esa-sceva/satcom-chunk-collection huggingface dataset:
# Radio Network Planning towards 5G mmWave Standalone Small-Cell Architectures
## 2 Problem Formulation and Simulation Methodology  
For the study of dense networks at high frequencies, two types of serving radio network nodes were considered in the planning process of our analysis: omnidirectional and tri-sectored microcells. When sectorized scenarios were considered, at each site three gNBs were co-located (reuse pattern 1 \(\times\) 3 \(\times\) 1). During the planning process, all three sectors or only a subset of them could be activated, according to the required signal strength in an area and/or the need to limit the overall interference of the system. Although the algorithm was developed to also examine tri-sectored macrocells with relay nodes, they were not considered here since the focus was to analyze the capacity capabilities of ultra-dense standalone small-cell architectures. Note that each cell can also have MIMO capabilities.  
Control Nodes (CNs) were used in our study in order to represent throughput requirements as well as coverage constraints. Specifically, \(K\) CNs were distributed in the area under study and each CNwas associated with a rate requirement, i.e., \(R_{CN,min}\). Introduced by system link budget calculations, the received power at each CN should always exceed a minimum power threshold.  
Furthermore, the optimization process was employed on discrete locations rather than on continuous x-y space, an approach already applied and tested for previous generation network deployments [2; 5; 6]. This way the complexity of the NP-hard problem of continuous space calculations was avoided [10], but also, this was closer to the actual planning process employed by network operators where existing sites are most certainly reused in any future network layout.  
The outline of the radio network planning process is shown in Figure 1. A more detailed description is provided in the next sections.
# AWMC: Abnormal-Weather Monitoring and Curation Service Based on Dynamic Graph Embedding
## 4 AWMC Description
### Functionalities and Illustrative Example  
The functionalities of the AWMC system are as follows.  
* The system shows the anomaly scores of each day for each city in a certain time interval and changes in the anomaly scores.
* The system shows weather data o thef temperature, humidity, vapor pressure, dew point temperature, local air pressure, sea-surface pressure, and ground temperature of each day for each city in a certain time interval, and changes in these weather data.
* The system shows the dispersion of seven types of weather data and marks outlier data points.
* The system shows the anomaly score of each city for one day on the map.  
Next, we discuss an illustrative example for our system. First, we click the marker on the map and select the city we want to check. Then, a box that includes the time selector pops up on the left side of the page. We can select the time interval that we want to check. Then, the line charts of anomaly scores and weather data for this city in this time interval appear. At the same time, the box plots of the weather data in this time interval also appear. Lastly, by clicking on any point in the anomaly-score line chart, the map shows the weather anomaly extent of all cities on the chosen day.
# Three Rapid Methods for Averaging GPS Segments
## 4 Experiments
### Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **HC-SIM** & **Length** & **Points** & **Time** \\ \hline Medoid [4] & 56.40\% & 98\% & 169\% & \(\sim\)1 h \\ CellNet [8] & 53.80\% & 65\% & 144\% & 49 s \\ CellNet [8] & 61.20\% & 96\% & 144\% & 48 s \\ Marteau [17] & 61.70\% & **99\%** & 145\% & 30 min \\ RapSeg-A & 62.00\% & **99\%** & **81\%** & **12 s** \\ RapSeg-B & 62.10\% & **99\%** & 79\% & 16 s \\ RapSeg-C & **62.20\%** & **99\%** & 77\% & 13 s \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the methods, as evaluated by HC-SIM accuracy. Length and points are given relative to the corresponding numbers in the ground truth; therefore, 100% denotes the expected result. The zoomed examples in Figure 14 demonstrate some issues that could be improved further. First, if the extracted data contain multiple lanes then the averaging might not be accurate enough. In segment averaging, such issues are ignored, and we assume that lane extraction is already done during the data preparation. Second, the segment might also include detours. The top, leftmost example includes shortcut used in summer, whereas in winter, it is part of the crossing skiing track and not used by pedestrians. Similar detour exists in the example below where the campus bus usually follows the southern road to the parking lot, but occasionally takes the longer detour northside of the small building. Third, the bicycle track near the river is partly extended by the nearby boat pier, which partially follows the bicycle track in parallel. As a result, the average segment can be slightly deviated towards the river. Fourth, in the case of GPS noise caused by tall buildings, the segment deviates away from the buildings. Further analysis by calculating Spearman correlation showed that the performance of RapSeg (all variants) correlates with the number of points (0.19-0.34) and with the number of trajectories (0.22-0.25). A positive correlation is expected, as more samples are expected to provide a better chance to derive the correct segment from the data. On the testing data, a negative correlation was observed between the performance and the variance (calculated using Hausdorff) between the trajectories in the set (\(-0.25\)). This is also as expected because it should be easier to find the correct segment average for a set with tighter packed trajectories. However, there is no correlation on the training set (0.06 Spearman correlation), which may be because the training set is smaller and populated by many sets containing only a few sample trajectories, which can skew the numbers. Figure 13: Visualizations of the three methods for handling the curvy model on sample trajectories (gray) from the test dataset. Solution is blue, ground truth is black, and the results are shown on top of OpenStreetMap.
