esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method
### Gateway Selection  
The vehicles that can receive messages from more than one cluster head change its color to grey and it's \(C_{id}\) is set as the concatenation of the Ids of all the cluster heads. The cluster head initiates gateway selection process when it receives the beacon message from grey vehicles. Using the received beacon messages, the cluster head computes the union of all neighboring CHs. All these neighbouring CHs are initially marked as uncovered. The cluster head repeats the following process until all neighboring CHs are covered: For each grey vehicle the cluster head computes the number of still uncovered CHs, and selects the grey vehicle with maximum value. The neighboring CHs that are covered by the currently selected grey vehicle are marked as covered. Once all neighboring CHs are covered the cluster head sends \(GW\) message consisting of selected gateway vehicles. Upon receiving the \(GW\) message, the selected vehicles change its colour from grey to red and the unselected vehicles change its color from grey to blue and updates its \(C_{id}\) to the Id of the cluster head with whom its relative mobility ratio is higher.
## 2 Materials and Methods
### Data collection and Processing  
#### 2.2.1 Field Survey Data  
The survey data include the FSC data carried out by government departments in 2017, and SP data surveyed in 2021. The main information of the FSC data includes average diameter at breast height (DBH), average tree height (H), stand age, survey date, stand volume per hectare, number of trees per hectare, elevation, depth of soil, etc. The FSC data were surveyed in 2017 and are the latest available forestry survey data. We chose 140 eucalyptus FSC, a total area of 379.2 ha, to carry out our simulation with the 3-PG model (Figure 1). The forest age of these FSC in 2017 was mostly concentrated in 0-4 and 9-12 years (Table 1).  
We investigated 17 eucalyptus plots with an area of 20 m \(\times\) 20 m in the study area. The forest age of the survey plots ranges from 1 to 13 years, the average DBH is 3.62-16.26 cm, and the average H is 4.02-19.69 m. We measured and recorded DBH and H for each tree with DBH greater than 5 cm in the plots. The planting time, management history, and environment information were also recorded through asking the owner. The model developed by [37] was used to calculate the biomass of each organ of each tree (Table 2). The biomass of each tree was summed to obtain the aboveground biomass of SP, and then converted to ACS by multiply carbon coefficient (0.4764) [37]. Considering eucalyptus has a very rapid growth rate and 3-PG model can simulate the forest structure monthly, the SP were surveyed about every six months (January 2021, July 2021, and December 2021). The data from the three surveys were used to verify the simulation accuracy of the model at the SP level. Some plots were harvested when conducting the second and third survey, and some plots were only measured for DBH. Finally, we collected a total of 44 DBH observations and 41 H observations for these plots after the three surveys.  
Figure 1: Location of the study area, spatial distribution of sample plots, and forest sub-compartment of eucalyptus. The base map is a true color composite of Sentinel-2 image.
# A BIM-Enabled Workflow for the Rehabilitation of Heritage Steel Bridges
## 4 Case Study for the Savarsin Heritage Steel Bridge  
The case study presented hereafter is focused on the Savarsin heritage steel bridge (see Figure 5), a historical structure located in Romania, crossing the Mures River on the local D707 road. The bridge, constructed in 1895, embodies the classical structural design characteristic of its era. Its primary load-bearing elements are parabolic lattice beams, featuring descending diagonals (in tension) and vertical struts (in compression). Spanning 159.2 m, the bridge consists of four simply supported truss girders, each measuring 39.8 m, with a length-to-height ratio of 1/6.4 (39.6 m/6.22 m). The main beams give the bridge a graceful and harmonious appearance, perfectly integrating into the picturesque landscape near the former summer residence of Romania's royal family. The bridge's stability is enhanced by a transversal bracing system located in the four central panels at the top of the longitudinal beams. Its load-bearing track structure comprises an orthogonal network of stringers and struts supported by Zores profiles. Above this framework there lies a 20 cm ballast layer, topped with a 5 cm layer of asphalt concrete, forming the final roadway surface. As a riveted structure, built using a construction method that was widely used in the past but which is less common today, the bridge holds significant historical value, adding further complexity to its maintenance. This combination of historical design and technical sophistication underscores its importance as an engineering marvel and also as a cultural landmark. For the structural elements of the bridge trusses, the elements in tension (i.e., bottom chord and diagonals) are depicted in blue, while the compression elements (i.e., struts and top chord) are depicted in red.  
