esa-sceva/satcom-chunk-collection huggingface dataset:
# Measuring the Lense-Thirring Orbital Precession and the Neutron Star Moment of Inertia with Pulsars
## 4 Measuring the LT Precession via Pulsar Timing
### Lense-Thirring Precession in \(\dot{x}\)
#### 4.1.1 PSR J1141\(-\)6545, a Young Pulsar with a Massive White Dwarf Companion
(The misalignment of the spin of a second-formed NS causes a much smaller effect on \(\dot{x}^{\rm LT}\), because such pulsars are generally much slower than any recycled pulsars. For this reason, only the misalignment of the first-formed, recycled object matters.) Nevertheless, even in that case, a lack of knowledge of \(\dot{\delta}^{\rm SO}_{\rm C}\) and \(\Phi_{\rm C}\) will still limit the measurement of the angular momentum of the NS and its Mol from a measurement of \(\dot{x}\). It is important to emphasise that, for Figure 7, the MoI of the WD is entered as a given, _not_ as a quantity we are trying to measure. The reason for this is threefold: first, unlike in the case of NSs, the MoI of WDs is reasonably well known; second, it tells us nothing about the state of super-dense nuclear matter, which does not exist in WDs; third, even if we tried to measure it, the fact that there is only one measurement (\(\dot{x}^{\text{SO}}\)) and three unknowns (\(P_{\text{WD}}\), \(\dot{\delta}^{\text{SO}}_{\text{c}}\), and \(\Phi_{\text{c}}\)) makes that measurement impractical. So, what do we learn from Figure 7? Two things: (a) the slower the spin of the WD, the larger is \(\dot{x}^{\text{LT}}\) relative to \(\dot{x}^{\text{QPM}}\); the reason is that the former varies with \(P_{\text{WD}}^{-1}\) and the latter with \(P_{\text{WD}}^{-2}\); (b) we can indeed constrain \(P_{\text{WD}}\) to a range from 10 to \(\sim\)150 s, if we take into account the configurations that emerge from a simulation that takes into account the system's orbital parameters (orange contours). This is a very fast spin period for a WD, and an unexpected confirmation of the evolutionary scenario described by [PERSON] and [PERSON] [82].
# Adaptive Equalization for Dispersion Mitigation in Multi-Channel Optical Communication Networks
### Optical Compensation
#### 1.1.1 Conventional Dispersion Compensation Fiber  
The CDCF is effective for channel dispersion compensation in the mostly used C and L bands. Additional compensation from linear dispersion slope characteristics can be achieved by reducing the CD related to the core of the CDCF [8].
# Intersection and Complement Set (IACS) Method to Reduce Redundant Node in Mobile WSN Localization
## 5 Evaluation
### Localization Error and Closeness  
The accuracy of the estimated location in the localization process can be described with localization error. The localization error per time is evaluated similar to the error calculation in MSL* wherein a low localization error represents high accuracy and vice versa. The estimation error can be measured [4] as follows:  
\[Error=\ \ \frac{1}{n}\sum_{i=1}^{n}||e_{i}-l_{i}|| \tag{9}\]where \(n\) is the total number of sensor nodes, \(e_{i}\) represents the estimation while \(l_{i}\) is the true location of the \(i\)-th node.  
For the closeness evaluation, the total average closeness value is obtained for every case tested according to Reference (3). A low closeness value represents a high quality location estimation and vice versa. The metric unit of estimation error used in this study is meter (\(m\)).
## 2 Experimental Section
### Gas Chromatography-Mass Spectrometry (GC-MS) Analysis and Data Processing  
A DB-VRX capillary column was used (30 m \(\times\) 0.250 mm ID and 1.4 \(\upmu\)m film thickness) inside of an Agilent 6890N GC coupled to a 5973N Mass Selective Detector. A helium carrier gas flow rate of 1.0 mL/min was used. The oven temperature was held at 35 \({}^{\circ}\)C for 4 min and then increased by 3 \({}^{\circ}\)C/min to 80 \({}^{\circ}\)C. The temperature was further increased to 120 \({}^{\circ}\)C at a rate of 10 \({}^{\circ}\)C/min and finally increased to a temperature of 240 \({}^{\circ}\)C at 40 \({}^{\circ}\)C/min and held for 4 min. The transfer line and source temperatures were held at 230 \({}^{\circ}\)C and 280 \({}^{\circ}\)C, respectively, and the MSD was operated in full electron ionization (EI) scan mode from 40 to 450 \(m\)/\(z\).  
