esa-sceva/satcom-chunk-collection huggingface dataset:
# Clustering-Based Nonstationary Massive MIMO Channel Modeling at 1.4725 GHz
## 3 Channel Model
During this process, the traditional observation by a human is replaced by the machine searching. Then, some potential links between data are now uncovered with the aid of the precise algorithm. The steps of the programming algorithm are shown in Figure 6.  
Figure 6: Flow chart of clustering and subinterval programming algorithm. In the first step, KPowerMeans algorithm is used to cluster the multipath in the sub-channel of the antenna array. This algorithm is one of the most commonly used clustering algorithms in the field of machine learning. It belongs to the iterative clustering algorithm, which is simple and scalable. The algorithm takes the PDP of the multipath as the initial object, searches for the peaks on the curve as the initial cluster core, and determines the number of clusters \(\{\rho_{i}|i=1,\cdots,k\}\). Then the MCD can be calculated as  
\[d_{MCD}(i,j)=\left|\rho_{i}-m_{j}\right| \tag{6}\]  
where \(\{m_{i}|i=1,\cdots L\}\) is the parameter set of multipath components. The parameters of multipath components and cluster cores include power, relative delay, departure angle, arrival angle, etc. Considering three-dimensional space, the angle information should include azimuth angle and elevation angle, so we have  
\[\rho_{i}\in[\tau_{i},\varphi_{AOA,i},\theta_{AOA,i},P_{i}] \tag{7}\]  
Then, the distance in the time-delay domain can be expressed as  
\[MCD_{\tau,ij}=\varsigma\frac{\left|\tau_{i}-\tau_{j}\right|}{\Delta\tau_{\max}} \tag{8}\]  
where \(\Delta\tau_{max}=\max_{ij}\{\left|\tau_{i}-\tau_{j}\right|\}\), and \(\varsigma\) means the weight coefficient of time-delay. \(\tau_{i}\) and \(\tau_{j}\) are the delay of the current cluster core and the current multipath component, respectively. The distance in the angle domain is  
\[MCD_{\theta,ij}=\frac{1}{2}\Bigg{|}\left( \begin{array}{c}\sin\phi_{i}\cos \varphi_{i}\\ \sin\phi_{i}\sin\varphi_{i}\\ \cos\phi_{i}\end{array} \right)-\left( \begin{array}{c}\sin\phi_{j}\cos \varphi_{j}\\ \sin\phi_{j}\sin\varphi_{j}\\ \cos\phi_{j}\end{array} \right)\Bigg{|}. \tag{9}\]  
In order to reduce the complexity, (9) can be simplified as  
\[MCD_{\theta,ij}=\frac{1}{2}\Bigg{|}\left( \begin{array}{c}\sin\phi_{i}\\ \cos\phi_{i}\end{array} \right)-\left( \begin{array}{c}\sin\phi_{j}\\ \cos\phi_{j}\end{array} \right)\Bigg{|} \tag{10}\]  
It should be noted that the distance in the angle domain should be calculated for the departure angle and arrival angle, respectively.
## 3 Results
### Results from BEST Procedure  
#### 3.3.1 Experimental Cumulative Infiltration and Infiltration Rate  
The initial moisture conditions of the topmost soil layer (0-5 cm) met the condition required by the BEST procedure (i.e., \(\theta_{\rm i}\) < 0.25 \(\theta_{\rm S}\)) [14] in most cases (80 per cent of samples). This allowed the correct application of the method and the possibility of observing the transient phase of the infiltration process in sufficient detail, although the moisture of the subsurface layer (5-10 cm) often did not meet the same condition.  
The dynamics of the cumulative infiltration and infiltration rate of each infiltration experiment are shown in Figure 6. In several cases, near-stationary conditions were reached before pouring the 15 volumes.  
Regarding LTAE NewSmoca, the ORG plots present higher infiltration rates than the INT and INT+ ones (which are quite similar). In LTAE Rotation, there is a less broad range of variation compared to that of LTAE NewSmoca, but more specific considerations on the effect of rotation or residual management cannot be deduced from Figure 6.  
