esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Metadata Systems for Name Disambiguation  
This section describes the chief metadata systems and databases for name metadata. Name metadata databases fall into one of two categories. They are either native databases, or they are an aggregation of names from several native databases. It is also possible to link individual name metadata records to  
\begin{table}
\begin{tabular}{|p{142.3 pt}|p{142.3 pt}|} \hline
**Element** & **Description / Notes** \\ \hline Preferred or authorized form of the name & Referred to as the _heading_ \\ \hline Other forms of the name, including earlier names, nicknames, pseudonyms, shortened or longer forms of the name, name in other languages or scripts, names associated with the person’s office (such as Governor of Colorado) & Referred to as _cross references_ \\ \hline Gender & \\ \hline Life events & Includes things such as place of birth and death, place associated with the entity’s output, titles held (including titles of nobility), job, nationality, elected or other offices held, military positions held; events associated with the person \\ \hline Institutional affiliations & University where degrees were earned, places of employment, including dates \\ \hline Notes & Notes that help identify the entity, e.g. “Author of the Norton anthology” or “Not the same as [PERSON], archaeologist” \\ \hline Family & Spouse, parents, children \\ \hline Works & List of books, articles, art, _etc._ associated with the person; need not be comprehensive \\ \hline Subject expertise or genres that the person normally creates in & For example: _Writes on supply side economics_ or _Illustrator of children’s books._ \\ \hline Languages the person normally writes or creates in or the person’s native language(s) & \\ \hline Brief biography & \\ \hline Unique identifier & Such as a record number \\ \hline Dates of the metadata record & When created and edited \\ \hline Links to contact information & E.g., author’s email address \\ \hline \end{tabular}
\end{table}
Table 1: A listing and description of the elements that might be included in a name metadata record. Some of this description is based on information in the publication entitled _Networking Names_ by [PERSON] [3].  
their counterpart records in other databases [7]. Organizations such as national libraries, database producers, standards organizations, and publishers are putting more effort into controlling and standardizing names in online databases. Some efforts invite participation from the people whose names are actually in the databases - the authors themselves.
## 3 Experimental Analysis and Results
### Quantitative Analysis
The reason for this is the number of iterations required in these methods to generate an optimal threshold value, whereas the computational cost is less for the Soundrapandiyan et al. and the proposed method. The Soundrapandiyan et al. method requires the background subtraction of the image, which is an additional step compared to the proposed method, and this additional step causes a higher computational cost than the proposed method. The simple mathematical structure of the proposed method to produce the optimal threshold value causes the lowest computational cost of all the methods listed in Table 4.  
Figure 6: Comparison of the PSNR values for the five methods.
## 7 Plasmon-Polariton Propagation in Linear Periodic Ionic System
### Possible Linkage with the Topological Model of Neuron Web
The saltatory conduction is apparently of a wave type because it is maintained even if a myelinated axon is divided into two disjoint pieces with ends separated slightly by an insulating barrier impossible to be crossed by any diffusive ion current. We have provided the model for the saltatory conduction based on the kinetic properties of collective plasmon-polariton modes propagating along linear and periodically myelinated electrolyte system, which in the case of axon is the thin cord of the nerve cell periodically wrapped by Schwann cells creating thick myelin sheath (Schwann cells myelinate axons in the peripheral system, whereas in the central neural system the white matter is built from axons myelinated by the oligodendrocyte cells). Plasmon-polaritons were previously investigated and understood based on the very well developed nano-plasmonics in metal-chains [15; 16]. It was demonstrated a long range low-damped propagation of plasmon-polaritons along metallic nano-chains [17]. Let us recall the main properties of these collective excitations occurring in metallic nano-chains on the conductor/insulator interface [18; 19] by hybridization of surface plasmons (i.e., fluctuations of charge density of electron fluid on the metal surface) with the electro-magnetic wave:  
1. Much lower velocity of plasmon-polaritons in comparison to the light velocity, which gives much shorter wavelength of plasmon-polaritons than light wavelength with the same frequency, this causes a strong discrepancy of momenta of plasmon-polaritons and photons with the same energy; thus, plasmon-polaritons do not interact with EM waves, i.e., photons cannot be excited or absorbed by plasmon-polaritons,
2. Radiative losses of plasmon-polaritons do not exist and plasmon-polariton attenuation is only due to Ohmic losses, which makes metallic nano-chains almost perfect waveguides for plasmon-polaritons,
3. Long range and practically undamped propagation of plasmon-polaritons is observed experimentally [17; 20]. Some of the unique properties of plasmon-polaritons in metallic nano-chains are of especial interest as they can be repeated in periodic linear alignments of electrolyte systems with ions instead of electrons as charged carriers. Due to larger mass of ions in comparison to electrons and lower concentration of ions in electrolytes than the concentration of electrons in metals, plasmon resonancesin finite ionic systems (e.g., in liquid electrolyte confined to the finite volume by appropriately formed membranes frequent in biological cell organization) occur in a different than in metals scale of micrometers instead of nanometers and for several orders lower frequency or energy (depending on ion concentration).
