esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient High-Dimensional Kernel k-Means++ with Random Projection
## 4 Experiments
### Findings
#### 4.6.1 Performance of Our Method for Centroid Initialization
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Dataset** & **d** & **k = 8** & **k = 10** & **k = 20** & **k = 50** \\ \hline \multirow{4}{*}{Reuters-21578 (D = 2003)} & baseline & 20.48 & 33.47 & 141.22 & 920.74 \\  & 200 & 8.61 & 11.49 & 26.28 & 135.16 \\  & 300 & 10.65 & 14.39 & 32.59 & 132.22 \\  & 401 & 13.03 & 17.89 & 41.63 & 175.59 \\ \hline \multirow{4}{*}{GLI-85 (D = 22283)} & baseline & 3.77 & 5.93 & 22.44 & 142.02 \\  & 2228 & 4.77 & 5.33 & 8.68 & 33.63 \\ \cline{1-1}  & 3342 & 6.7 & 7.24 & 11.21 & 37.07 \\ \cline{1-1}  & 4457 & 8.85 & 9.30 & 13.34 & 40.89 \\ \hline \multirow{4}{*}{SMK-187 (D = 19993)} & baseline & 5.96 & 8.88 & 32.27 & 194.97 \\ \cline{1-1}  & 1999 & 5.92 & 6.35 & 9.86 & 31.76 \\ \cline{1-1}  & 2999 & 8.48 & 8.97 & 12.97 & 38.41 \\ \cline{1-1}  & 3999 & 10.99 & 11.56 & 16.07 & 44.94 \\ \hline \end{tabular} The larger the dataset, the bigger the time improvement. There is a computational advantage for the proposed method with reduced execution time when k \(\geq\) 20 in general.  
\end{table} 
Table 1: Comparison with \(\epsilon\). tialization exists in most of the experiments with different parameters. For the GLI-85 and SMK-187 datasets, the major time reduction only happens on the large \(k\). When the \(k\) is small, the cost of random projection offsets the cost reduced from the center point initialization. In other words, the proposed solution is especially fit for solving problems in high dimensionality and when the \(k\) is large.
# New RFI Model for Behavioral Audience Segmentation in Wi-Fi Advertising System
## 1 Introduction
From the review of the existing behavioral segmentation applications, it is found that both clustering-based [4; 5; 6; 7; 9; 11] and the score-based [8; 10] segmentation approaches are widely used to interpret the behaviors of the audience. In clustering-based segmentation, clustering algorithms are utilized to identify natural groupings and patterns in the data without pre-established rules. On the other hand, score-based segmentation is a rigid approach that requires pre-established rules, such as using the quartiles concept as the rules to score the criterion [8; 10]. It will generate a huge number of group combinations that makes it exponentially more complicated when interpreting the patterns of the audience's behaviors. Therefore, clustering-based segmentation was chosen as the audience segmentation approach in this study to investigate and interpret the behavioral patterns of the audience. In this study, investigations have been conducted to evaluate the influence of the newly proposed RFI model in segmenting the behaviors of the audience that are interacting with the Wi-Fi advertising system. The Wi-Fi advertising system dataset that was collected in Malaysia was tested with the proposed RFI model to evaluate the engagement of the audience towards the advertisements. In addition to that, three types of clustering algorithms are tested to segment the audience according to their behaviors to give a better insight in understanding the audience behaviors in terms of their recency, frequency, as well as interest. Lastly, the segmented audience groups are interpreted with their representative  
Figure 1: General procedure of the public Wi-Fi advertising system. behavioral characteristics based on the dynamic-characteristics range table. The main contributions of this paper are summarized as follows:  
* To propose a new RFI model that is generally applicable to measure the audience behaviors in Wi-Fi advertising system. * To segment the audience behaviors into well-defined groups based on the RFI model. * To create a dynamic-characteristics range table to interpret the segmented behavioral characteristics of the audience based on their respective RFI values. The introductory section is followed by Section 2, which describes the related analysis methods and clustering algorithms in the existing publications. After that, Section 3 presents the procedures of the experiment's setup with the experimental results included in Section 4. Section 5 illustrates the discussion of this study, whereas the last section encapsulates the conclusions and summary of the whole study as well as the future work.
