated hybrid approaches which combine data-driven prognostics and model-based prognostics, hence benefiting from both their advantages (precision and applicability) [47].
## 6 Outlook  
The presented work shows that melting of ice can be monitored with AE. However, in this work only very little information (time of occurrence, AMPL, E, D, RT) were deduced from the acoustic signals and no spectral analysis was performed yet. Thus, additional information that can be deduced from a spectral analysis with well-known methods such as modal analysis or principal component analysis as done by [PERSON] and [PERSON] [20], [PERSON] [21] respectively. One question is if the two peaks in the histogram originate from different physical phenomena during the melting and if a detailed spectral analysis can differentiate the signals in the first and second peak. However, for this, it must be ensured that the spectral composition of the acoustic transients is not heavily influenced by the experimental setup and the changing physical properties of the sample during each measurement.  
In addition, the influence of physical parameters such as the cooling rate during the freezing, the purity of the sample and related freezing temperature and chemical composition of the sample to the acoustic activity have to be investigated. For this, a temperature measurement inside the ice/water mixture during the experiment would provide further insights into the freezing/melting behavior of the sample. Combined with repetitive measurements of the same setting and resulting statistics, fundamental properties of the sample could be derived only by recording and analyzing the acoustic signals during the melting.  
[PERSON] recorded and analyzed the presented data and was the major contributor in writing this paper. [PERSON] contributed in the design and the interpretation of the experiment. [PERSON] was mainly responsible for the technical clarifications and formulations regarding the daily aircraft maintenance. [PERSON] supervised and reviewed the analysis of the data and contributed to the interpretation of the results. All authors read and approved the final manuscript.  
The Research leading to these results has received funding from the \"NDTonAIR\" project (Training Network in Non-Destructive Testing and Structural Health Monitoring of Aircraft structures) under the action: H2020-MSCA-ITN-2016-GRANT 722134.  
Special thanks to [PERSON] (KU Leuven) for the technical support and to the M&E department of Brussels Airlines for daily cooperation.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AE & acoustic emission technique \\ NDT & non-destructive testing \\ PZT & piezoelectric \\ AC & alternating current \\ AMPL & amplitude \\ D & duration \\ RT & rise time \\ E & energy \\ THD & threshold \\ HDT & hit definition time \\ HLT & hit lockout time \\ PDT & peak definition time \\ \end{tabular}
# Optimizing Ambulance Allocation in Dynamic Urban Environments: A Historic Data-Driven Approach
## 3 Problem Description and Mathematical Model
### Notations  
Table 1 describes the notations used in this paper. In South Korea, each administrative region has its own fire station. We define \(\mathcal{B}\) and assumed that demand cells \(i\in\mathcal{I}_{b}\) in each borough \(b\in\mathcal{B}\) are only covered by ambulances assigned to locations \(c\in C_{b}\) within that borough. Let \(a_{cit}\) be 1 if location \(c\in\mathcal{C}\) covers demand cell \(i\in\mathcal{I}\) within the golden time, and 0 otherwise. To check for coverage, we used the Navigation API based on real-time traffic data. We define the golden time as 5 min.
# Fusion of Bilateral 2 DPCA Information for Image Reconstruction and Recognition
## 6 Discussion
### Limitations of DB2 DPCA  
Despite the above advantages, DB2 DPCA has some limitations. First, since DB2 DPCA is rooted in 2 DPCA and A2 DPCA, to calculate the feature matrices or the reconstruction images for DB2 DPCA, we need to calculate the corresponding results for 2 DPCA and A2 DPCA first, which is time consuming. However, other bilateral algorithms such as \((2\)D)\({}^{2}\)PCA also suffer from a similar problem. Second, since the original image height \(h\) and image width \(w\) are usually much larger than the feature numbers \(k\) and \(l\), the sizes of the feature matrices of DB2 DPCA tend to be much larger than those of the other three 2D algorithms. This is a major disadvantage of DB2 DPCA. There are three folds of meanings to this disadvantage: (1) DB2 DPCA needs a larger coefficient set for image representation than other 2D algorithms. (2) It will take longer to perform image classification by DB2 DPCA than by the other three 2D algorithms; therefore, the good classification performances of DB2 DPCA are obtained at the sacrifice of computational efforts. (3) There exist redundancies by pooling bilateral information together. This may explain why DB2 DPCA underperforms compared with 2 DPCA in face recognition in Figure 8a,d.
