esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Probiotics are dietary supplements that contain live microbial strains capable of colonizing the gastrointestinal tract (permanently or transiently) to improve human health [1, 2]. Probiotics are beneficial when the native flora of the gastrointestinal tract is disrupted; exogenously supplemented probiotics might temporarily colonize the tract, stabilize the microflora balance that ultimately restore the vital physiological function [1, 2, 3, 4, 5]. The term \"probiotics\" is generally limited to species belonging to the genus lactic acid bacteria (LAB), such as _Lactobacillus__us acidophilus_, _Lactobacillus__hamosus_, _Lactobacillus__gasseri_, _Lactobacillus__gasseri_, _Lactobacillus__gasseri_, and _Lactobacillus__plantarum_, as important probiotics because of their strain-specific properties that are beneficial to health [6, 7, 8, 9, 10, 11, 12, 13, 14, 15]. The current research in probiotics indicates that bacterial species should fulfill specific requirements, including resistance to high acid and bile concentrations, adhesion and establishment in intestinal epithelia, production of antimicrobial compounds, and modulation of immune responses [1, 16, 17, 10, 14, 16].  
Known for their ability to secrete various antimicrobial substances including bacteriocins, probiotics facilitate the immune response through the secretion of IgA to fight against potential pathogens, reduce allergic reactions, activate the mucosal barrier in the colon, increase the stability of commensal microflora, and decrease the adhesion of pathogens, as well as promote recovery [4, 5, 18, 19, 20]. Bacteriocins produced by _L.__gasseri_ are the best known of which gassericin-A produced by _L.__gasseri_ LA39 is isolated from infant feces [21]. It is reported that [PERSON] et al., isolated _L.__hramosus_ IMC 501 from the fecal matter of an elderly Italian, exhibited high adhesion to the gastrointestinal tract, and discouraged the growth of pathogens, particularly _Candidata albicans_[22]. Another probioticticisolated from kouniss, _L. casei_ [PERSON] was also found to have high acid and bile salt resistance, gastrointestinal persistence, antimicrobial activity, as well as cholesterol-reducing properties [23]. Due to extensive commercial value and the health benefits contributed by probiotics, continuous isolation and characterization of new strains have become an emerging trend as these beneficial bacteria are currently the most potent multi-faceted biotherapeutics [1, 9, 10, 14, 18, 24, 25, 26, 27, 28, 29, 30, 31].  
This study aimed to evaluate the probiotic potential of 20 _Lactobacillus_ strains isolated from fermented dates, along with their resistance to high acid and bile concentrations. In addition, the selected isolates were investigated for their various functional properties, such as adhesion to the intestinal wall, anti-oxidation, inhibition of glucosidase activity, cholesterol-lowering, and anti-inflammation.
# Local Balance and Information Aggregation: A Method for Identifying Central Influencers in Networks
## 5 Experiment
### Evaluation Index
#### 5.3.1 Kendall Coefficient  
The Kendall coefficient [35] is also known as the Kendall rank correlation coefficient. It is a statistical index that measures the sequential correlation of two random variables. It measures whether two variables are in the same order. The value range is usually between \(-1\) and \(1\), where:  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**DataSet** & **n** & **e** & \(\langle k\rangle\) & \(\langle AC\rangle\) & \(d_{max}\) \\ \hline Dolphins & 62 & 159 & 5.13 & 0.25 & 12 \\ USAir97 & 332 & 2126 & 12.80 & 0.75 & 139 \\ Bio-celegans & 453 & 2025 & 8.95 & 0.65 & 237 \\ Eurroad & 1174 & 1417 & 2.41 & 0.02 & 10 \\ Rt-assad & 2139 & 2788 & 2.00 & 0.02 & 152 \\ Soc-hamster & 2426 & 16,630 & 13.71 & 0.54 & 273 \\ CiteSeer & 3264 & 4536 & 2.80 & 0.24 & 99 \\ CA-GrQc & 5242 & 14,496 & 5.53 & 0.53 & 81 \\ CA-Erdos992 & 6100 & 7515 & 2.95 & 0.28 & 61 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Real network dataset.  
