esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Total Bacterial Count (TBC)  
In Figure 2A our results indicate a considerable reduction in total bacterial count (TBC) in probiotic sheep yogurt beverage with increasing honey concentrations, decreasing from 310.66 \(\pm\) 2.3 \(\times\) 10\({}^{6}\) cfu/mL (1% honey) to 229.33 \(\pm\) 4.61 \(\times\) 10\({}^{6}\) cfu/mL (5% honey). This may be attributable to honey's antibacterial qualities. Honey comprises bioactive chemicals, including hydrogen peroxide and phenolics, that can suppress bacterial proliferation. In Figure 2B, the TBC values in probiotic cow yogurt beverage demonstrate a slight reduction as honey content rises, decreasing from 353.33 \(\pm\) 6.11 \(\times\) 10\({}^{6}\) cfu/mL (1%) to 341.33 \(\pm\) 9.23 \(\times\) 10\({}^{6}\) cfu/mL (3%) and thereafter to 324 \(\pm\) 6.92 \(\times\) 10\({}^{6}\) cfu/mL (5%). Nonetheless, the figures remain at a quite high level, indicating that honey preserves bacterial viability to some extent. This indicates that high honey concentrations, while beneficial for initial bacterial growth, may cause osmotic stress or display antimicrobial effects at elevated levels. Honey contains bioactive compounds, such as hydrogen peroxide and phenolics, that can inhibit bacterial growth. In Figure 2C, TBC signifies the count of viable probiotic bacteria in blended yogurt beverage. At 1% honey, the total bacterial count (TBC) was recorded at 273.33 \(\pm\) 6.11 \(\times\) 10\({}^{6}\) cfu/mL. TBC attained a peak concentration at 3% honey (355 \(\pm\) 4.35 \(\times\) 10\({}^{6}\) cfu/mL), indicating optimal conditions for bacterial growth. At a concentration of 5% honey, the total bacterial count significantly diminished to 177.33 \(\pm\) 2.30 \(\times\) 10\({}^{6}\) cfu/mL, potentially due to the inhibitory effects of increased sugar concentrations or osmotic stress. Our findings align with those of ref. [29], who similarly discovered that honey's antibacterial capabilities diminished microbial populations in yogurt. Our findings also align with those of ref. [30], who examined honey's antibacterial constituents and saw significant impacts on the probiotic cultures in yogurt.
## 3 Methodology
### Image Preprocessing
#### 3.1.1 Gray Processing  
Gray processing is used to convert color images into grayscale images. In RGB color mode, the color of each pixel is co-determined by the R, G, and B components and each component may take a value between 0 and 255. When R = G = B, the resulting color is a gray color, and the value of R = B = G is called grayscale value. At this time, only 1 byte is needed for each pixel to store the grayscale value. Same as the value range of the three components, the grayscale value of each pixel varies from 0 to 255 and represents the different intensity of light. In general, image gray processing includes three common-used methods [50]: average method, weighted average method and maximum method. In this paper, the weighted average method is utilized to calculate the grayscale value for each pixel according to Equation (1). The effect of gray processing for the structural drawing image is shown in Figure 2b.  
\[\text{F}=0.2989\text{R}+0.5870\text{G}+0.1140\text{B}, \tag{1}\]
# Walking-Age Estimator Based on Gait Parameters Using Kernel Regression
###### Abstract  
Human gait motions differ depending on the age of the person. Previous studies have estimated age categories of walkers or have used age analysis for security or commercial surveillance purposes using images. However, few studies have estimated age from gait parameters alone. We estimated the age of people using kernel regression analysis based on their height, weight, and representative gait parameters, i.e., walking features that are interpretable with relative ease. Samples were obtained from 75 Japanese women aged 20-70 in a database. Through a variable selection based on sensitivity analysis, the established model estimated the ages of the women with a correlation coefficient of 0.78 with their actual ages, and the mean absolute error was 9.99 years. The sensitive variables included the minimum foot clearance, body weight, walking velocity, step width, and stride length. Estimation errors were significantly greater for elderly adults than for young people. Specifically, the mean absolute error for people in their 20s was 7.4 years, whereas that for those over 60 was 13.1 years. The proposed method uses gait parameters that can be measured with wearable devices, such as inertial measurement units; therefore, it offers an accessible approach to estimating a walker's age with moderate certainty and promoting healthcare awareness in daily life.  
