esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Phase-Field Model Validation for Dissolution of Minerals
## 4 Problem Description and Model Tests
### Benchmark with Analytical Model for One-Dimensional (1D) Congruent Dissolution  
The model consists of a one-dimensional domain with a size of 20 mm. The solid and liquid domains have a 3:17 ratio (see Figure 2). This is chosen to ensure that the length of the solution must be long enough for diffusion to take place in a system whose domain is considered to be semi-infinite.  
In the initial aqueous solution concentration \(c_{\mathrm{L}0}\) is specified as 0.1 mol/m\({}^{3}\). The concentration \(c_{S}\) of the solid was constant at 1 mol/m\({}^{3}\). The equilibrium concentration \(c_{\mathrm{Le}}\) was maintained at 0.4 mol/m\({}^{3}\) at the fluid-solid interface. The diffusion coefficient of the solute in the liquid \(D_{\mathrm{L}}\) is taken as 1.0 \(\times\) 10\({}^{-9}\) m\({}^{2}\)/s, while the diffusion of the solute in the solid \(D_{\mathrm{S}}\) is 1.0 \(\times\) 10\({}^{-15}\) m\({}^{2}\)/s. Neumann conditions (zero flux) were applied on the right and left side of the domain boundary. The parameters used in this study and their values are listed in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Description** & **Value** & **Unit** \\ \hline \(\Delta G\) & Gibbs free energy & 2233.23 & \(/m^{3}\) \\ \(A\) & curvature of the free energy density & 6.20 \(\times\) 10\({}^{3}\) & \(/m^{3}\) \\ \(\omega\) & height of the double well potential & 1.94 \(\times\) 10\({}^{4}\) & \(/m^{3}\) \\ \(L\) & interface mobility & 4.02 \(\times\) 10\({}^{-5}\) & \(m^{3}/(\int s)\) \\ \(k\) & gradient energy coefficient & 1.12 \(\times\) 10\({}^{-5}\) & \(/m\) \\ \(l_{0}\) & initial thickness of the diffuse interface & 1.0 \(\times\) 10\({}^{-4}\) & \(m\) \\ \(\sigma\) & interfacial energy & 1.1 \(\times\) 10\({}^{-1}\) & \(/m^{2}\) \\ \(D_{\mathrm{L}}\) & diffusion coefficient in solution & 1.0 \(\times\) 10\({}^{-9}\) & \(m^{2}/s\) \\ \(D_{\mathrm{S}}\) & diffusion coefficient in solute & 1.0 \(\times\) 10\({}^{-15}\) & \(m^{2}/s\) \\ \(c_{\mathrm{Se}}\) & saturation concentration in the solid phase & 1.0 & \(mol/m^{3}\) \\ \(c_{\mathrm{Le}}\) & saturation concentration in the solution & 4.0 \(\times\) 10\({}^{-1}\) & \(mol/m^{3}\) \\ \(R\) & gas constant & 8.31 & \(/(K\cdot mol)\) \\ \(T\) & ambient temperature & 2.93 \(\times\) 10\({}^{2}\) & Kelvin \\ \(K\) & equilibrium constant & 0.4 & [-] \\ \(t\) & calculation time & 2.88 \(\times\) 10\({}^{4}\) & \(s\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of benchmark parameters.  
Figure 2: Initial benchmark configurations and boundary conditions for 1D single-component dissolution of a planar solid.
# Towards Interactive Analytics over RDF Graphs
## 6 Translation of HIFUN Queries to SPARQL
### Analytics and RDF Schema Semantics  
The translation of SPARQL allows leveraging the RDF Schema semantics, specifically the RDF Schema-related inference that is supported by SPARQL. To give a simple indicative example, consider two properties directorOf and worksAt such that (directorOf, rdfs:subPropertyOf,worksAt) and suppose the following data:  
(p1,directorOf,brand) (p2,worksAt,brand1) (p3,worksAt,brand1) (p4,worksAt,brand1) (p1,livesAt,Athens) (p2,livesAt,Rhodes) (p3,livesAt,Corfu), (p4,livesAt,Corfu) If we want to compute the locations where the persons related to brand1 work at, and how many live at each place, we could use the following HIFUN query (_workAt_, _livesAt_, _COUNT_). If the inference of SPARQL is enabled, then the translated query will return Athens,1 Rhodes,1 Corfu,2 The key point is that the location of the director will be considered, since it is inferred that (p1,worksAt,brand1). Such inference would not be possible if we translated the data to the relational model.  
