esa-sceva/satcom-chunk-collection huggingface dataset:
# Scalable and Reliable Data Center Networks by Combining Source Routing and Automatic Labelling
## 3 The eTorii DCN
### Auto-Configuration: Automatic Label Assignment Based on GA3
When \(S1\) and \(S2\) receive these HLMACs, they would initially discard them (as they are longer than the ones they already have), but they could also leverage them to learn from the suffixes assigned by their neighbour core nodes and synchronize them. In the example, \(S1\) will understand that \(S2\) selected suffix 2, and \(S2\) that \(S1\) selected 7. Following certain priority, they could select a unique suffix and propagate it again, synchronising their values accordingly. For instance, \(S2\) could consider that \(1.7<2.2\) and then suffix 7 has a higher priority, hence propagating again its HLMAC to \(S3\), but now as 2.7 (instead of 2.2), which is synchronized with 1.7. As a result, all network nodes obtain HLMACs with synchronized suffixes (for simplicity, the present article omits the full assignment procedure, but it can be checked in GA3 [10]). Figure 3 shows an example of synchronized assignment. \(S7\) obtains 11.7.6, 1.7.6 and 2.7.6 (all suffixes are 7.6), while in Figure 2 (not synchronized) it obtained 1.7.6, 1.6.3 and 2.2.6. Synchronization of suffixes requires additional control messages (as detailed in GA3 [10]), but it grants additional properties for faster recovery after failures, as we will describe in the following sections. Figure 3: Address assignment example with synchronized suffixes. Figure 2: Address assignment example without synchronized suffixes. Note that label assignment goes from top (core) to bottom (Top-of-Rack (ToR)) switches, while end devices (usually hosts or servers) are left unaltered. When a host requires the creation of a route, it will be the ToR switch the one in charge to translate the physical (real) MAC of the host into an HLMAC. For example, host h1 could obtain label 11.7.6.1, 1.7.6.1 or 2.7.6.1, by simply adding a unique suffix (1) to the assigned address of the ToR switch that serves it, which is \(S7\). Finally, it is worth noting that the bigger the network topology, the bigger the redundancy and the bigger the amount of HLMACs to be assigned, because each HLMAC represents a potential route. However, the network manager can limit the assignment of addresses to any value. For example, nodes could just learn a limited set of labels and stop propagating them once reached, which reduces the amount of alternative routes, but accelerates the convergence time of the assignment; hence, it is up to the network manager to analyze the trade-off between those two aspects: resilience and scalability.
## 3 Methodology
### Statistical and Machine Learning Methods
#### 3.2.3 Correlation Analysis  
Correlation analysis was performed to assess the relationships between air pollutants (PM\({}_{10}\), NO, NO\({}_{2}\), CO) and meteorological parameters (WS, temperature, RH, and RainF) over the study period. The Spearman correlation coefficient, a non-parametric measure of association, was selected due to its suitability for non-linear and non-normally distributed environmental datasets [27].  
The Spearman coefficient ranges from \(-1\) to 1:  
* Positive values indicate a direct relationship.
* Negative values suggest an inverse relationship.
* Values near zero imply no significant association.  
The preprocessed datasets were aligned across consistent time intervals to ensure robust and reliable results. Seasonal variations in correlations were also analyzed to capture how pollutant-meteorological relationships vary under different climatic conditions. For instance, WS was found to play a critical role in pollutant dispersion during winter, while RainF had a stronger influence on pollutant removal during spring and summer.  