## 5 Analysis of Numerical Simulation Results
### Influence Analysis of Vertical Spacing between Pipes  
Based on the established model, the influence of vertical clear spacing were explored. The clear spacing between two pipes is 0.5 D and 2 D, respectively (D is the outer diameter of the jacking tube). Figure 12a shows the position of lower pipe jacking when the spacing is changed. Figure 12b shows the comparison of the ground settlement caused by pipe jacking with different vertical spacing. Table 3 shows the value of ground settlement caused by pipe jacking under different vertical clear spacing. It can be seen that, with the larger of pipe vertical distance, the buried depth of the lower pipe in the rock layer was increased, the influence of the lower pipe jacking on the ground settlement was decreased, and the total ground settlement decreases obviously. When the vertical clear spacing is 0.5 D, the total ground settlement is 6.6 mm, which is about four times the ground settlement when the spacing is 2.0 D.  
Figure 11: Comparisons of horizontal lateral displacement produced by pipe jacking.
## 3 Methods
### Target Information Inversion Method Based on GNSS-R Detection  
In signal processing, the detection results of different satellites can be separated by signal range codes. After acquiring multiple satellite detection results for the targets, the different target peaks in the range dimension can be distinguished when the targets keep a sufficiently large distance. Additionally, we can also distinguish them by the difference in beam direction. To acquire the precise coordinate information of targets in bistatic detection, we can determine the target coordinates by detecting the target range and velocity information, satellite information, and receiver information.  
\[N^{i,j}\cdot T_{P}\cdot\mathbf{C}+r^{i,j} =\sqrt{\left(x_{s}^{i}-x_{t}^{j}\right)^{2}+\left(y_{s}^{i}-y_{t }^{j}\right)^{2}+\left(z_{r}^{i}-z_{t}^{j}\right)^{2}} \tag{21}\] \[+\sqrt{\left(x_{r}-x_{t}^{j}\right)^{2}+\left(y_{r}-y_{t}^{j} \right)^{2}+\left(z_{r}-z_{t}^{j}\right)^{2}}\] \[\left(\mathbf{v}_{r}\cdot\mathbf{\theta}_{r}^{i,j}+\mathbf{v}_{s }^{i}\cdot\mathbf{\theta}_{r}^{i,j}\right)/\lambda+(\mathbf{v}_{t}^{i,j}\cdot \mathbf{\theta}^{i,j,1}+\mathbf{v}_{s}^{i}\cdot\mathbf{\theta}^{i,j,2})/\lambda\] \[=M^{i,j}\cdot PRF+fd^{i,j} \tag{22}\]  
The target coordinate information can be acquired by the satellite coordinate information from the signal data and the range detection results, as shown in Formula (21). We can also obtain the target velocities through the Doppler detection results as (22), where \(x_{s}^{i},y_{s}^{i},z_{s}^{i}\) are the coordinates of satellite \(i\), and \(x_{t}^{j},y_{t}^{j},z_{t}^{j}\) are the coordinates of target \(j\). \(N^{i,j}\), and \(r^{i,j}\) are the cycle number and distance during a cycle of detected results of target \(j\) and satellite \(i\), respectively. In (22), \(\mathbf{v}_{t}^{i,j}\cdot\mathbf{\theta}^{i,j,1}\) expresses the component of the velocity of target \(i\) in the direction of the bistatic angular bisector between satellite \(i\) and the receiver, and \(\mathbf{v}_{s}^{i}\cdot\mathbf{\theta}^{i,j,2}\) is the component of the velocity of satellite \(i\) in the direction of target \(j\). \(\mathbf{v}_{r}\cdot\mathbf{\theta}_{r}^{i,1}\) and \(\mathbf{v}_{s}^{i}\cdot\mathbf{\theta}_{r}^{i,2}\) are the corresponding velocity components between target \(i\) and the receiver. \(M^{i,j}\) and \(fd^{i,j}\) are the Doppler cycle number and Doppler value of the detected results of target \(j\) and satellite \(i\), respectively.