The ability to leverage the inference rules of RDF Schema in the context of analytic queries is especially important for datasets which are described with ontologies that contain high number of subClassOf and subPropertyOf relationships for achieving semantic interoperability across various datasets, like those in the cultural and historic domain [7; 42].
# Unsupervised Feature Selection for Outlier Detection on Streaming Data to Enhance Network Security
## 2 Background
### Feature Selection
However, statistical measures must be carefully chosen based on the type of input variable and the model outputs. From an online application perspective, filter approaches seem to be the most promising candidates since they do neither demand offline training nor rely on multiple iterations. Similar to labeling in ML, FS can be broadly classified from a supervision perspective into supervised and unsupervised methods with respect to use or ignore the target variables. Over recent years, unsupervised approaches have gained attention since acquiring labeled data is particularly expensive in both time and effort. Furthermore, sufficient label information is usually not available in real-world applications. With respect to the data perspective, FS can be classified according to [8] into static and streaming data.
# A Cloud-Based Real-Time Mechanism to Protect End Hosts against Malware
## 2 Background
### System Services Descriptor Table  
A kernel array called system services descriptor table (SSDT) stores all native API addresses. SSDT is not only a huge address index table, but also includes other useful messages such as the numbers of system services and the byte allocations of the arguments of each system service. By modifying an SSDT entry, we can hook a critical Windows native API to intercept the parameters passed to the native API. Nowadays, most of the Antivirus software still uses this technique to monitor and catch the abnormal behavior in the kernel.  
In a Windows system, a system service dispatcher gets a system call number as an index from a stub function and looks up the system service dispatch table (SSDT) to obtain the entry address of related system call service routine. After finding out the entry point, it jumps to the system call service routine to handle the request. Kernel function KiSystemService, shown in Figure 1, implements the system service dispatcher. However, before system service dispatcher gets the system call number, the stub uses EAX register, an extended (32-bit) processor register of x86 CPUs, to pass a system call number into the kernel.  
A system call number is divided into three parts. The 12 least significant bits of it are an index of the SSDT. Bits 12-13 are an index of the service descriptor table (SDT). Windows system reserves two bits for service descriptor tables, which means that there should be four SDTs, but Windows only uses two SDTs. One of the SDT tables is KeServiceDescriptorTable and the other is KeServiceDescriptorTableShadow. Both service descriptor tables hold a critical kernel structure called SystemServiceTable to record important information about an SSDT. Figure 2 illustrates how the EAX register and service descriptor tables are used to find the entry point of a service routine. Two SSTs are declared in KeServiceDescriptorTableShadow. One is SST0, which points to the base address of SSDT1. The other one stores information about SSDT2. SSDT2 is not used in our system. KeServicDescriptorTable declares only SST0, which also points to the base address of SSDT1.
# Lossless Compression of Large Aperture Static Imaging Spectrometer Data +
## 3 Compression Scheme  
Due to the unique properties of the LASIS, we divide the lossless compression of LASIS images into three steps: data rearrangement, prediction, and encoding. The three functional stages of this compression scheme are provided in Figure 3.
## 3 Biological Mechanisms in Bacterial Adhesion as Potential Subjects of Label-Free Monitoring
### Adhesive Molecules of Bacteria and Bacterial Entry into Host Cells
#### 3.1.5 Bacterial Entry into Host Cells
In general, phagocytosis is the route of invasion for bacteria, the accession into professional phagocytes, for instance monocytes, neutrophils, and macrophages [15]. _Salmonella_ and _Shigella_ use the “triggering” mechanism their penetration into host cells, where a brief contact with the surface of the host cell eventuates a fast cytoskeletal alteration in which explosive actin polymerization in the cytoplasm pushes out large membrane extensions, the membrane ruffles [15]. The “zipper”-type invasion procedure includes a single bacterial surface ligand that tightly contacts host cell adhesion molecules. Thus, the entry process is driven by the tight binding between cell adhesion molecules and bacterial adhesins [15]. Adapted with permission from ref. pseudopod-like structures surrounding the microbe and eventually results in the penetration of the bacterium into the so-called bacterial phagosome, a membrane-bound vacuole. The cadherin and integrin receptors that play a role in the internalin-mediated entry process are connected with the actin cytoskeleton via a multifactoral protein complex that links the actin filaments with the cytoplasmic domains of the receptors [17]. This method is used by _Listeria_ and _Yersinia_, Gram-positive and Gram-negative bacteria, applying a different host cell receptor [17; 316]. Figure 4 shows the possible ways of bacterial entry into host cells.
