esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Application of Modified Rockfall Hazard Rating System
### Rockfall Inventory  
The main purpose of a landslide inventory is to assess the distribution, type, and pattern of landslides from watershed to regional scale and is counted as the first step towards susceptibility, hazard, and risk assessment [56; 57]. Rockfall hazard is slightly different compared to typical large and shallow landslides and avalanches in terms of the affected area and economic losses along the transportation corridors. Through detailed fieldwork, the current study prepared an inventory of rockfalls, toppling, and debris slides from Besham to Dasu along the Karakoram highway. The road section primarily runs along the giant Indus River and, on the other side, is bounded by sub-vertical to vertical slopes prone to rockfall and toppling. A slope map of the study area was prepared using the 12.5 \(\times\) 12.5 m ALOS PALSAR Digital Elevation Model (DEM) ([[https://asf.alaska.edu/](https://asf.alaska.edu/)]([https://asf.alaska.edu/](https://asf.alaska.edu/)) 29 May 2019). Based on slope map and high-resolution GOOGLE Earth imagery, 30 locations were selected to collect the data. Local people were also interviewed for any historic rockfall activity and their observations regarding daily rockfall incidents.
## 3 Results
### Confusion Matrix of the Optimal Model  
To further assess the classification performance of the optimal model, the confusion matrix of the CWT-UVE-SVM classifier is shown in Figure 5. The model correctly identified all intact samples and misclassified only one mildly damaged sample as intact, which indicates strong separability capacity between undamaged and bruised fruit. Most errors occurred for the moderate and severe categories, as two moderately damaged samples were identified as severely damaged and one severely damaged sample was misclassified as moderately damaged. These confusions likely resulted from overlapping spectral responses at higher bruise levels, where tissue degradation leads to signal saturation. Overall, the matrix demonstrated the model's high reliability in differentiating cumulative damage levels in prunes.
## 4 Agricultural and Research Applications
### Improving Apiary Management
Our data from 50 colonies over several years indicates that the return rate for a healthy hive is in the mid-to-high 90 th percentile. Acute exposure to a poison may drop this to 80% or lower within a day. A chronic exposure event is more likely to play out over a series of days, with the colony losing another 1%-5% of the bees expected to return each day. If successful, our acoustic system may be able to provide comparable information for far less cost with a simpler system than a hive-entrance counter. Microphones are relatively inexpensive compared to counter devices. Our IR imaging of colonies was initiated so that a soldier who is unfamiliar with honey bee life histories could determine whether a sentinel colony was alive, of suitable population size, queen-right, and in good condition without knowing much about beekeeping and colony inspections. Similarly, growers who rent colonies for pollination have to trust their beekeeper. Some pay a premium for colonies of a certain size. Growers often hire bee brokers and grading crews, who score the condition and size of a percentage of 5%-15% of the colonies being rented. If we can successfully calibrate and verify that an IR camera can score hives with equal or better accuracy than opening the hive and visually inspecting a colony, any grower could verify the condition of colonies delivered without opening hives or knowing how to manipulate bees. In addition, it should be possible to score/grade every colony, not just a subset of the colonies offered for rent. Considering that the almond growers rent more than 1.6 million colonies each year and that 2015 rental fees were in the $ 140 to $ 170 range or higher per colony, it is important that the grower gets fair value for their investment. The beekeepers also gain advantages with use of an IR camera. Colonies are shipped from as far as the east coast to the west coast, with many being shipped in the fall or winter from cold climates where it is difficult, if not impossible, to inspect bees at the point of origin. Weak and dead colonies are often discovered in California after delivery when the hives are opened for feeding and inspection. Shipping a substandard or dead bee colony and its hive to California and returning it without being able to rent it is a major cost, since freight can run to $ 90 or more per colony each way. Small-scale backyard beekeepers may take an interest in these technologies, but inspecting one or two colonies in their backyard using these technologies would not be cost-effective. Although not necessarily needed for good bee management of a few colonies, some of these technologies may be warranted from the perspective of learning about bees or just the fun of trying something different. In Ireland a group of beekeepers bought eight of Arnia's hive monitoring systems and they are working with students from a local school to conduct simple experiments and learn more about bees.