# Performance Evaluation of a Long-Span Cable-Slayed Bridge Using Non-Destructive Field Loading Tests
## 2 Non-Destructive Field Loading Test for Pingnan Xiangszhou Bridge  
### Objective of Non-Destructive Field Loading Tests  
The purpose of the field loading test was to ascertain the bearing capacity and predict the future reliability of Pingnan Xiangszhou Bridge. The specific objectives of the load test included:  
1. Defining the real load of the bridge under static load conditions;
2. Verifying the rationality of the design, and providing reference for similar bridge design;
3. Verifying the validity of the finite element model, which provides the basis for the model's improvement and optimization;
4. Providing data for bridge monitoring and maintenance.  
For cable-stayed bridges, loading on the main girder is transferred to the piers and tower through the stay cables. Thus, the structural responses of the main girder and the stay cables are critical in the process of bridge operation. Therefore, the main measurement tasks of the load test of Pingnan Xiangszhou Bridge included deflection and strain testing of the main girder, strain testing of the cable tower, and cable force testing.
## 6 Summary  
A commissioning procedure based on the dispersion measurements at the screen downstream from the chicane has been developed. In order to achieve optimum bunch compressor operation, the first PITZ bunch compressor commissioning was performed to obtain the set of dipole magnet currents that provides zero momentum dispersion for a bunch charge of 50 pC. The procedure also yielded correlations between dipole magnet currents. By applying these chicane dipole current settings, the first bunch compression experiments were performed, where pyroelectric detector signals from the CTR station downstream from the bunch compressor were used to find a maximum compression phase of the booster cavity.  
In other words, we demonstrated zero-dispersion transport of the low-energy electron beam throughout the chicane with the high bending angle, where space charge and CSR effects drastically affect bunch compressor performance. With limited beam diagnostic stations downstream from the bunch compressor, we finally obtained the experimental results to verify the simulation study.  
Figure 10: Plot of maximum-compression booster phase as a function of bunch charge for 17 MeV/c beam. Red and blue markers represent IMPACT-T and OCELOT simulation results, respectively, while green markers represent corresponding experimental results.  
Figure 9: Plot of measured CTR pulse energy per electron bunch as a function of the booster phase for bunch charges of 30, 50, 100, and 200 pC.
## 2 Rack Cutters and Gear Surfaces
### Tooth Contact Analysis (TCA)  
We generate the manufactured gear surface, which can be represented in 3D. However, we assume that spur gear has a constant profile in the longitudinal direction to simplify the tooth contact. We focus on the contact analysis of spur gear pair under 2D simplification. A contact between two rotating gears must be defined to calculate the transmission error. To analyze the contact between the gears, we consider the relationship between the coordinate systems of the spur gear pair and their local coordinates \(\mathbf{C}_{S1}[X_{S1},Y_{S1},Z_{S1}]\) and \(\mathbf{C}_{S2}[X_{S2},Y_{S2},Z_{S2}]\). Figure 3 shows the local coordinates of the spur gear pair, where \(\psi\) is the rotation angle of gear and \(d_{g}\) is the distance between the two gears.  
Figure 2: Family of rack cutter surfaces.  
The Newton-Raphson method [25; 26] is used to solve Equation (9) and obtain the four unknown parameters \((u_{1},u_{2},\psi_{1},\psi_{2})\). In the \(i\)-th iteration, the method yields a solution \(\mathbf{X}_{i}\) satisfying  
\[\frac{\partial\mathbf{P}}{\partial\mathbf{X}}[\mathbf{X}_{i+1}-\mathbf{X}_{i} ]=-\mathbf{P}(X_{i})\quad(i=1,2,3\ldots),\quad\mathbf{P}=\big{[}\mathbf{P}_{1 }\ \mathbf{P}_{2}\ \mathbf{P}_{3}\big{]}^{T}, \tag{10}\]  
which converges to \(\mathbf{X}_{i}\longrightarrow\mathbf{X}\) as \(i\rightarrow\infty\). If one of the unknowns, say \(\psi_{1}\), is determined, we can find the solution \(\mathbf{X}\) for the parameters \((u_{1},u_{2},\psi_{2})\) from the Newton-Raphson iterations of Equation (10). The TE can be calculated as  
\[\begin{split} TE(\mu m)&=r_{b2}(\Delta\psi_{2})\\ &=r_{b2}(\psi_{2}-\frac{n_{1}}{n_{2}}\psi_{1}),\end{split} \tag{11}\]  
where \(n_{1}\) and \(n_{2}\) are the number of teeth of the driving and driven gears, respectively, and \(r_{b2}\) is the base circle radius of the driven gear.
