esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Data
#### 2.2.2 Data from Process-Based Models  
The Land Data Assimilation Systems (LDAS) use data from multiple ground and space observations that are integrated with advanced numerical models of physical processes to simulate fields of water and energy states and fluxes, which helps in filling gaps and minimizing errors in the observations. Different projects (e.g., NLDAS, GLDAS, FLDAS) of LDAS have been configured for specific domains and purposes [57]. There are some known issues with GLDAS version 1 data, which might present some challenges in the runoff, soil moisture and ET analysis performed with the GLDAS model. These issues are suggested to be solved in the newer version (GLDAS version 2.1) used in this study. The GLDAS 2.1 simulation started on 1 January 2000 using the conditions from the GLDAS version 2.0 simulation. The GLDAS-2.1 is revised with upgraded models using National Oceanic and Atmospheric Administration (NOAA)/Global Data Assimilation System (GDAS) atmospheric analysis fields [58], the disaggregated Global Precipitation Climatology Project (GPCP) precipitation fields [59] and the Air Force Weather Agency's AGRiculturalMETeorological modeling system (AGRMET) radiation fields. In this study, the surface soil moisture and evapotranspiration were selected from the GLDAS-2.1 Noah model from 2010 to 2019, with its 0.25 degree spatial resolution and monthly temporal resolution.  
FLDAS [60] is designed for semi-arid, food-insecure regions of Africa. Unlike GLDAS, which relies on global rainfall products (e.g., CMAP [61] and Princeton [62]), FLDAS uses a combination of the forcing data from Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) data and CHIRPS [44], which is designed for seasonal drought monitoring and trend analysis. In this study, the surface soil moisture and evapotranspiration were selected from the FLDAS dataset in Noah version 3.6.1 from 2010 to 2019, with its 0.1 degree spatial resolution and monthly temporal resolution.
## 3 Results and Discussion
### BET Analysis  
The as-prepared Pd/C catalysts were studied to evaluate their adsorption profiles, and it was found that all the prepared materials exhibited that, with the increase in the percentage of Pd, the amount of N\({}_{2}\) adsorbed increased, indicating enhanced interacting sites with the increase in the Pd percentage (Figure 4). The pore size distribution obtained from the studies revealed that the pore sizes of the 0.5-2% catalysts were found to be in the range of 200-600 nm, and the pore volume obtained indicated that the pores were deep channels in the range of 0.07-0.08 cm\({}^{3}\)/g. The catalyst with 3% Pd/C was found to possess a pore size distribution of 25-150 nm, and the pore volume indicated the formation of shallow pores. Moreover, the BET surface areas of the samples increased with the increasing of Pd content; the 0.5%, 1%, 2% and 3% Pd demonstrated surface areas of 837, 838, 841 and 933 m\({}^{2}\)/g, respectively.
# Traceability of Surgical Instruments: A Systematic Review
## 2 Traceability Surgical System Architecture Overview
### Layer 1  
This layer aims to identify the instruments. The identification of surgical instruments is divided into two distinct parts. First of all, there is the unique identification of instruments. It is based on the use of several technologies (RFID, dots tag, micro-percussion, laser engraving) to assign a unique identifier to each instrument through (RFID tag, barcode, Datamatrix code, and specific visual characteristics) [13; 14; 15; 16; 17]. Then, each type of instrument registration is appropriate for a particular reading technology (RFID reader, barcode reader, and camera). Apart from RFID technology, registration is symbolized by 1D-2D barcodes, a datamatrix, and alphanumeric characters.  
1. **RFID**: This wireless technology comprises an RFID tag, a reader, and an antenna [18]. It uses radio waves to transmit data from tagged objects to a reader, enabling automatic identification and real-time tracking [19]. This technology has advantages and disadvantages. Indeed, it does not disrupt workflow, which is crucial for maintaining the efficiency of medical procedures [18]. But RFID-tagged instruments may experience interference [20]. Also, RFID tags cannot be attached to all instruments, particularly those used in neurosurgery [21]. In addition, the integrated circuit chip of RFID tags is damaged after several washing cycles and sterilization at high temperatures, affecting the readability of crucial information [22]. Only a limited number of labeled instruments are identified simultaneously [23]. This can be restrictive in situations requiring the management of numerous surgical instruments.