As a testament to the engineering ingenuity and architectural sophistication of its era, the Savarsin Bridge is not only a vital piece of infrastructure but also a cherished landmark, with an emblemalal value for the local community. Its enduring legacy reflects the balance of modern preservation techniques and the cultural importance of maintaining such historical structures. Despite comprehensive rehabilitation efforts completed in 2008, the bridge remains in a state of partial disrepair, necessitating ongoing maintenance in order to safeguard its historical significance [60].  
The rehabilitation of the bridge stands as a remarkable achievement, exemplifying excellence in preserving heritage while ensuring structural integrity. Recognized by the European Convention for Constructional Steelwork (ECCS), the project received a prestigious award that highlighted its success in addressing both engineering and architectural challenges. Further acclaim came in 2010, when the bridge was honoured with the, underscoring the project's innovative approach to strengthening all structural elements through both direct and indirect methods.
# ADPSCAN: Structural Graph Clustering with Adaptive Density Peak Selection and Noise Re-Clustering
###### Abstract  
Structural graph clustering is a data analysis technique that groups nodes within a graph based on their connectivity and structural similarity. The Structural graph clustering SCAN algorithm, a density-based clustering method, effectively identifies core points and their neighbors within areas of high density to form well-defined clusters. However, the clustering quality of SCAN heavily depends on the input parameters, \(\epsilon\) and \(\mu\), making the clustering results highly sensitive to parameter selection. Different parameter settings can lead to significant differences in clustering results, potentially compromising the accuracy of the clusters. To address this issue, a novel structural graph clustering algorithm based on the adaptive selection of density peaks is proposed in this paper. Unlike traditional methods, our algorithm does not rely on external parameters and eliminates the need for manual selection of density peaks or cluster centers by users. Density peaks are adaptively identified using the generalized extreme value distribution, with consideration of the structural similarities and interdependencies among nodes, and clusters are expanded by incorporating neighboring nodes, enhancing the robustness of the clustering process. Additionally, a distance-based structural similarity method is proposed to re-cluster noise nodes to the correct clusters. Extensive experiments on real and synthetic graph datasets validate the effectiveness of our algorithm. The experiment results show that the ADPSCAN has a superior performance compared with several state-of-the-art (SOTA) graph clustering methods.  
structural clustering; adaptive peak selection; generalized extreme value distribution; distance-based similarity +
Footnote †: journal: Applied sciences  
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# Conformational Transitions of Double-Stranded DNA in Thin Films
## 2 Materials and Methods
### DNA Thin Films Obtained after 20 min and 40 min  
Upon increasing the time spent in an active vacuum from 10 min to 20 min and 40 min, respectively, DNA thin films show only minor spectral changes, as evident from Figure 3.  
Figure 2: Schematic representation of A- and B- forms of dsDNA structure. bp stands for base pair.  
These minor spectral changes include small frequency shift of the asymmetric \(\text{PO}_{2}{}^{-}\) vibration to 1233 cm\({}^{-1}\) and 1234 cm\({}^{-1}\) in 20 min and 40 min film, respectively, and intensity increase in symmetric \(\text{PO}_{2}{}^{-}\) vibration and intensity increase in a shoulder at \(\approx\)1030 cm\({}^{-1}\). In the 900-750 cm\({}^{-1}\) region, important A- and B-form markers (893, 860, 837, and 805 cm\({}^{-1}\)) stay silent; only small corrections in intensity can be found even though time spent in the chamber has increased four times (from 10 min to 40 min). Thus, observed spectral changes on \(\text{PO}_{2}{}^{-}\) vibrations indicate that small dehydration of the phosphate groups taking place in 20 min and 40 min film does not leave a significant impact on the conformation. Further structural transitions might have been prevented since constant pressure of about 10 mbar is established in the desiccator chamber after 2.5 min. This means that conformational changes are \"locked\" after equilibrium is established and only hydration of phosphate groups changes over time.