kernel regression; gait analysis; variable selection; walking age +
Footnote †: journal: Applied Sciences
## Materials and Methods  
### Study Area  
The study area is situated in the north of the Republic of Congo in the Sangha department. The climate across the study area is tropical humid characterized by an annual precipitation of 1600-2000 mm per year, with two dry seasons, one in June to August and a stronger one from December through to February [23; 24; 25]. The area is dominated by forests, which cover over 95% of the region. The different forest classes occurring in the study area are detailed by [PERSON] et al., [26] (illustrated in Figure 1). This map classifies humid forests into sub-classes according to their structure and phenology. Sub-classes such as swamp forests are found along the river network, while open to very open Marantaceae forests cover the majority of the study area. At the southern edge of the study area, savanna formations start to dominate. Fires in the region are relatively infrequent due to the high percentage of humid forest cover, and occur almost exclusively in the savanna regions in the south, the aquatic grasslands in the east and in and around villages where fields are being cleared.  
Figure 1: Vegetation map of [PERSON] et al., (2013) [26]. In black, the extent of the logging concessions (Source: Interactive Forest Atlas of the Republic of the Congo [27]). In red, the study area.  
The main commercial activity in the area is the extraction of round wood from concessions which are leased from the national authorities by private companies. These concessions cover an extensive part of forest in the north of the country, with wood quotas being extracted according to a management plan set out by concession managers in conjunction with the national authorities. Three UFA (unite forestiere d'amenagement) encompass the Marantaceae forests as illustrated in Figure 1: Pokola and Ngombe in the Sangha department and Loundoungou Toukalaka in the Likouala department. The Ngombe concession contains the largest proportion of the Marantaceae forests and the subsequent fires in 2016. The analyses presented in this study are focused on this area (the red box in Figure 1).  
The population density is very low (less than 1 inhabitant per km\({}^{2}\)[28; 29]). Indigenous populations occupy specific areas allocated in the logging concessions.  
The network of logging roads in the study area [30; 31] is used as a proxy of human accessibility to the forest and related risk of human induced fire outbreak. In concessions managed for timber production, secondary roads are usually closed after exploitation and a rapid regrowth of vegetation is observed after abandonment. [PERSON] et al., [30] observed a median persistence of four years for open roads before reverting to a vegetated state and 20 years before complete disappearance (no longer distinguishable from the surrounding forest).
# A Coalition Formation Game Approach for Efficient Cooperative Multi-UAV Deployment
## 3 A Coalition Formation Game Approach for Coverage Utility Maximization  
Notably, solving \(\mathcal{G}\) is challenging due to the enormous strategies (positions and coalitions), an effective method is required to simplify the process: Step 1 (Reward optimization): Carry out coverage optimization deployment under current coalition formation. Step 2 (Coverage utility maximization): Under the fixed deployment of UAVs, the coalition selection method is considered to maximize \(U1\). Step 3: Repeat the above steps until \(U1\) reaches maximization. This approach can not only avoid the computing complexity caused by multi-strategies, but also be able to work out the optimal solution of \(\mathcal{G}\).  
In distributed multi-agent control system, players interact with each other and make self-determined strategies. Motivated by that, the proposed UAV efficient deployment problem can be formulated as a game. According to Equation (6), the utility function consists of two items, and we focus on achieving global optimization. Formally, the game is denoted by \(\mathcal{G}=(\mathcal{N},\{S_{n}\}_{n\in\mathcal{N}},\{u_{n}\}_{n\in\mathcal{N}})\), where \(\mathcal{N}=\{1,2,\ldots,N\}\) is a set of UAVs, \(S_{n}=G_{n}\otimes C_{n}\) (\(\otimes\) is Cartesian product) represents a set of available joint state profiles, where \(G_{n}\) and \(C_{n}=\{1,2,\ldots,M\}\) represent a set of the available positions and coalitions for each UAV \(n\), respectively. Denote an element of \(G_{n}\) and \(C_{n}\) as \(g_{n}\) and \(c_{n}\). \(u_{n}\) represents the utility function and can be expressed as \(u_{n}(s_{n},s_{-n})\), where \(s_{n}\in S_{n}\) represents the state selection of UAV \(n\) and \(s_{-n}\in S_{1}\otimes S_{2}\otimes\cdots\otimes S_{n-1}\otimes S_{n+1} \otimes\cdots\otimes S_{N}\) is the state profiles of all the UAVs excluding \(n\), the same is true with \(g_{-n}\) and \(c_{-n}\).  
