esa-sceva/satcom-chunk-collection huggingface dataset:
# An Implementation of the HDBSCAN* Clustering Algorithm
## 3 Materials and Methods
### Main Development Phase
#### 3.4.2 Implement the HDBSCAN* Core Logic  
This is perhaps the most significant task in this work. To implement HDBSCAN* in Tribuo, the existing code from the working, minimal HDBSCAN* implementation is used as a basis for the code being added into the skeletons of the classes prepared in the previous task. As blocks of code are added, changes are made as required. For example, the input to the algorithm is a Tribuo specific Dataset <ClusterID> object, and the data should be transformed into an array of DenseVector objects which were described in Section 2. Differences such as this require several code modifications to achieve an implementation that will compile, at the very least. The changes must also be made carefully to avoid introducing subtle bugs before the unit tests can be integrated and executed. During the process, the code being added needs to be meticulously scrutinized, and detailed notes are kept about potential issues or improvements that should be made. These types of changes should only be attempted after the unit tests are integrated. The train method of the HdbscanTrainer class is the main entry point to the algorithm and an HdbscanModel object must be returned which provides access to the cluster assignments, and outlier scores. No intermediate state can be stored in an instance of an HdbscanTrainer object. The trainer could be invoked several times, possibly in parallel, with different datasets and must return a distinct, correct model object in every case. Furthermore, it is possible that a model object could be shared among client codes. The attributes of an HdbscanModel instance need to be immutable, so copies of the cluster assignments and outlier scores must be made before being returned. Additionally, there is some code required to generate the details of the clustering which is a requirement for the Provenance feature.  
Adding the unit tests, established as part of the working minimal implementation, to the new HDBSCAN* implementation in Tribuo is a critical aspect of this task. Once the code for the new implementation compiles, adding the unit tests immediately follows. Although the datasets for the unit tests already exist as.csv files, some initial effort is requiredto figure out how to load these as Dataset <ClusterID> objects. Recall that a Dataset <ClusterID> object is provided as input to an HdbscanTrainer instance's train method. It is a major milestone when the first unit test in the Tribuo Hdbscan module is successfully executed which makes the same assertions as those from the working, minimal HDBSCAN* implementation. This indicates that for a specific dataset, the reference implementation and the Tribuo Hdbscan implementation produce the identical cluster assignments and outlier scores. Further comparisons between these two implementations are performed in the upcoming Compare the HDBSCAN* implementations section.
# Smart Viniculture: Applying Artificial Intelligence for Improved Winemaking and Risk Management
## 5 Food Safety and Traceability in the Wine Industry
### Enhancing Traceability from Vineyard to Consumer  
AI significantly enhances traceability in the wine industry by leveraging data from IoT devices deployed throughout the supply chain. These devices include sensors, RFID tags, and smart labels, which collect and transmit real-time data on various aspects of wine production, from vineyard conditions to final distribution.
## 3 Recording of Atmospheric Conditions Using a Weather Monitoring Station to Predict RSSI Value  
Communication standards place a high demand on FSO systems throughout the year. The availability and reliability of FSO systems is an overall problem and in solving it, is necessary to analyze enough auxiliary input physical quantities, which characterize the atmospheric conditions along the entire transmission path in real time.  
Atmospheric data collection is provided by a weather station and sensors working on a Raspberry Pi 3 microcomputer. The monitoring station together with the hybrid FSO/RF system is shown in Figure 3.  
Monitoring the weather conditions between the two heads of FSO systems is an important task in determining and evaluating the reliability and availability of FSO systems. An important condition for securing an optical connection using an FSO line is direct visibility between the receiver and the transmitter. The full-duplex principle applies to this type of communication; hence, each device is a transmitter and a receiver at the same time. The list of monitoring station sensors is written in Table 1.  
The weather station created in this way is located directly at the transmitter/receiver; thus, it does not record weather conditions directly on the entire route. On the other hand, the subsequent monitoring system can record the entire transmission path, but not for the entire duration of the communication.