Verification of mass spectral identifications and retention time was completed using standards for 32 compounds of interest; heptane, undecane, 2-methylpentane, 1-pentanol, 2-ethyl-1-hexanol, ethanol, 1-hexanol, hexanal, nonanal, heptanal, octanal, 2-heptanone, 2-pentanone, 2-butanone, 2-nonanone, p-xylene, toluene, ethyl benzene, naphthalene, benzene, o-xylene, styrene, tetrachloroethylene, dimethyl disulfide, dimethyl sulfide, indole, pyridine, putrescine, cadaverine, hexanoic acid, propanoic acid, and butanoic acid. Although standards could not be analysed for every compound, this list of compounds was thought to represent the range of compounds expected based on a literature search.  
A minimum match threshold of 70% to the NIST library was used for compound identification, in addition to retention time comparison to standards where possible. A list of compounds of interest was generated from the decomposition soil samples for each experimental day by removing compounds at comparable levels in controls.  
Each compound was classified as being a(n) sulphur-containing compound, nitrogen-containing compound, aromatic, carboxylic acid, ester, aldehyde, ketone, alcohol, hydrocarbon, and/or other. Multiple classifications were assigned where required. Internal standard normalisation was performed to provide the relative amount of each compound present on the sampling tube. Internal standard precision was tested (n = 5 on 3 days) and was below 8% for sorbent tubes and below 11.5% for SPME; therefore it was deemed suitable to be used for providing a measure of relative quantification. The four experimental sites were treated as replicates and the average of each VOC's abundance was taken between pig carcasses. The averages were then summed within each class to provide data to be used for multivariate analysis. Principal component analysis (PCA) was performed based on the sum of the normalised peak areas in each chemical class using The Unscrambler X version 10.3 (CAMO Software).
## 3 Study Area and Used Data
### Data Processing  
SAR images were processed to select the primary image from the ascending and descending data. The remaining images were then aligned with the primary image. To improve the image quality, the images were multi-viewed in both the range and azimuth directions (multi-view ratio of 8:2), with a ground resolution of about 30 m, which is close to the SRTM acquired. Secondly, to suppress the phenomenon of incoherence, we built a short baseline set, using 36 days and 100 m as the temporal and spatial baseline thresholds to select interference pairs for ascending and descending. The temporal and spatial baseline combination of the constructed interference pairs is shown in Figure 4. Based on the interference pair selected from the temporal and spatial baseline, we used the DEM with a resolution of 30 m to generate the simulated topographic phase, and used the difference to eliminate the influence of terrain to obtain the differential interferograms. Meanwhile, the Goldstein filtering method [39] was used to filter the differential interferograms to improve the quality of the interferograms. Finally, the filtered interferograms were used for the final phase gradient calculation and stack fusion. Here, 0.15 was used as the coherence threshold mask to remove the phase gradient calculated by the corresponding interference pair.
# A Novel Discriminating and Relative Global Spatial Image Representation with Applications in CBIR
## 4 Datasets and Performance Evaluation
### Dataset Description  
To assess the effectiveness of the proposed research for image retrieval, experiments are conducted on the benchmark datasets used extensively in the literature. The first dataset used in our experiments is the Corel-1K [40] image dataset. The [PERSON]'s image dataset is comprised of a total of 1000 Corel images from diverse contents such as beach, flowers, horses, mountains, food, etc. The images are grouped into 10 categories with image sizes of \(256\times 384\) or \(384\times 256\) pixels. The second dataset is the Corel-1.5K image benchmark comprised of 15 classes with 100 images per category [40]. Figure 5 shows sample images from Corel-1K and Corel-1.5K, respectively.  