## 2 Materials and Methods
### Electrochemical Performance Evaluation  
The electrochemical performance of the composite fiber anodes was investigated using 2032 coin-type cells. The free-standing fiber anodes had a weight ranging from 3 to 7 mg. Lithium metal chips were used as the counter electrode, while glass microfibers were used as the separator. The electrolyte used consisted of 1 mol/L LiPF\({}_{6}\) salt in EC/DMC (1:1 by volume) solvent. The coin cells were then assembled in a high purity argon-filled glove box (Munich, Mbraun, Germany) with O\({}_{2}\) and H\({}_{2}\)O concentrations of \(<\) 0.5 ppm. The composite-fiber anode is 1 cm in diameter and 100-300 microns in thickness, depending on the weight of the free binder electrode (the mass of the composite fiber anode is usually between 2 mg to 4 mg). Next, 6 to 10 drops of the liquid electrolyte were added to each cell during the cell assembly in the glove box. The electrochemical performance was evaluated by carrying out galvanostatic charge-discharge experiments on using CR2032 coin type cells at a current density of 100 mA g\({}^{-1}\) between 0.05 and 3.0 V using a LANHE CT2001A battery testing system.  
Figure 1: As-spun \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\)/TiO\({}_{2}\)/C precursor fibers (**A**); the composite fibrous mat as collected after centrifugal spinning (**B**); the composite fibers after the carbonization process (**C**); free-standing and binder free \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\)/TiO\({}_{2}\)/C composite fibers held with a tweezer (**D**). The composite fiber anodes show excellent flexibility.  
The applied cycling current and specific charge/discharge capacities were calculated based on the mass of the composite-fiber anodes. Cyclic voltammetry was carried out using Biologic BCS-810, and electrochemical impedance experiments were performed using Autolab 128 N with a scan rate of 0.2 mVs\({}^{-1}\) and at frequencies from 0.1 Hz-1 kHz. Finally, rate performance tests were carried out on coin cells at current densities of 50, 100, 200, 400, and 500, mA g\({}^{-1}\) using an Arbin battery testing system. The current density was calculated with respect to the mass of the binder free electrode.
## 6 Real Jamming Results Analysis
### High jamming Scenario
#### 6.2.1 First Trajectory  
As shown in Figures 26 and 27, the GPS DF, GPS TF, GPS TF + Galileo SF, and GPS TF + Galileo TF-based solutions fail to provide the anticipated continuous positioning solution during jamming intervals. The inclusion of either the GLONASS SF or the GLONASS DF signals leads to a continuous positioning solution during jamming intervals. However, the GPS TF + Galileo TF + GLONASS DF-based solution has superior positioning performance compared to the GPS TF + Galileo TF + GLONASS SF counterpart.  
The minimum number of visible satellites using the GPS, Galileo, and GLONASS signals is 9, compared to less than one by using either GPS or GPS + Galileo signals, as shown in Figure 28.  
It is clear from Table 8 that the GPS TF-based solution is worse than the GPS DF solution due to the lower availability of the GPS DF compared to the GPS TF counterpart. Similarly, the GPS TF + Galileo TF solution shows worse performance than the GPS TF + Galileo SF counterpart, as the availability of the GPS TF + Galileo SF is less than the GPS TF + Galileo TF counterpart. The most accurate positioning solution is achieved through the GPS TF + Galileo TF + GLONASS DF combination. The RMSE of the GPS TF + Galileo TF + GLONASS DF positioning solution is improved by about 79% and 52%, respectively, compared to the GPS TF + Galileo TF counterpart.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Combination**} & \multicolumn{3}{c}{**Horizontal**} & \multicolumn{3}{c}{**Vertical**} \\ \cline{2-7}  & **RMSE** & **Mean** & **Max** & **RMSE** & **Mean** & **Max** \\ \hline GPS DF & 6.079 & 3.587 & 114.251 & 15.068 & 3.660 & 330.936 \\ \hline GPS TF & 2.958 & 2.881 & 7.301 & 4.152 & 2.517 & 23.592 \\ \hline GPS TF + Galileo SF & 2.721 & 2.573 & 6.517 & 3.425 & 2.265 & 21.818 \\ \hline GPS TF + Galileo TF & 2.307 & 2.155 & 6.009 & 2.070 & 1.625 & 8.898 \\ \hline GPS TF+ Galileo TF + GLONASS SF & 2.693 & 2.622 & 6.534 & 4.143 & 2.814 & 13.929 \\ \hline GPS TF + Galileo TF + GLONASS DF & 1.787 & 1.585 & 3.640 & 1.151 & 1.014 & 4.519 \\ \hline \end{tabular}
\end{table}
Table 7: RMS error, mean, and Max positioning errors with low actual jamming [second trajectory].  
