esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology and Case Study
### Methodology for Model Development  
With the aim to develop a model to predict the average speed (\(V_{\rm m}\)) and the 85 th percentile of the speed distribution (\(V_{85}\)) in the midpoints between vertical TCMs, various model types were proposed to correlate these speeds with the distance between TCMs as an independent variable (predicting variable). The significance of the model was verified by means of the Fischer-Snedecor test, with a \(p\)-value of the F statistic below 0.05 (95% of significance). Additionally, the individual significance of each coefficient and the intercept was tested by the Student's \(t\)-test if a \(p\)-value \(<\) 0.05 is obtained. Furthermore, if a linear regression model is adopted, some hypotheses that are assumed must be verified [58; 59; 60; 61; 62; 63; 64]: a linear relationship (checked by the high Pearson coefficient between variables, R), the independence of the observations (certified by the Durbin-Watson statistic near the range 1.5-2.5); the homoscedasticity (evaluated by the absence of any pattern in a plot of the standardized predicted values vs. standardized residuals), and errors are normally distributed (checked by a Shapiro-Wilk normality test).
## 2 Materials and Methods  
### Data Sources and Preprocessing  
1. IGS-TEC  
IGS-TEC (International GNSS Service Total Electron Content) is a global ionospheric Total Electron Content (TEC) product developed and disseminated by the International GNSS Service (IGS). This dataset is generated through multi-frequency signal differential techniques and ionospheric modeling methods, leveraging a globally distributed network of GNSS dual-frequency stations (e.g., GPS, BDS, Galileo). It is designed to deliver high-precision ionospheric parameters in support of navigation, communication, and space weather research.  
This study utilizes data from 2011 to 2019 with a temporal resolution of 1 h (UTC). The original TEC data with a grid size of 71 \(\times\) 73 was processed using bilinear interpolation, resulting in a global grid of 64 \(\times\) 64 dimensions that maintains a longitudinal resolution of 5.625\({}^{\circ}\) and latitudinal resolution of 2.8125\({}^{\circ}\). This method was selected primarily for its computational efficiency and simplicity in regridding existing gridded data, which is a standard approach for this type of preprocessing. While more advanced spatial interpolation techniques such as Kriging or Natural Neighbor Interpolation offer advantages in  
intensity and solar activity levels (e.g., sunspot numbers and flare activity). It also serves as a robust indicator of radio wave propagation conditions.  
This study employs hourly-level data from these three parameters, temporally and spatially aligned with IGS-TEC records (per-data-point alignment), as input features for ionospheric modeling. AE and SYM-H are incorporated to enable the model to capture magnetospheric energy injection-driven abrupt Ne variations, particularly those affecting the vertical electron density profile. As indicators of substorm (AE) and storm (SYM-H) activity, these indices reflect disturbances in ionospheric electric fields and plasma drifts, which significantly modify the vertical distribution of ionization in the F-layer, altering its shape and peak height. For instance, studies show that substorms (reflected in AE) cause significant disturbances in vertical plasma drift in the F-region [23]. Similarly, observations using Ne retrieval profiles during geomagnetic storms (characterized by significant SYM-H variations) have shown enhancements and uplifting of electron density structures [24], while F10.7 seeks to enable the model to uncover solar radiation's long-term modulation effects on ionospheric ionization rates.
## 5 Optimization Method
### Generating Initial Solution  
A feasible solution satisfying the optimization problem constraints is generated at this step. To this end, a greedy heuristic algorithm is executed. The algorithm aims to select for each demand of each network slice the PP nodes where the DU/CU entities are placed together with single routing paths to realize the FH and MH flows in the network.  
The considered greedy algorithm performs as follows:  
1. Preprocessing: In the preprocessing phase, the clusters are ordered in decreasing order according to their number of demands. Also, the set of demands is ordered to reflect the order of clusters; i.e., the first demands correspond to those in the first clusters. Moreover, for each cluster, the PPs are ordered in increasing order according to the length of the shortest path to the most distant RU in the cluster. The following steps process the clusters, demands, and PPs according to such order.