# Smart Internet of Things Power Meter for Industrial and Domestic Applications
### Research Methodology  
The research methodology employed in our work is as follows:  
* We reviewed the national legislation regarding power quality parameters (voltage, uptime, harmonics, etc.) that the grid operator needs to deliver to the clients;
* Conducting a thorough review and identifying the current interest in both the commercial and in the research field regarding smart power meters, integrating them in the Industrial IoT world and the importance of smart meters to domestic application;
* Identifying the technical characteristics that are scattered on various versions/products that would be a good solution to be merged together (e.g.,industrial protocols, wireless transmission, small-factor, battery-backed up, simple installation, flexible architecture);
* Reviewing various development platforms, demonstration boards, and test boards for the main components that can be used in the construction of the device to obtain the characteristics that we need;
* Proposing a flexible modular architecture that allows for using the best components while maintaining simplicity, cost-effectiveness, and small dimensions;
* Implementing at a test board level using development boards, breadboards, du-Pont wires, and other standard components typically used in prototyping;
* Designing and implementing the components on the PCB to achieve the previously mentioned characteristics;
* Programming and implementing a simple FFT-based solution for THD computation;
* Running a linear calibration procedure using an industrial calibrated commercial device;
* Testing various scenarios and compiling the results by using multiple calibrated power quality meters;
* Comparing the results and issuing opinions and conclusions based on the results obtained;
* Identifying improvement solutions to ensure compliance with specific international technical standards.
## 2 Case Study--Arsenic Contamination of Groundwater in Bangladesh  
### Background  
Bangladesh is a tropical country with a total area of about 147,570 km\({}^{2}\) and an estimated population of 160 million as of 2016 [26]. About 58% of the surface area is arable land and about 11% comprises forests and woodlands. The contribution of the agricultural sector to national GDP (Gross Domestic Product) is about 18%, while about 72% of its people are based in rural settings [26]. The per capita income in 2015-2016 is US$1466 [26].  
Although there is abundant groundwater in Bangladesh and the productivity of the aquifers are very high, variable water tables are observed in different parts of the country, more commonly shallow and below the ground surface by 1-10 m [27]. Groundwater is generally free from pathogenic microorganisms and, if not contaminated, can be reasonably safe to use. For these reasons groundwater has remained an appealing and readily available reserve for drinking water in many countries [27]. This has resulted in the widespread use of groundwater as the main source of drinking water, particularly as tube wells for the past forty years or more. The tube wells have contributed significantly to the reduction of mortality rate of diarrheal diseases [28]. Bangladesh achieved a remarkable success by providing 97% of the rural population with tube well water [28].  
However, the coverage for safe drinking water has been significantly reduced from 97% to 74% as a consequence of the extensive contamination of groundwater with arsenic [28]. Consequently, the mortality rate in Bangladesh was increased as a result of arsenic contamination of the drinking water [29]. About 12.6% of its population are still accessing this contaminated water [22]. This has led to the recognition of arsenic contamination as a catastrophic issue in Bangladesh [22]. To understand the cause of this problem, a knowledge of the geological characteristics of Bangladesh is useful.
# Double Dead-Time Signal Injection Strategy for Stator Resistance Estimation of Induction Machines
## 4 Experimental Validation
### Experimental Setup  
In order to guarantee the validity of the designed double dead-time algorithm, it has been validated in a test bench. The Table 2 shows the specifications of the metro machine used for the validation of the temperature estimation algorithm.  