# Prediction of Arrival Time of Vessels Considering Future Weather Conditions
## Appendix A  
Table 1 shows prediction results for the 30 import/export routes between Japan and Taiwan mentioned in Section 4.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Vessel Name**} & \multicolumn{2}{c}{**Target Voyage**} & \multirow{2}{*}{**Depature**} & \multicolumn{2}{c}{**Arrival Time**} & \multicolumn{2}{c}{**Prediction Accuracy**} \\ \cline{2-9}  & & & & **Actual** & **Proposed** & **Previous** & **Proposed** & **Previous** \\  & **Depature** & **Arrival** & **Time** & **Actual** & **Method** & **Research** & **Method** & **Research** \\ \hline TS TOKYO & NAGOYA & YOKOHAMA & 2020/3/22 & 2020/3/22 & 2020/3/22 & 2020/3/23 & 92\% & 71\% \\  & & 6:14 & 22-30 & 21:08 & 3:08 & 92\% & 71\% \\ \hline TS TOKYO & NANSHA & OSAKA & 2020/3/16 & 2020/3/19 & 2020/3/20 & 2020/3/20 & 95\% & 92\% \\  & & 4:58 & 23:14 & 3:49 & 6:12 & 95\% & 92\% \\ \hline TS TOKYO & KOBE & NAGOYA & 2020/3/21 & 2020/3/21 & 2020/3/21 & 2020/3/22 & 87\% & 78\% \\  & & 2:48 & 22:19 & 19:43 & 2:40 & 87\% & 78\% \\ \hline TS TOKYO & TOKYO & KEELUNG & 2020/3/24 & 2020/3/27 & 2020/3/27 & 2020/3/27 & 2020/3/27 & 91\% & 83\% \\  & & 6:11 & 5:40 & 11:46 & 18:03 & 91\% & 83\% \\ \hline TS TOKYO & KEELUNG & TAICHUNG & 2020/3/27 & 2020/3/27 & 2020/3/27 & 2020/3/27 & 94\% & 85\% \\  & & 11:52 & 19:43 & 20:12 & 18:32 & 94\% & 85\% \\ \hline TS TOKYO & TAICHUNG & KAOHSIUNG & 2020/3/28 & 2020/3/28 & 2020/3/28 & 2020/3/28 & 2020/3/28 & 67\% & 33\% \\  & & 3:52 & 12:26 & 15:13 & 18:10 & 67\% & 33\% \\ \hline TS TOKYO & KAOHSIUNG & NANSHA & 2020/3/28 & 2020/3/30 & 2020/3/30 & 2020/3/30 & 2020/3/30 & 81\% & 90\% \\  & & 18:38 & 0:08 & 5:41 & 3:01 & 3:01 & 81\% & 90\% \\ \hline TS TOKYO & LAEM & HONG & 2020/4/9 & 2020/4/13 & 2020/4/13 & 2020/4/12 & 2020/4/12 & 99\% & 94\% \\  & & 5:32 & 4:33 & 5:27 & 22:31 & 99\% & 94\% \\ \hline TS TOKYO & NANSHA & OSAKA & 2020/4/14 & 2020/4/17 & 2020/4/18 & 2020/4/18 & 90\% & 85\% \\  & & 10:24 & 23:06 & 7:44 & 11:50 & 90\% & 85\% \\ \hline TS TOKYO & KEELUNG & TAICHUNG & 2020/5/20 & 2020/5/20 & 2020/5/20 & 2020/5/20 & 96\% & 91\% \\  & & 9:44 & 17:05 & 16:48 & 16:24 & & \\ \hline TS TOKYO & TAICHUNG & KAOHSIUNG & 2020/5/20 & 2020/5/21 & 2020/5/21 & 2020/5/21 & 72\% & 32\% \\  & & 22:46 & 7:16 & 9:36 & 13:04 & 72\% & 32\% \\ \hline TS TOKYO & KAOHSIUNG & NANSHA & 2020/5/21 & 2020/5/23 & 2020/5/22 & 2020/5/22 & 90\% & 94\% \\  & & 15:35 & 1:54 & 22:26 & 23:58 & 9:57 & 60\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Routes.