Figure 6: Cumulative infiltration I (mm) and infiltration rate i (mm/s) for the experiments carried out in the LTAE NewSmoca (30 curves) and Rotation (60 curves). For graphical reasons, the durations of two experiments in the plots INT e INT+ appear truncated at 2000 s in the figure showing the cumulative infiltration. Their effective durations are 2536 and 5327 s, respectively.  
The cumulative infiltration curves obtained for LTAE NewSmoca exhibited an excessive concavity in several cases. This phenomenon usually indicates progressive soil sealing throughout the test, attributed to the disturbance caused by the manual pouring volumes of water [15; 34]. Under these conditions, as pointed out by [PERSON] et al. [34], the BEST-Slope and BEST-Intercept algorithms cannot be used, while the BEST-Steady algorithm can still give some useful indications of the values of \(\mathrm{K_{s}}\) and \(\mathrm{S}\). However, these are anomalous conditions for correctly applying the BEST method. Therefore, the results must be considered cautiously. In the present work, which has mainly comparative purposes, this uncertainty was considered tolerable to obtain rough indications of the differences between the different Long-term Agroecosystem Experiments. Furthermore, in order not to introduce inhomogeneities in the results, all the infiltration curves obtained in the NewSmoca LTAE were processed with the BEST-Steady algorithm alone, even in those cases where the other algorithms provided a satisfactory fitting to the experimental infiltration curves.  
Instead, for the plots considered in the LTAE Rotation, a satisfactory fit of the theoretical models of the BEST method to the experimental cumulative infiltration curves was generally attained with any algorithm. Therefore, the hydraulic parameters characterising the plots were obtained by averaging the estimates provided by all three algorithms.
## 4 Interference Characterization
### Outage Probability  
In the presence of inter-user interference, outage occurs when the SINR of a BSN is below an acceptable threshold \(\beta\), _i.e._,  
\[P_{o}=\Pr\left\{P_{SINRi}(t)<\upbeta\right\} \tag{5}\]  
We assume the noise is white noise, _i.e._, being constant over the whole frequency band.  
Denote the CDF of SINR regarding \(\upbeta\) as \(F_{SINR}\left(\upbeta\right)\). We have the outage probability of a BSN regarding the SINR threshold \(\beta\) is  
\[P_{o} =1-F_{SINR}\left(\upbeta\right)=1-\iint\limits_{x\geq 0,y\geq 0,x/y \leq\beta}f_{s}\left(x\right)f_{I}\left(y\right)dxdy\] \[=1-\int_{0}^{\infty}F_{s}\left(\beta y\right)f_{I}\left(y\right)dy \tag{6}\]  
where \(f_{s}\left(x\right)\) is the PDF of the desired signal \(x\), \(f_{I}\left(y\right)\) is the PDF of the interference signal \(y\), and \(F_{s}\left(x\right)\) is the cumulative distribution function (CDF) of the desired signal. This is the ratio of the desired signal to the interference is less than \(\upbeta\), _i.e._, \(x/y\leq\upbeta\), when the noise is negligible, _i.e._,  
\[\iint\limits_{x\geq 0,y\geq 0,x/y\leq\upbeta}f_{s}\left(x\right)f_{I}\left(y \right)dxdy=\int_{0}^{\infty}\int_{0}^{\beta y}f_{s}\left(x\right)f_{I}\left(y \right)dxdy\]
## 3 Results
### Community Composition and Isotopic Signatures
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **LP** & **IP** & **HP** \\ \hline N\({}^{\circ}\) & & & \\ \hline Community & 2942 (417) & 2777 (502) & 2926 (340) \\ Basal resources & 28 (28) & 51 (51) & 28 (28) \\ Invertebrates & 2793 (268) a  & 2526 (251) b  & 2848 (262) a  \\ Fish & 149 (145) a  & 251 (251) b  & 78 (78) c  \\ \hline \(\delta^{13}\)C (\%o) & & & & \\ \hline Community & \(-\)13.34 \(\pm\) 0.17 a  & \(-\)15.96 \(\pm\) 0.13 b  & \(-\)14.77 \(\pm\) 0.15 c  \\ Basal resources & \(-\)15.83 \(\pm\) 0.78 a  & \(-\)18.84 \(\pm\) 0.80 b  & \(-\)22.03 \(\pm\) 1.04 c  \\ Invertebrates & \(-\)12.65 \(\pm\) 0.22 a  & \(-\)15.63 \(\pm\) 0.19 b  & \(-\)14.42 \(\pm\) 0.18 c  \\ Fish & \(-\)14.67 \(\pm\) 0.21 a  & \(-\)16.30 \(\pm\) 0.17 b  & \(-\)16.09 \(\pm\) 0.15 b  \\ \hline \(\delta^{15}\)N (\%o) & & & & \\ \hline Community & \(5.94\pm\) 0.16 a  & 8.36 \(\pm\) 0.14 b  & 10.54 \(\pm\) 0.14 c  \\ Basal resources & \(3.65\pm\) 0.51 a  & 4.46 \(\pm\) 0.38 a  & 6.70 \(\pm\) 0.57 b  \\ Invertebrates & \(4.42\pm\) 0.16 a  & 7.31 \(\pm\) 0.21 b  & 9.82 \(\pm\) 0.14 c  \\ Fish & 8.79 \(\pm\) 0.17 a  & 9.41 \(\pm\) 0.15 b  & 13.21 \(\pm\) 0.16 c  \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters describing the communities in each Lake. LP, IP and HP: low, intermediate and high eutrophication. N\({}^{\circ}\) indicates the sample size. Numbers in parentheses indicate the number of samples analysed. Community indicates both fish and benthic invertebrates. Stable isotopes of \(\delta^{13}\)C and \(\delta^{15}\)N are reported as mean (%o) \(\pm\) s.e. For each parameter, different superscript letters (a,b,c) indicate differences between lakes (one-way ANOVA or Mann-Whitney test; \(p<\) 0.05).
## 1 Introduction  
Eye tracking is a powerful and precise method for analyzing visual attention, particularly effective in capturing non-conscious cognitive processes. Traditionally used by psychologists to investigate fundamental mental functions, this technique has gained recognition in other fields such as education and consumer behavior research due to its capacity to track real-time cognitive actions without relying on self-reported data [1].  
In consumer research, eye tracking offers an objective means to assess how visual stimuli influence purchasing behavior. By recording participants' eye movements, rather than asking direct questions, researchers can closely simulate real-world shopping scenarios. This allows for the identification of visual elements--such as color, texture, and packaging--that drive consumer decisions at the point of purchase [2].  
Beef products provide a compelling case for studying the role of visual attention, as consumers often rely on visual cues to assess quality. For fresh beef, color is the primary indicator, while packaging plays a similar role for cooked beef. Appearance is not only the first sensory attribute to be noticed but is also a key driver of appetite and product acceptability [3]. Prior research has shown that visual features significantly influence expectations related to tenderness, flavor, and overall eating quality--even before tasting the product [4,5].  
Despite these insights, consumers often face challenges in accurately predicting beef quality based on visual inspection alone, creating a major challenge for the meat industry. Understanding how consumers interpret visual cues--especially in the context of both raw and cooked forms of the same product--is essential for aligning marketing strategies and product development with consumer expectations.  
The existing literature confirms the relevance of visual attention in food choice [6,7], yet few studies have specifically focused on Brazilian consumers. Recent findings indicate that Brazilian consumers associate bright red coloration in fresh beef with higher quality, while a tough texture in cooked beef negatively affects choice and willingness to pay [8].  
Therefore, this study has two main objectives: (1) to evaluate how visual attention to different visual and quality cues affects the probability of purchase for both fresh and cooked beef among Brazilian consumers; and (2) to introduce a novel statistical approach to analyze eye tracking data in the context of food choice.  
By incorporating both raw and prepared forms of beef, this research captures a more complete understanding of how visual cues shape consumer decision-making throughout the purchasing and consumption cycle.