###### Abstract
In the end, we derived the minimum sensor data rate required to ensure safety. In addition, we proved that millimeter-wave communication is a promising candidate to exchange sensor data among vehicles and allow driving at high speed in the overtaking scenario. Figure 1: An illustration of the concept of cooperative perception. Although the red oncoming vehicle is invisible to the green ego vehicle due to the blue vehicle blocking it, the cooperative perception between the ego vehicle and blocking vehicle changes it to visible. The rest of this paper is organized as follows. Section 2 introduces related works and highlights our contributions. Section 3 explains the system model. Firstly, it introduces cooperative perception and explains the scenario analyzed in this paper. Secondly, it explains the vehicle movement assumed in the scenario and derives the distance to overtake safely. Finally, it explains the process of recognition and derives the required sensor data rate by combining with the vehicle movement. Section 4 shows and compares the safe overtaking realized by the conventional and millimeter-wave V2V communications. In more detail, it explains the V2V channel model, analyzes the antenna space for height diversity, and analyzes the result. Finally, Section 5 concludes this paper.
## 4 Numerical Results
time latency increased exponentially in accordance with exposure time. Consequently, an optimal exposure time exists, which produces the total smallest centroid error under the designed optical configuration and atmospheric turbulence. This optimal exposure time is one of the critical design factors considered to meet the pointing requirements of the beacon tracking system. Under weak turbulence conditions, the total centroid accuracy was 1.8 \(\upmu\)rad at an exposure time of 0.5 ms for the CMOS detector, and 1.2 \(\upmu\)rad at the exposure time of 0.3 ms for the QAPD detector. In moderate turbulence conditions, the total centroid accuracy was 2.7 \(\upmu\)rad at the exposure time of 0.4 ms for the CMOS detector, and 1.8 \(\upmu\)rad at the exposure time of 0.2 ms for the QAPD detector. The QAPD detector provided better performance than the CMOS detector under atmospheric turbulence in terms of the total centroid accuracy, especially in the short exposure time domain. This means that the QAPD  
Figure 4: Centroid error due to delay time (i.e., exposure time in the simulation) versus exposure time under two atmospheric turbulence conditions. Figure 3: Average centroid error due to noise sources versus exposure time under two atmospheric turbulence conditions. detector is more suitable for the PAT system, which requires accurate pointing capabilities and higher bandwidth for closed-loop beacon tracking. The fade probabilities for the two turbulence conditions are shown in Figure 7, defined as the probability when the SNR falls below 1 over log-normal tracking channels due to the intensity fluctuations produced by atmospheric scintillation. In other words, they mean probabilities when the received photoelectron counts are less than the threshold values of \(K_{thCMOS}\) and \(K_{thQAPD}\) for the CMOS and QAPD detectors, respectively. These threshold values increase as the exposure time becomes longer, since noise count is proportional to exposure time. The threshold values for fade probabilities were \(K_{thCMOS}=65.6\) and \(K_{thQAPD}=226.4\) at the exposure time of 0.1 ms, and \(K_{thCMOS}=141.8\) and \(K_{thQAPD}=2262.2\) at the exposure time of 10 ms, respectively. The big difference between the threshold values of two detectors at the exposure time of 10 ms occurred because the noise sources of the QAPD detector were dominated more by the exposure  
Figure 5: Total centroid error under weak atmospheric turbulence conditions. Figure 6: Total centroid error under moderate atmospheric turbulence conditions. time than the CMOS detector. This means that the CMOS detector will provide a better performance than the QAPD detector at long exposure times, in terms of fade probability. As the exposure time increases, the fade probability decreases, since the increase in received photoelectron counts is higher than the threshold value increment, which has to be taken into consideration in the design of the beacon tracking system.
## 3 Results and Discussion
### Limiting Oxygen Index (LOI)  
LOI is defined as the minimum oxygen concentration in the oxygen/air mixture that causes the material to burn. The measurement of the oxygen limit value was started from the reference sample for which 21% was chosen as the reference point [32]. In Table 2, the LOI values obtained for the materials, on which five to 15 triple layers were applied by the LbL method, are presented.  