## 3 Results and Discussion  
### Methylation Parameters  
Before GC analysis, the fatty acid components of lipids are converted to the simplest convenient volatile derivative, usually methyl esters [12]. The Lewis acid, boron trifluoride, in the form of its coordination complex with methanol, is a powerful acidic catalyst for the transesterification of fatty acids. The primary advantage of acid catalysis is the general applicability, with both bound and free fatty acids (FFA) being converted concurrently to FAME. Among the various acid-catalyzed reagents (such as methanol-hydrochloricacid, sulfuric acid in methanol, and acetyl chloride in methanol), boron trifluoride in methanol has a wide application as a good reagent to convert both the acyl-glycerols and the FFA into methyl esters [12]. Extraction temperature and duration are the most important factors contributing to the yield of FAMEs. To select a suitable temperature for derivatization method, we performed the FAME reaction at different temperatures (from 60 to 100 \({}^{\circ}\)C) with 30 min as fixed reaction time. The results of using five different temperatures (Figure 1) showed that the optimum temperature was 70 \({}^{\circ}\)C, with a significant difference in yield of all FAMEs (\(P<\) 0.05) in comparison with other temperatures. It was revealed that increasing temperature significantly decreased the concentration of fatty acids in the samples. This might be explained by fatty acids degradation during the methylation process. The optimum derivatization temperature by using conventional heating found from this study (70 \({}^{\circ}\)C) was in good agreement with other studies [13]. Although temperatures higher than 100 \({}^{\circ}\)C may provide equal or higher yields of FAMEs, the operation of derivatization above this temperature was not possible due to sample damage. Moreover, this can be explained because we used methanol, and high temperature may cause loss of solvent (the boiling point of methanol is 60 \({}^{\circ}\)C) as the vials were not completely gastight and transfer of double bond may have occurred. The temperature at 70 \({}^{\circ}\)C was, therefore, selected as an optimum point and used for further investigation.  
To identify the optimal reaction time, we performed conventional heating derivatization between 30 to 120 min (Figure 1) by using fixed temperature at 70 \({}^{\circ}\)C, the results showed significant difference between the tested conditions (\(p<\) 0.05). Moreover, the reaction time of 90 min was sufficient to complete the conversion of all fatty acids. The optimum time for FAME preparation may also depend on the type and size of sample. Samples with complex matrices may require a longer time for a complete lipid extraction [12].  
In conclusion, the preparation procedure of FAME for GC analysis without prior lipid extraction via the one-step method has a number of advantages as it is simple, time-saving, reduces the use of solvents, and minimizes the potentials for sample loss and contamination. By combination of the extraction-methylation processes, FAME preparation can be completed within 90 min and ready for GC analysis.
