esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Carrier-Launcher System Performance: Preliminary Analyses and Results
### Choice of Engines Needed to Perform the Parabolic Manoeuvre
To consider, in a first approximation, the effect of altitude at \(h=8000\text{ m}\), we may use the relationship based on density ratio:  
\[T_{max-PWengine}=T_{0}(\rho/\rho_{0})=435.9\text{ kN}\times(0.52517/1.225)=435.9 \text{ kN}\times 0.4287=186.9\text{ kN}, \tag{16}\]  
thus, estimating a reduction of the maximum thrust of about 57%. As a result, the maximum thrust of four PW4000-112 engines is not sufficient to perform the required manoeuvre. The Rolls-Royce UltraFan engine [56] develops a maximum thrust at sea level of about \(110,000\) pounds, i.e., \(489.3\text{ kN}\). For this engine, we obtain  
\[T_{max-RRengine}=T_{0}(\rho/\rho_{0})=489.3\text{ kN}\times(0.52517/1.225)=489.3 \text{ kN}\times 0.4287=209.8\text{ kN}. \tag{17}\]  
Finally, with four Rolls-Royce UltraFan engines we have  
\[4\text{ }T_{max-RRengine}=T_{max-carrier}=4\times 209.8=839.1\text{ kN}>T_{req}, \tag{18}\]  
and the thrust requirement is satisfied, although with almost zero margin. For the proposed carrier-launcher system, the altitude \(h=8000\text{ m}\) represents the maximum flight level at which the parabolic flight phase begins, when the thrust begins to be reduced. These preliminary calculations demonstrate that to practically accomplish this type of mission, four high-thrust engines must be installed on the flying wing carrier-launcher system.
## 3 Results  
### Multicollinearity Test  
Strongly correlated variables were eliminated using the multicollinearity test. Out of forty-two variables used on the test, eleven predictor variables with a VIF below the specified threshold were selected (Table 1). Atmospheric Resistant Vegetation Index (ARVI), Aspect, Chlorophyll Vegetation Index (CVI), Green Index (GI), Modified Soil Adjusted Vegetation Index (mSAVI), Elevation, Rainfall, Road, Soil type, Slope, and Temperature were the variables that satisfied the VIF threshold value. After excluding the collinear variables, the linear correlation coefficients ranged between 0.004907765 (Soil type-GI) and 0.8013945 (CVI-ARVI).
# A Novel K-Means Clustering Algorithm with a Noise Algorithm for Capturing Urban Hotspots
## 4 Experiment Results and Analysis
### Experimental Results and Comparison Analysis
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Taxi GPS Dataset** & **Clustering Algorithm** & **Average Running Time (s)** \\ \hline \multirow{6}{*}{Aracaju (Brazil)} & Noise K-means & 8.03867 \\  & FCM & 21.7398 \\  & K-means & 2.3773 \\  & K-means plus & 2.7005 \\  & Noise K-means & 10.86496 \\ \cline{2-5}  & FCM & 32.4686 \\ \cline{2-5}  & K-means & 3.232 \\ \cline{2-5}  & K-means plus & 3.3807 \\ \cline{2-5}  & Noise K-means & 9.87741 \\ \cline{2-5}  & FCM & 29.5736 \\ \cline{2-5}  & K-means & 3.1264 \\ \cline{2-5}  & K-means plus & 3.1248 \\ \cline{2-5}  & Noise K-means & 9.65121 \\ \cline{2-5}  & FCM & 26.9652 \\ \cline{2-5}  & K-means & 2.9121 \\ \cline{2-5}  & K-means plus & 2.9302 \\ \cline{2-5}  & Noise K-means & 8.34432 \\ \cline{2-5}  & FCM & 26.9652 \\ \cline{2-5}  & K-means & 2.6924 \\ \cline{2-5}  & K-means plus & 2.5276 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of the average running time (s). Figure 4: The SSE convergence curve for taxi GPS data (Aracaju (Brazil)). Figure 5: The SSE convergence curve for taxi GPS data (San Francisco (USA)). Figure 6: The SSE convergence curve for taxi GPS data (Rome (Italy)). As can be seen from Figures 4-8, all comparative clustering algorithms, except for FCM, could complete the convergence by iterating about 10 times, which shows that the proposed noise-based K-means clustering algorithm is feasible and suitable for basic partition clustering algorithms.