Definitions are given in the following to analyze the properties of the coalition formation game.  
**Definition 1** (**Coalition formation game, CFG [22])**.: _A (hedonic) coalition formation game is given by a pair \((\mathcal{N},P)\), where \(P=(\succ_{1},\succ_{2},\ldots,\succ_{n})\) denotes the preferences profile, specifying for each player \(i\in\mathcal{N}\) its preference relation \(\succeq_{i}\)._  
It should be noted that a coalition partition is denoted as a set \(\Pi=\{CO_{m}\}_{m=1}^{M}\) which partitions \(N\), where the subsets \(CO_{m}\) are called coalitions and are disjoint. Let \(CO_{\Pi}(i)\) denote the set \(CO_{m}\) i.e., if  
\[\forall i\in\mathcal{N}:CO_{m}\in\Pi,\quad i\in CO_{m}. \tag{8}\]  
Apparently, \(\mathcal{G}\) is a typical CFG. UAVs in \(\mathcal{G}\) make decisions through different preference relations, and form different coalition partitions. However, first of all, the UAV deployment should be discussed first given the coalition partition \(\Pi\).
## 3 Materials and Methods
### Experimental Set-Up
#### 3.4.4 Validation and Real Sample Assays  
Assays performed with urine samples were done after the optimal conditions were found. In this sense, for each assay, 1.2 mL of urine was added to 3.8 mL of buffer solution (pH 12.0, adjusted with HCl 5 mol L\({}^{-1}\)) with 5% of NaCl. For validation assays, 100 \(\upmu\)L of TCAs mix solution was added, except when performing blank assays. For the validation process several parameters were evaluated, such as linearity, analytical thresholds, selectivity, accuracy, precision, recovery, matrix effects and the process efficiency, according to previous works [50; 51]. To assess the developed methods selectivity, the optimized BA\(\upmu\)E-\(\upmu\)LD/LVI-GC-MS method was applied to urine control samples and the absence of interfering compounds at the studied TCAs retention time was verified. Calibration standards were prepared between 10.0 and 1000.0 \(\upmu\)L\({}^{-1}\) to assess the linearity (estimated with the lack-of-fit test), the coefficients of determination (\(r^{2}\)) and residuals dispersion. In order to determine intra and inter-day accuracy and precision, several assays (n = 6) were performed using different spiking concentrations of TCAs mix solution, including 10.0, 50.0, 500.0, and 1000.0 \(\upmu\)L\({}^{-1}\), corresponding to the lower limit of quantification (LLOQ), low, medium, and high concentrations, respectively. Interday assays were performed in three consecutive days and intraday assays were performed in the same day. The acceptance criteria were that the relative residuals and relative standard deviations (RSDs) \(\leq\) 15%, except for LLOQ values were \(\leq\)20% values were accepted. The analytical thresholds were assessed by the limit of detection (LOD) and LLOQ. The LOD corresponds to a signal-to-noise (S/N) ratio of 3/1, while LLOQ is the lowest concentration where it is possible to quantify according to accuracy and precision parameters, being the first point in calibration curve, and also it corresponds to a S/N < 10. Lastly, to obtain matrix effects, recovery yields, and process efficiency, three sets of samples, each at two different concentrations (25.0 and 750.0 \(\upmu\)E L\({}^{-1}\)), were prepared. Set A samples consisted of a mix solution with all the TCAs at the previously stated concentrations. Set B samples were fortified after the microextraction and before liquid desorption. Set C samples were spiked before the microextraction. The ration between absolute peak areas of sets A and B allowed for the calculation of matrix effects, between B and C for the recovery yields calculation, and between A and C for the calculation of the process efficiency.
