esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Overview of the Employed Techniques
#### 3.1.1 Core Python Packages
A block contains lines and their characters or an image. A line contains spans, and spans contain characters with identical font properties for all adjacent characters. To extract text with the Pymupdf tool, the target PDF file is loaded with the document object. Then, PageCount is initialized to split the document into pages. After that, the getText method is used for each page found to extract the text content and save it in a plain text file. Algorithm 3 shows the workflow of text extraction using this tool. ```
Input: pdf file Output: text file
1: Load the pdf file as document object
2: Initialize \(PageCount\) from the document object
3:for each page do
4: extract text using \(getText\)
5: append \(extracted\_text\) in memory
6:endfor
7:return\(extracted\_text\)
```  
**Algorithm 3** Text extraction using the Pymupdf tool  
Pdftottext  
Pdftottext is a simple Python package for extracting PDF-to-text. Although it is a Python package, Poppler [46] must be installed on the system before installing it via pip in Python. Poppler is a free PDF rendering library compliant with ISO 32000-1, and a fork of Xpdf-3.0 [47], developed by [PERSON]. This package must be used inside Python programs and has no single executable script. The Pdftotext package uses poppler to convert PDF-to-text. After loading the target PDF file as a binary object file, this tool uses poppler's PDF method to convert the PDF file to a plain text file. The poppler library is written in C++ language, so it takes less time than other tools. This tool first determines the layout of the PDF file, then converts the file into different pages and decodes the content into UTF-8 format. After that, it returns the PDF file as a plain text document for all pages. Algorithm 4 presents the workflow for converting text using the Pdftotext tool. ```
Input: pdf file Output: text file
1: Load the pdf file as binary object
2: Initialize Poppler \(PDF\) method and return \(page\)
3: Decode \(page\) to UTF-8 format
4: Return text contents as plain text file
```  
**Algorithm 4** Text extraction using Pdftotext tool
# Prediction of Remaining Fatigue Life of In-Service Bridge Cranes
## 4 Fatigue Residual Life Prediction of Bridge Crane  
### Fatigue Crack Expansion Correction Equation  
The main box girder of an overhead crane is mainly a welded structure, and the welded parts cannot avoid stress concentration and welding defects, so fatigue cracks will occur, and the starting point of these cracks is usually at the weld. These initial welding defects can be regarded as initial cracks, and their fatigue life mainly depends on the life of the crack extension. The bridge crane crack extension formula mainly uses the Paris crack extension formula to derive the fatigue life of structural components. However, the formula does not consider the effect of the stress ratio on the life of structural members, so the calculated fatigue life results deviate from the life of members in actual use. Inthis paper, [PERSON]'s formula [20] (R \(>\) 0), which only considers the effect of stress ratio in fracture mechanics, is used to derive the crack extension life.  
\[\frac{\text{da}}{\text{dN}}=\frac{\text{C}(\Delta\text{k})^{\text{m}}}{(1-\text{ R})\text{K}_{\text{c}}-\Delta\text{k}} \tag{5}\]  
where a is the crack length; C, m are material constants--according to the literature [23], for Q235 steel, c = 2.61 \(\times\) 10\({}^{-13}\), m = 3; \(\Delta\text{K}\) is the range of stress intensity factors, \(\Delta\text{K}=f\sigma\sqrt{\pi a}\)[20; 24; 25]; K\({}_{\text{c}}\) is the fracture toughness of the material; R is the stress ratio.
## 3 Simulation Results and Discussion  
The performance parameters analysed were those previously described, namely energy demand, indoor thermal comfort, and start-up lag time. The data gathered from 737,856 simulations are so vast that the following analysis and discussion of results is based on averaged values according to climate or dwelling. To answer the questions posed in the Introduction, this section is structured as follows: first, Section 3.1 compares the overall performance results for each city; second, in Section 3.2, five representative cities were selected to show results in more detail depending on the construction characteristics of the dwelling; third, Section 3.3 explores the effect of the thermal properties of wood on the three performance parameters to show whether the lowest conductivity woods should be discarded or not; next, Section 3.4 evaluates the global thermal performance of wood as a construction solution depending on the climate and dwelling construction characteristics. The combined influence of the thermal conductivity and thickness of wood floorings in radiant heating systems (i.e., thermal resistance) is shown for five representative cities. The intention here was to show whether a thermal resistance limit should be established depending on the climate and construction characteristics of the dwelling, and therefore if the established limit of 0.15 m\({}^{2}\) K/W is justified.