However, while Spearman correlation effectively identifies associations between variables, it does not infer causality. For example, a strong correlation between temperature and NO\({}_{2}\) does not necessarily indicate a direct causal link, as other underlying factors (e.g., industrial emissions) may mediate this relationship. These limitations emphasize the need for complementary analyses to draw robust conclusions.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameters** & **Min** & **Max** & **Mean** & **Median** & **SD** & **SE** \\ \hline PM\({}_{10}\) (\(\mathrm{\SIUnitSymbolMicro m}^{3}\)) & 6.49 & 121.24 & 31.92 & 27.14 & 16.24 & 0.51 \\ NO (\(\mathrm{\SIUnitSymbolMicro m}^{3}\)) & 2.22 & 145.21 & 25.8 & 20 & 19.35 & 0.60 \\ NO\({}_{2}\) (\(\mathrm{\SIUnitSymbolMicro m}^{3}\)) & 8.49 & 78.33 & 32.4 & 31.32 & 11.76 & 0.37 \\ CO (\(\mathrm{\SIUnitSymbolMicro m}^{3}\)) & 0 & 2.41 & 0.79 & 0.71 & 0.39 & 0.01 \\ WS (\(\mathrm{\SIUnitSymbolMicro m}/\mathrm{s}\)) & 0.5 & 4.1 & 1.58 & 1.5 & 0.59 & 0.02 \\ TEMP (\({}^{\circ}\)C) & \(-1.7\) & 29.1 & 15.94 & 16 & 7.02 & 0.22 \\ RH (\(\mathrm{\SIUnitSymbolMicro m}\)) & 22.6 & 99 & 72.06 & 73.5 & 12.43 & 0.39 \\ RainF (mm) & 0 & 49 & 2.30 & 0 & 5.94 & 0.19 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Descriptive statistics of air pollutants (PM\({}_{10}\), NO, NO\({}_{2}\), CO) and meteorological parameters (WS, temperature, RH, RainF) measured in Sakarya city during 2021–2023.  
By identifying these relationships, the correlation analysis lays a strong foundation for predictive modeling, elucidating how meteorological factors influence pollutant behavior. These insights are crucial for designing targeted air quality management strategies and implementing evidence-based interventions to mitigate pollution effectively.
## 2 The Equations of Coupled THC Model
### The Heat Transfer Equation  
According to the previous research [27], the thermal conductivity and thermal conductivity of porous medium are defined by an averaging model to account for both solid matrix and fluid properties, which are  
\[k_{eff}=(1-\phi)k_{s}+\phi k_{f}+k_{d} \tag{19}\]  
\[\big{(}\rho_{f}c_{f}\big{)}_{eff}=(1-\phi)\rho_{s}c_{s}+\phi\rho_{f}c_{f} \tag{20}\]  
where \(k_{eff}\) is the effective thermal conductivity, W/(m\(\cdot\)K); \(k_{s}\) and \(k_{f}\) are the thermal conductivity of solid matrix and fluid, respectively, W/(m\(\cdot\)K); \(k_{d}\) is the dispersion thermal conductivity, W/(m\(\cdot\)K); \(\big{(}\rho_{f}c_{f}\big{)}_{eff}\) is the effective volumetric heat capacity, J/(m\({}^{3}\cdot\)K); \(c_{s}\) is the specific heat capacity of solid material in porous media, J/(kg\(\cdot\)K); \(c_{f}\) is the specific heat capacity of fluid, J/(kg\(\cdot\)K).  
If the dispersion thermal conductivity is not considered, Equation (19) becomes  
\[k_{eff}=(1-\phi)k_{s}+\phi k_{f}+k_{d} \tag{21}\]  
The governing equations of heat transfer for porous media are  
\[(\rho_{f}c_{f})_{eff}\frac{\partial T}{\partial t}+\rho_{f}c_{f}\boldsymbol{v }\cdot\
abla T+\
abla\cdot\boldsymbol{q}=Q \tag{22}\]  
\[\boldsymbol{q}=-k_{eff}\cdot\
abla T \tag{23}\]  
where \(T\) is the absolute temperature, K; \(\boldsymbol{q}\) is the conductive heat flux, J/(s\(\cdot\)m\({}^{2}\)), \(Q\) is the heat source or sink, W/K\({}^{3}\).  
In summary, the governing equation of hydrochemical and hydraulic erosion is Equation (16), the governing equations of fluid flow are Equations (17) and (18), and the governing equation of heat transfer is Equation (22).  
Compared with the full coupled THMC processes reported by [PERSON] [28], some of the coupled THMC processes and some new coupled processes are studied using the equations of the coupled THC model, as shown in Figure 1.