The designed algorithm has been tested in a test bench composed by commercial elements, used in the metro of Bilbao by the company CAF Power & Automation. In this section the platform used for the validation of the strategy is described. The test bench is formed by two confronted squirrel cage induction machines. One machine is used as the traction motor and the other one is the load. The machines are fed by two VSI (\"Voltage Source Inverter\"), one for each machine. The control unit is composed by a DSP (\"Digital Signal Processor\") and a FPGA (\"Field-Programmable Gate Array\"). The structure is shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline Nominal Power & 179 & kW \\ Nominal Speed & 1758 & rpm \\ Maximum Torque & 1870 & Nm \\ Nominal Torque & 977 & Nm \\ Number of pole pairs & 2 & \\ Phase Effective Voltage & 675 & V \\ Stator Resistance (25 \({}^{\circ}\)C) & 111.2 & m\(\Omega\) \\ Rotor Resistance (160 \({}^{\circ}\)C) & 115 & m\(\Omega\) \\ Stator Inductance & 54.1 & mH \\ Rotor Inductance (referred to the stator) & 53.1 & mH \\ Mutual Inductance & 51.8 & mH \\ \hline \hline \end{tabular}
\end{table}
Table 2: Nominal parameters of the motor under test.  
Figure 4: Test bench structure diagram.  
To estimate the stator resistance on-line, the temperature of the stator is measured. For this purpose, different PT100 sensors have been installed in different parts of the machine. The algorithm has been implemented in a classical vector control. The tuning of the PI parameters of the injection strategy and the filter used to obtain the current offset has been made putting special interest into the estimation time. The implemented low-pass filter is a fourth-order filter with a cut-off frequency of 6.6 Hz. The current offset controller has a proportional gain (Kp) of 1 and an integral gain of (Ki) of 5.  
These parameters have been obtained in a manual tuning, looking for a low oscillation but giving more importance to a fast estimation, especially in the dead-time change. The injected voltage for the required current offset at the two different dead times has to be performed at the same working point. In order to avoid a condition variation during the estimation process, the algorithm has to work as fast as possible. This can be achieved using a low order filter or increasing the controller gains. However, this will cause an increment in the oscillation current DC component so a compromise between fast estimation and offset oscillation has been found.
# A Two-Stage Household Electricity Demand Estimation Approach Based on Edge Deep Sparse Coding
## 2 Deep K-SVD Algorithm  
### Basic K-SVD Algorithm  
Data compression includes two phases--encoding and reconstruction. The encoder converts the original load curve to an optimal format with less storage space and restores the load curve with minimal reconstruction error. Sparse coding is inspired by the working mechanism of brain neurons [28] and attempts to obtain a sparse and redundant dictionary set to describe the characteristics of the original overall load.  
Consider the given signal \(Y=[y_{1},y_{2},\cdots,y_{M}]\), which can be represented as a sparse linear combination of basic vectors in the overcomplete dictionary matrix \(D=\left[d_{1},d_{2},\cdots,d_{J}\right]^{T}\). In the load analysis and decomposition problem, \(y_{i}\) denotes the overall load and \(d_{j}=\left[d_{j,1},d_{j,2},\cdots,d_{j,N}\right]^{T}\) is the \(j\)th UBPs, which has \(N\) dimensions same as the original signal. The sparse representation of \(Y\) can be described as  
\[\begin{array}{c}\min_{D,X}\left\{\|Y-DX\|_{F}^{2}\right\}\\ \forall i,\|x_{i}\|_{0}\leq s_{0}\end{array} \tag{1}\] \[\begin{array}{c}\text{{subject to}}\quad x_{i,j}\geq 0,1\leq i\leq M,1\leq j\leq J\\ d_{j,n}\geq 0,1\leq j\leq J,1\leq n\leq N\end{array}\]where the basic vector \(d_{j}=\left[d_{j,1},d_{j,2},\cdots,d_{j,N}\right]^{T}\) denotes the jth equipment behavior patterns, which has \(N\) dimensions same as the original signal. \(x_{i}=\left[x_{i,1},x_{i,2},\cdots,x_{i,l}\right]^{T}\) is the coefficients vector of \(d_{j}\) which only a few elements are nonzero to realize the dimensionality reduction of the original load signal [28]. The first constraints on \(s_{0}\) is to ensure the compression efficiency of load profile and the second and third non-negative constraints on the coefficient vectors should be guaranteed because the UBPs are non-negative in practice.  