# Relating Sentinel-1 Interferometric Coherence to Moving Events on Grasslands
## 2 Materials and Methods
### SAR Data  
Sentinel-1A is a C-band SAR remote sensing satellite launched into orbit on 3 April 2014. It mostly operates in the Interferometric Wide swath mode (IW) providing dual-pol data (VV + VH). For this study, acquisitions from three geometries (defined by the relative orbit number or RON) were used: R58, R80, and R160. An overview of geometries is given in Table 1. For each geometry, acquisitions in Estonia were made every 12 days, with some exceptions. The data were acquired as Single Look Complex (SLC) products. Precise acquisition dates for each geometry are given in Table 2. The Sentinel-1 orbit maintenance strategy ensures relatively small orbital InSAR baselines on the order of 150 m [32].  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**RON** & **Ase/Desc** & **Acquisition** & **Sub-Swath** & **Incidence Angle** & \multicolumn{2}{c}{**Ground Range**} \\  & & **Time (UTC)** & & & & **Resolution az \(\times\) rg, m** \\ \cline{5-8}  & & & & **Near** & **Far** & **Near** & **Far** \\ \hline
58 & Ascending & 16:04 & 3 & 44.6 & 45.0 & 21.60 \(\times\) 4.98 & 21.60 \(\times\) 4.95 \\
80 & Descending & 04:34 & 2 & 39.0 & 39.5 & 21.70 \(\times\) 4.93 & 21.70 \(\times\) 4.87 \\
160 & Ascending & 15:56 & 2 & 37.9 & 38.4 & 21.70 \(\times\) 5.05 & 21.70 \(\times\) 4.99 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Relative orbit numbers (RONs) used in the study and their parameters [33]. The values are given for the study area. Azimuth (az), range (rg).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**RON** & & \multicolumn{3}{c}{**May**} & \multicolumn{3}{c}{**June**} \\ \hline
58 & 1 & & 13 & 25 & 6 & 18 & & 30 \\
160 & & 8 & & 20 & 1 & 13 & 25 & 25 \\
80 & 3 & & 15 & 27 & & 8 & & 20 & \\ \hline
**RON** & & \multicolumn{3}{c}{**July**} & \multicolumn{3}{c}{**August**} \\ \hline
58 & & 12 & 24 & & 5 & 17 & & 29 \\
160 & 7 & & 19 & 31 & 12 & 24 & 24 \\
80 & 2 & & 14 & 26 & 7 & & 19 & 31 \\ \hline
**RON** & & \multicolumn{3}{c}{**September**} & \multicolumn{3}{c}{**October**} \\ \hline
58 & & 10 & & & 4 & 16 & & 28 \\
160 & 5 & & 17 & & 29 & & 11 & 23 \\
80 & & 12 & & 24 & & 6 & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Acquisition dates for different RONs.  
Figure 1: Location of the study area in Estonia.
# JSSTR: A Joint Server Selection and Traffic Routing Algorithm for the Software-Defined Data Center
## 6 Evaluation
### Evaluation Results and Analysis
The probability of finding the optimal (or sub-optimal) solution of the joint server selection and traffic routing problem increases as the number of memeplexes and number of frogs increase. However, the computational complexity of the JSSTR algorithm also increases with increasing the number of memeplexes and the number of frogs. Otherwise, as the number of memeplexes and frogs in each memeplex decreases, the calculation accuracy and computation complexity decrease. Besides, compared with WLC-GA, the JSSTR and Travs algorithms need to collect server load information. However, the traffic generated by collecting server load information is rather small. For example, in a real scenario, when using the Simple Network Management Protocol (SNMP) to collect server load, the length of SNMP packets is usually less than 90 bytes. In order to collect the CPU utilization, RAM utilization and disk utilization, six SNMP packets will be generated. The server load information is usually collected every five seconds. Assume that there are 16 servers in the server  
Figure 8: Server load comparison for JSSTR, Travs and WLC-GA.  