## 2 Materials and Methods
### Implementation
#### 2.2.2 Conversion Scripts  
The SNOMED CT in RDF was generated with the SNOMED OWL Toolkit [21]. The resulting OWL files were additionally converted to the Turtle file format with a home-made tool. For the other terminologies, Python scripts were developed to convert the original terminologies to a machine-readable SPHN-compatible RDF format. For ATC, CHOP, ICD-10 GM and LOINC, codes are represented as classes (rdfs:Class) and names (labels) by the property rdfs:label, such as, in the axes of LOINC, which are represented as individual properties (e.g., \"hasComponent\", \"hasMethodType\"). Hierarchies of the codes are represented by the property rdfs:subClassOf. Where necessary, root classes were created under the SPHN namespace: [[https://biomedit.ch/rdf/sphn-resource](https://biomedit.ch/rdf/sphn-resource)]([https://biomedit.ch/rdf/sphn-resource](https://biomedit.ch/rdf/sphn-resource)). For LOINC, specific properties for representing the 6-axis given as contextual information for each LOINC term (e.g., \"hasComponent\", \"hasMethodType\") were created. For UCUM, the library of pre-built valid UCUM codes from the National Library of Medicine, National Institutes of Health, U.S. Department of Health and Human Services (v1.5, Released in June 2020 [22]) was used as an input file for the Python pipeline. UCUM codes are provided as owl:NamedIndividual, represented with the code rdfs:label and the label rdfs:comment. All developed scripts are available under the GNU General Public License v3.0 license on [[https://git.dcc.sib.swiss/dcc/biomedit-rdf/-/tree/master/ontology_pipelines](https://git.dcc.sib.swiss/dcc/biomedit-rdf/-/tree/master/ontology_pipelines)]([https://git.dcc.sib.swiss/dcc/biomedit-rdf/-/tree/master/ontology_pipelines](https://git.dcc.sib.swiss/dcc/biomedit-rdf/-/tree/master/ontology_pipelines)) (accessed on 30 October 2021).
# Research on Multiple-Axis Contour Error Suppression Method Based on Composite Layered Control
###### Abstract  
With the widespread application of multi-axis machining in the industrial manufacturing, aerospace, and military equipment sectors, the demand for machining ultra-precision components has been steadily increasing. Contour errors directly impact the quality of machined parts. In conventional multi-axis motion control systems based on cross-coupling, it is conventionally assumed that all individual axes are of equal significance during machine processing. However, in practical machining scenarios, diverse machining trajectories and accuracy requirements give rise to distinct control necessities for each axis. This complication leads to challenges in ensuring a consistent single-plane contour, thereby constraining the elevation of the overall contour accuracy. To address this issue, this study proposes a multi-axis contour error suppression method based on composite hierarchical control. The approach advocated in this paper initially ensures the precision of single-axis position control through the development of an advanced S-shaped function-based sliding-mode disturbance observer. Building on this foundation, the three-dimensional spatial contour is segregated into upper and lower layers. Subsequently, dedicated fuzzy PID cross-coupling controllers are devised for each layer. The experimental outcomes substantiate that in comparison to conventional cross-coupling control methods, the method introduced in this study, rooted in composite hierarchical control, not only guarantees the accuracy of single-plane contours but also further enhances the overall contour precision.  