## 4 Simulations
### Simulation Setting
#### 4.1.2 Performance Metrics  
In all simulations, the following metrics of routing performance were used: (1) Delivery Ratio: It is the average percentage of the number of packets that successfully achieved the destination and that were sent from by the source node over a certain period of time; (2) Delivery Delay: It is the average transmission time of the packets that are accepted by the destination; (3) Hop Count: It is the average number of the relay when packets are delivered successfully; (4) Number of Forwarding: It is the average number of packets during the forwarding process.
# Hybrid Model Predictive Control of Semiactive Suspension in Electric Vehicle with Hub-Motor
## 5 Simulation Results
The improvement of the evaluation index by HMPC is approximately the same for both the C-class road with a larger amplitude and the B-class road with a smaller amplitude, which proves the effectiveness of HMPC. Figure 19: Motor eccentricity. Figure 20: Tire dynamic load. Figure 21-23 show the time-domain transient response of passive suspension, COC, and HMPC for HM-EV evaluation indexes under bump road excitation (\(a=0.1\) m). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Road Excitation** & **Performance Index** & **Passive** & **COC** & **HMPC** \\ \hline \multirow{4}{*}{20 m/s C-class} & Vertical acceleration of sprung mass & 1.3325 & \begin{tabular}{c} 1.2261 \\ (\(\uparrow\)7.36\%) \\ \end{tabular} & \begin{tabular}{c} 0.9927 \\ (\(\uparrow\) 24.99 \%) \\ \end{tabular} \\ \cline{2-5}  & Motor eccentricity & \(1.1369\times 10^{-4}\) & \begin{tabular}{c} \(1.0376\times 10^{-4}\) \\ (\(\uparrow\) 8.73\%) \\ \end{tabular} & \begin{tabular}{c} \(8.6154\times 10^{-5}\) \\ (\(\uparrow\) 24.22\%) \\ \end{tabular} \\ \cline{2-5}  & Tire dynamic load & 689 & \begin{tabular}{c} 630 \\ (\(\uparrow\) 8.56\%) \\ \end{tabular} & \begin{tabular}{c} 607 \\ (\(\uparrow\) 11.9\%) \\ \end{tabular} \\ \hline \multirow{4}{*}{20 m/s B-class} & Vertical acceleration of sprung mass & 0.6618 & \begin{tabular}{c} 0.6086 \\ (\(\uparrow\) 8.03\%) \\ \end{tabular} & \begin{tabular}{c} 0.4909 \\ (\(\uparrow\) 25.21\%) \\ \end{tabular} \\ \cline{2-5}  & Motor eccentricity & 5.6844 \(\times 10^{-5}\) & \begin{tabular}{c} 5.2015 \(\times 10^{-5}\) \\ (\(\uparrow\) 8.49\%) \\ \end{tabular} & \begin{tabular}{c} 4.3713 \(\times 10^{-5}\) \\ (\(\uparrow\) 23.10\%) \\ \end{tabular} \\ \cline{2-5}  & Tire dynamic load & 344 & \
\begin{tabular}{c} 315 \\ (\(\uparrow\) 8.43\%) \\ \end{tabular} &
\begin{tabular}{c} 305 \\ (\(\uparrow\) 11.34\%) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The RMS values of HM-EV evaluation index. Figure 22: Vortical acceleration of sprung mass. In order to better compare the effect of different control methods, peak-to-peak (PTP) values were defined which represent the maximum values minus the minimum values. Table 4 shows the PTP values of each evaluation index under two types of bump road excitation. It can be seen from Figures 21-23 and Table 4 that HMPC can reduce the amplitude of the evaluation index to a greater extent under bump excitation compared to COC. In addition, the improvement of PTP values under different amplitudes bumps excitation of HMPC so it is basically remains the same, which reflects that HMPC can perform well under different amplitude of road excitation, further verifying the effectiveness of HMPC.