# Managing Access to Confidential Documents: A Case Study of an Email Security Tool
## 7 Limitations and Future Work  
Our work is not without limitations. Firstly, we were unable to recruit a more diverse sample despite advertising the study on a university mailing-list, social media, Craigslist, and through flyers. However, a majority of the interested individuals who ended up completing the study were students. Therefore, our results may be considered exploratory and cannot be generalized for all users. Including non-university participants, such as less tech-savvy users or employees who frequently share work-related sensitive information, would enhance the study by ensuring a more diverse sample.  
We asked simple questions to evaluate our participants' understanding of encrypted emails, such as whether someone could access their email content. Further research is needed to ask detailed and less technical questions to measure users' understanding between point-to-point and end-to-end encryption using secure communication tools. Additionally, we targeted first-time users, since we were interested in first impressions and the likelihood of its adoption for sharing and receiving sensitive content via email. A follow-up study is needed to compare the findings in this study with those of existing Virtru users.  
Virtru provides more controls to track the emails in the control center after they have been sent (e.g., checking if the email is accessed or shared by the recipients, providing validation reports, and listing all authorized recipients who access the email). It would be interesting to investigate what happens beyond such tracking, such as with the following questions:  
* Do users review their confidential emails to check if they have been accessed by recipients?* Are users concerned about their emails if they stay in the recipient's email inbox based on the set expiration date?
* What are the users' expectations of the email deletion concept (e.g., the undo feature vs. revoking access at any time)?  
A future study can be conducted to evaluate the effectiveness of risk communication notifications added to Gmail's web interface via a browser extension once Gmail users share sensitive data or confidential attachments.  
Aside from end-to-end encryption of email data, our participants were comfortable managing its access control using the expiration date and disabled forwarding features. However, some participants were confused about the attachment features (watermarking and persistent protection). A future study is needed to evaluate the impact of risk communication by presenting scenarios highlighting the importance of using the attachment features tailored to specific populations, such as a scenario showing how students watermark authorized recipients' names on their academic papers or school projects when sharing with other students to prevent recipients from leaking their sensitive data.
# Microbially Induced Desaturation and Carbonate Precipitation through Denitrification: A Review
## 2 The Denitrification Mechanism  
Organisms that are capable of denitrification, that is, denitrifying bacteria, are widely distributed with a high density in nature. These types of microorganisms are common in a variety of environments, and in agricultural soil they reach a population density of the order of 106 microorganisms/g of soil [21]. Typically, denitrifying bacteria constitute about 20% of the total microbial population that can grow under anaerobic conditions and their population corresponds to about 1% to 5% of the overall culturable soil microbiota [22]. More than 50 genera have been identified, including _Bacillus_, _Acaligenes_, _Diphorhotacter_, _Pseudomonas_, _Spirillum_, _Paracoccus_, _Thioobacillus_, and _Achromobacter_[23]. Thus, so far, many studies have used different denitrifying bacteria to link their nitrate reduction activity with either CaCO3 precipitation or biogas generation or the combination of both (e.g., [8; 13; 20; 21; 24]).  
Denitrification, or nitrate reduction, is an essential process in the global nitrogen cycle, in which the fixed nitrogen is cycled back into the atmosphere as N2 gas. Thus, it closes the global nitrogen cycle and keeps the ecosystem in balance. Most denitrifying bacteria undertake denitrification in the presence of organic carbon and nitrate when oxygen is scarce or unavailable [2].
## 2 Materials and Methods
### Characterization  
#### 2.3.1 Electron Microscopy  
The CNFs were examined using high-resolution transmission electron microscopy (HR-TEM) in a JEM 2100F (JEOL, Tokyo, Japan) 200 kV electron microscope with energy-disperse X-ray spectroscopy (EDS) at 20 kV. The sample preparation was performed dispersing a very small quantity of CNFs in dichloromethane in an ultrasound bath for ten minutes and then drying at ambient temperature before HR-TEM observation. A JEM-1010 (JEOL, Tokyo, Japan) 100 kV transmission electron microscope (TEM) was utilized to observe the CNFs incorporated into the calcium alginate films. Ultrathin samples of calcium alginate/CNFs with 60 nm sections were prepared on a Leica Ultracut UC6 ultramicrotome (Leica Mikrosysteme GmbH, Wien, Austria) and a Diatome diamond knife (Diatome Ltd., Nidau, Switzerland). The specimens were placed on TEM grids (300 mesh) coated in carbon film.