2. **Dots**: are pre-printed adhesive dots with a unique DataMatrix code [24; 25]. They can have different sizes, from 2 to 9.5 mm. The size versatility allows application on various instruments, including small ones such as microsurgery instruments. Additionally, the dots adhere strongly when exposed to heat during autoclave. However, these technologies may be subject to code alteration through deformation or fragmentation, which could affect the traceability process in the long term.  
Figure 3: Architecture of a surgical instrument traceability system.  
3. **Micro-percussion**: registration by micro-percussion consists of pressing the surface of surgical instruments. This mechanical process uses a fine tip that strikes the surface at high speed. Implementing micro-percussion marking requires several pieces of equipment, including a micro-percussion machine, a peripheral PC equipped with specialized software for registration management, and a controller responsible for encoding management [25]. Micro-percussion markings are less sensitive to corrosion but have poorer contrast [14].
4. **Laser engraving**: Laser marking is based on eliminating material through a laser beam directed toward the surface of the surgical instrument. Laser marks have excellent visibility but are quickly attacked by corrosion [14]. This technique also allows the registration of circular surface instruments.
# User Mobility Modeling in Crowdsourcing Application to Prevent Inference Attacks
## 5 Experiments
### Setting
#### 5.1.4 Complexity Analysis  
We evaluated the reduced graph generation Algorithm 5 that was to be used to protect privacy. The total time complexity can be broken down into several key steps: the main loop that iterates over all vertices in the graph \(O(n)\), nested loops that process the edges associated with each vertex (\(\sum_{i=0}^{n}i)=O(1)\), associated weights \(O(k)\), and inferences in the Bayesian network \(O(bn)\). The construction of the reduced graph adds an additional complexity of \(O(n_{s}\times m)\). Therefore, the total complexity can be approximated as follows:  
\[O(n\times k\times bn)+O(n_{s}\times m)\]  
The complexity variables of the algorithm are as follows: \(n\) is the number of vertices in the graph \(G\)--determining the storage space for vertices; \(m\) is the number of edges in \(G\)--reflecting the space needed for edges and associated data; \(k\) is the number of weight tuples per edge--indicating data density; \(bn\) represents the complexity of the Bayesian network inferences--impacting decision-making time; and \(n_{s}\) is the number of selected vertices in the reduced graph \(G_{reduced}\)--influencing the size of the final graph. This formula indicates that the algorithm is primarily influenced by the size of the graph, data density, and the complexity of calculations within the Bayesian network.  
The space complexity is primarily determined by the storage requirements for the vertices and edges in the graph, as well as the Bayesian network. The space complexity can be expressed as follows:  
\[O(n+m+bn_{space})\]  
where  
* \(bn_{space}\) is the space required to store the Bayesian network, reflecting the memory consumption of the inference model.  
This complexity shows that memory usage grows linearly with the size of the graph and the complexity of the Bayesian network. While the algorithm is efficient in handling large graphs in terms of memory, the requirements could become significant in scenarios with extremely large or dense graphs, potentially leading to challenges in environments with limited storage capacity.
## 3 Methodology
### Evaluation Strategy  
The improvement of OWCMs over corresponding GMs was analyzed by direct comparisons of the GM-/OWCM-derived fine-resolution SM with in situ SM measurements, as well as indirect intercomparisons between the aggregated OWCM-/GM-derived SM and RFSM. To minimize point-cell-scale differences during direct validation, in situ measurements from grid cells with low heterogeneity were used. Moreover, the performances of GM- and OWCM-derived SM products were also evaluated in terms of their ability to spatially and temporally match original RFSM estimates.
## 3 Analysis of Parameter for Designing Pickup Coil in PCB
### Magnetic Flux Incident Area  
Figure 6 and Table 2 presents the analysis results of the influence between the coil and conductor according to the area of the surface where the magnetic flux is incident in the coil, and the finite element analysis was conducted based on the PCB layout. Moreover, it includes the results of analyzing the mutual inductance of the coil and the self- inductance of the coil according to the incident area in the vertical coil structure and horizontal coil structures. In the vertical coil on the PCB, the magnetic flux incident area of the coil was determined by the number of layers from the bottom layer, whereas in the case of the horizontal coil, it can be set in various ways by the designer with a high degree of freedom. In addition, the distance between the coil and the trace was fixed to 0.47 mm to see only the influence of the change on the magnetic flux incident area.  