Analyzing the results of the coated polyamide matrices, a decrease in the oxygen index value was observed along with the increase in the number of triple layers applied, regardless of the type of deposition method used. Most likely, this effect was influenced by the way the layers were applied--predominantly on the surface. The application of the LbL method for the adsorption of materials constituting the polyelectrolyte solution and suspensions caused a uniform movement of the flame front towards the holder. The combustion rate for the 5L, 10L, and 15L composites was much smaller than for the reference material.  
Figure 3: Overall thermal stabilization effect of the triple layers on polyamide 6.  
LOI values for PA6 coated by using the LbL method show no flame-retardant action of the deposited layers. In general, the deposition of triple layers adversely affects the flammability of the pristine polymer matrix.  
The incorporation of MMT in an amount of 3% by weight resulted in a decrease in the LOI value by 0.1% compared to pure polyamide 6, which is within the measurement error. The presence of triple layers on the surface of the nanocomposite increased the value of the LOI; the highest LOI improvement was achieved for 15 LMMT.  
A comparison of the obtained results shows that the use of the LbL method for embedding the HP layers, MMT, and CC on the surface of the PA6 nanocomposite promotes the ignition of the polymeric material. During the combustion of the samples reinforced with MMT, the flame evenly covered the surface, and after its self-extinguishing, no visible smoke appeared, which was observed for the pristine polyamide. This insignificant effect could be related to both the introduction of aluminosilicate into the polyamide matrix, affecting the \"closure\" of the surface of the incenerated sample by creating a dense protective coating, as well as the depositing of phosphorus and copper compounds on the surface of the PA6/MMT nanocomposite.  
The MMT suspension and polyelectrolyte solutions/dispersions introduced into the matrix were most likely thermally decomposed first, resulting in the formation of carbonaceous char preventing the transfer of heat, energy, and oxygen between the flame and the material, thereby reducing the smoke emission and toxic decomposition products [33].
# Experimental Study and Modelling on the Structural Response of Fiber Reinforced Concrete Beams
## 4 Modelling
### Modelling Results  
Modelling results for FRC of the group CSf is shown in Figure 5. Model is approximating experimental data quite good. Difference between modelling and experimental data at the first peak is easy to explain--model is describing only macro-crack opening stage.  
Figure 5: Experimental and modelling data comparison for materials in the group CSf.  
Macro-crack growth and stage of concrete aggregates working as \"cohesive\" elements are absent in the model. Modelling results for FRC of the group CPPfS and the group CPPfD are shown in Figures 6 and 7. Model is \"in general\" approximating experimental data. At the same time, pull-out data for fibers oriented close to 90\({}^{\circ}\) are characterized by short length of pulled out tail. Fibers are not currying load and are breaking, see in Figure 8. After that only part of fibers is currying load.  
Figure 8: Crack surface in broken sample CPPfD with Durus PP fibers. Long fiber tails are not pulled out of the concrete, but mainly lifted up. Such fibers were oriented under small angles to the crack surface.  
Figure 6: Experimental and modelling data comparison for the group CPPfS.  
Figure 7: Experimental and modelling data comparison for the group CPPfD.  
Load bearing mechanisms at initial stage of the curve is different. For the group CPPfD we obtained necessary pull-out curve by back--analysis approach (by minimization difference between modelling and experimental data). Obtained pull-out curves are different from experimentally obtained, see in Figure 9. Here we can conclude--load bearing mechanisms in FRC with PP fibers is more complicated than simple pulling out fibers are bridging the crack. Bigger role is playing fibers oriented under smaller angles than \(90^{\circ}\).
