esa-sceva/satcom-chunk-collection huggingface dataset:
# Drone vs. Bird Detection: Deep Learning Algorithms and Results from a Grand Challenge
## 4 Gradiant Team
### Datasets Used  
In order to ensure a robust validation strategy, the authors manually split the videos in the train and validation data trying to maintain both sets as independent as possible. Hence, videos that were recorded at exactly the same location must belong to the same data split. A final selection of 16 videos was used as validation set.  
Most videos were recorded at high frame per second rates which results in a large amount of identical images. For avoiding this effect a subsampling was carried on by taking one out of each ten frames, in other words, only a 10% out of the total data was used for training and validating our model.  
Apart from challenge data, external datasets were used for training the final model. The first of them is the Purdue UAV dataset [20], which can be publicly accessed and contains a total of 70,250 frames that were also subsampled until reaching 27,864 frames with 10,461 bounding boxes. The second dataset is private and consists of 22,236 frames and 14,152 bounding boxes.  
Most of the images have a resolution of 1980 \(\times\) 1080 pixels and training a network with images of such dimensions is unmanageable. The effect of downscaling input images would result in a loss of information, which is critical for detecting very small drones. Areas of small objects would embrace only a few pixels after being resized; for this reason, image crops of 720 \(\times\) 720 pixels were extracted from each image, following a sliding windows approach.  
Furthermore, different techniques for data augmentation were applied such as the addition of different types of noise, such as Gaussian or Median blur. Color transformations such as RGB shift, HSV shift, Additive Random Brightness and other transformations (e.g., random rotations or random cutouts) were also performed. All these operations were added in a stochastic fashion during the training phase so images in each data batch were randomly altered.
## 1 Introduction
### Problem Statement and Objectives  
The primary research problem addressed in this paper is the following: How do different sequential neural network architectures (GRU, LSTM, and Transformer), when inserted as head modules within the same Siamese retrieval framework, compare in their ability to match multilingual job descriptions with candidate resumes drawn from a production-scale IT pipeline?  
The specific objectives are the following:  
* To fill the gap left by past studies that fixed a single backbone.
* To train these models using a triplet loss function on a real-world dataset of job descriptions and candidate resumes from the IT sector.
* To evaluate and compare the performance of the GRU, LSTM, and Transformer head models using ranking metrics (Top-K Accuracy and MRR) and qualitative analysis (t-SNE visualization).
* To analyze the results highlighting the relative strengths and weaknesses of each sequential model for this specific task.
# Semi-Automatic Fractional Snow Cover Monitoring from Near-Surface Remote Sensing in Grassland
## 6 Discussion  
We presented a semi-automatic procedure to obtain the FSC in grassland cover, highlighting the potential of the PhenoCam in Oakville. All the findings were exclusive for our grassland cover and for winter weather conditions. Despite the use of just the Blue band in previous research, we focused on the other spectral bands that could improve the FSC estimation. Assuming that there is no change in elevation, we also did not use a Digital Elevation Model (DEM) to georeference the image and calculate the total area.  
One of the main reasons for the need for this high temporal resolution data is the variability of snow cover. It takes one day to go from 0 to 100 FSC and sometimes less than five days for snow to completely melt (Figure 16).  
Figure 16: Snow melting and its respective FSC in Oakville. (**a**) FSC = 100%, date: 12 October 2019; (**b**) FSC = 97%, date: 13 October 2019; (**c**) FSC = 94%, date: 14 October 2019; (**d**) FSC = 4%, date: 15 October 2019 and (**e**) FSC= 0, date: 16 October 2019.  
Figure 15: FSC, temperature, snowfall, and rainfall for the winters of (**d**) 2018–2019 and (**e**) 2019–2020 in Oakville.  
Other studies have used the Blue band because the three bands have similar behaviors in mountainous regions [19]. Figure 17 shows that in separate cases of 100% FSC, the three bands have different histograms that could be related to sunlight. We highlighted that previous studies did not have MoNIR band, and implementing it in our study allowed us to have fewer cases of misclassification because of shadows.  
Furthermore, previous studies have used 127 as the minimum value in the Blue band because they found that areas with snow cover in this band have a value greater or equal to 127. In this research, we used the Blue/MoNIR average, and if these values were greater than 100, the threshold value was set at 100. Figure 18 shows that in this region, the Blue band value, when there is snow present, was greater than 80. The difference between these values shows that depending on the region (mountainous or a valley) and the land cover (forest, grassland, etc.), the Blue band will have different behavior.  