Figure 28: Number of visible satellites for different GNSS signal combinations [first trajectory].  
Figure 27: Vertical positioning error for different GNSS signal combinations [first trajectory].
# Research on Localization Algorithms Based on Acoustic Communication for Underwater Sensor Networks
## 6 Conclusions  
The localization in UWSNs is similar to the indoor positioning for the WSN where the TOA, AOA, TDOA, and RSS algorithms can be used in UWSNs. However, there are still many differences between the acoustic communications. Because of the unpredictable properties of the underwater environment, more factors that may affect the accuracy of the localization need to be taken into account and more difficulties need to be overcome. In our paper, we first describe the acoustic communication, network architecture, and routing techniques of UWSNs. Different from other survey papers, the positioning algorithms are classified into five aspects based on various criteria, namely, computation algorithm, spatial coverage, range measurement, the state of the nodes and communication between nodes. In each class, the subcategories are contained. Lots of pioneering papers in the field are collected and compared. Moreover, a comprehensive comparison is presented. General challenges and open issues about positioning algorithms based on acoustic communication for UWSNs are also discussed.  
**Acknowledgments: This work was supported in part by the National Natural Science Foundation of China and the Civil Aviation Administration of China under Grant U1733110, in part by the Overseas Academic Training Funds, University of Electronic Science and Technology of China under Grant 201506075013, and in part by the Program for Science and Technology Support in Sichuan Province under Grant 2014 GZ0100 and Grant 2016 GZ0088.**Author Contributions: [PERSON] and [PERSON] conceived and designed the taxonomy method and the structure of this paper; [PERSON] and [PERSON] collected the information and analyzed the data; [PERSON] wrote the paper.**Conflicts of Interest: The authors declare no conflict of interest.**
## 2 Materials and Methods
### Criteria for Selecting Optimal Number of Latent Classes  
To select the optimal number of clusters, three performance evaluation metrics are used: Akaike information criterion (AIC), Bayesian information criterion (BIC), and log-likelihood (LL). Both AIC and BIC are calculated based on the value of the log-likelihood (LL). LL is the logarithm of the likelihood ratio, which is a test comparing the fit of two models by examining the predictive power of one model compared to the other [37]. According to [PERSON] and [PERSON] [3]. AIC and BIC are computed for each latent class model, and are defined as in Equations (2) and (3), where \(k\) is the number of estimated parameters in the model, and \(n\) is the number of observations used in the model. Comparing AIC and BIC values, the model with the smallest value is considered a better fit. The difference between AIC and BIC is that BIC chooses models with fewer parameters than AIC does.  
\[AIC=-2 LogLikelihood+2k \tag{2}\]  
\[BIC=-2 LogLikelihood+k\;ln(n) \tag{3}\]
## 3 Results  
### Evaluation of the Object Detection Model  
The results of the object detection model are summarized in Table 1, which presents the AP values for each of the three major coronary arteries (RCA, LCA-LAD, and LCA-CX) across five folds. For the RCA, the AP values ranged from 0.68 (fold5) to 0.72 (fold2 and fold4), resulting in a mean of 0.71. The LCA-LAD showed the lowest AP of 0.62 in fold2, while the highest was 0.78 in fold4, with a mean of 0.70. Meanwhile, the AP for the LCA-CX varied between 0.57 (fold4) and 0.64 (fold3), producing an overall mean of 0.61. Notably, the highest single-fold AP observed was 0.78 (LCA-LAD in fold4), whereas the lowest was 0.57 (LCA-CX in fold4). These findings indicate that the model performed best in detecting the RCA, followed by the LCA-LAD, and lastly the LCA-CX.  