* A Kendall coefficient of \(-1\) indicates a complete negative correlation, meaning the order of the two variables is entirely opposite;
* A Kendall coefficient of 1 indicates a complete positive correlation, meaning the order of the two variables is fully consistent;
* A Kendall coefficient close to 0 suggests no correlation, indicating the order of the two variables is unrelated. The formula is shown in Equation (11): \[\tau(R_{1},R_{2})=\frac{S_{c}-S_{d}}{0.5n(n-1)}\] (11) where \(S_{c}\) represents the number of consistent binary pairs and \(S_{d}\) represents the number of inconsistent binary pairs.
## 4 Discussion
The effect of low-density foliage can be intensified when the data collection is done during leaf-off season. However, point density over 4 points/m\({}^{2}\) was considered sufficient to measure hardwood trees with a height over 25 m consistently (Figure 13). As USGS increases its LiDAR data acquisition effort to higher densities (8 pulses/m\({}^{2}\)), point densities of future 3 DEP LiDAR data will likely also increase. There are other factors that could also affect the accuracy of tree height measurement. First, a human error could be introduced while measuring height in the field. When collecting inventory data across a large geographic region, the \"time window\" allowed for data acquisition is relatively small, especially in areas where there are large elevation gradients like in the western USA. Long data collection period for large areas can often lead to multi-year and multi-vendor contracts, causing potential complications in project management and inconsistencies in data attributes and quality. Second, tree canopy structures originating from outside a plot boundary, i.e., over-hanging branches, can cause an overestimation in tree height measurement. Third, our DTM-based nDSM generation method can produce a discontinuous CHM over a smooth canopy surface when the terrain slope is rugged. The discontinuous CHM can occur when a single ground height is assigned within the entire 1.524-by-1.524-m pixel area of DTM (Figure 3). Conversely, a continuous terrain model derived by mesh or triangulated irregular network (TIN) can be used to model more smooth ground surface [37]. Fourth, an airborne LiDAR data has its own source of error, such as sensor position, GPS-IMU integration, signal attenuation by the atmosphere, divergence of laser footprint, and aircraft vibration. However, the amount of the LiDAR error (Table A1) is considered relatively small compared to that of the other causes. Fifth, a more rigorous filtering algorithm can be used to remove spurious LiDAR returns.
# An Extensible Automated Failure Localization Framework Using NetKAT, Felix, and SDN Traceroute
## 4 Use-Cases
### Use-Case 2: Locating Configuration Error  
With this use-case, we emulated the possible effect of an issue with the controller application. In cases where the application is acting up, a possible failure can be that the switch acts as a black hole and does not forward (certain) packets. Another issue could be that the switch relays the packet via the wrong port, but this error type would cause a loop of packet forwarding that would bring down our emulation in a quite short time. These kinds of failures are inspired by issues caused by network policy or controller code changes. The first can happen whenever the network is extended with new services or when arming it against new vulnerabilities. Since network controllers are modular, many modules from different vendors can serve the same functionality. Changing such modules (e.g., based on a financial reason) would effectively change the code the controller is running and, in a worst-case scenario, can result in unforeseen side effects. Another example why controller code can be changed is to make it more effective. By opening up the network control, SDN makes the development of controllers and control modules much faster, which is beneficial for the overall process, but quickly changing code makes network behavior harder to track.  
In this use-case, as Figure 13 shows, the path segment reaching from _sw1_ to _h1_ is correctly identified as being the one having problems. The link between _h1_ and _sw3_ is marked as the most problematic one. This comes from the fact that _h6-h8_ cannot establish a connection with _h1_. This is also the cause why the links connecting these hosts with their switches are marked as being faulty.  
Figure 12: Failing links as identified by the framework in the layout difference use-case.  