The results in Table 2 show that the amount of magnetic flux passing through the magnetic flux incident surface increases as the coil width increases, and the mutual inductance value increases accordingly; this demonstrates that the amount of change in the incident magnetic flux is determined according to the size of the incident area. Moreover, the \(L_{c}\)/\(M\) value for the horizontal coil (Model-C) is less than that for the vertical coil (Model-A), i.e., it can be seen that the mutual inductance value between the coil and conductor increases based on the same self-inductance value. Therefore, to increase the mutual inductance value to increase the sensor sensitivity for a specific bandwidth, an appropriate magnetic flux incident area through which the magnetic flux passes must be selected.  
Figure 6: FEM results under magnetic flux incident in different areas: (**a**) vertical structure coil model and (**b**) horizontal structure coil model.
# Studies on Seismic Performance of Precast Concrete Columns with Ground Splice Sleeve
## 2 Test Setup
### Material Strength  
#### 2.1.1 Steel Bar Strength  
HRB400 steel bars are utilized to make the specimens. Table 1 lists the actual measured yield strength \(f_{y}\) and ultimate strength \(f_{u}\), which are obtained through material tests on three steel bars. In Table 1, the yield stain is expressed as \(\varepsilon_{y}\)= \(f_{y}/E_{s}\) and the elastic modulus is \(E_{s}~{}=~{}2.0~{}\times~{}10^{5}\) MPa.  
\begin{table}
\begin{tabular}{r r r r} \hline \hline \(d\) (mm) & \(f_{y}\) (MPa) & \(f_{u}\) (MPa) & \(\varepsilon_{y}\) (\(\times\)10\({}^{-6}\)) \\ \hline
8 & 427.4 & 626.7 & 2137 \\
12 & 455.4 & 608.4 & 2277 \\
16 & 458.3 & 625.8 & 2292 \\
20 & 439.9 & 649.0 & 2200 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Actual tensile strength of the steel bars.  
Figure 2: Elevation of columns (distances in mm): (**a**) prefabricated column; (**b**) cast-in-situ column. Intensity grade of rebar used in the columns is HRB400 whose nominal yield strength is 400 MPa, and the diameter of the preformed hole is 45 mm.
## 2 Background
### Reliability Block Diagram
#### 2.3.1 Minimal Paths and Minimal Cuts  
Minimal path is a set of components organized in series whose guarantees system operation [23]. On the other hand, minimal cut is a set of components whose implies system failure [1, 22, 23].  
For example, Figure 4a has only one minimal path (\(\{\)B\({}_{1}\)B\({}_{2}\)B\({}_{3}\)\(\}\)) and has three minimal cuts (\(\{\)B\({}_{1}\)\(\}\),\(\{\)B\({}_{2}\)\(\}\),\(\{\)B\({}_{3}\)\(\}\)).
## 3 Results
### Vertical Distribution of Environmental Parameters  
The specific activity of \({}^{137}\)Cs in top layer of the lake sediments from North Aral Sea was 13.6 Bq/kg, corresponding to the sampling time of 2018. The main accumulation peak (29.3 Bq/kg) appeared at 18 cm, which may correspond to the Chernobyl nuclear leak that occurred in the former Soviet Union in 1986 [50; 51]. The peak at 28 cm (2.29 Bq/kg) corresponds to year 1954 [52], while the peak at 24 cm (13.3 Bq/kg) may correspond to year 1963 [53; 54]. The age of the sediment core was interpolated from the \({}^{137}\)Cs time-markers. The linear fitting curve of depth (x) and age (y) was Y= -2.37X + 2022.4 (r\({}^{2}\) = 0.98, \(p\) = 0.01).  