Figure 2: (**a–d**) show axial views of CCTA images. (**a’-d’**) display the corresponding bounding boxes for the ground truth (yellow dashed lines with yellow filling) and inference results (RCA: cyan, LCA-LAD: magenta, LCA-CX: green). The center points of the bounding boxes are marked with yellow circles for the ground truth and green ”\(\times\)” marks for the inference results.
## 4 Challenges and Limitations of Nanosensor Deployment
### Signal Processing and Data Interpretation  
#### 4.1.1 Unique Challenges in Nanosensor Data Management  
Unlike conventional sensors, nanosensors can be sensitive to nanoscale perturbations, resulting in amplified noise or erroneous readings. This sensitivity necessitates the development of specialized signal processing algorithms that can discern actual structural changes from data noise, possibly arising from minute environmental variations or material inconsistencies [87].
# Evaluation of Nitrogen and Phosphorus Removal
## 1 Introduction
MBBR systems have simple and compact designs, can operate under varying temperatures, and sustain the growth of microorganisms [12]. Incorporating inoculant at the startup of bioreactors is typically done to establish populations of nitrifying bacteria [13; 14]. When designed to accommodate influent wastewater characteristics, sufficient hydraulic retention time [15], and appropriate ratio between carrier volume and tank volume [12], MBBR systems have been shown to effectively treat municipal wastewater [11; 16], industrial wastewaters [17], and dairy processing wash waters [18]. The main component of this system is a denitrifying woodchip bioreactor; a passive treatment technology consisting of a bed of saturated woodchips that provide anerobic conditions and a carbon (C) source for microbial denitrification, in which nitrate (NO3\({}^{-}\)) is converted to inert dinitrogen gas (N2) or, in some cases, nitrous oxide (N2O) [19]. Woodchip bioreactors have been shown to effectively improve water quality from wastewater sources containing elevated N concentrations [20]. While most bioreactor studies focus on NO3\({}^{-}\) removal, reductions of other water quality parameters such as P [21], BOD [22], and fecal indicator bacteria (_Escherichia coli_) have also been reported [23]. Designs that consider variables such as optimal NO3\({}^{-}\) loading rates [24] and hydraulic retention time [25] have been successful in a number of settings treating agricultural tile drainage [19; 26], dairy farm wastewaters, [27], aquaculture effluent [28], and septic tank effluent [23]. This system is the first application of a denitrifying bioreactor used to treat silage leachate or silage bunker runoff. To address the lack of reliable treatment technologies for silage storage bunker runoff, this study evaluated the nutrient removal performance of an innovative treatment system including treatment tanks with MBBR, followed by denittifying woodchip bioreactors, on a research farm located in Vermont, USA. The objectives of this study were to:  
1. Characterize the N and P composition of storm-driven silage storage bunker runoff. 2. Evaluate concentration reductions and nutrient mass removal of the two components of the treatment system (i.e., the treatment tanks with MBBR, and the denitifying woodchip bioreactors) during storm events. 3. Consider the design of the system and its effectiveness in treating silage storage bunker runoff in order to offer recommendations for future designs.
## 5 Experiment Results and Discussions
### The Proper Time Window for Detecting Known Attacks
Additionally, Figure 5a demonstrates the impact of increasing the time window of OpenFlow traffic analysis on the detection results of K-NN trained models; a longer time window decreases the K-NN classifiers detection models performance. For the SVM classifier, Figure 5b presents the detection performance using different time windows. The SVM trained model acquires the highest detection rate with 89% recall, 81% precision, and 85% F1 score when the time window is equal to 2 min. When the time window is equal to 20 min, the detection performance is decreased drastically to 35% recall, 7% precision, and 11% F1-score. The NB classifiers trained models did not behave differently from K-NN and SVM. When the time window increases, the detection performance decreases and vice versa. As shown in Figure 5c, the NB classifier has the highest detection performance with 96% recall, 85% precision, and 91% F1 score when the time window is equal to one minute. When the time window is equal to 20 min, the NB classifier has the lowest detection performance with 37% recall, 11% precision, and 17% F1 score. The experimental results in both the physical and simulated SDN environments demonstrate the crucial rule of time window on the detection performance of machine learning classifiers. A longer time window led to a decrease in the detection performance of saturation attacks.