## 3 Applied Methodology
### Calculation of the Indicators
#### 3.2.1 Indicator 1: Distance from Active Channel to Stakes or Floodplain Margins  
From a perspective of fluvial dynamics study, we sample the extracted points of the active channels with the raster proximity map to the margins of the floodplain (Figure 9A). By performing this action, we may know how far each point of the channel to the margins of the floodplain is. This sampling is completed for the two dates. Finally, this version of indicator 1 gives information about the migration of the channel within the floodplain through time. For instance, in the highlighted sector in orange (Figure 10A), the channel was around 8 m away from the closest margin of the floodplain in 1953, but around 50 m away in 2018. Thus, this sector can be considered as unstable. On the contrary, the sectors that present the same distances values in both 1953 and 2018 can be considered as stable.  
Figure 9: Raster proximity maps used for the calculation of the indicators along the Clamoux River. Raster proximity map of the floodplain margins (**A**); raster proximity map of the edges of the vineyard (**B**); raster proximity map of the 1953 active channel (**C**); raster proximity map of the 2018 active channel (**D**).  
In a perspective of risk management, we sample the extracted points of the channels with the raster proximity map to the margins of the edges of the vineyard (Figure 9B). By performing this action, we may know how far each point of the channel is to the margins of the edges of the vineyard. This sampling is completed for the two dates. Finally, this version of indicator 1 shows the most actives sectors (in terms of bank erosion) of the channels for the vineyard and their evolution through time. This version of indicator 1 may help the river manager to identify the sectors where it could be relevant to build an embankment along the channel. In Figure 10B, on the highlighted sector in orange, in 1953, the channel was around 20 m away from the vineyard, and in 2018, it was around 50 m away. Thus, we can consider that the lateral dynamics of the river for the vineyard has decreased in this sector. If we have data relative to the elevation of the different items of the floodplain, we may better interpret this indicator by taking into account the elevation.
## 2 Related Work
### Coverage Enhancement Method Based on Adjusting Sensing Direction  
Nodes' sensing direction scheduling problem is an NP-hard problem, and it is difficult to obtain accurate mathematical solutions [15]. Intelligent optimization algorithms can obtain approximate optimal solutions through iterative optimization [16]. [PERSON] et al. [17] improved the particle swarm optimization algorithm for coverage enhancement in DSNs and proposed a node redeployment method. This algorithm uses an overlap rate as the fitness function. By iteratively adjusting the sensing direction of directional sensor nodes, the overlap rate of network nodes is reduced, and the coverage performance of the network is improved. [PERSON] et al. [18] proposed a new coverage enhancement method based on the Discrete Army Ant Search Optimizer (DAASO) to address boundary constraints and coverage maximization problems. [PERSON] and [PERSON] et al. [19] improved the Gravity Search Algorithm (IGSA) and proposed a coverage control strategy to improve the monitoring quality of DSNs. Their method consists of coverage enhancement and node sleep. The coverage enhancement method improves network coverage. Node sleep reduces redundancy and improves energy utilization.  
Algorithms based on geometry and virtual forces are also commonly used methods for sensing direction adjustment problems [20]. [PERSON] et al. [21] designed a coverage optimization scheme based on the Voronoi diagram. The geometric relationship between Voronoi units and node sensing range is analyzed, and the directional sensor is rotated to maximize the coverage area of each Voronoi unit. [PERSON] et al. [22] designed a DSN coverage enhancement strategy based on virtual force and PSO algorithm. This method establishes a sensing direction adjustment model by analyzing and calculating the forces acting on nodes. Based on this, the PSO algorithm is used to solve the optimal sensing direction set.