Figure 17: (**a**) Snow cover in Oakville. (**b**) Red-Green-Blue bands histograms and (**c**) MoNIR band histogram.  
Figure 18: Blue band value compared with (**a**) Expert 1 and (**b**) Expert 2 FSC values.  
One of the biggest issues related to the FSC from near-surface remote sensing is shadows. These can create misclassifications and significantly decrease accuracy. The use of the MoNIR band helps to improve the classification (Figure 19).
## 2 Theory and Methodology
### Sparse Adaptive Leja Interpolation
#### 2.4.3 Post-Processing  
Once available, the sparse interpolation can be used as an inexpensive surrogate model that can replace the original model in computationally demanding tasks, such as sampling-based estimation of statistics. Specific statistics can be computed directly after the interpolation terms. One possibility is to first transform the Lagrange basis into a gPC basis [49] and then use the well-known formulas of gPC approximations for the estimation of moments or sensitivity indices [8; 9]. Another approach is to apply the expectation operator along with the functional corresponding to the statistic directly upon the interpolation, as to derive an appropriate quadrature scheme, similar to the univariate case presented in Section 2.2.3. For example, assuming a Leja-based interpolation given by:  
\[\mathcal{I}_{\Lambda}\left[g\right]\left(\mathbf{y}\right)=\sum_{\mathbf{i} \in\Lambda}s_{\mathbf{i}}L_{\mathbf{i}}\left(\mathbf{y}\right),\]the expected value of the QoI can be estimated as:  
\[\mathbb{E}\left[g\right]\approx\mathbb{E}\left[\mathcal{I}_{\Lambda}\left[g \right]\right]=\sum_{\mathbf{i}\in\Lambda}s_{\mathbf{i}}w_{\mathbf{i}}, \tag{13}\]  
where the quadrature weights \(w_{\mathbf{i}}\) are given as products of the respective univariate weights, i.e.,  
\[w_{\mathbf{i}}=\mathbb{E}\left[L_{\mathbf{i}}\right]=\mathbb{E}\left[\prod_{n=1 }^{N}l_{n,i_{n}}\right]=\prod_{n=1}^{N}\mathbb{E}\left[l_{n,i_{n}}\right]=\prod _{n=1}^{N}w_{n,i_{n}}.\]  
Clearly, analytical formulas similar to Equation (13) can be derived for other statistics as well.
## 3 Results
### Year 2021 Optimization of the Irrigation Water Volume for Each Clone
#### 3.2.4 Berry and Must Quality Parameters and Oenological Potential
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Aromatic Compounds (mg L-1)** & **4** & **94** & **188** & **276** & **372** & **ANOVA** \\ \hline Ethyl acetate & 103a & 102a & 117b & 119b & 139c & *** \\
1-Propanol & 52d & 38a & 42b & 37a & 47c & *** \\
2-Methyl 1-propanol & 103b & 106b & 88a & 126c & 138d & *** \\
3-Methyl 1-Butanol acetate & 0.66a & 0.88b & 0.63a & 0.61a & 0.81b & *** \\
3-Methyl 1-butanol & 160c & 162c & 119a & 136b & 156c & *** \\ Ethyl hexanoate & 2.49 & 2.42 & 2.48 & 2.40 & 2.37 & 0.8 \\ E-Hexanol & 3.994 & 3.28b & 2.57a & 3.904 & 3.95c & *** \\ Ethyl octanoate & 1.19d & 0.54b & 1.14c & 0.58b & 0.44a & *** \\ Linalo & 0.013b & 0.014b & 0.014b & 0.011a & 0.013b & *** \\
3-Methyl 10 1-propanol & 3.49d & 2.84b & 2.85b & 2.51a & 3.18c & *** \\ \(\beta\)-Damascenone & 0a & 2.18c & 2.42d & 2.09b & 2.04b & *** \\ Cirtonello1 & 0.019a & 0.023b & 0.025b & 0.019a & 0.023b & *** \\ Ethyl dodecanoate & 0.085c & 0.063b & 0.096d & 0.059 ab & 0.054a & *** \\ Hexanoic acid & 9.00c & 8.41a & 9.25d & 8.70b & 8.46a & *** \\
2-Through ethanol & 53b & 44a & 45a & 46a & 52b & *** \\ Ethyl tetradecanoate & 0.019 & 0.017 & 0.019 & 0.017 & 0.015 & 0.18 \\ Nerolidad & 0.075c & 0.041a & 0.059b & 0.062b & 0.058b & *** \\ Octanoic acid & 1.90c & 0.088a & 1.46b & 0.83a & 0.97a & *** \\ Ethyl hexadecanoate & 8.29c & 5.28a & 8.45c & 5.87b & 5.61 ab & *** \\ Decanoic acid & 0.14b & 0.099a & 0.13b & 0.10a & 0.11a & *** \\
9-Decanoic acid & 4.01d & 4.00 cd & 3.99b & 0a & 3.99c & *** \\
**\(\Sigma\)Total (mg L-1)** & 49\% & 479b & 438a & 486 bc & 560d & *** \\ \hline \hline \end{tabular} \({}^{\prime\prime}\)ns” not significant; *** indicate significant differences at the 0.1, 0.05, 0.01, and 0.001 levels of probability, respectively. In each column and for each factor, different letters indicate significant differences according to [PERSON]’s multiple range test at the 95% confidence level.  