# Understanding Safety Performance of Prefabricated Construction Based on Complex Network Theory
## 3 Methodology
### Data Collection  
#### 3.1.1 Identification of Safety Risks of Prefabricated Construction  
Due to the distinction in construction technology, the safety risks of prefabricated constructions are unlike those of general constructions. Hence, the whole life cycle of the prefabricated building project is divided into four stages: design, off-site manufacture, transportation, and on-site assembly. On this basis, a comprehensive literature review was carried out to screen special safety risks of prefabricated construction. The literature search was not limited to the field of safety risks of prefabricated construction. Still, it was screened in the context of schedule risks, cost risks of prefabricated construction, and even general construction and engineering risks. Following sorting of the previous eligible research results, the risk factors were categorized into different stages, and meanwhile, a preliminary safety risk list was formed. In addition, to be in accord with the actual situation of the prefabricated building industry in China, ten professionals in this field were invited to conduct semi-structural interviews to modify and adjust the initial safety risk list. The interviewed team members included one civil supervision engineer, two assistant designers, two project managers, and five project safety consultants. Each interviewee had a minimum of five years of working experience in prefabricated construction and three years in safety management. Prior to the discussion, respondents were informed of the detailed definition of each safety risk. The interview mainly focused on two aspects: (1) retain/eliminate the safety risk identified in the literature review and justify them; (2) supplement other prefabricated construction safety risks not included in the preliminary risk list, if necessary. The safety risk list was further verified and expanded leveraging expert knowledge, the risks identified from the literature were deleted or modified, two new risks were added (R16, R24), and the original 36 risks were revised as 37 risks with new risk ID. Ultimately, a comprehensive list of safety risks for a typical prefabricated building project was obtained, as provided in Table 1. The list ascertains 4 safety risks in the design stage, accounting for 10.81%; 19 safety risks in the stage of the off-site manufacture, accounting for 51.35%; 7 safety risks in the transportation phase, accounting for 18.92%; and 33 safety risks of on-site assembly, accounting for 89.16%, some of which participate in multiple stages. Figure 2: Research framework.
## 2 Material and Methods
### Feature Extraction and Selection
#### 2.2.1 tsfresh Library  
Features were extracted using the Python library tsfresh [39]. The library is composed of a combination of 63 time-series characterization techniques. The library calculates 794 features (based on estimating trends, seasonality, and periodicity of the data) by default and shortlists the features based on automatically configured hypothesis tests [12]. The library uses standard APIs for time-series (pandas) and machine learning (scikit-learn) packages and provides exploratory analyses. A list of the calculated features and their respective runtimes is documented in [40].
# Guidelines for Telemetry Studies on Snow Leopards
## 4 Implementation  
A detailed description specifying capture methods, the safety measures that will be employed, and the qualifications and experience of the staff should be included in the study plan. The plan should specify an assessment of the potential risks for both animals and staff and outline the procedures to follow to mitigate risks when they are presented. It is advised to review all possible capture methods and seek advice from multiple, experienced parties on which methods are best suited for the proposed study area [32].  
Several different heuristics for collar size and weight limits have been employed, e.g., the collar weight should not exceed 2, 3, or 5% of the animals' weight [30; 33; 34]. [PERSON] et al. [35] showed that when animals moved at a high speed, forces exerted by the collars increased dramatically: a collar weighing 3% of a Cheetah's (_Acinonyx jutatus_) body weight exerted forces equivalent to 54% of the body weight. Care should be taken to select a collar model as light as possible; for fields that are dependent on agility and speed, collars should probably weigh less than 2% of the animals' body weight. It is necessary to select a collar model that can be adjusted to fit the animal, thereby avoiding ulcerations [1; 36]. If capturing a young or thin animal, the collar should be fitted so that the animal has room to grow e.g., [37]. Care must be taken to ensure that the collar is not fitted so loose that it can get wedged in the animal's mouth or that a front leg or object can get caught [11]. If the information on sex-specific neck sizes is available for the target species, these should be used as minimum sizes for young individuals.  
Collars should preferably be fitted with a programmable mechanism (drop-off) that detaches the collar from the animal at a time decided by the research team [38], or at a minimum a break-away zone consisting of a material that will degrade over time (e.g., cotton [39]) ensuring that the collar eventually detaches. Realtime locations can expose the animals to poaching risks or stress from tour operators, especially for charismatic and elusive species. It is therefore critical that real-time locations are not shared publicly and that VHF-frequencies and GPS-collar data are only accessible by personnel that require the information in their work.