Locating \(h1\) as having a problem can help in identifying the root cause of the problem but at the other end of the path, at _sw1_. Analyzing which tests failed and which passed for the marked links can help in better understanding the root cause of the failure without applying further tests. This use-case demonstrates a network issue of which symptoms both used monitoring tools can detect but eventually their combined results take us closer to figuring out the root cause of the problem.
# Agriculture 5.0 in Colombia: Opportunities Through the Emerging 6G Network
## 4 Results and Discussion
### Current Colombian Agricultural Outlook
#### 4.3.2 Pest and Disease Detection  
In Colombia, integrated systems using wireless sensor networks, remote sensing, and deep learning have been applied to detect coffee leaf rust (_Hemileia vastatrix_) in Caturra crops. [PERSON] et al. demonstrated [65] an F1-score of 0.775 using UAVs equipped with multispectral cameras and convolutional neural networks deployed at the edge, improving early diagnosis in rural field conditions. With 6G, these workflows could transmit high-resolution images in real time, enabling automated alerts and improved responsiveness in crops such as coffee, rice, and cacao.
## 3 Results and Discussion
### Influence of the reactant Addition Order on the Morphology of the Aluminum Oxides  
The experimental results from Sections 3.2 and 3.4 reveal that there is a certain inheritance relationship between the precursor morphology and the morphology of the aluminum oxide. Figure 16 shows the microscopic morphology of aluminum oxide prepared with different reactant addition sequences. When an aluminum chloride solution was added dropwise to the ammonium bicarbonate solution, the aluminum oxide morphology after high-temperature calcination appeared as hexagonal plates with long sides and short angles, with a thickness of approximately 100 nm and a radial size of approximately 1200 nm. During the calcination process, boehmite undergoes dehydration, changing from a dense crystal face to a porous crystal face, but the overall morphology remains largely unchanged. A small amount of sodium basic aluminum ammonium carbonate in the precursor decomposes under heat and is absorbed by the growth of hexagonal plate-shaped aluminum oxide crystals. In contrast, when ammonium bicarbonate solution was added dropwise to the aluminum chloride solution, the morphology of the aluminum oxide was primarily rod shaped. During the calcination process, sodium basic aluminum ammonium carbonate decomposes under heat, transforming from irregular rod-shaped structures with a length of approximately 800 nm and a diameter of approximately 60 nm to short rod-shaped aluminum oxide structures with a length of approximately 200 nm.  
Figure 15: Schematic diagram of boehmite crystal structure.  
The N2 adsorption-desorption results are shown in Figure 17. The curves in the N2 adsorption-desorption isotherm plot exhibit a type IV adsorption branch with an H3 hysteresis loop. According to the BJH calculation, as shown in the pore size distribution curve, these shapes indicate the presence of a significant amount of mesopores. The specific surface area of alumina prepared by adding aluminum chloride dropwise to ammonium bicarbonate (68.54 m\({}^{2}\)/g) is higher than that prepared by adding ammonium bicarbonate dropwise to aluminum chloride (48.01 m\({}^{2}\)/g). This indicates that alumina prepared by adding aluminum chloride dropwise to ammonium bicarbonate possesses abundant active sites and superior adsorption capacity.
