esa-sceva/satcom-chunk-collection huggingface dataset:
# Utilizing Sentinel-2 Satellite Imagery for LULC and NDVI Change Dynamics for Celephu, Bhutan
## 2 Materials and Methods
### Image Classification Methods
#### 2.3.2 LULC Mapping  
The current study focused on image classification for four primary LULC classes, namely water bodies, vegetation, built-up areas and barren land. The barren land typically includes sand and gravel river banks, landslide areas, and excavated construction areas. The RF classifier was used for two separate image classification schemes for the period between 2016 and 2023 and enabled comparative statistical analysis of LULC change dynamics occurring over seven years, including mapping of LULC change detection. LULC focuses on categorizing and classifying the types of land use activities and surface covers that indicate the spatial distribution of various land features. Statistical data was extracted from the image classification reports and a comparative LULC analysis was performed for Gelephu in 2016 and 2023.  
Further to validate the LULC maps, the current study used a rapid validation method using the true colour composite from the 2023 Sentinel-2 image and Open Street Map (OSM) data. The building footprint of the built-up class was particularly taken into account during the validation. To validate the LULC map, two sets of building polygons were used. In the absence of OSM data in the southern zone of the study area, the building polygons from the true colour composite of the 2023 Sentinel-2 image have been meticulously drawn to  
Figure 3: Colour composites of the study area (The Red polygon marks the boundary of the study area). (**a**) The band set 4-3-2 (true colour); (**b**) 5-6-2 (healthy vegetation); (**c**) 5-4-3 (colour infrared, vegetation); (**d**) 7-6-4 (false colour, urban).  
accurately represent the locations and sizes of the buildings. For the northeast zone, OSM building footprint extraction was used for validation. The main objective of this validation process was to compare the identified building shapes with the corresponding pixels categorized as built-up areas on the Gelephu 2023 LULC map. The procedure involved overlaying the building shapes onto the LULC map allowing for a comparison between these two sets of data.
# Design and Performance of a XBee 900 MHz Acquisition System Aimed at Industrial Applications
## 4 Experimental Tests of the Monitoring System
### Jitter at the Measuring Instant in Multiple Sampling Operations  
This test is aimed at checking the use of the multiple sampling capability. In these operations, the sampling time is left to the Smart Measuring Node microcontroller between two broadcast measurement commands. This approach increments the payload of the messages but allows for a higher sampling resolution.  
For that purpose, a broadcast period of 120 ms was selected, and several sampling periods were used, namely 60, 40, 30 and 24 ms, yielding to 2, 3, 4 and 5 samples per broadcast measurement command. Table 6 shows the jitter at the measuring instant.  
Results prove that the value of jitter increments when the schedule is tighter. This may become acceptable for some operations depending on the dynamics of the process.
## 3 Results and Discussion
### Viscosity Measurements  
Figure 1a-d presents the measurements of the viscosity (\(\eta\)) related to frequency (0.1-100 Hz) of the desserts with the addition of a different amount of dried vegetable powders ((a)--beetroot; (b)--onion; (c)--championon; (d)--carrot). Depending on the amount and type of dried material added, the change in the viscosity of individual products was observed.  
Figure 1: _Cont_.  
In the case of desserts with the addition of beetroot and carrot, the viscosity of individual products decreased with the more considerable amount of added powder. On the other hand, in the desserts with the addition of onion powder, the highest values in the initial phase of the measurement were observed for the product with a 1% addition (15,580 Pa-s). The values of the tested features in desserts with dried champignon powder remained at a similar level for products with 1 and 3% and decreased for those with the 5% addition. Many factors, such as the temperature, molecule size, and complexity of hydrocolloid concentration in foods can affect the viscosity of the final product [40]. The high viscosity of the final product is connected with the use of WPC80. During the heat treatment process, its composition of different protein fractions, such as \(\beta\)-lactoglobulin and \(\alpha\)-lactalbumin, can influence the viscosity of dairy desserts and raise its final value [41]. Most of the experiments concerning dairy products prepared with the addition of fiber or dried vegetables are focused on different types of yogurt. For example, [PERSON] et al. (2017) tested yogurt enriched with green olive powder. They noticed a higher value of described feature than the product without powder. It was described that green olives had raised solid content in the final product, which might increase the viscosity [42]. Different researchers also reported an increase in the number value of the feature when the fiber is added to yogurt [43]. We noticed this kind of dependence in our product's desserts with betroot and carrot addition. In the case of samples with the addition of onion powder and champignon powder, the tendency was the opposite. It may be related to the structure of a given additive and its incorporation into the product. Compared to other powders, it caused the liquid consistency of a dairy dessert.