## 5 Improved Performance of Harmonic Injection by the Phase Perturbation Method
### Trip Generation (Section C)  
Sections A and B are intended to obtain an impedance value at PCC. The accurate value of the impedance itself is not the aim of the strategy. For this reason, the present section looks for creating a trigger signal indicating that a change in the impedance has occurred. To do this, a \(\delta\) pulse will be created. This \(\delta\) pulse contains the information of a grid-connected to grid-disconnected transition. The \(\delta\) signal generation is obtained according to [29]. As a difference with respect to [29], instead of using a delay and average calculations, the \(\delta\) pulse detection is replaced by filters; see Figure 15a. In this sense, it is possible to decrease the memory and the required computational time.  
Figure 15b shows how the trip generation works. The first order low-pass filter, the faster one (about ms), intends to prevent erroneous island detections. Taking into account the time needed for the DFT, it should be set with a rising time close to the utility voltage period. Then, the second order filter, the slower one (about s), is designed to take off with a lower slope compared with the first order filter. In this sense, the \(\delta\) pulse signal shows a fast rising behavior when a grid impedance change occurs and is slowly reduced to zero with a dynamics dominated by the second order filter response. Note that the \(\delta\) has dynamic times in the order of the fast filter (ms).
## 5 Experiments
### Summary of Performance  
The performance requirements of the ground station detection system, as presented in Section 4, are compared with the results of the three experiments in Sections 5.2-5.4 and summarized in Table 4. For comparison with the performance of the proposed method, the performance of AR markers obtained using a similar experimental system is also presented in Table 4.  
As shown in Table 4, the proposed method achieved the required performance in all three performance categories.  
In terms of detection performance for low-resolution markers, the proposed method was found to be able to detect RCMs up to a marker size of 10.2 [pixels]. This corresponds to the detection of a 2 [m] diameter color marker at an altitude of 100 [m] above the ground and at a ground distance of 2839 [m]. Since the expected operational range is 2000 [m], the sensor system has enough of a margin. Its performance is half that of AR markers in terms of marker drawing size.  
The proposed method also has a marker direction detection accuracy of 0.12 [deg.], which is 0.02 [deg.] more accurate than the required accuracy. It can therefore be used for antenna control in the range of \(\pm\)0.29 [deg.]. However, when compared to AR markers, the detection accuracy was found to be inferior, entailing twice as much error.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Item** & **Detection Performance for** & **Marker Direction** & **Detection Performance** \\  & **Low Resolution** & **Detection Accuracy** & **of Markers Against Motion Blur** \\ \hline Performance requirements & Smaller than 14.5 [pixel] & Smaller than 0.14 [deg] & Greater than 0.0120 [-] \\ Proposed method & 10.2 [pixel] & 0.12 [deg.] & 0.0175 [-] \\ (Color markers) & 10.2 [pixel] & 0.06 [deg.] & 0.0011 [-] \\ AR marker & 20.0 [pixel] & 0.06 [deg.] & 0.0011 [-] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of performance.  
Figure 18: Effect of the proposed method on motion blur resistance with varying exposure time: (a) maximum angular velocity detectable by RCM vs. exposure time; (b) motion blur tolerance vs. exposure time. The blue markers represent the experimental results from Figure 17, while the orange markers represent the results from additional experiments.  
The proposed method's marker detection performance for motion blur was 0.175 [-]. This performance is found to be applicable to angular velocities up to 29.8 [deg./s] in the assumed operational environment.