# Remote Crop Mapping at Scale: Using Satellite Imagery and UAV-Acquired Data as Ground Truth
## 4 Discussion
Other studies reported higher accuracies for multi-class models using optical and/or SAR data, but these studies had access to larger ground-truth data sets and/or more cloud-free optical imagery [7; 14; 16]. To tighten the confidence interval and improve accuracies for individual categories, labeling efforts should be expanded in future studies [15]. Ideally, independently gathered ground-truth data (especially for testing) would also be incorporated into a full-scale study to highlight and remove any biases in the model. To further assess the model, we compared crop-specific areas calculated by the model with areas reported by the SAS for the same 2019 Season A. Although the estimated areas for maize and beans agreed with the survey results, the estimated area for cassava did not. The classification accuracy of cassava was lower than the other classes included in the model. The spectral signature of cassava overlapped with the spectral signature of the \"other vegetation\" class, which was undercounted by the model compared with the 2019 SAS. Additionally, some of intercropped maize was likely counted as cassava based on the similarity in the spectral information. Although not a major source of testing error in the model, bananas were undercounted in relation to the 2019A SAS. Again, the harvested area was less than the total cropped area: 32% of the crop was harvested during Season A. The examples of bananas included in the training set were all from mature, healthy plants. Additionally, we did not include banana plants that were growing in residential areas, which is commonly practiced in Rwanda. To reduce noise in the training dataset, we decided early in the labeling effort to include only cells that were dominated (i.e., at least 75%) by a single class type, and we included only cells with banana plants that were surrounded by other cells with banana plants. Banana plants that were growing next to structures were likely classified in the non-vegetative class in our model because it was the dominant landcover. Again, as more higher resolution satellite imagery becomes available, the addition of mixed-landcover classes to the labeling taxonomy will help capture bananas growing in residential areas. In the process of creating the country-scale map, we used existing shapefiles of water bodies and national parks to mask out known areas. The accuracy of these shapefiles affects the accuracy of the land areas calculated from the map generated using the trained model. Therefore, as these shapefiles are updated and improved, the precision of any models downstream in the mapping process will benefit. Future studies will be positively affected by the availability of up-to-date shapefiles for urban areas and other large-area non-vegetative structures that do not change as quickly as agricultural regions. These shapefiles can be created using commercial imagery as a source of ground-truth datasets for training the model, leaving UAV-acquired imagery for the ground-truthing of areas of intensive agricultural growth.
## 4 Results
### Flow State Construct: J48 Decision Tree and Logistic Regression Analysis
#### 4.2.2 Logistic Regression  
In line with answering RQ2, the study utilized logistic regression for each of the seven nodes to obtain the OR of contrasting conditions represented in the leaves, which helped identify each factor's contribution. Table 2 displays the results of the logistic regression analysis. In particular, the factor in Node 1 (cognitive load) illustrates that the OR of the low-load group vs. the high-load group was 5.6, showing a significant difference (\(p\) < 0.001). This suggests that the low-load group was more likely to reach a high EEG-F than the high-load group, with a 5.6 OR. Among all the nodes, only the factor in Node 7 (difficulty), with the conditions of a low cognitive load and low self-efficacy with low LT-tp, appeared insignificant (\(p\) = 0.133), leading to its exclusion from the final construct. In the following section, Figure 3 presents the final construct of the flow state.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Node** & **Variables** & **Omnibus \(\chi^{2}\)** & **Sig.** & **Group** & **Wald’s \(\chi^{2}\)** & **Sig.** & **OR** \\ \hline
1 Cognitive load & 108.88 *** & \textless{}0.001 & L vs. H & 93.46 *** & \textless{}0.001 & 5.6 \\ \hline \multicolumn{5}{c}{H load (OR = 1.0)} \\
2 & Self-efficacy & 53.74 *** & \textless{}0.001 & H vs. L & 41.52 *** & \textless{}0.001 & 11.8 \\
4 & (H efficacy) LT-tp & 18.80 *** & \textless{}0.001 & H vs. L & 15.77 *** & \textless{}0.001 & 7.1 \\
6 & (H LT-tp) Difficulty & 4.97 * & 0.026 & L vs. H & 4.79 * & 0.029 & 3.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of logistic regression analysis for electroencephalogram (EEG)-detected, real-time flow states (EEG-Fs).