# NTARC: A Data Model for the Systematic Review of Network Traffic Analysis Research
###### Abstract  
The increased interest in secure and reliable communications has turned the analysis of network traffic data into a predominant topic. A high number of research papers propose methods to classify traffic, detect anomalies, or identify attacks. Although the goals and methodologies are commonly similar, we lack initiatives to categorize the data, methods, and findings systematically. In this paper, we present Network Traffic Analysis Research Curation (NTARC), a data model to store key information about network traffic analysis research. We additionally use NTARC to perform a critical review of the field of research conducted in the last two decades. The collection of descriptive research summaries enables the easy retrieval of relevant information and a better reuse of past studies by the application of quantitative analysis. Among others benefits, it enables the critical review of methodologies, the detection of common flaws, the obtaining of baselines, and the consolidation of best practices. Furthermore, it provides a basis to achieve reproducibility, a key requirement that has long been undervalued in the area of traffic analysis. Thus, besides reading hard copies of papers, with NTARC, researchers can make use of a digital environment that facilitates queries and reviews over a comprehensive field corpus.  
network traffic analysis; data curation; reproducible science; meta-analysis +
Footnote †: journal: Applied Sciences
# Analysis and Design of A PMQR-Type Repetitive Control Scheme for Grid-Connected H6 Inverters
## 4 Simulation Results and Analysis
### Controller Design
#### 4.1.4 The Phase Advance Compensator \(z^{m}\)  
It can be seen from the phase-frequency characteristic diagram in Figure 12 that both the control object \(P_{0}\left(z\right)\) and the designed second-order low-pass filter \(S_{1}\left(z\right)\) have a certain phase lag. Therefore, it is necessary to select an appropriate phase lead compensation link \(z^{m}\) to compensate the system phase shift without affecting the amplitude-frequency characteristics of the system. A fourth-order phase advance compensator was employed in this paper. As can be seen from Figure 13, under the operation of the different order phase lead compensator, the magnitudes of the system have not been affected at all. However, only when \(m\) is selected as four, the system can obtain a good zero phase shift characteristic. Hence, this paper applies the fourth-order phase lead link.  
Figure 12: Bode plot of the system after magnitude compensation.
## 3 Results
### Obstacle Avoidance Performance
#### 3.1.2 Obstacle Response During Perpendicular vs. Angled Approaches  
During testing, the drone's reaction to obstacles differed depending on how it approached them--either perpendicularly as seen in Figure 7a or at an angle as seen in Figure 7b. When flying straight toward an obstacle, the Multi-Ranger Deck reliably detected the object within the 0.35 m threshold and immediately retreated 0.2 m to avoid a collision. These perpendicular encounters typically resulted in smooth, consistent avoidance maneuvers, as the object was well aligned with the front-facing sensor.  
However, when the drone encountered an object at an angle, avoidance behavior became more nuanced. Angled obstacles--such as those turned or placed diagonally--were not always detected symmetrically by the front or side sensors. This asymmetry increased the possibility of the drone clipping a propeller on the edge of the object, particularly when the contact point fell between two sensor fields. Despite this, the drone successfully avoided collisions in these situations by responding  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Obstacle Location** & **Avoidance Success Rate (\%)** \\ \hline Forward & 100 \\ Backward & 100 \\ Left & 100 \\ Right & 100 \\ Angled & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Obstacle avoidance success rates based on the direction of obstacle encounter relative to the drone.  
Figure 7: **(a)** Typical scenario of drone encountering obstacle perpendicularly. **(b)** Scenario of drone encountering an obstacle at an angle.  
of the drone's boundary was first triggered. The system adjusted, initiating a horizontal or vertical avoidance maneuver depending on which sensor crossed the safety threshold first. A demonstration video of these two scenarios is available in Supplementary Materials (Video S1).  
Furthermore, the drone could approach obstacles at awkward angles due to its ability to rotate and then fly forward. Although the control system does not include a specific gesture for diagonal flight, this functionality effectively allows the drone to fly at an angle relative to its original grid orientation. As a result, obstacles may be encountered off-axis, challenging the avoidance system's ability to interpret directionality accurately. Nonetheless, the obstacle detection logic proved adaptive enough to handle these situations, maintaining safe distances and adjusting flight paths as needed.