2. Placement of DUs with routing of FH flows: In this step, the consecutive clusters are processed, for which PP nodes are selected for the realization of DU functions according to a first-fit policy. Namely, the nearest feasible PP \(v\in\mathcal{V}^{\text{PP}}\) is chosen for each cluster \(c\in\mathcal{C}\), such that (a) it has enough capacity for DU processing for all demands in the cluster and (b) the FH flows can be realized with a latency and bandwidth guarantee over the shortest paths between the RUs belonging to the cluster and the PP selected.
3. Routing of MH flows for eMBB demands: Here, the consecutive demands belonging to the network slices of type eMBB are processed. In particular, the shortest feasible routing paths, satisfying the latency and bandwidth constraints, are selected to realize MH flows between the previously chosen PPs (with DU processing) and the hub node, where the CU functions are performed for eMBB.  
Figure 3: A flowchart of the PBA method.  
4. Placement of CUs with routing of MH flows for URLLC demands: Finally, PP nodes for the CU processing are selected for URLLC network slices. Namely, for each slice \(s\in\mathcal{S}^{\text{URLLC}}\), the PPs are ordered in decreasing order according to the overall DU load of the demands belonging to this slice. Next, the first feasible PP \(v\in\mathcal{V}^{\text{PP}}\) is chosen, such that (a) it can accommodate the CU load of the slice and (b) for which the MH flows can be realized in the network over the shortest paths with the latency and bandwidth guarantee.  
The above algorithm provides a set of initial paths \(\widetilde{\mathcal{P}}\) for the second step of the PBA (i.e., the CG procedure). Moreover, the PBA implementation in this work assumes that the CU processing for URLLC slices is performed in the PP nodes selected by the greedy algorithm. Therefore, the paths realizing the MH flows for URLLC demands have to terminate at these PP nodes, and only such paths are considered in the set of candidate paths \(\mathcal{P}\) in the CG procedure in Section 5.3.
## 2 Materials and Methods
### Reagents Preparation  
Stock solutions of electrolytes (KCl (0.5 M), KH\({}_{2}\)PO\({}_{4}\) (0.5 M), NaHCO\({}_{3}\) (1 M), MgCl\({}_{2}\) (0.15 M), (NH\({}_{4}\))\({}_{2}\)CO\({}_{3}\) (0.5 M), NaCl (2 M), and CaCl\({}_{2}\) (0.3 M)) were used to prepare simulated salivary fluid electrolyte solution (SSF), simulated gastric fluid electrolyte solution (SGF) and simulated intestinal fluid electrolyte solution (SIF) [19]. Each of these solutions (SSF, SGF, and SIF) was prepared in volumetric flasks of 100 mL by pipetting appropriate volumes of stock solutions of electrolytes and adding distilled water to make up the final volume of 100 mL. The final concentrations of electrolytes were: SSF (18.875 mmol L\({}^{-1}\) KCl, 4.625 mmol L\({}^{-1}\) KH\({}_{2}\)PO\({}_{4}\), 17 mmol L\({}^{-1}\) NaHCO\({}_{3}\), 0.056 mmol L\({}^{-1}\) MgCl\({}_{2}\), and 0.06 mmol L\({}^{-1}\) (NH\({}_{4}\))\({}_{2}\)CO\({}_{3}\) adjusting pH to 7 with 1 M HCl or 1 M NaOH if required); SGF (8.625 mmol L\({}^{-1}\) KCl, 1.125 mmol L\({}^{-1}\) KH\({}_{2}\)PO\({}_{4}\), 31.25 mmol L\({}^{-1}\) NaHCO\({}_{3}\), 0.15 mmol L\({}^{-1}\) MgCl\({}_{2}\), 0.625 mmol L\({}^{-1}\) (NH\({}_{4}\))\({}_{2}\)CO\({}_{3}\), and 59 mmol L\({}^{-1}\) NaCl, pH was adjusted to 3 with 1 M HCl), SIF (8.5 mmol L\({}^{-1}\) KCl, 1 mmol L\({}^{-1}\) KH\({}_{2}\)PO\({}_{4}\), 106.25 mmol L\({}^{-1}\) NaHCO\({}_{3}\), 0.4125 mmol L\({}^{-1}\) MgCl\({}_{2}\)), and 48 mmol L\({}^{-1}\) NaCl, pH was adjusted to 7 with 1 M HCl.  