The third dataset used to validate the efficacy of the proposed RGSIR is the Corel-2K image benchmark. Corel-2K is a subset of Corel image dataset and is comprised of 2000 images classified into 20 semantic categories. Example images from this dataset are shown in Figure 6.  
Figure 4: The influence of the number of bins on the performance of RGSIR.  
Figure 5: Randomly selected images from each class of Corel-1K and Corel-1.5K image datasets [40].  
The forth dataset is the Oliva and Torralba (OT) dataset [41], which includes 2688 images classified into 8 semantic categories. This dataset exhibits high inter and intra-class variability, as the river and forest scenes are all considered as forest. Moreover, there is no specific sky category, since all the images contain the sky object. The average image size is \(250\times 250\) pixels and the images are collected from different sources (i.e., commercial databases, digital cameras, websites). This is a challenging dataset as the images are sampled from different perspectives, varying rotation angles, different spatial patterns and different seasons. Figure 7 shows the photo gallery of images for the OT image dataset.  
The last dataset used is our experiments is the RSSCN image dataset [33], released in 2015, comprised of images collected from Google Earth. It consists of 2800 images categorized into 7 typical scene categories. There are 400 images per class, and each image has a size of \(400\times 400\) pixels. It is a  
Figure 6: Class representatives from the Corel 2000 image dataset [40].  
Figure 7: Class representatives from the OT image dataset [41].  
challenging dataset, as the images in each class are sampled at 4 different scales, with 100 images per scale under varied imaging angles. Consistent with related work [33], the dataset is stochastically split into two equal image subsets for training and testing, respectively. Example images from this dataset are shown in Figure 8.
## 6 Parallelization of Algorithms  
The proposed tabu search algorithm (in short TSA) has been parallelized using the MPI library using the scheme of independent search processes with different starting points (MSSS according to [PERSON] [19] classification) On the cluster platform, there were parallel non-cooperative processes implemented with mechanism for diversifying startup solutions based on the Scatter Search idea. Each of the processors modified the solution generated by the MERTA algorithm by performing a certain number of swap moves proportional to the size of the problem and the number of a processor. A parallel reduction mechanism (MPI_Bcast) was used to collect the data. MWPTSA (Multiple-Walk Parallel Tabu Search Algorithm) parallel algorithm's pseudocode is given in Algorithm 3, and the scheme in Figure 2.  
```
Input :\(\Pi^{0}=\{\pi_{1}^{0},\pi_{2}^{0},\ldots,\pi_{p}^{0}\}\) - set of initial solutions; \(p\) - number of MPI processes; Output :\(\pi^{*}\) - the best-found solution;
1 parfor\(i=1,2,\ldots,p\)do
2\(\pi_{i}\leftarrow\pi_{i}^{0}\); (current solution, local for the process)
3\(\pi_{i}^{*}\leftarrow\pi_{i}^{0}\); (local best solution found so far)
4 repeat
5\(LT_{i}\leftarrow\emptyset\); (tabu list)