## 3 Results
### Variant II--Use of Dripping Filters
#### 3.2.3 Nitrate Nitrogen  
The nitrate concentration in the system with PPA as the biological filter filling increased until day 44 and reached 32.150 mg N-NO3/dm3. It decreased slightly afterwards. A similar trend was observed in the system with PSC. The highest concentration of 30.00 mg N-NO3/dm3 was observed on day 40 and it decreased slightly afterwards. A different situation was observed in the system with CF. The concentration of nitrate nitrogen increased to 29.800 mg N-NO3/dm3 on day 36; afterwards it decreased until the end of the experiment, reaching 25.00 mg N-NO3/dm3 on the last day. In the system with CFsf, the nitrate concentration increased until day 32 and reached 32.15 mg N-NO3/dm3 and it decreased slightly afterwards (Figure 9).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Stage I**} & \multicolumn{3}{c}{**Stage II**} \\ \cline{2-6}
**Filling** & **Increase (d)** & \multicolumn{2}{c}{**Maximum Concentration (N-NO2/dm3)**} & **Decrease (d)** & \multicolumn{2}{c}{**Average \(\pm\) SD (N-NO2/dm3)**} & **Duration (d)** \\ \hline PPA & 1–17 & 12.500 & 17–27 & 0.480 \(\pm\) 0.139 & 18 \\ PSC & 1–19 & 12.900 & 19–30 & 0.438 \(\pm\) 0.137 & 15 \\ CF & 1–8 & 5.780 & 8–15 & 0.385 \(\pm\) 0.126 & 30 \\ CFsf & 1–17 & 11.700 & 17–26 & 0.261 \(\pm\) 0.178 & 19 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Nitrite nitrogen concentration in RAS, in which test fillings (PPA—polypropylene aggregate, PSC—polyethylene screw caps for PET bottles, CF—commercial fittings HXF12 KLL, CFsf—control submerged filter for dripping filters, commercial fittings HXF12 KLL) were used for two separated stages: Stage I ((high nitrite concentration) and Stage II (low nitrite concentration).  
Figure 8: Nitrite nitrogen concentration in experimental systems using dripping biological filters: PPA—polypropylene aggregate, PSC—polyethylene screw caps for PET bottles, CF—commercial fittings HXF12 KLL, CFsf—control submerged filter for dripping filters, commercial fittings HXF12 KLL.
# Travel Route Planning with Optimal Coverage in Difficult Wireless Sensor Network Environment
## 2 Related Work
### Data Mule Based Schema  
In this schema, the network employs a mobile collector to walk through the sensor field along a regular way for data gathering. Once the mobile collector detects any sensor in its transmission, it will stop for data gathering using single hop communication. The mobile collector will return to the static sink at fixed periods for data uploading. Following are some representative protocols using this schema.  
In reference [25], the authors presented a data dissemination method with a two-tier layer called TTDD. In TTDD, a grid structure is built by the source nodes. Firstly, the source node calculates the location of the virtual dissemination points and the sensors with the closet distance to the dissemination points are elected as dissemination nodes for data forwarding. Once sinks request information of the event, the query message is disseminated to the source node by forwarding of the dissemination nodes. Then, the data package is returned to the sink along with the previous dissemination path. If one disseminated node receives the same query message from a different sink, it will only forward one of them and drop the others.  
In reference [26], the authors presented a data dissemination schema combined with multiple mobile sinks called MSDD. In MSDD, a two-level structure is constructed for data transmission. In the top level, mobile sinks cooperatively collect the information of source nodes utilizing a special schema. In the bottom level, the area of interest (AOI) is partitioned into several regions by grids. The dissemination nodes are introduced to assist sensors in forming different regions. A sink walks in the AOI randomly and once the event occurs, the query-driven routing path will be established. Simulation results validate the better performance of the presented method compared to TTDD.  
In reference [27], the authors presented a new ideal to enhance the lifetime of the network by rotating the nodes with a heavy burden. Some mobile sensors are introduced to be the candidates for these \"hot spots\" and once the residual energy of these nodes is lower than a threshold value, the mobile sensors will exchange their location with the weak nodes.  
In reference [28], the authors presented a dynamic routing schema with a mobile collector. They explore the routing method for WSN in a circular sensor field. The whole sensor field is partitioned into sectors for clustering, and a mobile data collector is utilized to move around the edge of the sensor field for data gathering. Additionally, this schema presented a dynamic routing method for intercluster data forwarding. The closest CH from the sink is chosen as its agent for direct communication, and then the other CHs are connected into a chain for data forwarding.