# Optimizing Healthcare Delivery: A Model for Staffing, Patient Assignment, and Resource Allocation
## 5 Results and Discussions
### Resource Allocations  
The resource allocation illustrations in Figures 1 and 2 present how healthcare schedule optimization research allocates resources across time slots for the two model procedures. The visualizations show resource consumption and availability during optimization. Each row represents a resource, and each column is a time slot in the heatmap visualization. The darker blue heatmap colors imply more resource allocation at each time slot, whereas the white colors indicate less. The scale on the right side of the graph ranges from 0.0 to 1.0, with 1.0 representing the highest allocation of resources. This scale indicates the percentage of resources allocated at each time slot. For instance, if the scale shows 0.6 at a particular time slot, this means that 60% of the resources are allocated at that time slot. These graphs can be used to analyze hospital resource allocation efficiency and effectiveness by highlighting peak and off-peak resource utilization, gaps and overlaps in resource distribution, and possible areas for improvement or optimization. For example, Figure 1 shows that more resources are allocated in the first hour than in the second. Also, Figure 2 demonstrates that the first two hours had more resources than the next three. This could imply a higher demand for resources in the morning than in the afternoon or an over-allocation of resources in the early hours that should be reassigned to the rest of the hours.  
\[\text{resource\_matrix}=[a(r,t)\mid r\in R,t\in T],\]  
where  
* \(T\) is the set of time slots;
* \(R\) is the set of resources;
* \(r\) is a resource from \(R\);  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **First Output** & \multicolumn{2}{c}{**Second Output**} \\ \hline
**Staff Member** & **Overtime Hours** & **Staff Member** & **Overtime Hours** \\ \hline  
\hline \end{tabular}
\end{table}
Table 7: Staff overtime hours for first and second outputs.
## 5 Case Study
### Results
#### 5.3.1 Usability Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**IBM CSUQ** & **Question** & **M * & **M * & **\(\sigma\) * \\ \hline \multirow{8}{*}{SYSUSUSUSU} & 1. Overall, I am satisfied with how easy it is to use this system. & 6.04 & 6 & 1.01 \\  & 2. It is simple to use this system. & 6.08 & 6 & 0.99 \\  & 3. I can effectively complete my work using this system. & 5.9 & 6 & 1.13 \\  & 4. I am able to complete my work quickly using this system. & 6.02 & 6 & 0.91 \\  & 5. I am able to efficiently complete my work using this system. & 5.92 & 6 & 1.12 \\  & 6. I feel comfortable using this system. & 6.04 & 6 & 1.21 \\  & 7. It was easy to learn to use this system. & 6.04 & 6 & 1.09 \\  & 8. I believe I became productive quickly using this system. & 5.9 & 6 & 1.18 \\ \hline \multirow{8}{*}{INFOQUIAL} & 9. The system gives error messages that clearly tell me how to fix problems. & 5.3 & 6 & 1.37 \\  & 10. Whenever I make a mistake using the system, I recover easily and quickly. & 5.62 & 6 & 1.19 \\  & 11. The information (on-screen messages and guidance or other documentation) & 5.86 & 6 & 0.97 \\  & provided with this system is clear. & 6.08 & 6 & 1.07 \\  & 12. It is easy to find the information I need. & 5.84 & 6 & 1.02 \\  & 13. The information provided with the system is easy to understand. & 5.84 & 6 & 1.00 \\  & 14. The information is effective in helping me complete my work. & 5.84 & 6 & 1.00 \\  & 15. The organization of information on the system screens is clear. & 6.1 & 6 & 0.93 \\ \hline \multirow{3}{*}{INTERQUIAL} & 16. The interface of this system is pleasant. & 5.98 & 6 & 1.13 \\  & 17. I like using the interface of this system. & 6.04 & 6 & 1.14 \\  & 18. This system has all the functions and capabilities I expect it to have. & 5.88 & 6 & 1.06 \\ \hline OVERALL & 19. Overall, I am satisfied with this system & 5.92 & 6 & 1.19 \\ \hline \hline \end{tabular}  
* average (\(\mu\)), median (M), and standard deviation (\(\sigma\)).  
\end{table} 
Table 3: Results of the average, median, and standard deviation measures of the IBM CSUQ tool. Figure 7: Most used words to describe the web application. * Number of students.