6 Generate neighborhood \(\mathcal{N}(\pi_{i})\) taking under consideration tabu list \(LT_{i}\) as well as aspiration criterion;
7\(\delta=\arg\min_{n\in\mathcal{N}(\pi_{i})}\mathcal{F}(\pi)\);
8 if\(\mathcal{F}(\delta)<\mathcal{F}(\pi_{i}^{*})\)then
9\(\pi_{i}^{*}\leftarrow\delta\);
10\(\pi-\delta\);
11 Add move attributes to the local tabu list \(LT_{i}\);
12 untilStop criterion (achieving solution with assumed or better value of the cost function);
13
14 Reduce \(\pi_{i}^{*}\), \(i=1,2,\ldots,p\), to the best value with using tree-based parallel calculating scheme;
15 return\(\Theta^{*}\)
```  
**Algorithm 3**Multiple-Walk Parallel Tabu Search Algorithm (MWPTSA)
# Dynamic Spectrum Access for Internet of Things Service in Cognitive Radio-Enabled LPWANs
## Appendix A
First, \(\widetilde{p}T=0\) and \(\widetilde{p}e+p_{c_{1}-1}R(I-R)^{-1}e=1\) in the expression (5) are rewritten as  
\[p_{0}A_{0}+p_{1}B_{1}=0 \tag{15}\]  
\[R^{2}B_{c_{1}}+R(A_{c_{1}}-d(A_{c_{1}}))+D=-Rd(A_{c_{1}}) \tag{5}\]0  
\[R^{2}B_{c_{1}}+R(A_{c_{1}}-d(A_{c_{1}}))+D=-Rd(A_{c_{1}}) \tag{5}\]1  
The dimension of the sub-matrix in the above equation is \((c_{2}+1)\times(c_{2}+1)\). Now we define the matrix \(H_{k}\), \(k=1,\text{ }\cdots,\text{ }c_{1}-2\) as  
\[H_{k}=\left[ \begin{array}{cc}-A_{k}D^{-1}&I\\ -B_{k+1}D^{-1}&0\end{array} \right] \tag{18}\]  
From (15), the following expressions are obtained:  
\[(p_{k-1},p_{k})=(p_{k},p_{k+1})H_{k} \tag{19}\]  
and  
\[(p_{0},p_{1})=\left(p_{c_{1}-2},p_{c_{1}-1}\right)H_{c_{1}-2}H_{c_{1}-3}\ldots H _{1} \tag{20}\]  
By combining (15), (17), and (20), the following expression is also obtained:  
\[p_{c_{1}-1}\Big{(}-\big{[}A_{c_{1}-1}+RB_{c_{1}}\big{]}D^{-1},I\Big{)}H_{c_{1} -2}\ldots H_{1}\left[ \begin{array}{c}A_{0}\\ B_{1}\end{array} \right]=0 \tag{21}\]  
Consequently, using (17), (21) and the normalization condition (\(\widetilde{p}T=0\) and \(\widetilde{p}e+p_{c_{1}-1}R(I-R)^{-1}e=1\)), the steady-state probabilities can be determined. Finally, the computational procedure is presented for getting the mean number of CU calls in the dynamic spectrum access strategy. \(N_{2}\) is given as following:  
\[N_{2}=\sum_{n_{1}=0}^{c_{1}}\sum_{n_{2}=0}^{\infty}n_{2}p(n_{1},\ n_{2})=\sum_{n_ {1}=0}^{c_{1}}\sum_{n_{2}=0}^{r-1}n_{2}p(n_{1},\ n_{2})+\sum_{n_{1}=0}^{c_{1}} \sum_{n_{2}=r}^{\infty}n_{2}p(n_{1},\ n_{2}) \tag{10}\]  
Here, the second item with infinite parameter can be solved as following:  
\[ \begin{array}{l}\sum_{n_{1}=0}^{c_{1}}\sum_{n_{2}=r}^{\infty}n_{2}p(n_{1}, \ n_{2})\left(P_{k}=P_{r-1}R^{k-r+1},\ k\geq r\right)\\ =\sum_{n_{1}=0}^{c_{1}}\sum_{n_{2}=r}^{\infty}n_{2}P_{n_{2}}=\sum_{n_{1}=0}^{c_ {1}}\sum_{n_{2}=r}^{\infty}n_{2}P_{r-1}R^{n_{2}-r+1}\\ =\sum_{n_{1}=0}^{c_{1}}P_{r-1}R^{-(r-1)}\sum_{n_{2}=r}^{\infty}n_{2}R^{n_{2}}= \sum_{n_{1}=0}^{c_{1}}P_{r-1}R^{-(r-1)}R\frac{\partial}{\partial R}\left( \frac{R^{r}}{I-R}\right)\\ =\sum_{n_{1}=0}^{c_{1}}P_{r-1}R^{-(r-1)}R\left(\frac{rR^{r-1}(I-R)+R^{r}}{(I-R )^{2}}\right)\\ =\sum_{n_{1}=0}^{c_{1}}P_{r-1}\left(\frac{rR(I-R)+R^{2}}{(I-R)^{2}}\right)\\ =\sum_{n_{1}=0}^{c_{1}}\left[rP_{r-1}R(I-R)^{-1}+P_{r-1}R^{2}(I-R)^{-2}\right] \\ =P_{r-1}R^{2}(I-R)^{-2}e+rP_{r-1}R(I-R)^{-1}e\end{array}  \tag{11}\]