## 4 Results
### Correlation Plot  
Figure 8 explains the correlation plot for the GDC metric, the ensemble model with multiple annotations on the x-axis, and the remaining models on the y-axis. The correlation plot shows the same dataset-related conclusion as the previous plots. For example, when the SA and MA models are compared, the correlation plot shows wide spreads of points for the Knee and SIJ datasets, which indicates the models' disagreement. Also, in the SIJ dataset, the graph shows two clusters. One at the bottom indicates that the MA model is scored higher for those samples, and the other at the top right indicates the agreement between the models. For the Lung and Heart datasets (for the Heart dataset, the y- and x-axis ranges are very small, which is why we consider them as a clear cluster), the plot shows a cluster of points at the top right very tightly around the lines, which suggests a strong correlation between the models. Moreover, when the MA model is compared with the MC models, we see the same distribution for all the graphs, which clearly indicates that changing the dropout rate has little impact on the model's performance. For example, for the Knee and SIJ datasets, the plot shows a wide distribution of the points as an indicator of disagreement between the models (also true for the regression model). One interpretation of Knee dataset results is that the dataset size is relatively small. Again, for the Lung and Heart datasets, the graph shows a cluster of points in the top right for all MC dropout versions, and the points are very tight to the line as proof of the agreement between the models. Yet, some outliers appear directly below the cluster, representing samples of MC models that score low GDC. Finally, comparing the MA and regression models shows a strong agreement for the Heart dataset. For the Lung dataset, the points are widely spread on the right side of the plot, which suggests the MA model outperforms the regression model. The IQR of the regression model in Figure 6 is much wider than that of other models, which draw the same conclusion as shown in a correlation plot.  
Figure 7: Shows Bland-Altman plots for the GDC metric.
## 3 Results
### Effect of Different Additives on the Aerobic Stability of Whole-Plant Millet Silage
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Items** & **Additives** & **LAB (log CFU/g)** & **Yeasts (log CFU/g)** & **General Bacteria (log CFU/g)** \\ \hline \multirow{4}{*}{At silo opening} & CK & 7.19 \(\pm\) 0.18 b & 7.18 \(\pm\) 0.24 b & 7.37 \(\pm\) 0.9 a** \\  & LP & 8.02 \(\pm\) 0.10 a & 7.51 \(\pm\) 0.08 ab* & 6.15 \(\pm\) 0.08 c** \\  & S & 7.98 \(\pm\) 0.20 a & 7.69 \(\pm\) 0.28 ab & 5.94 \(\pm\) 0.10 c* \\  & MIX & 8.19 \(\pm\) 0.13 a & 7.92 \(\pm\) 0.11 a* & 6.63 \(\pm\) 0.15 b \\ \hline \multirow{4}{*}{After 7 d aerobic stability test} & CK & 6.98 \(\pm\) 0.18 B & 6.93 \(\pm\) 0.18 C & 6.77 \(\pm\) 0.12 \\  & LP & 8.14 \(\pm\) 0.05 A & 8.33 \(\pm\) 0.02 AB & ND \\ \cline{1-1}  & S & 7.90 \(\pm\) 0.14 A & 8.04 \(\pm\) 0.14 B & 7.58 \(\pm\) 0.50 \\ \cline{1-1}  & MIX & 8.18 \(\pm\) 0.16 A & 8.47 \(\pm\) 0.11 A & 7.16 \(\pm\) 0.26 \\ \hline \hline \end{tabular} Note: CK, LP, S, and MIX represent control, _Lactiplantiobacillus plantarum_, sucrose, combination of _Lactiplantiobacillus plantarum_ and sucrose addition treatments, respectively; M represents raw materials. Different lowercase letters indicate significant differences (\(p<0.05\)) between different treatments after the aerobic stability experiment. * Indicates significant difference between unscaling and aerobic stability experiments in the same treatment (\(p<0.05\)), ** indicates that the difference between at silo opening and after aerobic stability test in the same treatment is extremely significant (\(p<0.01\)); ND: not detected. LAB: lactic acid bacteria.  
\end{table} 
Table 5: Effects of different additives on microbial counts at silo opening and after aerobic stability test of whole-plant millet silage.
## 3 Results
### Classification Results and LLCC Statistics  
The U-Net model used in this study experienced 400 epochs. The metrics of model training show that overall accuracy and loss value on training and testing data converge to above 0.97 and below 0.04, respectively. The training indicators are finally obtained through randomization parameter training. Patches per epoch and patches per batch were set to 300 and 1. The patch sample rate was 16, and the value of class weight used to highlight feature pixels at the beginning of training was set between 0.58 and 2.55. Loss weight was set to 0.46 for highlighting feature pixels when checking training effects.  
Figure 8 presents all the classification results based on NDVI datasets. In this figure, (a), (b), and (c) represent MLC classification results of five land-cover types except changed land based on time series NDVI, mean NDVI, and single NDVI, respectively. The classification images from single NDVI and mean NDVI have many areas of confusing classification. The difference in the classification results of the main urban area in the north of Shangyu is particularly obvious. In these areas, many agricultural lands are wrongly identified as buildings in the mean NDVI, while most of them are classified as tidal flats in the single NDVI result. This phenomenon is largely due to the lack of temporal details, resulting in less information available for classification methods. For LUCC detection, (d) and (e) are the classification images of all six land-cover types based on time-series NDVI processed by MLC and U-Net, respectively. Additionally, the main difference is that a lot of agricultural land is misclassified as changed land in MLC. This is consistent with the statistics of the results (Figure 9).  