# Quantifying Total Phosphorus and Heavy Metals in Residential Septage
## 3 Results and Discussion
### Total Phosphorus and Metal in Septage  
3.2.1. Total Phosphorus Concentrations and Masses  
The results indicate that TP concentrations were elevated in the sludge and scum layers relative to the liquid layer (Figure 3). Median concentrations of TP in the sludge (10.60 mg L\({}^{-1}\)) were double the concentrations observed in the scum (5.30 mg L\({}^{-1}\)). Despite differences in median TP concentrations, this difference was not statistically significant (\(p\) = 0.44), which was likely due to the large variability in TP concentrations. The scum layer contained TP concentrations that ranged from 1.00 to 835 mg L\({}^{-1}\), whereas sludge concentrations ranged from 0.70 to 252 mg L\({}^{-1}\). The liquid layers typically contained the lowest concentrations of TP relative to the scum and sludge layers. The median TP concentration across all tanks in the liquid layer was 1.77 mg L\({}^{-1}\), and the concentrations ranged from 0.48 to 56.20 mg L\({}^{-1}\). The concentration differences were statistically significant when comparing the liquid to the sludge (\(p\) < 0.01) and scum (\(p\) = 0.01) layers. The masses of TP tended to be elevated in the sludge layer, followed by the liquid and scum layers (Figure 3). The sludge layer contained the greatest median TP mass (35.60 g), which was approximately 5 and 9 times greater than the liquid (6.70 g) and scum (3.79 g) layers, respectively. These differences were statistically significant at \(p\) < 0.01. The median TP mass in the liquid layer was approximately double the median of the scum layer, although this difference was not significant (\(p\) = 0.18). Trends between the scum and liquid layers reversed for TP masses because there were 12 septic tanks (32% of the total) where the scum layer was less than 1 cm at the time of pumping, and thus there was little to no TP present. However, the scum layer contained the highest maximum TP value of 3162 g, which was about 3 and 15 times greater than the max values in the sludge and liquid layers, respectively. The concentrations of TP in liquid wastewater measured by the current study overlapped with those of past studies [3, 24, 25, 26, 31, 49, 50, 51, 52]. These studies found that phosphorus concentrations in liquid wastewater can be variable, ranging from 1.20 to 39.5 mg L\({}^{-1}\). In the current study, median TP concentration in liquid wastewater was within the low end of this range (1.77 mg L\({}^{-1}\)). Furthermore, 23 septic tanks were within this range of liquid TP concentrations. Of the 14 tanks that were outside this range, 11 were less than the range, 1 exceeded the range, and 2 were missing data due to laboratory error. For the tanks that were lower than the range, the median difference between their observed TP concentration and the minimum range value was 0.3 mg L\({}^{-1}\) (0.03-0.72 mg L\({}^{-1}\)). The tank that exceeded the range exceeded it by 16.7 mg L\({}^{-1}\). The current study found congruence in TP concentrations in liquid wastewater.
## 3 Results and Analysis
### Flexural Strength  
Figure 10 illustrates the flexural strength development of the cement mortar made with varying replacement levels of the TPSW materials as partial sand substitutes under a fixed water-to-cement ratio of 0.5, with the control group serving as the baseline. At 3 days, the flexural strength of the control group was 10.4 MPa. For the 5% replacement, the strength was 9.5 MPa (\(-\)8.7%); for 10% replacement, it was 8.9 MPa (\(-\)14.4%); and for 15% replacement, it was 7.8 MPa (\(-\)25.0%). At 28 days, the flexural strength of the control group increased to 13.3 MPa. The 5% replacement group achieved 12.1 MPa (\(-\)9.0%), the 10% replacement group reached 10.8 MPa (\(-\)18.8%), and the 15% replacement group achieved  
Figure 8: Compressive strengths of cement mortars with different TPSW amounts at different ages.  
Figure 9: Compressive strengths of cement mortars at 28 days with different W/C ratios and TPSW amounts.  
10.1 MPa (\(-\)24.1%). At 91 days, the control group achieved a flexural strength of 14.9 MPa, while the 5% replacement group reached 13.5 MPa (\(-\)9.4%), the 10% replacement group achieved 12.5 MPa (\(-\)16.1%), and the 15% replacement group recorded 11.5 MPa (\(-\)22.8%).  
When the water-to-cement ratio was fixed at 0.5 and the curing period was extended to 91 days, a 5% replacement level resulted in a flexural strength reduction of only 9.4%. However, when the replacement level exceeded 10%, the strength reduction consistently surpassed 10%. These findings suggest that a 5% replacement level under these conditions was the most effective for utilizing waste materials, thus achieving the goal of recycling and reusing TPSW materials.