cross-coupling control; composite layered method; multi-axis motion system; contour error suppression +
Footnote †: journal:  
Article  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], C. Research on Multiple-Axis Contour Error Suppression Method Based on Composite Layered Control
## 3 Integrated Driving HIL Simulator for Evaluation of Cooperative Eco-Driving System
### Interaction between Simulators  
Figure 4 shows the interface and data flow for the interaction between each component composing the IDHIL simulator. First, to synchronize traffic scenario data between the ASM ModelDesk and QualNet, QualNet receives traffic scenario data periodically from the ethernet-connected ASM PC through Local. QualNet immediately updates the traffic data of virtual nodes which broadcast V2X messages based on the current state every 100 ms. Whenever a virtual HV node receives virtual V2X messages from the surrounding virtual node, QualNet passes the messages to the packet converter through ethernet. The packet converter converts virtual V2X messages into real V2X messages and transmits them to the DUT through the 802.11p wireless channel. DUT passes traffic data received from the surrounding nodes to the attached application PC through ethernet. The application PC operates the cooperative eco-driving application and informs a driver of the application results by visual, audible, and actual methods, which helps a driver drive more fuel-efficiently. In addition, the application results and the surrounding traffic data are used to operate hybrid control algorithms running on HCU, and thus the application PC passes the results and data to the HCU running on MicroAutoBox through the controller area network (CAN). In the MicroAutoBox, the HCU operates a hybrid control algorithm, e.g., a driving mode decision algorithm, by using the current vehicle state data of the HV from the HIL simulator and surrounding traffic data from the application PC. During the simulation, the driving controller transmits a control data of the driver through the USB to the ASM PC, which leads to the operation of ECUs within MicroAutoBox and Simulator Mid-Size.  
Figure 3: V2X network emulation using V2X communication devices.  
Components with ethernet connection exchanged packets through the UDP within the private local network formed using only a network switch to avoid unwanted connections, and thus they had a low delay of within 1 ms. Wireless communication between the packet converter and DUT had an extremely low communication delay within 0.1 ms because they were in a lab testing environment having a line-of-sight (LOS) and very short distance between them. Therefore, it was assumed that the communication delay between the packet converter and the DUT was included in the communication delay of the virtual V2X message received by the virtual node mapped to the HV within QualNet.
## 3 The Influence of Structure Parameters
### The Thickness of Dielectric Layer  
The thickness of the dielectric layer \(H_{\text{d}}\) is another factor which could affect the structural color. Therefore, we changed the thickness of the dielectric layer \(H_{\text{d}}\) from 30 nm to 120 nm while keeping other parameters unchanged, and ran the simulation again. Results show that when the thickness of  
Figure 5: The reflection spectra as a result of changing the incident light’s tilt angle. **(a)** The reflection spectra with the tilt angle being \(0^{\circ}\). The maximal reflectivity is 87.87%. **(b)** The reflection spectra with the tilt angle being \(10^{\circ}\). The maximal reflectivity is 83.65%. **(c)** The reflection spectra with the tilt angle being \(20^{\circ}\). The maximal reflectivity is 75.9%. **(d)** The reflection spectra with the tilt angle being \(30^{\circ}\). The maximal reflectivity is 70.5%. **(e)** The reflection spectra with the tilt angle being \(20^{\circ}\) and the electric field direction rotated by \(90^{\circ}\). The maximal reflectivity is 82.1%. **(f)** The reflection spectra with the tilt angle being \(30^{\circ}\) and the electric field direction rotated by \(90^{\circ}\). The maximal reflectivity is 77%.  
the dielectric layer is less than 70 nm, the reflectivity and the bandwidth of the red wave band increase as the thickness of the dielectric layer increases, but the reflectivity of the blue and green wave bands decreases. When the dielectric layer is thicker than 70 nm, the reflectivity and the bandwidth of the red wave band increase, and so does the reflectivity of the green and blue wave bands. Therefore, we can draw the conclusion that when the thickness of the dielectric layer \(H_{\text{d}}\) is 70 nm, the reflected light is the closest to the spectral red, which is the result we want, as shown in Figure 6.
## 5 Discussions  
Section 3 analyzed the morphology of roof structures during the IWS and FMS of the GSWF. It explored how these varying roof structures apply loads to the GSR surrounding rock. Despite the different mining stages, the static loads of the advanced working face area in the GSR remain relatively consistent. These loads primarily consist of the lateral static load from the gob and advanced static load of the working face. The lateral static load from  
Figure 18: Changes in maximum vertical stress and energy density within the immediate roof, along with vertical stress in coal pillar, across varying coal pillar widths.  
the gob increases initially and then decreases with distance from the gob. Consequently, advanced static load on the solid coal side of the GSR shows an asymmetric stress distribution [58]. Stresses are highest near the gob and decrease further away; this evolution law is consistent with the numerical simulation results of vertical stress distribution in front of the working face shown in Figure 14. Furthermore, the theoretical analysis of stress distribution based on the roof structure aligns with the numerical simulation results.  