Figure 9: Server load balancing degree comparison for JSSTR, Travs and WLC-GA.  
cluster. In that case, the traffic speed generated by collecting servers' load is about 13.5K bits per second, which is rather small compared to the bandwidth of the link. In conclusion, all the above evaluation results and analysis indicate that JSSTR can achieve better network resource utilization and higher network load balancing with acceptable less increase in computation complexity compared with WLC-GA. Additionally, compared with the traversal-based method, it can achieve nearly the same network resource utilization, network load balancing, server resource utilization and server load balancing while clearly decreasing the computation complexity.
# A Comprehensive Review of Stingless Bee Products:
## 2 Stingless Bee Honey
### Antioxidant Properties
#### 2.1.3 Total Flavonoid Content  
A study of the influence of the origin and bee species with regard to the antioxidant properties by [PERSON] et al. [13] showed that the total flavonoids of _Heterotrigona itama_ and _Genitorigona thoracica_ honey range from 2.8 to 9.31 mg QE/100 g. In that study, honey was collected from different locations across Peninsular Malaysia, and the author stated that the flavonoid content varied according to different botanical and geographical origins of nectar collected by the bees. Next, a study by [PERSON] et al. [30] concerning the antioxidant capacity of honey produced by stingless bees, namely, _Genitorigona thoracica_, _Heterotrigona itama_, and _Heterotrigona erythrogastra_ ([PERSON], 1902), found that flavonoid content ranged from 12.41 to 17.67 mg/mL, with _Heterotrigona itama_ demonstrating the highest value. The author stated that phenolic and flavonoid compounds in the honey provided the ability to donate an electron from a hydroxyl group to an unpaired electron of free radicals, which is related to the reducing power.  
A study by [PERSON] et al. [42] on various Malaysian stingless bee honey samples from the _Trigona_ sp. showed that the total flavonoid content ranged from 36.67 to 194.98 mg GA/100 g. The author also stated that phenolic and flavonoid content make for the strong antioxidant content of honey, which has potential in scavenging free radicals. Furthermore, a study by [PERSON] et al. [43] comparing European honeybee honey with stingless bee honey revealed that stingless bee honey has a higher flavonoid content than European honeybee honey, for which the values were 97.88 to 101.5 mg CE/kg and 64.72 to 66.98 mg CE/kg, respectively. The author stated that the high value of polyphenols in stingless bee honey results in higher colour intensity.
## 2 Materials and Methods  
### Study Area  
Our study area is the Hitardalur valley and, in particular, the area surrounding the Hitardalur landslide, which is located on the eastern side of the Fagraskogarfjall mountain in the Vesturland region, western Iceland (Figure 1). The elevation in the Hitardalur valley ranges from 89 to 743 m above sea level. The valley floor is partly covered by a lava field (Hagahraun) [46]. Thick sedimentary layers can be found within the stratigraphic sequence. The rock is rather dense as the cavities have been filled with minerals. Quaternary volcanic formations, created during the last glaciation and Holocene, can be found within the vicinity of the landslide. Faults and fractures are prominent within the region and can be divided into three groups, i.e., NE-SW, N-S and NW-SE oriented [47]. The faults and fractures that belong to the NE-SW and N-S groups formed mainly during the formation of the lava pile and are older than 8 million years. NW-SE oriented fractures belong to the so-called Snaefells fracture zone which extends from Kerlingarskar\(\varnothing\) in the north to Borgarfjordur valley in the south. The NW-SE fracture zone was very active 8 to 4.5 million years ago and is considered to be still active [48].  
On the morning of the 7 July 2018, a large landslide blocked the Hitara river and led to the creation of a landslide-dammed lake. Water from the landslide-dammed lake found a new way through the highly porous lava field. The landslide originated in an area of the Fagraskogarfjall mountain that shows evidence of earlier displacements [45]. The year 2018 had more rainfall than usual in Iceland [49], likely leading to a higher water pressure in cracks and fractures that further weakened the rocks,  
Figure 1: (**a**) The study area around the Hitardalur landslide shown on a Sentinel-2 image acquired on 17 July 2018 and (**b**) its location in western Iceland; background data © ESRI.  