## 2 Background
### SDN Security Issues
#### 2.3.1 Application Plane Security  
One of the key points of the SDN paradigm is the ability to control a network via software. Since most of the network functions can be implemented as SDN applications, malicious applications, if not stopped early enough, can spread havoc across a network.  
The main point is that there is no standard or specifications allowing SDN applications to manage network services and functions (i.e., to control the control plane) [36]. Hence,applications can pose a threat to the whole network. The myriad of vendor and third-party applications developed in different independent development environments using different programming models and paradigms could create interoperability limitations and security policy collision. Typically, in OpenFlow-based applications, the applications that manage the control plane are developed by parties other than the controller vendors, and this can lead to a security privilege issue. In fact, these applications can inherit the privileges of access to network resources and can manage the behavior of the network if proper security mechanisms are not provided for malicious activities [37].  
[PERSON] et al. [38] demonstrated the need to have secure mechanisms in the SDN paradigm to establish secure connections between the controller and applications. Otherwise, due to the abstractions created by the SDN controller, malicious applications can exploit these abstractions to inject malicious configuration commands and theoretically break the network.  
[PERSON] et al. [39] identified three different classes of applications that can impact network security in the SDN paradigm. First, there are the network-sensitive applications that require particular network characteristics. Second, there are applications that provide services for the network, such as a firewall. Finally, there are applications that combine the characteristics from the first and second classes.  
In general, malicious applications can bypass access control and will be a real challenge in SDNs. SDN-aware applications can locate and directly communicate with the SDN controllers, while SDN-agnostic applications can communicate indirectly with application datagrams in specific formats.
## 3 Methodology
### Model Optimization  
The best performing model identified unique words in the corpus that optimally distinguish among distinct topics. However, stop words manifest as noise that reduces the separability of term distributions among document clusters, which effectively decrease the performance of topic modeling. This research invented a method of stop word engineering to reduce noise, beyond removing common English stop words.  
Figure 3 illustrates the two-part workflow to conduct stop word engineering and find the best mix of algorithms in the NLP pipeline. The workflow began by setting the hyperparameter for the number of topic model topics to identify the same number of SME-identified topics and initializing the stop word list to the database search terms. Subsequently, the workflow iteratively selected from an exhaustive combinatorial mix of NLP algorithms in the pipeline {tokenizer, normalizer, vectorizer, filter, topic modeler} to calculate the H-index. The workflow then selected the NLP pipeline that maximized H-index, and then iteratively updated the stop word list. The selected stop words were those that repeated more than once (appeared more than twice) in the combined list of top 20 keywords that the model identified for each topic. The iteration terminated when no keywords repeated more than once. The workflow then iterated through the NLP pipeline again, using the updated stop word list, to identify the best performing pipeline. The pipeline selection terminated once the best NLP pipeline stayed the same.  
The rationale for setting the termination criteria to a maximum of a single keyword repetition allowed for each document to reflect a mixture of topics, albeit with one topic dominating.  
The rationale for setting the stop word keyword count window to the top 20 was that preliminary analysis established it as a point of diminishing returns in term importance. The Results section plots the keyword frequency distribution of the best model to illustrate and validate this observation. That is, the dominant keywords contribute most toward identifying latent topics, whereas infrequent keywords manifest as noise.  
The literature established that topic models share the same flaw in requiring a user to define the topic count for modeling [32]. Hence, the model optimization part of the workflow used the best NLP pipeline from the pipeline to iterate the number of topics to model and calculate the purity, dispersion, and H-indices. The workflow then evaluated topic alignment by selecting the model that provided the peak H-index. The SME then mapped model-assigned topics to the SME categories and empirically assessed a level of qualitative alignment. The workflow then utilized uniform manifold approximation and projection (UMAP) to visualize document clustering by topic assignment probability in a dimensionally reduced feature space, and subsequently to visualize the level of topic assignment confusion.