Enzyme solutions were prepared daily and preincubated at 37 \({}^{\circ}\)C before use (\(\alpha\)-amylase 1000 mg L\({}^{-1}\) in SSF, pepsin 31,660.61 mg L\({}^{-1}\) in SGF, pancreatin 8000 mg L\({}^{-1}\) in SIF). Bile salts were prepared in SIF (25,000 mg L\({}^{-1}\)).
# A Review on Neural Network Based Models for Short Term Solar Irradiance Forecasting
## 4 Solar Irradiance Forecasting Based on Deep Learning Techniques
### Solar Irradiance Forecasting Model Based on the LSTM Algorithm
The experiments were conducted at half-hourly and hourly data intervals, as well as during all four seasons. The model accuracy can be seen to vary from season to season in Table 3. The suggested model demonstrates that the lowest inaccuracy happened in the winter for the half-hourly forecast horizon, since Florida has substantially more rain from May to October, resulting in less predictable PV generation. Regardless, the model frequently showed a gradual increase in inaccuracy for hourly solar irradiance forecasting.
# Deep Learning-Based Log Parsing for Monitoring Industrial ICT Systems
## 2 Related Work  
Log parsing is a fundamental issue in the operation of ICT systems in practice. Previous studies on log parsing have applied various clustering algorithms based on different criteria such as word frequency or feature words. However, these methods often suffer from low accuracy, scalability, or robustness. Heuristic algorithms have also been proposed to address some of these issues, but they require manual tuning and domain knowledge. Recently, machine learning techniques have been explored for log parsing, which can potentially overcome some of the drawbacks of traditional methods.  
One of the earliest efforts is the Simple Logfile Clustering Tool (SLCT) [8], which sets the tone for successive clustering-based methods. It uses the frequency of fixed words (tokens) in the log text as the basis for clustering. LogSig [9] uses word pairs as the feature of one log record and then aggregates them into groups. The log template of each group is generated based on the common pairs. LKE [10] takes the strategy that two clusters of log records are simply joint if their distance is below a particular threshold, which suffers from an obvious accuracy loss on complex log sets.  
Other than the conventional clustering-based methods, many log parsers use heuristic algorithms to analyze logs. IPLoM [11] heuristically parses logs by record length, which, however, requires careful preprocessing since inappropriate preprocessing may cause incorrect splitting. Drain [12] enhances IPLoM by maintaining a trie (prefix tree) for faster searching, which, however, does not address the problem caused by the length-based parsing in IPLoM. POP [13] aims to solve this issue by merging groups in the last step, which surely improves the parsing recall and the overall parsing accuracy.  
The natural language processing (NLP)-based log parser [14] uses latent Dirichlet allocation (LDA) to perform the classification after the tokenization, semantic processing, vectorization, and model compression steps. LogDTL [15] constructs a deep transfer neural network model for log template generation, which applies the transfer learning technique for data training augmentation purposes. NuLog [16] employs masked-language modeling to vectorize input tokens with random masking. A two-layer neural network encoder processes the vectorized input, and a Softmax linear layer produces a result matrix that maps to event templates.  
However, unlike LogParser, none of the methods above manages to address the issue of parsing logs from multiple sources, especially from homologous data sources with a different parsing task.