In the statistics, the area of changed land is up to 89.87 square kilometers, accounting for 6.4% of Shangyu according to MLC results. It is too sensitive to change information extraction, especially in a farmland area. The statistical area of changed land in U-Net is fairer. According to the regional overview issued by the government of the study area in 2022, the area of vegetation cover in Shangyu district is 538.54 square kilometers (other land-use types are not indicated or do not match). The difference is only 13.3 square kilometers (2.5% of the actual area), which illustrates that the method based on time-series NDVI and U-Net in vegetation cover extraction is very effective.  
Figure 7: Annual NDVI value curves (each curve represents the NDVI value of a typical feature, and each point in the curve represents the mean value of 10 typical features).  
Figure 8: Classification results of (**a**) MLC method without change detection based on time-series NDVI; (**b**) MLC method change detection based on mean NDVI; (**c**) MLC method change detection based on single NDVI; (**d**) MLC method including change detection based on time-series NDVI; (**e**) U-Net method including change detection based on time-series NDVI.  
Figure 9: Area statistics of each ground feature comparing MLC and U-Net.
## 2 Materials
### Satellite Data  
In this research, we accessed all satellite data via the Google Earth Engine (GEE) Python application programming interface (API) [60]. From the GEE, we downloaded the S1 data--one of the missions of ESA's Copernicus initiative--together with NASA's SMAP data on the location of the SM stations. Their ensured continuity for the future and sensitivity to changes in vegetation and soil properties makes both satellites a viable option for SM monitoring [61; 62; 63; 64].
# A Review on Observer Assistance Systems for Harvested and Protected Fish Species
## 2 Related Research
### Fish Species Estimation  
The estimation of fish species can be carried out using the automatic identification of fish species from images or videos combined with electronic monitoring systems [29]. Therefore, the labor force for reporting catch statistics can be reduced and the accuracy of reporting can be increased. Image analysis approaches are increasingly being used to gather fish species information. These approaches have the advantage of automation, efficiency, veracity, and accuracy as opposed to the traditional manual methods. Previous studies have addressed how image analysis can be used to identify marine fish types.  
A research paper [30] developed a nearest-neighbor classifier for identifying nine species of fish using shape and color features and directed an acyclic graph multiplexing for distinguishing six species of fish using wavelet-based texture features as inputs. In some cases, class-supported vector machine classifiers [31] have been developed. In one study [32], a method using image processing and statistical analysis was developed to recognize 4 species of fish by color, shape, and texture characteristics, and the morphological characteristics of 27 fish species were evaluated, resulting in three types that were significantly different from each other. Automated morphological recognition has been successful on different fish species [33]. In addition, a study [34; 35] succeeded in recognizing 15 fish species in underwater videos by combining hierarchical trees and Gaussian mixture models. Representatively, the YOLO [36; 37; 38] and SSD [39; 40] methods have been used to estimate fish abundance using autonomous imaging devices and genetic programming-based classifiers.  
Regarding model and harvest estimation, there are no distinctive studies, but the representative studies include the crowdsourcing paradigm and image stitching studies described in Section 2.1. It is expected that these techniques can also be used to estimate harvest. The crowdsourcing paradigm makes it faster, cheaper, and easier to collect the data needed for deep learning [23]. One problem, however, is that while crowdsourcing can provide tremendous benefits with vast amounts of data, it requires a specific strategy to verify the quality of the data collected.  
This verification must take place by the use of pre-trained CNNs and fast region-based CNNs to detect fish and recognize fish species in images from the ImageCLEF dataset [32], or by using pre-trained CNNs as in [41] to video underwater. There is also a method for recognizing fish species from images with random noise using CNNs and transfer learning, such as in [42], and there is also a study [43] that automatically identified species of tuna and billfish. However, in most studies, there is a part that requires learning through labeling. A deep dependence can therefore be expected from the data used in the study.
# Optimal Algorithms for Sorting Permutations with Brooms
## 3 Sorting Permutations with a Single Broom
### Algorithm \(A_{b}\)  
Let \(S_{min}\) be the star marker that is closest to its home residing on the path and \(P_{max}\) be the largest path marker that is not homed. Our algorithm \(A_{b}\) for single broom that transforms a given input configuration to a sorted configuration, consists of the following 3 steps in the given order.  