## 8 Discussion
This preliminary finding referring to the potentially better applicability of higher signal frequencies can be promising for further analysis of daytime Swarm observations with respect to LAIC. EPBs are ionospheric irregularities that are often suspected to be the ones that can be confused with SIIDs and vice versa. The EPB studies, since the beginning of the Swarm mission, started a generation of Swarm-based IBI products [69], which have been analyzed in this study together with the other parameters. Several cases of unconfirmed EPBs appearing together with the Swarm Ne signal disturbances are found in this study, as is one case of jointly existing confirmed EPBs and disturbances in the Swarm Ne signal at a 300 km wavelength. The number of coincidences between the seismic record and Swarm disturbances is, however, much larger than the coincidences of Swarm Ne disturbance and EPBs. Thus, two alternatives are possible in the mentioned infrequent cases; the first is the masking of SIIDs by the EPBs, which was investigated in [65]; the second is a relation of EPBs with preseismic signals in the ionosphere, which was suggested by [PERSON] et al. [70]. Assuming that EPBs are related to a geomagnetic source, and that the geomagnetic observations are applied in the development of EPB products like the Swarm L2 IBI product, the first idea can be used as a basis for the conclusions. The comparative analysis of Swarm disturbances, EPBs, and other factors lead to the conclusion that disturbing signals are a mixture of geomagnetic, solar, seismic, and other effects that are observable together. This study provided many examples of composite Ne disturbances being measured by Swarm and revealed the existence of an evident part of the disturbance signal, dependent on seismic activity. [PERSON] and [PERSON] [65] suggested that the separation of disturbed signals with different origins can be possible with the use of spectral tools. This study uses STFT for disturbance pattern recognition; however, the conclusion is that the needs are larger, i.e., the spatiotemporal models of the other disturbing factors are also necessary, aside from the spectral decomposition. The use of global models of geomagnetic and solar activity level can, together with the spectral signal decomposition, provide the tools for some kind of regularization of detected disturbances and enable recognition of signals of different origins. Figure 16: Pearson correlation coefficients of Swarm A/B/C Ne PSD with cumulative sum of seismic energy (black), ap (green), Dst (orange), and F10.7 (red), respectively (**a**) directly between daily Swarm Ne PSDs, cumulative sum for the short period of 5 days, ap, Dst, and F10.7, (**b**) using 5-day moving averages of all series except F10.7 (original daily F10.7 is used due to its apparent long-term character), (**c**) 7-day averages of all with original F10.7, and (**d**) 10-day averages of all with original F10.7. N and D denote nighttime and daytime tracks, respectively. The wavelength of Ne signal sampling is also given in km.
# A Novel Network Protocol Syntax Extracting Method for Grammar-Based Fuzzing
## 3 Methods
### Single-Layer Protocol Field Information Extraction  
Single-layer protocol field information extraction refers to extracting all fields' attribute information owned by a protocol from the protocol dissector file. Attributes information includes field name, offset, length, type, optional value, etc. There are three main ways to obtain fields' attributes information:  
* From static arrays. Each protocol dissector file defines one or several static structure arrays called \"header fields\" in which the names, identifiers, data types, lengths, and optional values information for all fields are stored, we call these fields \"standard fields\". In the subsequent parsing process, the standard fields are used to judge the validity of the extracted per packet type fields and at the same time can give supplement information to the valid fields.
* From the buffer reading function. By analyzing the packet buffer read function, the variable name, offset, and length information of each field is extracted.
* From the protocol tree. By tracing the process of the protocol tree generating in the main parsing function, the identifier, offset, length, variable name, and byte order of each field is extracted.  
Table 1 lists the attribute information included in protocol fields and the corresponding obtaining ways, wherein \"Static Array\" means that it can be extracted from the structure array \"header fields\", \"Buffer Read\" means that it can be obtained from the buffer reading function, and \"Proto Tree\" means that it can be extracted from the generation process of the protocol tree.  
Figure 2: Wireshark protocol dissector framework. ([[https://github.com/wireshark/wireshark/tree/master/](https://github.com/wireshark/wireshark/tree/master/)]([https://github.com/wireshark/wireshark/tree/master/](https://github.com/wireshark/wireshark/tree/master/)), accessed on 10 March 2024).
# Feature Construction Using Persistence Landscapes for Clustering Noisy IoT Time Series
## 8 Conclusions  
The use of persistent homology (an important topic in topological data analysis) for analyzing time series is a novel emerging area of scientific enquiry, which connects mathematics, computer science, and statistics. Persistent landscapes are especially useful constructs for clustering a large number of time series with different temporal dependence properties. As lower-order persistence landscapes contain more important topological features than those of higher orders, in many situations it may be unnecessary and computing-intensive to use persistence landscapes of all orders to elicit useful features of time series. Previous research on this topic has not considered the problem of _selecting the order_ of the persistence landscapes for effective clustering. In this paper, we present the elbow method, a data-based approach to selecting the optimal order of persistence landscapes. The novelty of our approach was to only use persistence landscapes up to the selected order as features, for  
Figure 5: Distribution of locations in the \(C=3\) clusters.  
faster clustering of a large number of time series. Note that if we have labeled time series, these features can also be used in supervised learning algorithms (classification).  