# A New Indicator to Assess Public Perception of Air Pollution Based on Complaint Data
## 4 Discussion  
This paper shows that the areas with the strongest public perception of air pollution are mainly located in the southwest of Shandong Province, including Zibo, Laiwu, Jinan, Zaozhuang, Linyi, and other cities. The cities mentioned above primarily focus on industrial development.  
Therefore, we used POI data to explore the relationship between the high value of APPI and enterprises' space distribution. We used word cloud analysis-based complaint data content to discuss the problems of public complaints, because the public's perception was the strongest in 2015, and we took it as an example.  
Word cloud was initially proposed by [PERSON], associate professor of Journalism at Northwestern University [32], a visual display of 'keywords' with high frequency in the text. Words with increased frequency will be presented in a larger form, while the words with low frequency will be presented in a smaller format. Word cloud image filters out a large number of low-frequency and low-quality text information so that the viewer can appreciate the text's theme as soon as they scan the text. The distribution of APPI and industrial POI points (a) and the results of complaint content based on word cloud analysis (b) in Shandong Province in 2015 are shown in Figure 6.  
Figure 6a describes that cities with the strongest public perception of air pollution, such as Jinan, Zibo, Laiwu, Weifang, Zaozhuang, Linyi, and Qingdao, are also the densest  
Figure 6: Superposition results of industrial sites and APPI in Shandong Province in 2015 (a) and content analysis of air pollution complaints based on word cloud (b).  
enterprises. The word cloud result shows that the most frequent word is \"enterprise\" (Figure 6b), followed by \"pollution,\" \"villagers,\" or \"village\" in the complaint content. It indicates that the public is more concerned about the impact of enterprises on air pollution. Moreover, the high-frequency vocabulary words of \"village\" and \"villagers\" mean that these enterprises may be located in the suburbs, such as the Zhangdian District of Zibo.  
The more enterprises there are, the more the pollutants, and the stronger the public perception of air pollution will be. Although coastal cities also have high-density industries and enterprises, their self-purification ability is relatively strong, the public perception of air pollution is stable.  
As an international tourism city, Qingdao mainly focuses on the development of tourism services and enterprises. The public may have higher requirements on its air quality, so the perception of air pollution (APPI) is also stronger.  
In this section, we can conclude that the public's strong perception of air pollution is also the high-density area of enterprises. The word cloud analysis of public complaints' content well confirms this conclusion, indicating that the index APPI proposed in this paper can effectively evaluate the public perception of air pollution.
# Escalator: An Autonomous Scheduling Scheme for Convergecast in TSCH
###### Abstract  
Time Slotted Channel Hopping (TSCH) is widely used in the industrial wireless sensor networks due to its high reliability and energy efficiency. Various timeslot and channel scheduling schemes have been proposed for achieving high reliability and energy efficiency for TSCH networks. Recently proposed autonomous scheduling schemes provide flexible timeslot scheduling based on the routing topology, but do not take into account the network traffic and packet forwarding delays. In this paper, we propose an autonomous scheduling scheme for convergecast in TSCH networks with RPL as a routing protocol, named Escalator. Escalator generates a consecutive timeslot schedule along the packet forwarding path to minimize the packet transmission delay. The schedule is generated autonomously by utilizing only the local routing topology information without any additional signaling with other nodes. The generated schedule is guaranteed to be conflict-free, in that all nodes in the network could transmit packets to the sink in every slotframe cycle. We implement Escalator and evaluate its performance with existing autonomous scheduling schemes through a testbed and simulation. Experimental results show that the proposed Escalator has lower end-to-end delay and higher packet delivery ratio compared to the existing schemes regardless of the network topology.  
Keywords:wireless sensor network; TSCH; convergecast; autonomous scheduling; RPL +
Footnote †: journal: Sensors
# Application of Machine Learning Ensemble Methods to ASTRI Mini-Array Cherenkov Event Reconstruction
## 4 Parameters  
The reconstruction of Cherenkov events detected by ASTRI Mini-Array telescopes requires the use of several parameters (see Figure 2) to fully characterize their properties. In our study, we have utilized multiple sets of parameters to analyze and interpret the data. Each set of parameters includes a specific subset of the parameters listed below, commonly employed in Cherenkov analysis [2].  