# Study on Strength and Microstructure of Red Clay Reinforced by F1 Ionic Soil Stabilizer
## 3 Results and Discussion  
### Effect of F1 on Basic Physical Properties of Red Clay  
The basic physical parameters of solidified red clay with different F1 doses are displayed in Table 2. As the F1 dose increased, the clay's plastic limit increased initially and then decreased. The liquid limit and plasticity index decreased at first and then increased. The samples with 0.5 L/m\({}^{3}\) F1 exhibited a 45.74% higher plastic limit, 8.4% lower liquid limit, and 43.8% plasticity index than the virgin red clay, indicating the clay's water adsorption capacity and water sensitivity was reduced by F1. The F1 can hydrolyze to produce a high concentration of H\({}_{3}\)O\({}^{+}\) with a strong exchange potential energy, which can disrupt the electric double layer through ion exchange. The result was a thinner adsorbed water film and smaller inter-particle distance, which modified the soil's micropore structure and the interaction between soil grains, weakened capillary action, and resulted in increased plastic limit and reduced liquid limit and plasticity index [27,28].  
A similar trend could be observed in optimal water content, maximum dry density, and permeability coefficient, which also exhibited extrema around an F1 dosage of 0.5 L/m\({}^{3}\). At this dose, the optimum moisture content was 12.12% higher, the maximum dry density was 5.8% higher, and the permeability coefficient was 41.8% lower than virgin red clay. These effects were because F1 had reduced the thickness of combined water film and inter-particle spacing through ion exchange and the hydrophobic action of sulfonated oil, enhancing particle agglomeration under compaction, increasing compactnessm and reducing the permeability coefficient. The reduced water film also reduced water sensitivity, increased the resistance between particles, and increased the optimal moisture content of solidified red clay.
## 3 Methods
### Voxel-Grid Downsampling  
The point density of a point cloud obtained by a three-dimensional (3D) scanner varies depending on the distance between the scanner and the target of scanning. Objects that are far from the 3D scanner have a lower point density, whereas objects that are close to each other have a relatively higher point density. Meanwhile, the proposed method applies DBSCAN based on the x- and y-coordinates of the point cloud. From the perspective of DBSCAN operating on the x- and y-coordinates, the desired results cannot be obtained if the point density of the point cloud is not uniform. Therefore, to apply the proposed method, it is necessary for the point cloud to have a uniform point density. To achieve this, the method uses voxel-grid downsampling. Voxel-grid downsampling creates a voxel with a length equal to the previously defined voxel size, as shown in Figure 4. The voxel-grid downsampling recreates the representative point located at the center of the voxel instead of the points located inside the voxel. The voxel size was set to 0.05 m, which was appropriate for reducing the weight of the point cloud while preserving the shapes of the inner columns or the thickness of the inner wall. The authors utilized the voxel-grid downsampling algorithm in Open3d (ver. 0.17.0).