## 3 Initial Conditions and Definitions of the Algorithm
### Pheromone Marks for an Indirect Communication between the Agents  
The agents do not use targeted communication from agent to agent. Variable numbers of agents are coordinated based on their interactions with the evaporating pheromone marks in the environment \(S_{real}\). Each cell of the cellular space \(S_{real}\) is capable of being marked by pheromone values. The pheromones gradually evaporate over time. Thereby, each cell(\(x\),\(y\),\(z\)) \(\in\)\(S_{real}\) accumulates a time volatile pheromone value  
\[S_{real}(x,y,z)=\{Pex_{n}(x,y,z)(t)\}, \tag{2}\]  
where \(Pex_{n}(x,y,z)(t)\) is the pheromone trace value (which indicates when the cell was visited for the last time) in a given cell at (\(x\), \(y\), \(z\)) coordinates of the subspace \(s_{\{n,i\}}\) at time \(t\): a cell(\(x\),\(y\),\(z\)) \(\in\)\(s_{\{n,i\}}\)\(\lor\)\(s_{\{n,i\}}\)\(\in\)\(S_{real}\).  
The entrance \(e_{j_{m}}\) is at a position where an agent may enter the subspace \(s_{\{j,i\}}\), and \(e_{j_{m}}\subseteq E_{j}\subset s_{\{j,i\}}\). The entrance cells of the space \(S_{real}\) include the following two pheromone values,  
\[e_{\{j_{m}\}}=\{Pena_{\{j,m,i\}}(t),Penv_{\{j,m,i\}}(t)\} \tag{3}\]  
where \(Pena_{\{j,m,i\}}(t)\) (\(Penv_{\{j,m,i\}}(t)\)) is the level of the pheromone requiring the addition (removal) of an agent into (from) the group operating in the \(s_{\{j,i\}}\) subspace, respectively. The \(Pena_{\{j,m,i\}}(t)\) (\(Penv_{\{j,m,i\}}(t)\)) value indicates that there are too few (many) robots in the given subspace to accomplish the exploration/monitoring task, respectively.  
Each agent \(a_{\{f,k,v\}}\) has an empty map of the environment \(S_{a_{\{f,k,v\}}}\) (3D cellular grid) at the start. In each iteration (the time slot, in which an agent moves from one cell to another), the actual cell of the agent's environment map \(S_{a_{\{f,k,v\}}}(x,y,z)\) is updated based on the measured pheromone values of the actual cell \(Pex_{n}(x,y,z)\) of the exploration space \(S_{real}(x,y,z)\). The agent uses the map \(S_{a_{(f,k,v)}}\) for navigation and as a type of memory.  
The agents produce three types of pheromones:  
1. The first type indicates that there are too few agents in the given subspace to accomplish the task (exploration and monitor).
2. The second type indicates that there are too many agents.
3. The third type indicates the exploration/monitoring ability.  
The agent representation is supplemented by the following pheromone variables,  
\[a_{\{f,k,v\}}=\{Pmax_{\{f,k,v\}},Pa_{\{f,k,v\}},Pr_{\{f,k,v\}}\}, \tag{4}\]  
where \(Pmax_{\{f,k,v\}}\) is the value of a level of the pheromone mark that indicates the exploration/monitoring ability, and \(Pa_{\{f,k,v\}}\)\((Pr_{\{f,k,v\}})\) is the value of the pheromone required for adding (removal of) an agent to (from) the group \(As_{k}\), respectively. An agent's pheromone mark values are constant during the agent's life.