\end{table} 
Table 13: Average values of aromatic compounds (concentration and composition) of wines at the end of malolactic fermentation, for clones 372, 276, 188, 94, and 4 (under control conditions) in 2021.
## 4 Security and Performance Analyses
### Security Analyses
#### 4.2.7. Resisting Stolen Verifier Attacks (Threat Model 3)  
The proposed scheme does not require a verifier table maintained by \(MCS\) to authenticate \(Pat\) and \(Doc\). Hence, stolen verifier attacks are not an issue.
## 3 Prioritizing Geo-Spatial Data Collection  
The proposed approach is based on the integration of prioritization and optimization stages in an iterative scheme. The general work-flow of the implementation stages, sketched out in Figure 3, entails a first phase where priorities and constraints are collected in the form of geo-spatial layers. The prioritization of the data collection is realized by using the collected information to generate a raster map to give the above-mentioned focus maps [11]. Focus maps combine different geo-information layers representing individual criteria into a single raster representing the inclusion probability of each point, that is, the probability of the point being selected for surveying conditional on the sampling probability of each of the other layers (probability pooling). This in turn drives the subsequent data collection phase. In order to implement an efficient survey based on the use of a mobile mapping system, a set of sampling points is generated according to the computed focus map, and suitably routed on the existing road network. This allows one to realize the further optimization of the overall data collection by encoding additional survey constraints in the routing algorithm.  
The mobile mapping system is then deployed and driven according to the planned route. The georeferenced, omnidirectional images collected during the survey can be suitably analyzed by one or more operators, and the extracted information stored in a geo-database. Moreover, the outcomes of the survey can be used to repeat the prioritization and optimization process by accounting for both the original constraints and for the information collected in the field, therefore realizing an iterative, incremental data collection process.
# Comparison of Enhanced Noise Model Performance Based on Analysis of Civilian GPS Data
## 4 Modelling the Noise
### Moving Average Model  
Rather than using the past values of the random variable, a moving average model of order \(q\), abbreviated as MA(\(q\)), regresses on past errors, \(E_{t-k}=X_{t-k}-\mathbb{E}(X_{t})\) for \(k=0,\ldots q\), and is defined as  
\[X_{t}=\mu+\sum_{\ell=0}^{q}g_{\ell}E_{t-\ell}.\]  
The process has \(q+2\) parameters. One may cast any stationary AR(\(p\)) process as an MA(\(\infty\)) model and an invertible MA(\(q\)) model as an AR(\(\infty\)) process. Nevertheless, MA(\(q\)) models are useful, as one cannot fit an AR(\(\infty\)) model on a finite number of observations.  
Unlike autoregressive processes, for MA processes, any estimator based on a finite amount of observations inherently carries some loss of information. Resultantly, parameter estimation is harder than for AR processes of comparable order.  
The qualitative behaviours of the autocorrelation and partial autocorrelation functions, \(\widehat{\rho}(h)\) and \(\widehat{\rho_{p}}(h)\), are similar to those in autoregressive processes, but with roles interchanged. The autocorrelation function vanishes for \(h>q\), while the partial autocorrelation function is dominated by damped exponential and/or periodic functions. In the examples shown in Figures 3 and 4, the autocorrelation function vanishes too slowly to be described by a low-order MA process.