1. \(\log_{10}(SIZE)\) is the decimal logarithm of the total content in photo-electrons of the cleaned image;
2. \(WIDTH\) is a Hillas parameter [2]: the minor half axis of the ellipse that best represents cleaned image;
3. \(LENGTH\) is a Hillas parameter: the major half axis of the ellipse that best represents the cleaned image;4. \(DENS\) is defined as: \[DENS=log_{10}\frac{SIZE}{WIDTH\times LENGTH}\] (1)
5. \(CONC\) is the concentration of the image defined as the ratio of the sum of the intensity in the two brightest pixels over \(SIZE\);
6. \(NUSEDTEL\) is the number of telescopes used for the stereoscopic reconstruction;
7. \(M3 LONG\) is the third momentum descriptor of the image elongation;
8. \(TELIP\) is the telescope impact defined as the distance between the stereo reconstructed core position and the given telescope;
9. \(STMAXH\) is the stereo reconstructed shower maximum height;
10. \(LEAKAGE\) is defined as the ratio of the sum of the pixel signals at the edge of the camera over \(SIZE\);
11. \(NUMOORE\) is the number of core pixels in the image (see [10] for more details on the image cleaning method);
12. \(NUMBOUNDARY\) is the number of boundary pixels in the image (see [10] for more details on the image cleaning method);
13. \(DELTA\) is the angle between the Cherenkov photon emission direction and the projection of the photon arrival direction onto the plane perpendicular to the Cherenkov radiation cone;
14. \(ASYM\) is the distance from the highest pixel to the center of the image, projected onto the major axis;
15. \(ECCE\) is the eccentricity of the image defined as: \[ECCE=\frac{\sqrt{(LENGTH^{2}-WIDTH^{2})}}{LENGTH}\] (2)
16. \(ELONG\) is the elongation of the image defined as: \[ELONG=1-\frac{WIDTH}{LENGTH}\] (3)
17. \(NUMISLAND\) is the number of islands of the image. This parameter is used only for filtering.  
Figure 2: Sets of parameters used in our analysis. “GH SET” refers to sets used in gamma/hadron discrimination while “EN SET” refers to sets used in energy estimation. A full box indicates that the corresponding parameter is part of the set. “GH SET 3” and “EN SET 1” are highlighted since they are our top-performing sets, as we will discuss in Sections 5.5 and 6.3.  
Figure 2 reports the different sets of parameters used in our analysis. Each set was tested against the benchmark combination (GH SET 0).
## 2 Materials and Methods
### Data Analyses  
#### 2.4.1 Device Accuracy and Reliability  
Data from day one were incomplete and were not used for the calculation of social networks. RTK rover G was not used on day one after physical damage during transport. Data analysis showed that RTK rover C had temporary recording failures. Data from day four were not analysed for social networks as RTK rover F failed to record on this day due to operator failure (the device was not accurately started). Data from days two  
Figure 4: RTK rovers fitted on the back of sheep via harnesses (**a**), and fixed base station (**b**) in the middle of the paddock. The two masts (**b**) were for the GNSS amplified antenna, and RTK correction transmitter.  
and three were complete and were used for calculations of social networks and sampling rates as described in subsequent sections. The analysis of the GNSS data recorded by the RTK rover was carried out using R packages [40]. All records commencing from when the animals were introduced into the paddock until they were removed remained in the analysis, including the GNSS error around the paddock boundaries to evaluate the GNSS accuracy. All available locational data for animals were plotted in the R statistical package per day (5 h a day). The fence line coordinates were also plotted using ggplot [41] in R [40] based on the measurements obtained while walking around the fence line. To examine the device accuracy, position differences were calculated based on the difference between the instantaneous measurement from a GNSS rover moving along the paddock's fence line, compared with the fence line interpolated from a GNSS survey of the paddock corners. The calculated location errors were then plotted using ggplot [41] in R [40] with the normalised counts (i.e., count in each bin divided by total count so all values are <1.0) for all bins to make the comparison easier.