## 3 Results
### Selection Response  
We compared the average GEBVs and EBVs of BW, AFE, and EN25-44 WK across successive generations for male ducks, female ducks, and the overall population. As shown in Figures 1 and S5, the GEBVs and EBVs for BW in males, females, and the overall population showed a decreasing trend over generations, despite some fluctuations. For reproductive traits, the direction of selection was more apparent. The GEBVs and EBVs for the AFE in males, females, and the overall population decreased progressively across generations. From the 13 th to the 15 th generation, the AFE advanced by approximately one day per generation. Correspondingly, the GEBVs and EBVs for the EN25-44 WK increased progressively across generations. From the 13 th to the 15 th generation, the egg number increased by one egg per generation. Compared to the EBVs, the range of the GEBV changes across successive generations was broader, indicating potentially higher predictive accuracy. In summary, these results suggest that genomic selection has the potential to improve breeding accuracy and is significant for optimizing the growth and reproductive traits of Pekin ducks.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Traits 2** & **BW** & **BSL** & **NL** & **AFE** & **EN25-44 WK** & **EWT** & **ESI** \\ \hline
**BW** & **0.19 (0.02)** & 0.52 (0.05) ** & 0.12 (0.07) & 0.11 (0.06) & -0.14 (0.07) * & 0.19 (0.06) ** & 0.03 (0.06) \\
**BSL** & 0.39 (0.09) ** & **0.27 (0.02)** & 0.39 (0.06) ** & -0.01 (0.06) & 0.06 (0.06) & 0.24 (0.05) ** & 0.07 (0.06) \\
**NL** & 0.27 (0.11) ** & 0.25 (0.09) ** & **0.23 (0.02)** & -0.04 (0.06) & 0.12 (0.06) * & 0.09 (0.06) & -0.01 (0.06) \\
**AFE** & 0 (0.1) & -0.22 (0.08) ** & -0.08 (0.09) & **0.37 (0.02)** & -0.91 (0.02) ** & 0.06 (0.05) & -0.07 (0.05) \\
**EN25-44 WK** & -0.04 (0.11) & 0.22 (0.08) ** & 0.14 (0.09) & -0.89 (0.04) ** & **0.27 (0.02)** & -0.03 (0.06) & 0 (0.06) \\
**EWT** & 0.32 (0.10) ** & 0.24 (0.08) ** & 0.08 (0.09) & -0.06 (0.08) & 0.16 (0.09) * & **0.39 (0.02)** & -0.03 (0.05) \\
**ESI** & -0.01 (0.11) & 0.14 (0.08) * & -0.09 (0.09) & -0.17 (0.08) * & 0.03 (0.09) & 0 (0.09) & 0.36 (0.02) \\ \hline  & & & & & & & & \\ \end{tabular} Note: 1: Heritability is given on the diagonal (bold for SNP-based heritability, italic bold for pedigree-based heritability), SNP-based genetic correlations are shown above the diagonal, and pedigree-based genetic correlations are shown below the diagonal. The standard errors of the estimates are provided in parentheses. In the correlation coefficients between two traits, **’’** indicates \(p\leq 0.01\), and **’**’** signifies \(p\leq 0.05\). 2: BW, body weight at 6 weeks of age; BSL, body slope length; NL, neck length; AFE, age at first egg; EN25-44 WK, egg number from 25 to 44 weeks of age; EWT, average egg weight at 40 weeks of age; ESI, average egg shape index at 40 weeks of age.  
\end{table}
Table 4: Genetic parameters of growth, reproductive, and egg quality traits 1.