# Fault Detection Algorithm Based on Dynamic Global-Local Preserving Projection
## 2 Background Techniques
### LPP Algorithm  
The LPP method is a linear approximation of the Laplacian feature map for extracting features from the data while preserving the local manifold information of the data [37; 38]. For the initial dataset \(X\), a transformation matrix \(W\) is calculated and projected into a low-dimensional space to obtain the dataset \(Y\) after dimensionality reduction. The objective function is as follows:  
\[\begin{array}{l}J_{LPP}=\min\limits_{a}2\sum\limits_{ij}\left(y_{i}-y_{j} \right)^{2}W_{ij}\\ subject\ to:\ a^{T}XDX^{T}a=1\end{array} \tag{3}\]  
where the diagonal matrix \(D(d_{ii}=\sum\limits_{j}w_{ij})\) represents the distance between sample points.  
The larger the value of \(d_{ii}\) is, the more important the corresponding point \(y_{i}\) is. The weight matrix \(W\) represents the distance relationship between the sample points in the dataset. Its size can be calculated using the kernel function, as shown in Equation (4).  
\[\begin{array}{l}\text{W}_{ij}=\left\{\begin{array}{ll}e^{-\frac{\left|x_{ i}-y_{j}\right|}{t}}&if\ x_{j}\in\Psi_{k}(x_{i})\ or\ x_{i}\in\Psi_{k}(x_{j})\\ 0&otherwise\end{array}\right.\end{array} \tag{4}\]  
where \(t\) is the parameter of the heat kernel function. Its value range is \(5m\sigma^{2}\leq t\leq 10m\sigma^{2}\) (\(m\) is the number of sample variables and \(\sigma\) is the sample variance). The appropriate valuesfrom this range are derived based on manual experience. \(\Psi_{k}(x_{i})\) is the set of the first \(k\)-nearest neighbors of the sample.
## 3 Material and Methods
### Statistics and Data Analysis Approaches  
The following methods were processed in R environment, using RStudio v. 1.1.463 console (RSStudio, PBC, Boston, MA, USA). PCA was conducted by using \"prcomp\" function, in order to visualize general trends of the data regarding VOC bacterial profiles. HCA associated with heatmap was generated by applying \"pheatmap\" package, cluster distance was measured by using Pearson correlation coefficient and [PERSON]'s minimum variance method (\"ward.D2\") was the applied clustering criterion. For both PCA and HCA, data scaling was performed prior analysis (by subtracting variable's mean and dividing it by the standard deviation). Venn diagram and networks aiming to represent the volatiles coincident among the bacterial strain groups were prepared, using \"VennDiagram\" and \"sna\" packages, respectively. General plots were produced with the aid of \"ggpupr\" and \"ggpplot2\" packages. Dot plots aimed to reveal the trend presented by the detected volatiles after the application of bio-AgNPs in relation to the corresponding unstressed strains, in terms of binary logarithm of compound fold-change. Weighted plot used as input the pathway impact and significance (in terms of -log\({}_{10}\) p) calculated by the pathway analysis tool of the web server MetaboAnalyst 4.0 [84]. The following parameters were employed: over-representation analysis = hypergeometric test; pathway topology analysis = relative-betweenness centrality; model organisms = _Escherichia coli_ K-12 MG1655 and _Klebsiella pneumoniae_ MGH 78578 (serotype K52)--such an online tool uses pathways that are described and stored at the Kyoto Encyclopedia of Genes and Genomes (KEGG database) [85]. Ultimately, using the \"igraph\" package, we performed a network analysis to show links between the relevant metabolites (fold-change in relation to unstressed culture with \(p<0.05\)) and possibly related biochemical processes. Spearman correlation analysis was conducted to verify the reproducibility of VOC profiles obtained from analyzed replicates--coefficients \(\geq\)0.8 were obtained for replicates belonging to the same assay; the Mann-Whitney U test was performed to point out discriminating features, considering \(p<0.05\) as the relevance criterion. The latter procedures were conducted by using IBM SPSS Statistics v. 24 (IBM Corp., Armonk, NY, USA).