## 4 Security Approach
### Initial Key Upload  
Whenever a new node connects to the network for the first time, its public key must be stored in the mapping system in a secure way. For security and usability reasons, we propose to use cryptographic smart cards to store the public key, private key and identifier of any node at the users side [23]. The smart card is issued by the global authority--or one of its delegates--and initially generates a public-private key pair on chip. One of the major advantages of smart cards is the property that the private key never leaves the smart card. Before shipping the smart card to the user, the public key is derived from it and stored in the mapping system. In this way, the GA is never in possession of the private key and the public one is copied to the mapping server within a secure environment. For further details of this initial bootstrap see [23].
## 3 Algorithm Validation  
### Pick Functions  
In this paper, we selected three high-dimensional, single-peak benchmark functions; three high-dimensional, multi-peak functions; and three low-dimensional, multi-peak functions to verify the feasibility and accuracy of the IESSA algorithm. Table 1 shows the basic information of the three sets of benchmark functions [20].  
To solve the nine benchmark functions, we used the SSA, GWO, CSSOA, PSO, and IESSA algorithms discussed in this paper. Each experiment had a population size of \(N\) = 30, a maximum number of iterations \(T\) = 100, and assumed that 20% of the population consisted of discoverers and scouts. For each test function, we conducted 30 independent experiments and compared the solutions to derive the mean and standard deviation. We also carried out the optimal solution derived from the coarsening algorithm. The mean value represents the convergence accuracy of the algorithm, and the standard deviation represents the stability of the algorithm, using the same standard test functions. The results of the five algorithms solved are shown in Table 2, including their means and standard deviations.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Title** & **Function** & **Dim** & **Initial Range** & \(\mathbf{F_{\min}}\) \\ \hline Unimodal test function & \(F_{1}(x)=\sum\limits_{i=1}^{n}x_{i}^{2}\) & 30 & \([-100,100]\) & 0 \\  & \(F_{2}(x)=\sum\limits_{i=1}^{n}|x_{i}|+\prod\limits_{i=1}^{n}|x_{i}|\) & 30 & \([-10,10]\) & 0 \\  & \(F_{3}(x)=\sum\limits_{i=1}^{n}\left(\sum\limits_{j-1}^{i}x_{j}\right)^{2}\) & 30 & \([-100,100]\) & 0 \\ Multimodal test function & \(F_{4}(x)=\sum\limits_{i=1}^{n}-x_{i}\sin\left(\sqrt{|x_{i}|}\right)\) & 30 & \([-500,500]\) & \(-418.9829\times 5\) \\  & \(F_{5}(x)=\sum\limits_{i=1}^{n}\left[x_{i}^{2}-10\cos(2\pi x_{i}+10)\right]\) & 30 & \([-5.12,5.12]\) & 0 \\  & \(F_{6}(x)=\frac{1}{4000}\sum\limits_{i=1}^{n}x_{i}^{2}-\prod\limits_{i=1}^{n} \cos\left(\frac{x_{i}}{\sqrt{i}}\right)+1\) & 30 & \([-600,600]\) & 0 \\ Fixed-dimension test functions & \(F_{7}(x)=\left(\frac{1}{500}\sum\limits_{j=1}^{25}\frac{1}{j+\sum\limits_{i=1} ^{2}\left(x_{i}-x_{i}\right)^{6}}\right)^{-1}\) & 2 & \([-65,65]\) & 0.998 \\  & \(F_{8}(x)=\sum\limits_{i=1}^{11}\left[a_{i}\frac{x_{i}(p_{i}^{2}+p_{i}x_{2})}{p _{i}^{2}+p_{i}x_{3}+x_{4}}\right]^{2}\) & 4 & \([-5,5]\) & 0.00038 \\  & \(F_{9}(x)=-\sum\limits_{i=1}^{4}c_{i}\exp\left(-\sum\limits_{j=1}^{4}a_{ij}\left( x_{j}\cdot p_{ij}\right)\right)\) & 3 & \([1,3]\) & \(-3.75\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic information on the three sets of benchmark functions.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **SSA** & & **GWO** & & **PSO** & & **CSSOA** & & **IESSA** \\  & **Ave** & **Std** & **Ave** & **Std** & **Ave** & **Std** & **Ave** & **Std** & **Ave** & **Std** \\ \hline F1 & \(2.6\times 10^{-46}\) & \(1.4\times 10^{-45}\) & 0.015 & 0.015 & 5.80 & 2.96 & \(1.6\times 10^{-42}\) & \(8\times 10^{-42}\) & 0.0 & 0.0 \\ F2 & \(2.1\times 10^{-22}\) & \(9.4\times