## 3 Experimental Results and Analysis
### Comparative Experiment

Table 11: The classification accuracy of models for dataset (1). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Supervised Model**} & \multicolumn{3}{c}{**Unsupervised Model**} \\ \cline{2-9}  & **LDA** & **1D-CNN** & **S-CNN** & **CUFL** & **AAE** & **VAE** & **M-SSL** \\ \hline Birch & 57.65 & 65.601 & 67.09 & 71.50 & 76.48 & 74.17 & 77.20 \\ Larch & 71.94 & 71.325 & 72.10 & 80.28 & 81.08 & 80.72 & 81.18 \\ Spruce & 57.40 & 69.69 & 64.64 & 73.26 & 72.00 & 70.82 & 74.42 \\ Mongolia & 56.40 & 48.96 & 65.18 & 79.11 & 70.98 & 74.27 & 75.24 \\ Willow & 54.72 & 65.35 & 65.65 & 54.03 & 64.18 & 60.36 & 76.10 \\ Polar & 62.82 & 63.45 & 64.62 & 62.37 & 63.77 & 62.77 & 73.25 \\ OA (\%) & 54.37 & 60.95 & 67.85 & 75.69 & 75.08 & 70.28 & 79.69 \\ AA (\%) & 55.05 & 63.36 & 70.04 & 67.29 & 70.39 & 68.68 & 77.92 \\ Kappa (\%) & 53.89 & 61.02 & 67.69 & 62.84 & 69.13 & 64.38 & 74.64 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: The classification accuracy of models for dataset (2). Figure 6: Tree classification maps of models for dataset (1). the study concludes that the classification capability of the traditional machine method is slightly inferior to the deep learning model.
# Hyperspectral Imaging for Detecting Plastic Debris on Shoreline Sands to Support Recycling
## 3 Results and Discussion
### HSI Analyses
#### 3.2.2 Principal Component Analysis  
The results of the performed PCA of the two different experimental setups are reported in Figure 8.  
While the PCA score plot related to samples used to build the sand/plastic/other materials model is shown in Figure 8a, the one related to the sand/plastic model is shown in Figure 8b, and the one regarding the samples used for the polymer-type setup is in Figure 8c. Regarding the sand/plastic/other materials experimental setup, PC1 and PC2 explained 55.88% and 16.01% of the total variance, respectively. While regarding the sand/plastic experimental setup, PC1 and PC2 explained 47.46% and 27.5% of the total variance, respectively. The spectral data of the samples were clustered into different groups according to their spectral signatures. The PCA score plot for the samples in the calibration set used to build the polymer-type model reveals that PC1 captures 54.46% of the total variance, while PC2 captures 37.17%. The plot shows a clear grouping of samples into five different clusters.
# A New Architectural Approach to Monitoring and Controlling AM Processes
## 6 Summary  
This paper proposes a functional architecture for controlling AM processes. That architecture has two internal control loops: real-time and near-real-time. Real-time functions include data acquisition, preprocessing, curating, and tagging from in situ sensors. Data analysis focuses on using individual samples to characterize the melt pool for small collections of that data. Near-real-time functions focus on individual layers. Those functions include data fusion, residual stress estimation, 3D opaque model reconstruction, defect detection, and part quality prediction. The outer-most, layer-wise loop is for near-real-time control. This loop makes adjustment of scan path and process parameters for the next layer based on the monitoring information from the previous layers.  
In addition to these internal, control-loop functions, the paper discusses some of the offline functions associated with training and retraining the AI/ML models used by those analysis functions. The paper includes an example of a near-real-time, control function: melt-pool size. It describes both the collection method and the analysis methods. There are two methods: the K-means clustering method and the elbow method. The elbow method determines the optimal number of clusters, and the clustering method identifies which data points go in which cluster. The melt-pool area for each cluster is the control metric. The area of each cluster is calculated; based on the area (too big or too small), defects are determined. Control decisions, such as changing the laser power and scan speed, can be made based of the area.
## 3 FRBs in the Era of SKAO/ngVLA
### Quantum Computing Searches  
Recent advances in quantum computing justify a brief look at their potential application in FRB searches, even if these will only be realised in the far future. FRB searches with radio telescopes have two traits that make application of quantum algorithms highly interesting. The first is that, after the receivers record the initial voltages, the subsequent beam forming, dedispersion, and box car searching are inherently highly parallel. This lends itself well to encoding the respective directions, dispersion measures, and pulse widths as entangled states. Then, the very large number of currently required identical classical operations--say, 1000 tied-array beams \(\times\) 10,000 trial DMs \(\times\) 10 boxcar widths--can be replaced by single quantum operations. The second trait is that this large amount of data can, in the end, be meaningfully reduced to a binary classification operation: there either is or is not a FRB in the data. Such collapse operations are highly suited for quantum computers. Once detected, the FRB can be characterised fully using the relevant buffer data.  