## 2 Background
### Measurement Strategy Phase
#### Purpose and Scope of FSM for Google Analytics Implementation  
The purpose of measuring the functional size of Google Analytics implementation is to use the measured size as follows:  
* Estimate the effort required to implement, maintain, or extend a Google Analytics setup, particularly in terms of tracking user interactions (e.g., page views and button clicks) and event-based data collection.
* Estimate Resources and Costs: Estimate the resources, time, and costs associated with deploying and managing analytics tags, triggers, and variables on a website or application.
* Provide Benchmarking for Optimization: benchmark different implementations (e.g., different websites) for optimization and improve analytics accuracy and efficiency.  
The scope of measuring the functional size of Google Analytics implementation defines the boundaries of what will be measured in Google Analytics implementation. This ensures that the measurement is focused and only includes the necessary elements. The scope of Google Analytics includes the following.  
* _Software Being Measured_: The software being measured is Google Tag Manager (GTM) and consists of the following components:  
1. _Tags_: These are code snippets or tracking scripts that collect data and send them to various platforms (e.g., Google Analytics). Tags are essential components of GTM that define what data are collected and where they are sent. However, they do not constitute a functional process in the COSMIC context. Instead, tags are invoked (i.e., fired) as part of a functional process when certain conditions are met, such as user interactions including button clicks or page views.  
Figure 3: COSMIC method measurement process [63].  
2. _Triggers_: These conditions specify when a tag is fired. For example, a trigger can specify that a tag should fire when a user clicks a button or visits a page. Triggers are also considered components of GTM, as they define the conditions under which functional processes are activated (e.g., user interactions such as button clicks or page views).
3. _Persistent Storage_: Persistent storage in GTM in the COSMIC sense is defined as follows: * _dataLayer_: This is a structured JavaScript object that holds information on user interactions and website data. It serves as storage that transfers information between the website and GTM. * _Variables_: Variables in GTM are dynamic placeholders that store values (such as page URLs, button names, and form field values) and pass them to tags and triggers during the data collection process. Variables are components that provide the necessary data to tags and triggers, but do not execute the functional processes themselves. Variables help define the conditions for when a trigger should fire or what data a tag should send.
## 4 Conclusions  
The effect of carbon nanotube concentration on chloride penetration in nano-modified mortars was investigated, and their physical, electrical, and mechanical performance were assessed. Mortar workability and air content in fresh conditions were kept constant among variable nanofiller concentrations, thus as to not influence the ion penetration inside the nano-modified specimens, and allow for rationalization of the findings by means of variable nanotube loading, exclusively. The main conclusions can be summarized as follows:  
1. Using the rapid chloride permeability test method (RCPT) for plain and nano-reinforced mortars, all mortars were observed to exhibit high ion permeability. It must not be neglected that the RCPT-measurable permeability reflects not only chloride ions, but the total of ions contained to the mixture.
2. Nano-inclusions were found to affect the permeability of the mortars since the absorbed ion chlorides increased with nanotube concentration. Diffusion coefficient (Da) did not follow the same trend; it increased up to 0.4 wt. % of nanofiller content and decreased for higher contents of 0.6% and 0.8%. Porosimetry findings are compatible with the observed behavior.
3. The flexural and compressive properties of mortars exposed to salt spray fog present improved values compared to the virgin specimens. The observed improvement was rationalized upon sodium chloride filling the pores, which resulted in a decrease in apparent porosity and an increase in material strength.
4. Electrical resistivity, measured incrementally every 10 mm below the surface of the specimen, presented notable variations as a result of the specimens' subjection to NaCl solution. For mortars with 0, 0.2, and 0.4 wt. % CNTs, resistivity increased up to two orders of magnitude with interrogation depth. On the other hand, mortars with percolated conductive nanofiller networks at CNT concentrations of 0.6 and 0.8 wt. % CNTs, exhibited increases in electrical resistivity up to five orders of magnitude with respect to the surface value.  
Conceptualization, K.G.D. and T.E.M.; methodology, K.G.D., N.M.B., and T.E.M.; validation, P.T.D., L.K.T., and D.A.E.; formal analysis, P.T.D., L.K.T., D.A.E. and N.M.B.; investigation, P.T.D., L.K.T., D.A.E., and N.M.B.; data curation, P.T.D., L.K.T., D.A.E. and N.M.B.; writing--original draft preparation, P.T.D. and K.G.D.; writing--review and editing, P.T.D. and K.G.D.; visualization, X.X.; supervision, K.G.D., N.M.B. and T.E.M.  