###### Abstract  
A quantitative X-ray Photoelectron Spectroscopy (XPS) study has been undertaken on different experimental data sets of ZrN thin films deposited using reactive Bipolar Pulsed Dual-Magnetron Sputtering (BPDMS) on silicon/stainless steel substates, to obtain dense, pure and homogeneous coatings, free from morphological defects. Zirconium nitride (ZrN) occupies a central role within the class of transition metal nitrides (TMN) for its excellent properties, such as high hardness, low resistivity and chemical/thermal stability when its stoichiometric ratio is 1:1. Many deposition techniques, reported in the literature, tried to obtain oxygen-free and defect-free structures, but they proved a hard task. In this paper it has been demonstrated, using quantitative XPS, that stoichiometric, pure and homogeneous ZrN films have been grown at certain deposition conditions, optimized also via optional accessories mounted on the deposition apparatus. Almost all the films considered for microanalytical characterization resulted as completely oxygen-free, pure (with a lowest-detection limit of 1%) and homogeneous. Apart from these features, a stoichiometric ratio (N/Zr) close to one was calculated for six samples of the ten investigated, with a precision of \(\pm\) 0.01. In this frame XPS, widely known for being a highly surface-sensitive technique (average depth resolution of 20-30 A), and powerful for characterizing the chemical composition of materials, has been extensively employed to extract information both in the surface regions and in depth. A cluster ion beam Ar+ \({}_{2500}\) facility on our main XPS chamber has not proved adequate for depth-profiling acquisitions. Therefore, Ar+ ion sputtering was performed instead. To the best of our best knowledge, the results achieved in the present paper possess a level of accuracy never reached before. Rigorous calibration procedures before and during experimental spectrum acquisitions and a careful and scrupulous data processing using software CasaXps v.2.3.24 PRI were carried out to ensure a low percentage error. Progress has also been made for shake-up satellite extraction and interpretation from Zr 3d high-resolution spectra with the help of the literature milestones reported in the text. The total absence of oxygen inside most of the films prevented the formation of zirconium oxide compounds during deposition, which are generally resonant with the binding energy of the shake-up satellite peaks and hide them. A little summary about the experimental shake-up satellite peaks revealed and extracted from the Zr 3d region, after Shirley background subtraction and data processing, will be presented in the last subparagraph of the \"Results\" section for the ZrN samples analyzed. Figures of Zr 3d deconvoluted spectra for in-depth area analysis have been reported. The quantitative satellite contribution to the Zr 3d total area would not be included in stoichiometric calculations.  
ZrN; coatings; quantitative XPS; BPDMS; spectroscopy
## 1 Introduction  
The principal goals of recent solar cell processing based on crystalline Si (c-Si) are increasing the conversion efficiency under long-term stability and reducing the solar cell cost per watt. To improve the conversion efficiency, different structural modifications of solar cells have been investigated [1, 2]. A special solar structure, passivated emitter and rear cells (PERCs), is globally drawing increasing interest owing to its high efficiency and appropriate production cost, and could be a good candidate for the future development of solar cells [3, 4]. One of the most renowned features of PERCs is its rear-side passivation based on a dielectric layer or stack of layers, which can either chemically saturate the surface dangling bonds or offer a strong surface field, thus playing a critical role in reducing surface recombination. Several dielectric materials such as silicon dioxide, silicon nitride, aluminum oxide (Al\({}_{2}\)O\({}_{3}\)), and amorphous silicon are regarded as appropriate choices because of their superior passivation ability [5]. The Al\({}_{2}\)O\({}_{3}\) has a very low interface defect density with strong field-effect passivation at the Al\({}_{2}\)O\({}_{3}\)/Si interface [6]. Hence, applying Al\({}_{2}\)O\({}_{3}\) to PERCs has become more prevalent. The quality of the interface between Si and Al\({}_{2}\)O\({}_{3}\) is crucial for improving the performance of PERCs. The damaged interfaces, which can cause considerable defects and absorption losses of long-wavelength light, should be avoided.  