Some initial exploration of quantum computing for parallel radio-telescope operations has recently been performed [48]. Although that work focuses on image-domain data, we see analogies to the time-domain data processing required for FRB searching. While the classification part itself is not computationally expensive, first quantum computing steps have recently been reported for a pulsar dataset [49] where the candidates parameters were still produced classically.  
Real-life application will require technology to convert the voltages into qubits, algorithms that can separate the FRBs from RFI, and quantum computers with a much larger qubit number and lower noise than will be available even in the near future. Based on manufacturer road maps these may arguably only materialise in the 2040s.
# Muscle Activity and Biomechanics of Sprinting: A Meta-Analysis Review
## 3 Results
### Meta-Regression--Sprint Speed vs. Muscle Activation
#### 3.5.1 Goodness-of-Fit Tests  
R-Squared (R\({}^{2}\)) = 0.881. R\({}^{2}\) represents the proportion of variance in muscle activation explained by sprint speed. A value of 0.881 indicates that 88.1% of the variability in muscle activation is explained by sprint velocity, suggesting a strong predictive relationship. Generally, R\({}^{2}\) values above 0.7 indicate a highly explanatory model, which is the case here.  
Adjusted R-Squared = 0.869. The adjusted R\({}^{2}\) accounts for the number of predictor variables and avoids overfitting. Since Adjusted R\({}^{2}\) (0.869) is very close to R\({}^{2}\) (0.881), it confirms that the model is not overfitted and provides a reliable estimate of the relationship.  
F-Statistic = 74.20, \(p\) < 0.001. The F-statistic tests whether sprint speed significantly predicts muscle activation. The very high F-value (74.20) suggests a strong model fit. The \(p\)-value (<0.001) confirms that the regression model is statistically significant, meaning sprint speed is a meaningful predictor of muscle activation.
## 2 Related Works
In the development of LBAR, a pseudo-random method was used to find routes, which speeds up the search for an efficient route and considers the energy balance. Furthermore, the pheromone trail updating mechanism took energy and path length information into account, resulting in a network lifespan extension. However, this method was unable to determine the cluster technique's optimality. [PERSON] et al. (2019) [PERSON] et al. (2019) introduced the energy-efficient Scalable Routing Technique (SRA), a flexible, low-energy, and adaptive clustering hierarchical routing algorithm to sustain network lifespan despite network size growth. To minimize the stress on CH, SRA implemented a three-layer hierarchy structure and used multi-hop broadcast for intra-cluster communication. In this work, SRA was compared to different WSN routing schemes using network performance as a function of network size changes. According to simulation data, SRA outperformed benchmarking techniques through load balancing and energy efficiency for WSNs. However, this proposed method did not analyze the computational time and complexity. [PERSON] et al. (2019) [PERSON] et al. (2019) presented Privacy-Preserving Data Aggregation (PPDA), a new aggregating technique that is both energy-efficient and secure. A sensor network was structured into an aggregation tree, and the leaf nodes of the tree were connected to form multiple chains, according to the proposed strategy. It reduced the number of leaf nodes in the basic aggregation tree. The simulation study findings revealed that the proposed algorithm is more efficient when compared to conventional aggregation techniques while maintaining superior privacy protection. PPDA performed well in terms of efficiency and accuracy of aggregation outputs. However, this technique was unable to develop a data aggregation model to reduce duplicate data. It has been discovered that a great deal of effort has gone into controlling CH selection and improving data transfer across SN while the sink is moving. However, there has been a sprinkling of research that has concentrated on both aspects at the same time using any hybrid strategy. Using a hybrid method to combine the best features of both optimization techniques to obtain the best network performance.