# Design Optimization of Truss Structures Using a Graph Neural Network-Based Surrogate Model
## 4 Results and Discussion
### Ten-Bar Planar Truss
#### 4.1.1 Data Preparation  
The initial stage involved generating truss structural design samples with element cross-sectional areas within a defined space of \(\textbf{A}\ \in\ [1,\ 15]\) in 2. Specifically, 1000 truss structural design samples were generated, whereby for each truss sample, 10 design variables were randomly assigned values within the specified range. The trusses were then analyzed using a finite element model, and the nodal displacements determined from the analysis were stored along with other information pertaining to the truss configuration. These data were subsequently utilized to create graphs representing the truss structures. Since the 10-bar planar truss is two-dimensional, and the applied loads are vertical, five node features are sufficient to effectively represent the truss. As shown in Figure 6, these features include the truss joint coordinates \((x,\ y)\), the vertical load \((F_{y})\) applied at the joints, and the two binary features (0 or 1) indicating the degrees of freedom associated with the joint translations. Additionally, each edge has a single feature that pertains to the corresponding truss element's cross-sectional area, \(A\), while each node contains two output features that correspond to the nodal displacements \((\delta_{x},\ \delta_{y})\) obtained via FEM.
## 6 Applying Anomaly Detection in Water Systems
### Training Methods  
As already mentioned in the previous sections, anomalies are rare obscure events in practice so labelled anomalous training data is hard to come by. This means that supervised learning as a classification problem would not yield good results because there might not be enough positive examples to train the algorithm. Based on the discussion in the previous section though, supervised learning can be used to learn the system behaviour and predict the system state given some time-series inputs. As with their fault detection counterparts, if the actual value is outside a predetermined error margin from the predicted value, it could then be flagged as an anomaly. In this case, labelled training data as it pertains to anomalous states is not required because the algorithm is learning how the system operated under normal condition and anything not adhering to that would then be classified as an anomaly. Anomalous examples are still required but not in the training process, they will rather be used to evaluate the proposed algorithms as will be discussed in the next section.  
To use parametric schemes, the probability distribution of that data has to be known beforehand. This knowledge can then be used to fit the training set to the model by calculating statistical parameters such as mean and standard deviation. The probability that given time-series inputs fall within normal system parameters can then be calculated and if the value is smaller than some predetermined threshold it is classified as an anomaly. This does not require a labelled training set so it can also be viewed as an unsupervised learning problem. Their non-parametric counterparts do not make any assumptions about the underlying probability distribution of the data but are similar in the way an anomaly is classified. The lack of labelled anomalous training data can thus be solved by ignoring anomalous data in the training process and rather using the little that is available for evaluation purposes.
## 3 Public Transport-Assisted Data-Dissemination System  
The proposed framework depicts the Public Transport-Assisted Data-Dissemination System (PTDD), which consist of smart cities that are equipped with wireless sensors and data centers to handle massive data dissemination for several categories of applications, as shown in Figure 1, using a set of buses picked up at each bus stop. PTDD is composed of a central controller and a data center, along with RSUs deployed at bus stops and onboard units on buses. Smart meters, video surveillance data, and air pollution data are some of the delay-tolerant applications and can tolerate delays ranging from seconds, to minutes, to hours.  
Over the last few years, we have witnessed the rapid growth of vehicles in urban areas together with the increase of internet-enabled devices integrated into vehicles [36]. Vehicles are being used as mobile nodes to create a mobile ad hoc network. They move randomly and communicate either with moving vehicles or fixed equipment such as RSUs. This alternative communications network layer of public transport networks will include public vehicles moving around the city. The flowchart given in Figure 2 gives an overview of the overall workflow of the proposed system. We will first apply the MADM methodology for the selction of the best network in the heterogeneous network, and next, we will offload data onto selected public vehicles to carry it until the destination for energy-efficient data transmission.  
Figure 1: Public Transport-Assisted Data-Dissemination System (PTDD)
## 2 Preliminaries
### Coding Gain and Diversity  
The coding gain signifies the relative horizontal shift in the BER versus the SJNR curve at asymptotic values of SJNR, whereas the diversity order is defined as the slope of fall of the same curve at high values of SJNR. Mathematically, we can write the asymptotic BER expression as [27]  
\[\lim_{\gamma\to\infty}\text{BER}(\gamma)\propto(G_{c}\gamma)^{-G_{d}}, \tag{12}\]  
where \(G_{c}\) corresponds to the coding gain, while \(G_{d}\) symbolizes the diversity order of the defined FSO system.