## 5 System Simulation and Analysis
### Simulation Results and Analysis
In Figure 13(b), the variable motor deflection angle in PSO fuzzy PID control varies the most at 0-2 s. When the rotational speed of the quantitative pump steps up to 600 r/min at 3 s, the deflection angle of the variable motor increases with it, and the rotational speed does not change significantly. Combining the analysis in Figure 13(b) with the data from the three control approaches in this case, the enhanced CPSO fuzzy PID control proves to be the best. The ascension time, settling time, and overshoot for the three control approaches in Figure 14(a) are presented in Table 7. According to the calculations, the enhanced CPSO fuzzy PID control approach reduces the ascension time by 37.5% and 80%, and the settling time by 83% and 80%, compared to the fuzzy PID and PSO fuzzy PID control, respectively. At the same time, the system exhibits no overshoot with the PSO fuzzy and enhanced CPSO fuzzy control approaches. In Figure 14(b), when the input quantitative pump speed is a random value of 400-500 r/min, the variable motor deflection angle magnitude varies with the quantitative pump speed, and the rotational speed does not change significantly. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Control Approach** & **Ascension Time/s** & **Settling Time/s** & **Overshoot/\%** \\ \hline Fuzzy PID & 0.8 & 2.5 & 19 \\ PSO fuzzy PID & 1.8 & 1.8 & — \\ Enhanced CPSO fuzzy PID & 0.5 & 0.5 & — \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Step input of 500–600 r/min. Figure 15: Random input of 400–500 r/min. Combining the analysis in Figure 10b with the data from the three control approaches in this case, the enhanced CPSO fuzzy PID control proves to be the best. In summary, when the variable motor swashplate deflection angle factor varies in a small range from 0.001 to 0.0011, the variable motor-speed input is only affected by the quantitative pump speed input, rather than the variation in the swashplate deflection angle factor. When different quantitative pump speeds are entered, the speed of the variable motor can eventually be maintained at about 1500 r/min. This is because there is a positive relationship between the base value of the variable motor deflection angle and the speed of the quantitative pump. When the swashplate deflection angle is compensated for in a small range, this relationship remains essentially unchanged. Meanwhile, the enhanced CPSO fuzzy PID control approach significantly reduces the overshooting amount, settling time, and ascension time in the speed-control system, which is contrasted with the other two control approaches. The enhanced approach enhances the stability and rapidity of the system, and it greatly meets the control requirements of the system.
# A Compact Wearable Textile Antenna for NB-IoT and ISM Band Patient Tracking Applications
## 4 Conclusions  
A triple-band wearable textile monopole antenna is proposed for patient tracking applications. The designed antenna provides good gain (3.7 dBi, 5.3 dBi, and 9.6 dBi) and wide impedance bandwidths in all three reported frequency bands (1.8 GHz, 2.45 GHz, and 5.8 GHz). A bending analysis is performed to test the antenna's usefulness in different bending scenarios. Owing to its wideband behavior, this antenna can operate in the reported frequency bands even if the antenna's resonances shift. The proposed antenna radiates in a boresight direction (outside the human body) with wide impedance bandwidths and lower SAR values. It can communicate up to 100 m of distance, which makes it a suitable candidate for NB-IoT 1.8 GHz, ISM RFID 2.45 GHz, and 5.8 GHz wearable patient tracking applications.  
Conceptualization, D.S., R.N.T. and S.K.; methodology, D.S., R.N.T. and S.K.; formal analysis, D.S.; measurements, D.S. and R.N.T.; software, D.S.; writing--original draft preparation, D.S.; writing--review and editing, D.S., R.N.T., S.K., S.S. and L.M.; supervision, L.M. All authors have read and agreed to the published version of the manuscript.  
This research receives no funding.  
Not applicable.  
Complete data is available in the research paper.  
The authors declare no conflicts of interest.
# Study on the Effectiveness of Multi-Dimensional Approaches to Urban Flood Risk Assessment
## 2 Methodology
### Hazard
#### 2.3.2 Straight-Line Distance to the Road  
The distance to the nearest road, measured in a straight line, is also a pivotal factor during urban flood events, particularly for evacuation and emergency response. In crises, areas with well-developed road networks enable faster evacuation and facilitate rescue operations, thereby streamlining recovery efforts. By using GIS analysis, one can calculate how far each structure is from accessible roads to gauge its overall accessibility and disaster response capabilities. In this study, data from Open Street Map (OSM) was employed to conduct this evaluation.