## 4 Discussion
### Effects of Vegetation Canopy Structure on LUE  
For subtropical mixed forests, canopy structure is not a main factor affecting LUE change. The estimation of PRI is complicated by the light conditions in the tree crown, especially for the closed crown with complex light conditions, which affects the estimation of LUE [48]. Therefore, we attempted to combine optical data with lidar data in order to infer the impact of forest structure on plant photosynthesis. The spatial distribution difference of LAI\({}_{\text{e}}\) does not show a great impact on the change of LUE. In our study, we believe that the uneven terrain factors in the study area have an impact on the accuracy of the LAI\({}_{\text{e}}\) extracted by the TLS. For the topographic relief, the data of TLS cannot obtain the complete point cloud data of the canopy, especially the upper part of the canopy, which affect the LAI\({}_{\text{e}}\) extraction results. In addition, the study area is located in the subtropical region with a humid and hot climate. The cloud layer is thick and has a strong scattering and refraction effect on the solar light [41]. Most of the light reaching the crown is scattered light, which leads to more multiple scatterings and more extensive light conditions [11; 47; 48]. This result is inconsistent with other results [11; 15; 49]. The higher the structural complexity of the canopy, the more it will pass through the canopy, resulting in more light interception, resulting in more multiple scatterings and wider radiation conditions [50; 51; 52]. The reason for the inconsistency with the results found by [PERSON] et al (2018) may be that the direct radiation received by the boreal forest is higher than that of tropical forests, and PAR and illumination conditions are one of the most important factors affecting LUE [11].
## 3 Analytical Methods and Applications
### Data Mining  
Data mining is about discovering patterns from large datasets. Spatial data mining is the application of data mining techniques to spatial data. Spatial data mining follows the same functions as data mining, with the end objective to find patterns in geography, extracting knowledge from geospatial data [53]. Multiple geo-located objects may be involved, being dynamic in space and/or time. Their characteristics, trajectories and behavioral patterns in space and time can become aggregated and clustered, classified and associated, for more advanced and complete analytics and accurate numeric predictions.  
An example is the MobEyes project [54], which performs urban monitoring by means of a vehicular sensor network, using opportunistic dissemination of road information to vehicles. Other examples involve smarter traffic lights for optimizing traffic management [55] and a smarter road system [56], allowing road authorities to obtain a broader view of the problems that can occur on the transportation network they manage.  
Besides urban transportation management, aggregated location sensing has been employed in the analysis of behavior and interest of visitors in different types of cultural heritage-related visits [57], as well as in logistics [15], for tracking vehicle location, mechanical systems and condition of cargo, and for monitoring drivers' habits and performance.  
Furthermore, large-scale location measurements are important in management of construction sites and operations, by means of real-time monitoring of the position of assets and materials for day-to-day management and planning [15]. Infrastructure asset management was performed in [58], targeting location-based services in smart cities. Electric utilities such as the Seattle City Light employ real-time aggregated location measurements to produce accurate outage reports, achieve situational awareness, logistics support, and advanced analytics [15]. Interesting applications are the ones concerning animal monitoring, such as using collar tags to recognize the activity of goats [59], or employing ultra-low power sensors for sustainable wildlife monitoring [60]. Domestic animal monitoring is important for assessing their overall welfare and detecting early enough possible diseases. Wildlife monitoring is useful for protecting wild animals against poachers or for estimating life threads due to changing weather and climate conditions, or disaster scenarios.  
Finally, crowdsourced geo-tagged information has been used for assessing in real time earthquake hazards, and to facilitate and coordinate emergency response [61].
## 4 Field Monitoring and Stress Analysis
### Monitoring Test Setup, Materials, and Methods
During on-site dynamic strain testing, intense blasting vibrations caused collisions between data cables and surrounding structures, while mechanical equipment and welding operations introduced significant signal interference. To ensure measurement accuracy, the following measures were implemented before testing: data cables at borehole exits were wrapped with foam sleeves, and the remaining segments were enclosed in stainless steel junction boxes to prevent impact damage. Testing instruments were grounded using steel rebars to eliminate electromagnetic interference. All mechanical equipment was relocated, and welding operations were suspended during testing. Instrument connections were established and activated only after confirming the absence of interference sources. Sonic testing was completed prior to blasting. The ultra-dynamic strain amplifier was activated just before the blast and deactivated afterward. The field-testing configuration is shown in Figure 21. Each blasting round required approximately 15 min for complete monitoring, and the procedure was repeated over five consecutive rounds. Figure 19: YL-LCT single-transmitter dual-receiver sonic probe. Figure 20: Layout of wave velocity testing boreholes.