Interestingly, the most striking result to emerge from Figure 2, showing the vertical distributions of environmental parameters, is the occurrence of three obvious discontinuities that divide the PTE profiles into four relatively obvious stages. Below 21 cm, the PTE concentration is low, and after a rapid  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multirow{2}{*}{**Range**} & \multirow{2}{*}{**Minimum**} & \multirow{2}{*}{**Maximum**} & \multicolumn{3}{c}{**Mean**} & \multirow{2}{*}{**Std. Deviation**} \\ \cline{4-5}  & & & & & **Statistic** & & **Std. Error** \\ \hline V (mg/kg) & 97.9 & 29.5 & 127 & 91.6 & 6.94 & 38.0 \\ Cr (mg/kg) & 62.2 & 18.2 & 80.4 & 59.3 & 4.53 & 24.8 \\ Co (mg/kg) & 10.8 & 3.18 & 14.0 & 10.2 & 0.772 & 4.23 \\ Ni (mg/kg) & 33.2 & 7.72 & 40.9 & 27.4 & 2.28 & 12.5 \\ Cu (mg/kg) & 32.3 & 5.16 & 37.5 & 22.8 & 2.11 & 11.5 \\ Zn (mg/kg) & 72.6 & 17.4 & 90.0 & 61.2 & 5.00 & 27.4 \\ As (mg/kg) & 14.6 & 3.78 & 18.3 & 11.4 & 0.797 & 4.37 \\ Cd (mg/kg) & 0.317 & 0.0453 & 0.362 & 0.192 & 0.0176 & 0.0966 \\ Pb (mg/kg) & 18.2 & 4.64 & 22.8 & 15.4 & 1.23 & 6.74 \\ LF (m\({}^{3}\)/kg) & 14.0 & 1.90 & 15.9 & 10.7 & 0.960 & 5.26 \\ HF (m\({}^{3}\)/kg) & 13.4 & 2.00 & 15.4 & 10.3 & 0.929 & 5.09 \\ \({}^{137}\)Cs (Bq/kg) & 29.3 & 0 & 29.3 & 13.0 & 1.17 & 6.42 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary statistics for 12 environmental parameters: \({}^{137}\)Cs, magnetic susceptibility (MS) with high (HF) and low frequency (LF) and potentially toxic elements (PTEs) in the North Aral Sea sediment core.  
increase to a depth of 9 cm, another spike in PTE concentration occurs. From 9 to 2 cm, the increase in PTE concentration is gradual. A significant change in PTE concentration is noted again in the surface sediments, but the trend is opposite to that of the first two mutation points, with the PTE concentration starting to decrease.
# Bridge Crack Detection Based on SSENets
## 2 Materials and Methods  
### Datasets  
In order to meet the experimental requirements, we used the bridge crack dataset created by [PERSON] et al. (2015) as input for training and testing. A total of 2068 initial images of the dataset were collected by Phantom 4 Pro's Complementary Metal Oxide Semiconductor (CMOS) surface array camera with a resolution of \(1024\times 1024\). In order to construct positive samples (images with cracks) and negative samples (images without cracks), the initial images were divided into four parts. Sub images were filtered, cropped and flipped, then 6069 images with resolution of \(224\times 224\) were obtained. The combination of images and labels was used as the dataset. We chose 4856 images as the training set and 1213 images as the testing set. As shown in Figure 1, the flow chart of the crack detection task was divided into two parts: training and testing. By inputting the training set into SSENets, we can get a trained crack classifier. It can be used to detect whether there are cracks in the testing set, and finally get the output of the task. In the test, we used the sliding window technique to traverse the whole image. The structure of SSENets will be described in detail below.
## 2 Materials and Methods
### Satellite Images  
This study used multispectral images from the European Space Agency (ESA) satellites Sentinel-2A and Sentinel-2B [26]. The Sentinel-2 system, which was developed and operated by the ESA and manufactured by a consortium organized by Airbus DS, obtains images of the entire Earth every ten days from the same angles. Both satellites operate cooperatively to obtain images at frequent intervals. The Sentinel-2 images consist of 13 bands, ranging from visible to near-infrared. The filming area ranges from 56\({}^{\circ}\) S to 84\({}^{\circ}\) N and encompasses both land and sea. The bands have spatial resolutions of 10, 20, and 60 m.  
In this study, only bands with spatial resolutions of 10 and 20 m were used. The multispectral Sentinel-2 images were used to observe chlorophyll in plants because this satellite is primarily used to observe vegetation with a combination of visual observations and various wavelength-specific bands [27]. Sentinel-2 is also used to monitor disaster situations such as forest fires because it has a short observation cycle and provides free images with sufficient spatial resolution. Sentinel-2 images can be accessed from the Copernicus database [28]. In this study, images taken before and after the forest fires in Okgye town, Gangneung city, were compared. The images from 26 March, 20 April, and 8 August 2019 were used for regular monitoring before and after the outbreak. Table 1 presents the details of the satellites used in this study.