and thus contributed to landslide initiation [50]. [PERSON] et al. [45] estimated that approximately 7 million m\({}^{3}\) of material was released and that a total of 10-20 million m\({}^{3}\) of material was displaced. The landslide deposition covers an area of approximately 1.5 km\({}^{2}\) on the valley floor and is up to 30 m thick. The fall height was about 450 m over a runout length of approximately 2.3 km at a runout angle of approximately 12\({}^{\circ}\). The length of the riverbed covered with debris was estimated to be around 1.6 km. The Hitardalur landslide is considered to be one of the largest landslides in Iceland in recorded history [51]. Figure 2 shows field photographs of the Hitardalur landslide, the landslide-dammed lake and the river finding a new path after emerging from the dammed lake.
# Evaluation of Horizontal Shear Performance of Composite Girders Using Separable Shear Connectors
## 3 Evaluation of Horizontal Shear Performance
### Evaluation of Horizontal Shear Strength  
The horizontal shear-strength results for each specimen were compared with various design standards, as detailed in Table 4 and Figure 11. The overburden load used to calculate the friction resistance included the self-weight of the concrete girder members and the weight of the steel blocks used during the experiment. Interface conditions were applied under the \"smooth\" conditions in Table 1. The specimens were fabricated according to the KDS standards. Additionally, most fabrication criteria satisfied the AASHTO and ACI-318, except EC2. Cases that did not satisfy certain standards involve the pull-out phenomena in the girders because of the insufficient embedding strength of the shear connectors, which reduces the horizontal shear strength. The M-DY specimens generally exhibited higher horizontal shear strength than the rebar specimens using conventional rebar shear connectors because of the effect of preventing anchor nut pull-out by placing a steel plate above the anchor nut in the embedded part of the shear connector, as illustrated in Figure 5b. By contrast, the S-DY specimens exhibited approximately 10% lower strength compared with the rebar specimens. This phenomenon could be attributed to lower shear resistance, as the anchor nut portion was completely pulled out from the concrete block under high shear force. Therefore, based on the horizontal shear test results, in the flexural test evaluation of composite girders, improvements were made to increase resistance against shear connector pull-out by drilling through-holes under the anchor nut of the S-DY shear connector and inserting rebar to increase the bonding strength.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Specimen**} & \multirow{2}{*}{\(A_{s}\)} & \multirow{2}{*}{\(\rho_{\text{eff}}\)} & \multicolumn{6}{c}{**Test Results**} & \multicolumn{6}{c}{**Predicted Results**} & \multicolumn{6}{c}{**Shear Strength Ratio**} \\  & & & **(\%/\%)** & & **(\%/\%)** & & **(\%/\%)** & & **(\%/\%)** & & **(\%/\%)** & & **(\%/\%)** & \\  & & & & **(\%/\%)** & & **(\%/\%)** & & **(\%/\%)** & & **(\%/\%)** & & **(\%/\%)** & & **(\%/\%)** & \\ \hline \multirow{6}{*}{Ethory} & 42 & 77.42 & 1.35 & 351.23 & 1.36 & & & & & & & & & & \\  & & 27.742 & 1.35 & 403.17 & 1.46 & & & & & & & & & & \\  & & 37 & 74.724 & 1.35 & 308.93 & 1.54 & & & & & & & & & & \\  & & 41 & 76.02 & 1.20 & 354.53 & & & & & & & & & & \\  & & 42 & 76.02 & 1.20 & 374.29 & 1.45 & & & & & & & & & & \\  & & 43 & 76.02 & 1.20 & 374.29 & 1.45 & & & & & & & & & & \\  & & 43 & 76.02 & 1.20 & 373.45 & 1.44 & & & & & & & & & & \\  & & 5 & 270.2 & 1.20 & 382.5 & 1.32 & & & & & & & & & & \\  & & 3 & 76.02 & 1.20 & 346.57 & 1.34 & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of horizontal shear strengths.  
Figure 10: Reconstruction simulation (S-DY-s3): (**a**) Slab composition; (**b**) Remove shear connector; (**c**) Fracture (girder); (**d**) Fracture (slab).