In this study, four Si wafers with distinct rear-side surface morphologies were fabricated. Three of the wafers displayed uniform and continuous pyramids with various sizes, and the other has a relatively planar rear-side surface similar to a mirror. All samples were then completed to form entire PERCs in order to compare their open-circuit voltage (\(V_{\mathrm{oc}}\)), short-circuit current density (\(J_{\mathrm{sc}}\)), and fill factor (FF).
# A Software-Defined Networking Framework to Provide Dynamic QoS Management in IEEE 802.11 Networks
## 3 Proposed SDN-Based Framework for Implementing DEDCA
### How to Communicate the Configuration Changes  
Once _requesting_ and _giving terminals_ have been identified and the new \(CW_{min}\) values have been obtained by the SDN controller, it is necessary to communicate the new configuration to the cameras.  
As said before, there is no a direct communication channel between the controller and the stations. Again, we are going to use OpenFlow protocol and the programmable capability of the SDN access point to solve this limitation.  
For each _requesting_ and _giving terminal_, the controller will create an ALARM response message, whose structure is \(ALARM:1:cw_{min\_value}\). As in the case of the Alarm messages, the Alarm response message is encapsulated into an UDP packet that is sent to the corresponding station by the Access Point. To force the sending, the SDN controller will generate _packet_out_ messages that contain the frame in the payload field, and indicate the instruction of sending the frame to the wireless port (step 3 in Figure 4).  
The stations, that are listening for UDP packets on the predefined port, will receive the Alarm response and will apply the new \(CW_{min}\) configuration by means of modifying its value in the network-device firmware (see Section 4.2.1).  
This procedure will be used also to restore the initial configuration of the _requesting_ and _giving terminals_ once the high-demanding situation, generally of limited duration, has finished.  
Figure 5: The choice of _requesting_ and _giving stations_ depending on the camera that generates the ALARM. Due to the symmetry of the scenario only 4 cases are shown. (**a**) CAM11 generates the ALARM. (**b**) CAM9 generates the ALARM. (**c**) CAM10 generates the ALARM. (**d**) CAM12 generates the ALARM. The cameras marked in green are the _requesting stations_ and the cameras marked in pink are the _giving stations_.
## 2 Materials and Methods  
### Datasets  
In this study, we collected all the required subcellular localization annotations and mRNA sequence datasets from the RNALocate database. In the RNALocate database (version 2.0), this version provides more accurate localization annotations than the first version, facilitating the construction of a reliable benchmark dataset. If the experimental data in a paper confirms the specific localization of an mRNA, then this localization information will be annotated and assigned to the mRNA. If an mRNA has multiple localization annotations, these annotations will be merged, enabling the mRNA to have multiple labels. These datasets are consistent with those used by the Clarion method [19]. Specifically, we used 84,972 mRNA subcellular localization records from the RNALocate database as our initial dataset. After initial statistics, we found that these data covered 150 different subcellular localization types. Given the small number of entries and incomplete information for some subcellular localization types, we decided to exclude those with fewer than 3000 entries. After this screening, we obtained nine major unique transcripts, including exosomes, nuclei, cytoplasm, chromatin, nuclear matrix, ribosomes, nucleoli, and cell membranes. To reduce the impact of sequence redundancy on classifier performance, we further processed these transcripts using the CD-HIT-EST tool [20], setting an 80% sequence similarity threshold to reduce redundancy. This step ensured that the similarity between any two nucleotide sequences was below 80%. Finally, we obtained 36,971 mRNAs as the benchmark dataset for subsequent experimental analysis. The distribution of our dataset is shown in Table 1.  
Additionally, this study categorized the sequences based on the number of subcellular localization tags annotated for each sequence, as shown in Figure 1. Specifically, among the 36,971 mRNA sequences, there are 12,884 mRNA sequences with a single tag, 4060 mRNA sequences with two tags, 3442 mRNA sequences with three tags, 3165 mRNA sequences with four tags, 3518 mRNA sequences with five tags, 4258 mRNA sequences with six tags, 4079 mRNA sequences with seven tags, 1443 mRNA sequences with eight tags, and 122 mRNA sequences with all nine tags.