## 2 Materials and Methods  
### Inert Dusts Used  
The DE deposit was obtained from a single mine located in the area of Elassona (39.894524, 22.185065), in the Prefecture of Thessaly, Greece. The zeolite was obtained from a single mine located in the area of Orestiada (41.506950, 26.531530), in the Prefecture of Thrace, Greece. Regarding the DE formulations used in these bioassays, two enriched in diatoms and with different particle size DE powders (namely DE5 and DE6) were created and evaluated. More specifically, DE5 had 70% semi fractured diatoms content and 80% of the particles were smaller than 45 \(\upmu\)m, while DE6 contained 68% of totally fractured diatoms with 99% of the particles being smaller than 45 \(\upmu\)m. These powders were created by separating the diatoms from other elements from a given DE formulation with high clay content, containing only 40-60% discoid and cylindrical structure diatoms, using air separation methods. Supplementary data about the geographical origin of this DE deposit, the processing to create these new, enriched in diatoms, DE formulations and their insecticidal activity against several stored product insects are available in the work of [PERSON] and [PERSON] [28]. Data about the insecticidal activity of DE deposits of the same wider area are available in the literature with various results [12; 19; 29]. The geochemical analysis measurements (X-ray Powder Diffraction, XRPD) of zeolite sediments were carried out by The Mineral Lab Inc., Golden, CO, USA. The zeolite sentiments were found to contain 85% clinoptilolite, 9% plagioclase feldspar, and <3% quartz and mica/illite, while <5% of the minerals could not be identified.
# Verification Methodology for Simulation Models of the Synchronous Generator on Transients Analysis
## 2 Modeling Methods for Transients Calculation  
The calculation of the sudden three-phase short-circuit was performed on the synchronous hydro generator model with the following nominal data: power 30,000 KVA, voltage 10,500 V, current 1650 A, power factor 0.9, frequency 50 Hz, pole number 48, and rotation speed 125.
## 6 Information Fusion Strategy
### Verification with Checkpoints  
The final areas obtained for each crop are spatially compared with checkpoints and the reference areas from Section 5.1. The search of the checkpoints is done through crowdsourcing from the site _Panoramio_, owned by Google (Mountain View, CA, USA), which possesses a database of geo-located tag photos uploaded by users around the globe. Panoramio only allows outdoors photos, which is advantageous to us because it implies removing a lot of data clutter. Every time a user uploads a picture, it is revised for compliance with this criterion, which ensures we only find relevant pictures when searching in agricultural areas. The site could be accessed as a layer in _Google Earth_ and _Google Maps_. The total number of checkpoints used in the region can be seen in Figure 10, which were distributed in the following way: 65 for banana, 33 for coffee, 56 for maize, 58 for rice and 82 for sugar cane. As a sample of the verification of the results through the checkpoints, in Figure 11, the estimated areas for coffee in Costa Rica are shown, including the location and images of the checkpoints used to validate the results.  
Figure 10: Checkpoints with crowdsourced images in the regions of Central America (**left**) and the Caribbean (**right**) used to validate the obtained areas.  
Figure 9: Estimated areas of the selected crops in the region of the Caribbean.
# Efficient Online Engagement Analytics Algorithm Toolkit That Can Run on Edge
## 4 Experimental Results
### Face Orientation  
The confusion matrix in Figure 7 shows the evaluation of face orientations on CMU Face dataset using the threshold function described in Equations (6) and (7) of Section 3.2. This extremely simple algorithm using a nose landmark achieved 94.78% on both Accuracy and F1-score on the CMU dataset. This is comparable to the algorithm in [50], which requires more complex feature extraction and classification.
## Materials and Methods
### Behavioural Repertoire
#### GNSS  
Both alpacas and a total of 14 ewes from the flock of 180 were equipped with UNE Tracker II GNSS [17] (Figure 1). Collared ewes were randomly assigned by selecting every 10 th animal through a set of yards. Alpaca collars were programmed to record position (latitude and longitude) of the collared animals every 150 s (with a sequential burst of 5 logs 10 s apart) for 41 days. Ewe collars were programmed to record position of the collared animals every 10 min (with a sequential burst of 5 logs 10 s apart) continuously for 41 days. The alpaca collars were programmed to record logs more frequently because of an ability to change batteries at day 20 of the experiment. The ewe collars were programmed to record logs less frequently in order to conserve battery strength for the entire 41-day period, as handling of lambing ewes was not feasible. A limitation of GNSS collars was the inability to accurately determine specific behaviours exhibited by animals, however periods of high and low activity based on change in speed was expected to indicate behavioural synchronicity between the two species.