## 5 Experiments and Performance Evaluation
### Perception Experiment
#### 5.1.2 Experimental Scenarios  
In order to test the SOP perception effect, we deployed multiple WiFi, Bluetooth, ZigBee signal nodes in the actual scene, and used the built perception system for signal perception testing. The experiment was chosen to be carried out in a two-story underground parking lot where has no 2.4G wireless equipment. Figure 27 shows a real view of the test site.  
Figure 24: SOP perception system.  
Figure 25: The structure and data flow of the SOP perception system.  
In advance, we make sure that there was no external interference in the 2.4 GHz frequency band in the test site by using an Agilent spectrum analyzer. The test results are shown in the Figure 28.  
After external interference check, we arranged the signal nodes in different areas. The layout need consider the influence to perception with different numbers and combinations of signal sources. The deployed nodes are shown in Figure 29.  
Figure 28: Spectrum analyzer detection results show no interference source: (**a**) max hold model; (**b**) clear/write model.  
Figure 26: System software interface.  
Figure 27: Experimental environments: (**a**) B1 of parking lot; (**b**) B2 of parking lot.  
On the B1 layer, we deploy multiple types of signal nodes at the same time to test the system's signal perception ability in a complex wireless environment where multiple signals coexist. On the B2 layer, we only deploy one type of signal at a time to test the system's ability to percept specific signal at different distance. The floor plan and signal source layout position are shown in Figure 30. The green line represents the walking route of tester.
## 4 ITS Threats Analysis and Classification
### Classification of ITS Attacks and Cryptographic Countermeasures
#### 4.4.5 Attacks on Privacy and Countermeasures  
The privacy of ITS entities and their messages is a key requirement, and all sensitive data should be protected, including the identities of the drivers, their driving behaviors and the historical vehicle locations [80]. However, some ITS road safety applications require the transmission of vehicle-centric data (e.g., location, speed, heading, _etc._) to notify the surrounding vehicles and infrastructures about potential road hazards. Moreover, when an issue arises (e.g., accidents, road traffic offense, malicious users), the ITS system operators should be able to identify the identities of the involved drivers and vehicles. There is thus a clear trade-off between the privacy and security requirements.  
Several attacks on privacy exist [18; 76]. One typical attack on privacy consists of the tracking of the vehicles and/or users during their journeys. Indeed, ITS entities are generally equipped with Wi-Fi- or Bluetooth-enabled devices, which broadcast various information in clear text (e.g., identifiers, MAC addresses, devices types, _etc._). This information can be collected by third party entities to triangulate the positions of users and track their movement within an urban environment. The main countermeasure consists of using randomized and/or temporary identifiers (e.g., MAC and IP addresses) to unlink them from the vehicles and drivers. Another approach consists of the usage of pseudonyms in order to ensure anonymous communications [26; 81].