1. While (\(\exists\) a \(S_{min}\)), efficiently home \(S_{min}\);
2. While (\(\exists\) a \(P_{max}\)), efficiently home \(P_{max}\);
3.  
Efficiently home the markers in the star that need to be homed.  
An illustration of algorithm \(A_{b}\) for a single broom is given in Figure 2. In the initial setup, we have two unhomed star markers on path, i.e., 3 on \(v_{7}\) and 2 on \(v_{8}\). \(S_{min}\) is 3 in the first iteration and in the next it will be 2. Homing markers 3 and 2 take 5 swaps in total. Then, there will be no star markers residing on the path. Thus, we can start with Step 2 of our algorithm. \(P_{max}\) is 8 and a swap with 7 homes both the markers. Only the star markers 5 and 4 at \(v_{4}\) and \(v_{5}\) respectively need to be homed. Our algorithm homes all markers in total of 9 swaps, exactly the same number of swaps that are taken by the algorithms of [PERSON] et al. [5], [PERSON] et al. [6] and [PERSON] [10].  
Step 1 and Step 2 of \(A_{b}\) have the following properties:Star markers residing on the path always move to the left in the increasing order of their distances from their respective homes;
2. A path marker moves right to its home; thereby moving the smaller path markers towards the center;
3. Swap involving two star markers will not take place on a path edge.  
The next section covers the analysis and correctness proof of the algorithm \(A_{b}\).
# Age- and Sex-Adjusted Reference Intervals in Tear Cytokine Levels in Healthy Subjects
## 2 Materials and Methods
### Tear Collection and Tear Inflammatory Molecule Analysis  
A non-stimulated basal tear fluid sample (4 \(\upmu\)L) was collected from the external annulus of the eye using a 4 \(\upmu\)L calibrated glass microcapillary tube (Cat. 1-000-0040, Drummond Scientific, Broomall, PA, USA) in a non-traumatic manner, avoiding reflex tearing as much as possible, as previously described [21, 22], until the capillary tube was completely full of liquid. Samples were diluted 1/5 in a 0.5 mL microtube (Sarstedt AG&Co, Numbrecht, Germany) containing ice-cold Cytokine Assay Buffer (Merck Millipore, Millipore Iberica, Madrid, Spain) and frozen at \(-80\)\({}^{\circ}\)C until analysis.  
Eighteen cytokines in tear samples were quantified using a Luminex IS-100 equipment (Luminex Corporation, Austin, TX, USA) with Millipore commercial kits HCYTO60K-17-plex and MPXHCYP2-62K-IL-23 single plex, (both from Merck-Millipore, Millipore Iberica, Madrid, Spain). The concentrations of epidermal growth factor (EGF), fractalkine, interferon (IFN)-\(\gamma\), interleukin (IL-10, IL-12p70, IL-15, IL-17A, IL-18, IL-1 receptor antagonist (IL-1 RA), IL-2, IL-6, IL-7, IL-8, interferon gamma-induced protein 10 (IP-10), monocyte chemotactic protein (MCP)-1, tumor necrosis factor (TNF)-\(\alpha\), vascular endothelial growth factor (VEGF), and IL-23 were analyzed according to the manufacturer's instructions. Briefly, 10 \(\upmu\)L of the 1/5 diluted sample were incubated under agitation overnight at 4 \({}^{\circ}\)C with beads coated with antibodies specific for each molecule. After washing, the beads were incubated with biotinylated human antibodies for 1 h, followed by incubation with streptavidin-phycoerythrin for 30 min. Standard curves were used to convert fluorescence units to concentration units (pg/mL). Data were stored and analyzed with the \"Bead View Software\" (Upstate-Millipore Corporation, Watford, UK).  
Regression on order statistics (ROS) method [23] was used to impute values below the limit of detection. Molecules that were detected in less than 50% of the samples were not further analyzed. Levels of all other cytokines were analyzed as base 2 log-transformed variables.
# Experimental Study of LoRa Transmission over Seawater
## 4 Experimental Results and Analysis  
The experimental results with analysis are organized into two groups, based on the propagation condition; inside the groups, data is further divided based on the transceivers' operating frequency. At the end of the section, a discussion and result generalization in different aspects is given, as well as potential application areas.  
During each experiment, RSS, SNR, and PRR were logged. The results are presented both in time domain and also cumulatively, using histograms. The plots in time domain show current values represented by dots, while their moving average values calculated on the 1-min window are shown with solid lines.  
The distribution of the moving average values, as well as the difference between current and moving average values, are presented in the form of histograms. In that way, we aimed to improve link analysis and potentially separate interference caused by reflections and terrain shielding from radio-interference.