We evaluated our approach through a simulation study, which showed that using features up to the optimal selected order produced the best clustering performance in most cases. We also applied our algorithm to clustering weather stations in the US, based on daily temperatures from sensors, in interesting and meaningful ways.  
It is interesting to note that, unlike the simulated cases, the same clustering labels that we obtained from our algorithm were also obtained for the temperature series by using only the first-order persistence landscape, or all orders of persistence landscapes as features. In future work, it will be useful to demonstrate the usefulness of our algorithm for IoT time series with different types of complex dependence patterns. If the IoT series exhibit nonstationary behavior, we could construct persistent landscapes starting from higher-order spectra, whose fast computation is discussed in [34]. Other useful extensions of our approach would include an investigation of the robustness properties of persistent-homology-based statistical learning for time series, and developing alternatives to the elbow method.  
Conceptualization, R.C. and N.R.; methodology, R.C. and N.R.; writing--review & editing, R.C. and N.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Precoder and Decoder Co-Designs for Radar and Communication Spectrum Sharing +
## Abbreviations  
The following symbols are used in this manuscript:  
\[\begin{array}{ll}\alpha&\text{scalar, complex path loss}\\ \mathbf{A}&\text{transmitted signal matrix under block fading assumption}\\ \mathcal{A}&\text{communication user set}\\ \mathcal{A}_{r}&\text{communication user set that is interfered by radar}\\ \mathcal{A}_{c}&\text{communication user set that is not interfered by radar}\\ \beta&\text{baseband subcarrier}\\ \beta_{R}&\text{radar baseband subcarrier}\\ \beta_{C}&\text{communication baseband subcarrier}\\ \mathbf{B}&\text{division multiplexing modulation matrix}\\ \mathbf{\bar{B}}&\text{demodulation matrix}\\ \mathcal{B}&\text{radar user set}\\ \mathbf{C}&\text{channel coding matrix}\\ d&\text{scalar, number of degrees of freedom}\\ \mathbf{D}&\text{interference plus noise covariance matrix}\\ h[f_{1}]&\text{scalar, channel impulse response received for first subcarrier}\\ \mathbf{H}&\text{channel matrix}\\ K&\text{total number of communication users}\\ L&\text{total number of blocks/pulses}\\ M&\text{total number of subpulses}\\ N&\text{column dimension of precoder matrix, which is user-desired frequency DoFs.}\\ N_{sc}&\text{number of transmitted subcarriers}\\ N_{p}&\text{column dimension of data matrix }\mathbf{S}\\ \mathbf{P}&N_{sc}\times N\text{-dimensional precoding matrix}\\ \mathbf{Q}&N_{sc}\times N\text{-dimensional decoding matrix}\\ \mathbf{s}(N)&\text{data vector for }N\text{th time slot}\\ \mathbf{S}&N_{p}\times M\text{-dimensional data matrix}\\ T_{p}&\text{entire pulse duration}\\ \mathbf{W}&N_{sc}\times M\text{-dimensional Gaussian noise matrix}\\ \mathbf{Y_{T}}&N_{sc}\times LM\text{-dimensional transmitted signal matrix}\\ \mathbf{Y_{R}}&N_{sc}\times LM\text{-dimensional received signal matrix}\\ \tau_{T,j}&\text{scalar, delay between transmitted antenna and target}\\ \tau_{R,j}&\text{scalar, delay between received antenna and target}\\ \theta&\text{target's direction of arrival}\\ \mathbf{\Omega}&\text{selection matrix}\\ \mathbf{1}_{N\times M}&N\times M\text{-dimensional all-ones matrix}\\ \end{array} \tag{1}\]
###### Abstract  
Grout voids in the tendons of a post-tensioning bridge reduce their strength. Grout voids are also severe flaws causing corrosion of the steel strands in the tendons. Detecting voids during construction and operation of the tendons is essential to prevent tendon failure, which is critical to bridge safety. This study presents a method for inspecting external tendons for voids in the grout below the protective duct pipe using terahertz electromagnetic waves. Due to low attenuation in the high-density polyethylene duct and the large reflectivity difference between the duct/grout and the duct/void interfaces, terahertz waves are suitable for detecting voids in the grout inside tendons. For this study, we developed a mobile frequency-domain terahertz A-scanner that can be used to measure terahertz A-scan data in real time. It is shown that the mobile terahertz A-scanner can be used to assess the area of the grout void in external bridge tendons.  