## 4 Results and Discussion
### Comparison of Different Models  
In order to demonstrate the superiority of the improved YOLOv5 algorithm in detecting sewage discharge outlets compared to other object detection models, we conducted a comparative analysis involving SSD, Faster-RCNN, YOLO_MobileNetV3, YOLOv5, YOLOv6, YOLOv7-tiny, YOLOv8 and the enhanced YOLOv5 model. Utilizing the same dataset for testing, each model was trained and evaluated using different neural networks, with the results presented in Table 3. Figure 15 provides a more intuitive comparison of the evaluation metrics of the four detection models. The improved YOLOv5 model achieved the highest mAP compared to other algorithms. Furthermore, the enhanced model, while ensuring processing speed, compared various metrics such as mAP for sewage discharge outlet detection, further demonstrating that incorporating C3 Ghost, BiFPN, and CBAM attention mechanisms into the original YOLOv5 model effectively enhances the accuracy and efficiency of sewage discharge outlet detection, making it more suitable for this specific task. The results indicate that the detection mAP of the YOLOv5-BiFC model is higher than that of the SSD, Faster-RCNN, YOLO_MobileNetV3 models, YOLOv6, YOLOv7-tiny and YOLOv8 suggesting that the improved YOLOv5 model outperforms the other models in detecting sewage discharge outlets. The recognition results are illustrated in Figure 15, where (a) represents images universally recognizable by all detectors, and (b) represents images where the model trained with improvements shows better target recognition compared to other models.  
Figure 14: Results of training. (**a**) Train loss and Val loss Graph of YOLOv5-BiFC (**b**) mAP of YOLOv5 and YOLOv5-BiFC.
## 7 Performance Evaluation and Results
### Results and Discussion
#### 7.3.4 Complex Segment 4  
In complex segment 4, the abnormal traffic flow patterns occurred in Segments C and D where these two segments are located close to an exit, which is similar to the traffic flow patterns in CS1. This leads to an observation that abnormal traffic flow on I-95 highway usually occurred at the curved road that has an exit within the next half a mile. The proposed model achieved 80% in terms of \(R^{2}\) and \(AR^{2}\) and outperformed all other base-line models in all the segments.
# Relationship between [PERSON]'s Apparent Power and Instantaneous Power in Three-Phase Systems
## 2 Formulations of Apparent and Instantaneous Power in Three-Phase Wye-Configured Systems  
In this section, the expressions for Buchholz's apparent power, the instantaneous power, and their respective components are established at the point of common coupling (PCC) of the source and load of a three-phase, unbalanced, non-sinusoidal power system using the source neutral point (N) as voltage reference, as demonstrated in Figure 1.
# Mapping Wild Leek through the Forest Canopy Using a UAV
## Materials and Methods  
### Study Area and Context  
Gatineau Park is a 361 km\({}^{2}\) protected area extending over 50 km northwest from Ottawa-Gatineau, in Southern Quebec, Canada. Its proximity to a large and densely populated urban area makes it an intensively used area, receiving 2.7 million visits per year [17]. It contains rich and diverse ecosystems that provide habitats for over 5000 species, including 156 species on federal and provincial lists of species at risk [46]. The park's forest is a mix of deciduous and coniferous temperate and northern species [46]. The southern portion of the park, where wild leek is predominantly found, is dominated by sugar map, beech, trembling aspen, and red oak [47]. Gatineau Park is primarily a conservation park, and the harvest of wild leek has been strictly prohibited since 1980 [6].  
The study area includes two roughly rectangular sites located inside the park. Wild leek is present in both areas, in varying amounts (Figure 2):(1) Area 1 (0.059 km\({}^{2}\)), contains dense and large patches of wild leek.  
(2) Area 2 (0.027 km\({}^{2}\)), contains smaller and more dispersed patches of wild leek.  
Other spring species emerge at the same time as wild leek and in similar environments. For example, ferns--_Pteridophyta_ sp., white trillium--_Trillium grandifloraum_, red trillium--_Trillium erectum_, blue cohosh--_Caulophyllum thalictroides_, trout lily--_Erythronium americanum_, and squirrel corn or dutchman's breeches--_Dicentra_ sp., are among species frequently seen in Gatineau Park in early spring (Figure 3). However, unlike wild leek, they have sparse coverage, have a less vibrant green colour, and do not generally develop into large and dense areas [18; 48]. White trillium is most similar to wild leek, as it is abundant and forms slightly denser patches than other spring ephemerals. Although white trillium patches can occasionally cover areas of tens of square meters, its density is low compared to that of wild leek. Previous observations [18] indicate that wild leek patches manifest as a vibrant green color in high resolution aerial photos, while white trillium appears as a fainter green.  