The load transmitted from the roof fracture of the adjacent gob to the advanced area of the solid coal side of the GSR is related to the coal pillar width and the distance to the GSWF. When the coal pillar width is small, the lateral roof of the adjacent gob transmits a large load to the solid coal side of the GSR. Additionally, when the distance to the GSWF is small, the load on the solid coal side of the GSR in front of the working face transmitted from the GSWF is relatively large [29; 47; 59]. These loads accumulate, leading to energy accumulation in this region (Figures 5 and 9). The accumulated energy decreases as the coal pillar width and the distance to the GSWF increase. This trend aligns with the numerical simulation results, which show the energy density distribution of the roof in front of the working face (Figure 16). The theoretical analyses are consistent with numerical simulation results, confirming the reliability of both approaches.  
The analysis of the roof structures of the GSWF and its loading characteristics on the surrounding rock of the GSWF and GSR, as discussed in Section 3, is grounded in classic mine pressure theory and key strata theory [10]. While variations in stratigraphic configurations, lithology, and mining conditions lead to differences in the roof structures, they also make a difference in the load characteristics and stress-energy levels in the GSR. Despite these differences, the results presented in this paper are broadly applicable and reflect typical patterns. They offer valuable insights for studying and preventing coal bursts in GSRs under similar geological and mining conditions.
# Triangular Sierpinski Microwave Band-Stop Resonators for K-Band Filtering
## 6 Design of the 26 GHz Coupled Hexagonal Resonators  
The design of the 26 GHz small hexagonal resonators was performed by simulating the composition of the rescaled triangles in the same way as the 20 GHz resonators. Three resonators were coupled and mirrored with respect to the central conductor of the CPW to obtain a hexagonal arrangement. The simulation is shown in Figure 13, where it is evident that the best performance among them (electrical matching, corresponding to a deeper notch in the resonance) was obtained for the composition of the C1 triangles despite the shift in the expected frequency of operation. This further demonstrates the difficulty in treating the entire structure like the simple composition of single resonators. Looking at Figure 11, where the expected S\({}_{21}\) of the single resonators is shown, the C1 structure is not necessarily the best one, but it is characterized by a sharp peak around 27 GHz, which is almost the same for the hexagonal resonator but with an enlarged band of operation. An additional optimization for most of the studied structures should concern the electrical matching of the I/O lines.
# Energy Efficient Routing in Wireless Sensor Networks: A Comprehensive Survey
## 2 Communication Model
### Coherent/Non-Coherent
_SPIN for Broadcast Networks_ (SPIN-BC) [36] is another variation of SPIN protocol that uses one to many communications. The source sends the ADV message to all nodes in its range and interested nodes wait for a predetermined time before they send an REQ message. In case they receive an REQ message from another interested node, they cancel their REQ message to limit unnecessary requests. The source, in the case that it receives the REQ message, broadcasts the DATA message once, regardless of the number of REQ messages. SPIN-BC uses cheap one to many communications. It also achieves good scalability and generally performs better than SPIN-PP. However, there is a waiting time before sending the REQ message. _SPIN with ReLiability_ (SPIN-RL) [36] utilizes the SPIN-BC algorithm with an ADV message tracking mechanism and a limit of data transmission at sources. The ADV messages contain information of the source while receiving nodes store them for later use. An interested node sends an REQ message and, in the case that it does not receive the DATA, it resends the REQ message. The source node then waits for a specific time before it broadcasts the DATA again to the interested nodes. Generally, SPIN-RL is a more reliable protocol than SPIN-BC. Furthermore, SPIN-RL provides remarkable loss tolerance and good scalability. Yet, processing tasks performed increase overhead. A synoptic overview of the characteristics of the aforementioned negotiation based routing protocols belonging to the Communication Model category is presented in Table 3.