terahertz wave; nondestructive inspection; voids in grout; external tendon; post-tensioning bridge
###### Abstract  
Glucansucrase secreted by _Streptococcus mutans_ and composed of virulence genes alters oral microbiota, creating adherent environment for structural bacteria colony forming dental biofilm. The present investigation studied the inhibitory and binding potentials of mangiferin against _S. mutans_ and its enzyme glucansucrase implicated in biofilm formation. Antibacterial activity against planktonic _S. mutans_ was carried out. Using reverse transcription PCR, the expression of crucial virulence genes, _gtfB_, _gtfC_, _gtfD_, _gtpB_, and _comDE_ were determined. The effect of mangiferin on teeth surfaces biofilm was ascertained by scanning electron microscopy (SEM). Docking analysis of _S. mutans_ glucansucrase and mangiferin revealed the binding energy of \(-7.35\) and ten hydrogen interactions. Antibacterial study revealed that mangiferin was not lethal to planktonic _S. mutans_, but a concentration-dependent inhibition of glucansucrase activity was observed. The inhibitory effect of water-insoluble glucan synthesis was apparently more marked relative to water-soluble glucan synthesis attenuation. Mangiferin significantly downregulated the expression of the virulence genes, indicating a mechanism involving glucanotranferases, specifically inhibiting colony formation by attenuating bacterial adherence. SEM images revealed that _S. mutans_ biofilm density was scanty in mangiferin treated teeth compared to non-treated control teeth. Our data therefore suggest that mangiferin inhibited _S. mutans_ biofilms formation by attenuating glucansucrase activities without affecting bacteria growth.  
mangiferin; _S. mutans_; glucansucrase; dental plaque; biofilm
# Calibrating Microscopic Traffic Simulation Model Using Connected Vehicle Data and Genetic Algorithm
## 4 Results
### Validation Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Errors in Time-Space** & **Before** & **After** & **\% of Change** & **Units** \\
**Diagrams** & **Calibration** & **Calibration** & **\% of Change** & **Units** \\ \hline Mean & 366.89 & 246.97 & 32.68\% & m\textasciitifmin \\ Median & 203.55 & 235.69 & \(-\)15.79\% & m\textasciitifmin \\ Standard Deviation & 494.95 & 184.44 & \(-\)27.44\% & m\textasciitifmin \\ Minimum & 3.71 & 4.72 & \(-\)27.23\% & m\textasciitifmin \\ Maximum & 2479.92 & 705.35 & 71.56\% & m\textasciitifmin \\
25 th percentile & 23.39 & 85.17 & \(-\)264.11\% & m\textasciitifmin \\
75 th percentile & 558.05 & 375.83 & 32.65\% & m\textasciitifmin \\ Variance & 244,973.86 & 340.17.95 & 86.11\% & (m\textasciitifmin)\({}^{2}\) \\ Skewness & 2.51 & 0.55 & 78.16\% & - \\ Kurtosis & 8.46 & \(-\)0.40 & 104.69\% & - \\ Coefficient of Variation & 1.35 & 0.75 & 44.64\% & - \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Statistical results of the errors in the validation process. Data gathered from the simulations and RTMS are used to compare the average vehicle speed before and after the calibration of parameters with the ground truth data. The validation process's average speed data from 7 to 9 AM are displayed in Figure 8. Although they still require improvement, the calibrated parameters considerably decreased the differences between the simulation and the ground truth, as shown in the figure. Figure 8: Average speed comparison of ground truth and simulation results before and after implementing calibrated parameters in the validation process. These calibrated traffic simulation models have significant practical applications in traffic engineering. For instance, the improved model accuracy can support enhanced ramp metering strategies, where precise predictions of traffic flows are essential to managing merging and lane-changing scenarios effectively. Similarly, the reduced error variability allows for better corridor management, enabling traffic engineers to design adaptive signal timings and manage traffic congestion dynamically. These applications demonstrate how the proposed calibration framework can translate into actionable insights for real-world traffic management and planning tasks.