Figure 3: Examples of spring plants found in similar environmental conditions to the wild leek in Gatineau Park, Quebec including (**a**) ferns, (**b**) patches of emerging white trillium and red trillium, (**c**) blue cohosh (late May), (**d**) trout lily, and (**e**) squirrel corn or dutchman’s breeches. Photos were taken in May 2016.  
Figure 2: Location of two areas where imagery was acquired for mapping wild leek by means of pre-programmed UAV flights. Two separate flights were conducted, one for each area, on 10 and 11 May 2017. All coordinates refer to WGS 1984, UTM Zone 18N.
## 2 Methodology
### Numerical Method
#### 2.2.2 Three-Dimensional Gas Flow Model (3D GFM)  
To simulate the steady-stage gas temperature of the reactor, the commercial software CFD-ACE 2015 was used for building the 3D GFM considering the flow dynamics of the reactor. This section summarizes the governing equations modeled in the 3D GFM while detailed descriptions can be found in [33]. Figure 2b shows the numerical domain including the reactive zone, glass plate, Teflon block, and ambient air environment. Due to the reactor symmetry, the 3D GFM is simplified with the symmetric plane assigned to reduce the model size without losing the accuracy. The steady-state flow dynamics of the background gases is modeled by solving the mass and momentum conservation equations as:  
\[\
abla\cdot\left(\rho\overrightarrow{V}\right)=0 \tag{7}\]  
\[\
abla\cdot\left(\rho\overrightarrow{VV}\right)=-\
abla p+\
abla\cdot\tau+ \overrightarrow{S}_{M} \tag{8}\]where \(\rho\) is the density of the background gas (i.e., air or He), \(\overset{\rightarrow}{V}\) is the velocity vector, \(p\) represents the pressure of the background gas, \(\tau\) is the viscous stress tensor, and \(\overset{\rightarrow}{S}_{M}\) is the momentum source term to calculate the buoyance force resulting in the cooling of natural convection on the reactor surface. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No. ** & **Reaction** & **Rate Constant (a)** & \(\epsilon\epsilon V\)(b) & \(\Delta E\epsilon V\)(c) & **Ref.
# LSTM and Bat-Based RUSBoost Approach for Electricity Theft Detection
## 1 Introduction
Figure 1: Types of non-technical loss (NTL). Conventional models such as the recurrent neural network are hard to train on large electricity consumption data and fail to capture long-term temporal correlations. For this reason, in this paper, we use the LSTM model because it is a sequential model with the long short term memory concept, which shows great significance in the learning of sequential temporal correlations. Moreover, the RUSBoost technique effectively handles the class imbalanced problem and avoids the classifier being biased. In addition, the bat algorithm is used for parameter tuning, finding an optimal learning rate for RUSBoost; this further enhances the performance of the model. This model is efficient at detecting electricity thieves. For validation, the proposed model is compared with state-of-the-art techniques. The simulation results validate the performance of our proposed model. The key contributions of this paper are as follows:  
* The smart meter data collected from the State Grid Corporation of China (SGCC) [12] have missing values and outliers. In this paper, we perform data pre-processing using interpolation and normalization methods. These methods help to get the dataset on a common scale and compute the missing values. * In order to better extract and memorize features from large time series data, we utilize the LSTM block, which efficiently extracts useful information to truly represent electricity theft cases. * In order to tackle the imbalanced data, RUSBoost is employed to handle the class imbalance problem and performs better than existing data balancing techniques. It performs two operations: RUS first under-samples the data, then Adaboost predicts final classification. This technique improves its performance by learning from previous mistakes, which shows the effectiveness of the model. * Along with RUSBoost, a metaheuristic method--the bat algorithm--is utilized for the efficient parameter optimization of a classifier. * Moreover, for comparative analysis, the precision, recall, F1-score and receiver operating characteristics (ROC) curve are used to compute the accuracy of the model. The rest of the work is organized as follows. In Section 2, we present a detailed overview of the literature. In Section 3, the proposed model is described. The experimental results are discussed in Section 4. Finally, the conclusion and future work are given in Section 5.