## 3 System Design of Mobile Robot Person-Following System
### Path Planner  
Path planning in mobile robots is a key technology and is defined as finding a collision-free path connecting the current point and the goal point in the working environment. The advantages of path planning include minimizing the traveling time, the traveled distance, and the collision probability. The mobile robot should navigate efficiently and safely in highly dynamic environments to recover its target. The recovery procedure based on the output of the target trajectory prediction module is comprised of three steps. The first step is the target trajectory prediction, which was described above. The second step is the planning of the trajectory between the current position of the robot and the LPP by the global planner. This trajectory is shown as the green path in Figure 6b. The third step involves the local path planner. In this study, the default global planner was based on the move_base ROS node. The global path planner, which allows the robot to avoid static environmental obstacles, is executed before the mobile robot starts navigating toward the destination position. The local planner was based on the timed-elastic-band (TEB) planner in the ROS. To autonomously move the robot, the local path planner monitors incoming data from the LiDAR to avoid dynamic obstacles and chooses suitable angular and linear speeds for the mobile robot to traverse the current segment in the global path (Equation (10); see more details in [37; 38]).
## 2 Materials and Methods
### Spatial Autocorrelation Analysis  
#### 2.9.1 Hot Spots Analysis  
Analyzing hot spots remains crucial for uncovering the data trends and patterns and detecting and mapping regions with statistically significant high spatial values clustering (hot spot), as well as low values (cold spot), for both NDBI and LST. The process could be easily achieved using Getis-Ord Gi* statistics [61]. Hence, a hot spot analysis was performed via random points (n = 1463) selected from the NDBI and LST images across the study area by employing the ESRI(r) ArcGIS (version 10.2(r)) software program. NDBI and LST pixel values corresponding to each point were obtained by using the extraction tools of the software. The output from the analysis was statistically interpreted using the z-score and \(p\)-value. A greater z-score plus lower \(p\)-value indicated high clustering of features with high values (hot spot), while a smaller (-) z-value plus lower \(p\)-value suggested feature clustering with lower values (cold spot). A z-score close to 0 indicated no clustering of either high or low values within the regarded feature [62]. In the current study, NDBI and LST's hot and cold spots were classified into seven classes based on their Gi* values. Gi* > 0 was regarded as a hot spot with 99%, 95%, and 95% confidence; Gi* < 0 was regarded as a cold spot with 99%, 95%, and 95% confidence; and Gi* = 0 was regarded as \"not significant\". The results obtained from these analyses were used to investigate the spatiotemporal patterns of a hot and cold spots with regards to NDBI and LST over the study area. Univariate Local Moran's \(I\) test (\(p<0.05\)) was performed to determine any significant clustering (spatial autocorrelation) for both NDBI and LST over the study area by using GeoDa, an open-source software product [63].
# Diverse Humanoid Robot Pose Estimation from Images Using Only Sparse Datasets
## 3 Diverse Humanoid Robot Pose Dataset (DHRP)
### Sparse Dataset and Annotation Process  
We collected 42 publicly available video clips for each target robot from the official websites of their respective providers and their YouTube channels. These were divided into 29 clips for training and 13 clips for evaluation, ensuring that both the training and evaluation sets included front, side, back, and partial views for each robot. Example images from the target humanoid robot dataset are shown in Figure 3.  
The video clips were manually annotated for uniformly selected frames using V7 Darwin annotation software [53]. The numbers of annotated frames for each target humanoid robot are listed in Table 2. In total, we collected 3.8k training samples and 1.5k evaluation samples, forming a small-scale real-image dataset. On average, only 347 images were used per robot for training.  
It is important to note that the annotation process was labor-intensive, and even with the collected video clips, capturing the entire shape of each robot was not fully possible. Nevertheless, our experiments show that, despite the limitations of this sparse dataset, when augmented with our data generation techniques described in Section 3.3, our pose estimation method achieved satisfactory keypoint detection accuracy on the evaluation dataset, maintaining robustness over time in the evaluation video clips (see Section 5).  
Figure 2: Joint configurations of humanoid robots used in the Diverse Humanoid Robot Pose Dataset (DHRP). (**a**) Apollo. (**b**) Atlas. (**c**) Darwin-OP. (**d**) EVE. (**e**) FIGURE 01. (**f**) H1. (**g**) Kepler. (**h**) Optimus Gen 2. (**i**) Phoenix. (**j**) TALOS. (**k**) Toro. Note: Phoenix lacks lower-body data in the dataset.