# Dynamic Initial Weight Assignment for MaxSAT
## 1 Introduction
### Our Contribution  
Based on the above discussion and given the importance of the MaxSAT problem, our focus in this research was to study possible approaches to developing and/or enhancing the current state-of-the-art DLS algorithms for unweighted MaxSAT. More specifically, the main contributions of this research focused on:  
* Investigating mechanisms that have a general effect or improve the current state-of-the-art MaxSAT solving mechanisms, as they successfully deal with real-world problems with huge sizes and variables exceeding millions in number, which a decade ago was not possible
* We were interested specifically in experimentally investigating whether employing the dynamic initial weight allocation [24] within the SAT solvers had a widespread impact on the performance of algorithms that handle MaxSAT problems. We were further interested in testing whether this impact reflected negatively on the solver's performance or led to a higher performance level
* Based on the above two points, we designed the paper to address unweighted MaxSAT problems because the transition to weighted partial MaxSAT problems is implicit and requires only some very minor changes. Consequently, we experimentally investigated the dynamic initial assignment implemented in the DDFW+Initial Weight assignment technique (InitWeight) [24] and compared it against the state-of-the-art algorithms: YalSAT [25], ProbSAT [26], Sparrow [27], and DDFW [28]. These algorithms are generally applied to solve SAT problems; therefore, they are particularly suited to our purpose to extend and investigate the performance of the dynamic initial weight assignment in handling MaxSAT, where we adjusted the solvers to preserve and store the solutions that were reached and the time within which those solutions were reached.  
In the next Section 2, we discuss the preliminaries used in the paper. In Section 3, we discuss a general overview of the dynamic local search approaches. Then, in Section 4, we illustrate the improvement gains by our approach, and then, we analyze the results. Finally, the conclusion is drawn in Section 5.
###### Abstract  
In this paper, a novel method based on the Poggio-Miller-Chang-Harrington-Wu-Tsai (PMCHWT) integral equation is presented to study the electromagnetic fields excited by vertical or horizontal electric dipoles in the presence of a layered region which consists of \(K\)-layered dissipative media and the air above. To transform the continuous integral equation into a block tridiagonal matrix with the feature of convenient solution, the Rao-Wilton-Glisson (RWG) functions are introduced as expansion and testing functions. The electromagnetic fields excited by an electric dipole are calculated and compared with the available results, where the electric dipole antenna is buried in the non-planar air-sea-seabed, air-rock-earth-mine, and multilayered sphere structures. The analysis and computations demonstrate that the method exhibits high accuracy and solving performance in the near field propagation region.  
electromagnetic propagation; layered dissipative media; surface integral equation; PMCHWT +
Footnote †: journal: Applied Physics Letters
## 4 DHD-MEPO Algorithm
### Calculating Mobile Node Coverage Redundancy  
When repairing holes, to ensure cell coverage on the basis of meeting the actual application requirements, we need to select redundant mobile nodes in the target region to repair, using the resources of redundant mobile nodes to find hole coverage as effectively as possible and successfully improving the efficiency of the nodes. Therefore, candidate repair nodes need to be identified. When selecting candidate repair nodes, we first select from within the cell with the highest cell coverage. Assuming that certain mobile sensor nodes are densely distributed within a cell with a cell coverage of 90% or more, according to the calculated redundancy of the target points within the mobile node coverage cell, we judge that the redundancy is higher than 90% as a candidate repair node. Taking mobile node \(s_{11}\) in Figure 7 as an example, the quantity of target points within the coverage cell of \(s_{11}\) is 10. The statistics of these 10 target points being covered by other neighbor nodes around \(s_{11}\) are shown in Table 2, where 1 means covered by neighbor nodes and 0 means not covered.  
Figure 7: Mobile node \(s_{11}\) coverage situation.  
The set of candidate repair nodes \(R_{m}\) is determined by calculating the redundancy of each mobile node within the cell. The redundancy of mobile node \(s_{i}\) covering the target point is calculated as follows:  
\[Red_{s_{i}}=\frac{\sum_{j=1}^{h}{{{Cover}}_{nei}}}{h}*100 \tag{17}\]  
where \(h\) is the total quantity of target points covered by mobile node \(s_{i}\); \({{{Cover}}_{nei}}\) indicates whether target point \(e_{j}\) is covered by neighboring nodes of node \(s_{i}\). If it is covered by neighbor nodes, \({{{Cover}}_{nei}}\) is 1, otherwise it is 0. The greater the cumulative value of \({{{Cover}}_{nei}}\), the greater the chance of the mobile node becoming a repair node.