Figure 12: Mechanical properties of the nano-modified mortars before and after the exposure to the salt spray chamber.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# On Sharing Spatial Data with Uncertainty Integration Amongst Multiple Robots Having Different Maps
###### Abstract  
Information sharing is a powerful feature of multi-robot systems. Sharing information precisely and accurately is important and has many benefits. Particularly, smart information sharing can improve robot path planning. If a robot finds a new obstacle or blocked path, it can share this information with other remote robots allowing them to plan better paths. However, there are two problems with such information sharing. First, the maps of the robots may be different in nature (e.g., 2D grid-map, 3D semantic map, feature map etc.) as the sensors used by the robots for mapping and localization may be different. Even the maps generated using the same sensor (e.g., Lidar) can vary in scale or rotation and the sensors used might have different specifications like resolution or range. In such scenarios, the 'correspondence problem' in different maps is a critical bottleneck in information sharing. Second, the transience of the obstacles has to be considered while also considering the positional uncertainty of the new obstacles while sharing information. In our previous work, we proposed a 'node-map' with a confidence decay mechanism to solve this problem. However, the previous work had many limitations due to the decoupling of new obstacle's positional uncertainty and confidence decay. Moreover, the previous work applied only to homogeneous maps. In addition, the previous model worked only with static obstacles in the environment. The current work extends our previous work in three main ways: (1) we extend the previous work by integrating positional uncertainty in the confidence decay mechanism and mathematically model the transience of newly added or removed obstacles and discuss its merits; (2) we extend the previous work by considering information sharing in heterogeneous maps build using different sensors; and (3) we consider dynamic obstacles like moving people in the environment and test the proposed method in complex scenarios. All the experiments are performed in real environments and with actual robots and results are discussed.  
information sharing; multi-robot systems; positional uncertainty; path planning; mapping 2019
# Analysis of the Water Quality Improvement in Urban Stream Using MIKE 21 FM
## 5 Model Verification and Analysis Results
### Model Simulation Results
#### 5.3.4 Comparison of Total Phosphorus (TP) before and after the Business  
Figure 15 shows the TP concentration change at the analysis spots. As presented in Figure 15b-d, the TP concentration was maintained constant after the discharge of the ozone-treated reclaimed water. Before the supply of the ozone-treated base flow, the TP concentration was higher in the summer season due to the seasonal effect. However, after the discharge of the reclaimed water, it was maintained at a relatively constant concentration due to the ozone-treated reclaimed water. If the TP concentration of the Gulpo treated sewage water is lowered before the discharge, a higher water quality improvement is expected.  
Figure 14: Comparison of the TN change at analysis spots.  
Figure 15: Comparison of the TP change at analysis spots.  
After supplying the base flow, the water quality improvement on the DO and BOD concentrations was captured. Regarding TN and TP, the Gulpo treated sewage water's water quality and ozone-treated water is higher than that of the stream water. Therefore, if the sewage treatment discharged water's TN and TP concentration standards are strengthened before the discharge, water quality improvement is expected.  
Table 3 shows the mean, maximum, minimum, and standard deviation for the results of Figures 12-15.  
As a result of analysis of four analysis points through the supply of ozone treated reclaimed water, BOD decreased by about 30% and DO increased by 12%. It can be judged that there is an effect of improving water quality according to the supply of reclaimed water.  
In the case of TP, it decreased by 6.3%, but in the case of TN, it increased by 32.2%.  
The concentration was increased due to the high concentration of TN in the reclaimed water itself. Overall, there is an improvement effect on BOD and DO, but it is judged that TN and TP should be managed according to the quality of the reclaimed water discharged.  
The simulation results indicated that in the Gulpocheon midstream corresponding to the downstream of Cheongcheoncheon and Gyesancheon, water quality is significantly affected by the discharged amount of the base flow alongside the water quality concentration. For the future planning of the Gulpocheon water quality improvement, the reclaimed water's water quality and discharge flow should be considered. It is expected that the reclaimed water supply after the discharge of the ozone-treated water will result in increased flow after the junction of Cheongcheoncheon and Gyesancheon and, in turn, more favorable and easier maintenance of the stream in terms of hydraulics and water quality.