The non-negative K-SVD algorithm is effective and flexible for solving the optimization problem in (1) or training the dictionary. The initial dictionary matrix should be predetermined, and the following steps should be repeated until the required number of iterations is reached or the constraints are satisfied.  
(1) Sparse coding: The orthogonal matching pursuit (OMP) algorithms are introduced to obtain the approximate solution of the sparse coefficient vector \(x_{i}\) that corresponds to each load profile \(y_{i}\).  
\[\hat{x}=arg\ min\left\|y-D\hat{x}\right\|_{2} \tag{2}\]  
(2) Dictionary updating: The dictionary vector is updated with fixed coefficient vectors.  
Figure 1 shows a visualization of sparse coding, where the original signal is finally represented by a linear combination of six UBPs. Notably, the number of columns in the feature dictionary, \(J\), should be greater than the number of rows in the original signal \(N\), which is determined by the redundancy of the dictionary. The sparsity of sparse coding is primarily reflected by the condition that most coefficient vectors are zero.
## 4 Methods (ASMAG Algorithm)
### Snow Cover Mapping, Snow Line Altitude and Snow Cover Ratio Retrieval  
#### 4.2.1 Snow Cover Mapping and the Snow Cover Ratio (SCR) for Individual Glaciers  
The snow cover mapping algorithm is the core part of ASMAG. As a single threshold applied to the topographically corrected TOAR image of the entire study region has not provided useful results, the method is applied to individual glaciers where it selects a threshold automatically. We follow the approach presented by [PERSON] (2011) [52] and create a histogram of the Ekstrand corrected NIR band TOAR for each glacier after a mask (derived from the manually adjusted glacier inventory) has been applied. The NIR band also has the advantage of (in general) not being saturated over snow in 8-bit Landsat data [49]. The histogram is then used to identify a threshold to separate snow from ice. As histograms and thus optimum thresholds strongly vary, we tested the suggestion of [PERSON] et al. (2013) [54] and use the Otsu algorithm [55] for an automatic detection of the threshold.  
The Otsu method is also included in IDL and automatically performs clustering-based image thresholding by assuming that the image contains two classes (snow and ice) and has thus a bimodal histogram [54; 56]. As long as the amount of debris on a glacier is small compared to the clean ice area, debris will be included in the darker ice class. The optimum threshold for separating the two classes is found when their combined spread is minimal, or their inter-class variance is maximal [52]. In Figure 3 for three glaciers (no. 5, 17 and 20 in Figure 1) and two dates we show the Ekstrand corrected images, the mapped SC, the reflectance histograms along with the thresholds, and the location of the points (red) used for validation of the results. The latter are selected in obvious and in transition regions to provide a meaningful validation of the method. The SCR is calculated for each glacier and Landsat scene by dividing the snow-covered area by the total glacier area, similar to the accumulation area ratio (AAR) on glaciers [17].  
Figure 3: Snow cover mapping for glacier no. 17, 5 and 20 and different Landsat sensors. Left: The Ekstrand corrected NIR band. Center: The snow classification (light blue). Right: The respective histogram of the Ekstrand corrected image along with the automatically derived threshold. The histograms are not always exactly bimodal. The red dots indicate the location of sample points used for validation.