# Sensitivity Analysis of a Numerical Model for Percutaneous Auxicular Vagus Nerve Stimulation
###### Abstract  
Background: Less-invasive percutaneous stimulation of the auricular branch of the vagus nerve (pVNS) gained importance as a possible nonpharmacological treatment for various diseases. The objective is to perform a sensitivity analysis of a realistic numerical model of pVNS and to investigate the effects of the model parameters on the excitation threshold for single and bundled axons. Methods: Sim4 Life electrostatic solver and neural tissue models were combined for electromagnetic and neural simulation. The numerical model consisted of a high-resolution model of a human ear, blood vessels, nerves, and three needle electrodes. Investigated parameters include the axon diameter and number, model temperature, ear conductivity, and electrodes' penetration depth and position. Results: The electric field distribution was evaluated. Model temperature and ear conductivity are the non-influential parameters. Axons fiber diameter and the electrodes' penetration depth are the most influential parameters with a maximum threshold voltage sensitivity of 32 mV for each 1 um change in the axon diameter and 38 mV for each 0.1 mm change in the electrodes' penetration depth. Conclusions: The established sensitivity analysis allows the identification of the influential and the non-influential parameters with a sensitivity quantification. Results suggest that the electrodes' penetration depth is the most influential parameter.  
s _Article_  
Sensitivity Analysis of a Numerical Model for Percutaneous Auxicular Vagus Nerve Stimulation
## 3 Results and Discussion
### Fracture Tests on Notched Specimens  
The results of the fracture tests, in terms of LCCs, are summarized in Table 2. The notch tip radius, the critical loads of the corresponding three repetitions, and the average critical load are denoted in the table by \(\rho\), P\({}_{\text{i}}\) (i = 1, 2, 3), and P\({}_{\text{avg}}\), respectively. Two of the tested U-notched specimens are shown in Figure 7.  
As is clear in Figure 7, unlike the tensile mode I loading (i.e., the notch opening mode), under which only a single crack initiates from the notch border and propagates suddenly, a noticeable damage zone first forms in the neighborhood of the U-notch blunt edge during  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material** & **Elastic Modulus, E (GPa)** & **Poisson’s Ratio** & **Ultimate Compressive Stress, \(\sigma_{u}\) (MPa)** \\ \hline PMMA & 1.70 & 0.34 & 86 \\ GPPS & 2.14 & 0.34 & 68 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mechanical properties of the tested polymers under compression.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Material** & \(\rho\) **(mm)** & **P\({}_{1}\) (N)** & **P\({}_{2}\) (N)** & **P\({}_{3}\) (N)** & **P\({}_{\text{avg}}\) (N)** & **SD (N)** \\ \hline \multirow{4}{*}{PMMA} & 0.5 & 6663 & 6474 & 6897 & 6678 & 173.0 \\  & 1.0 & 7471 & 6879 & 7545 & 7298 & 298.0 \\  & 2.0 & 8575 & 8493 & 8681 & 8583 & 76.9 \\  & 4.0 & 9065 & 9464 & 9271 & 9266 & 162.9 \\ \hline \multirow{4}{*}{GPPS} & 0.5 & 4028 & 3839 & 4121 & 3996 & 117.3 \\  & 1.0 & 4543 & 4323 & 4231 & 4365 & 130.8 \\ \cline{1-1}  & 2.0 & 5396 & 5129 & 4996 & 5173 & 166.3 \\ \cline{1-1}  & 4.0 & 5950 & 6004 & 6423 & 6125 & 211.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimentally achieved load-carrying capacities (LCCs) for the U-notched specimens made of PMMA and GPPS. SD: standard deviation.  
Figure 6: Examples of specimens at the onset of the compressive stress. (**a**) PMMA; (**b**) GPPS.  
the compression test and, second, a crack propagates from this region. This failure evidence for notched polymeric samples under compressive stresses has also been reported in [33].
###### Abstract  
Functional near-infrared spectroscopy (fNIRS) is an effective non-invasive neuroimaging technique for measuring hemoglobin concentration in the cerebral cortex. Owing to the nature of fNIRS measurement principles, measured signals can be contaminated with task-related scalp blood flow (SBF), which is distributed over the whole head and masks true brain activity. Aiming for fNIRS-based real-time application, we proposed a real-time task-related SBF artifact reduction method. Using a principal component analysis, we estimated a global temporal pattern of SBF from few short-channels, then we applied a general linear model for removing it from long-channels that were possibly contaminated by SBF. Sliding-window analysis was applied for both signal steps for real-time processing. To assess the performance, a semi-real simulation was executed with measured short-channel signals in a motor-task experiment. Compared with conventional techniques with no elements of SBF, the proposed method showed significantly higher estimation performance for true brain activation under a task-related SBF artifact environment.  
functional near-infrared spectroscopy; sliding-window analysis; general-linear model; scalp-hemodynamics artifact; real-time signal processing +
Footnote †: journal: Applied Sciences