# Similarity Analysis for Time Series-Based 2D Temperature Measurement of Engine Exhaust Gas in TDLAT
## 4 Statistical-Based Mean Difference Test and Similarity Analysis
### [Step 1] Mean Difference Test
#### 4.1.2 Results of Mean Difference Test in [Step 1]  
Table 2 is a summary of the paired \(t\)-test results. For 153 sets, 1377 test results would be obtained when each test is performed from 1 to 9 s. If the \(p\)-value is less than 0.05, the null hypothesis is rejected. The \(p\)-values of 1 s, 3 s, 8 s, and 9 s were less than 0.05, i.e., the means of all cases were not equal to each other. In 2 s, the \(p\)-value was less than 0.05 except in four sets (i.e., 251 vs. 301, 451 vs. 501, 451 vs. 601, and 501 vs. 601). In 4 s, only one set (351 vs. 401) showed a \(p\)-value greater than 0.05 and the remaining 152 sets had a \(p\)-value of less than 0.05. In 5 s, 6 s, and 7 s, the \(p\)-value was greater than 0.05, while the remaining 152 sets had a \(p\)-value less than 0.05. Therefore, except for eight sets among 1377 sets, the \(p\)-value in all sets was less than 0.05 and hence, there was a mean difference between the cycles.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Time** & \multicolumn{4}{c}{\(T_{R\alpha c}\)} \\ \cline{2-6}  & **(s)** & **31** & **51** & **\(\cdots\)** & **901** & **1001** \\ \hline
1 & & 404.833 & 400.000 & \(\cdots\) & 405.432 & 404.189 \\
2 & & 406.775 & 401.665 & \(\cdots\) & 407.081 & 405.842 \\
3 & & 404.947 & 399.784 & \(\cdots\) & 405.436 & 404.455 \\
4 & & 407.458 & 401.846 & \(\cdots\) & 407.028 & 405.633 \\
5 & 1.0 & 409.400 & 403.506 & \(\cdots\) & 408.581 & 407.223 \\
6 & & 407.556 & 401.614 & \(\cdots\) & 406.820 & 405.720 \\
7 & & 404.951 & 399.982 & \(\cdots\) & 405.459 & 404.137 \\
8 & & 406.842 & 401.604 & \(\cdots\) & 406.900 & 405.606 \\
9 & & 404.999 & 399.722 & \(\cdots\) & 405.051 & 405.159 \\ \hline \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) \\ \hline
806 & & 398.712 & 399.182 & \(\cdots\) & 400.164 & 400.543 \\
807 & & 397.870 & 397.965 & \(\cdots\) & 402.270 & 402.782 \\
808 & 9.9 & 396.147 & 396.544 & \(\cdots\) & 401.184 & 401.859 \\
809 & & 397.183 & 397.668 & \(\cdots\) & 401.351 & 401.646 \\
810 & & 396.221 & 396.327 & \(\cdots\) & 403.357 & 403.785 \\ \hline \hline \end{tabular}
\end{table}
Table 1: \(T_{R\alpha c}\) per 18 candidate cycles used in [Step 1].
## 2 Airborne Instruments and Interface with the ATR-42
### Auxiliary Airborne Instruments  
**STORM Radar:** STORM, developed at LATMOS [11, 12], is a C-Band radar (5.35 GHz) based on an FM/CW (Frequency-Modulated/Continuous Wave) transmitted waveform. It transmits alternatively in the H and V polarizations, and simultaneously receives in both of these channels. The antenna is mounted so as to point towards the ground with a 20\({}^{\circ}\) incidence angle (measured with respect to nadir), when the aircraft's attitude is horizontal. STORM data are acquired over the ocean to provide information concerning the wind and waves, to provide complementary data to the CAROLS radiometric measurements, which are sensitive to wind, sea surface temperature and sea surface salinity.  
**GPS GOLD-RTR System:** The GPS system was supplied by the IEEC (Institut d'Estudis Espacials de Catalunya or Institute for Space Studies of Catalonia) [13, 14]. It is composed of three antennas and a GPS Open Loop Differential Real Time Receiver (GOLD-RT). The aim is to retrieve information concerning sea surface roughness, in the form of the effective Probability Density Function (PDF) of the sea surface slopes.  
**Infrared Radiometer (IRR):** The infrared radiometer is part of the standard equipment of the research ATR42. It is used to provide surface temperature estimations, simultaneously with the CAROLS measurements.  
**Visible Wide Angle Camera (VWAC):** It is located slightly forward of the slant radiometer antenna and allows the areas covered by the CAROLS nadir antenna measurements to be identified.  
Figure 5: Stability of the CAROLS measurements. Non corrected data correspond to one calibration point; corrected data correspond to two calibration points before and after data acquisition.