esa-sceva/satcom-chunk-collection huggingface dataset:
# Proteomics in Forensic Analysis: Applications for Human Samples
## 2 Proteomics Methods: Sample Preparation Techniques, Data Acquisition, and Data Analysis
### Data Analysis  
Targeted proteomic data analysis is simple because all _m/z_ values are predetermined for specific peptides. In untargeted proteomics, peptides are unknown and can be identified using different approaches. One of the most common peptide identification methods is the database search. In this method, theoretical spectra are generated from peptide sequences (database) via in silico digestion. Typically, search engines compare each observed spectrum to all peptides in a database that has masses within a predefined tolerance. The database search approach finds the best peptide-spectrum match (PSM) from a series of peptide candidates in the sequence database. A protein sequence database limits the space required for the software to be searched. Therefore, a database search is generally the most common approach for peptide identification when a protein sequence database is available [107]. Various software tools have been developed for database search, such as SEQUEST [108], Mascot [109], XITandem [110], MaxQuant [111], PEAKS DB [112], Comet [113], and MSFragger [114]. Other statistical modeling tools such as PeptideProphet [115], Percolator [116], and IDPicker [117] are usually combined with database search tools to re-score PSMs reported by these database search tools.  
The second approach to identifying peptides from MS/MS spectra is a spectral library search. In this approach, a spectral library is built from MS/MS spectra of known peptides, and the observed MS/MS spectra are compared to the actual experimental spectra in the library. This method reduces the search time, but requires the establishment of a spectral library [118]. However, when using this approach, a peptide present in the samples will not be detected if its spectrum is not in the library. Spectral library searches can be performed using software tools including SpectraST [119], XIF Hunter [120], M-SPLIT, QuickMod [121], Pepitone [122], and MSFepSearch [123].  
Another method is de novo peptide identification, which uses information in MS/MS spectra to determine peptide sequences without using sequence databases. it considers all possible amino acid combinations, and thus, the search space is remarkably larger than that of the database search. As a result, de novo sequencing is slower than the other approaches. Using this method, multiple sequences are generally derived at equal probabilities for each spectrum. De novo sequencing is generally not used as a primary tool to identify peptides from MS/MS spectra. It is useful when the species is unknown, or a sequence database is not available. De novo sequencing can also be applied after database search to determine unassigned peptide sequences [124]. Some software tools for this approach are Lutefisk [125], Shererga [126], PEAKS [127], PepNovo [128], NovoHMM [129], and DeepNovo [130].
# Short Text Aspect-Based Sentiment Analysis Based on CNN + BiGRU
## 4 Experimental Study, Performance Evaluation, and Comparison
### Aspect-Level Feature Extraction Based on TF-IDF Vectorization  
#### 4.2.1 TF-IDF Algorithm  
The TF-IDF algorithm is a feature extraction method recognized by academia. Compared with other algorithms, TF-IDF extraction is more accurate. Therefore, this study used the TF-IDF feature extraction algorithm to implement vectorization processing for 5G mobile phone review text. Through TF-IDF calculation, we were able to find whether a certain word was critical in this text sample. The specific calculation formula is as follows:  
\[f(\omega)=TF(\omega)*IDF(\omega)=TF(\omega)*\frac{\log N}{n(\omega)+1} \tag{16}\]  
The values of \(TF(\omega)\) and \(IDF(\omega)\) are calculated separately using Formula 3, and then the total TF-IDF weight value is obtained and sorted in descending order. Choosing the first few keywords with the highest scores played a role in dimensionality reduction. The workflow is shown in Figure 4.
## 4 SNR Estimation
### SNR Thresholds for Adaptive Modulation Switching  
The SNR switching thresholds are determined from the bit error ratio (BER) curve for \(M\)-PSK that is invoked in the non-adaptive STBC assisted for a \(2\times 2\) MIMO-OFDM system, when targeting a BER of about \(10^{-3}\)[39; 45]. Table 2 outlines the switching thresholds that are derived from the BER curve for \(M\)-PSK for the transmission SUI-5 channel shown in Figure 8.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Modulation** & **SNR Threshold** \\ \hline QPSK & SNR \(\leq\) 9 dB \\ \hline
8-PSK & 9 dB \(<\) SNR \(\leq\) 12 dB \\ \hline
16-PSK & 12 dB \(<\) SNR \(\leq\) 16 dB \\ \hline
32-PSK & 16 dB \(<\) SNR \(\leq\) 19 dB \\ \hline
64-PSK & SNR \(>\) 19 dB \\ \hline \hline \end{tabular}
\end{table}
Table 2: \(M\)-PSK modulation switching threshold for \(2\times 2\) MIMO-OFDM system.
## 2 Short-Term and Long-Term Collision Probability Calculation Models
### Long-Term Collision Probability Calculation Model  
The long-term orbit safety of LEO megaconstellations is not only complex and time-consuming in calculation, but also requires high modeling and hardware conditions. Moreover, due to the influence of orbit prediction errors, there is a significant deviation between the predicted position of the long-term orbit of space targets and the actual position, and the calculation results still have a large random deviation.  
This section makes simplified assumptions about the debris and collision models, and calculates the density change of space debris in the shell where the constellation is located in accordance with the generation and death of space debris. This process is performed to avoid detailed modeling and analysis of complex scenarios for collisions in the life cycle of the constellation. A \"particle-in-a-box\" (PIB) collision probability calculation method is used to establish a long-term collision probability calculation model. The process of establishing the model is shown in Figure 5.
## 3 Results  
### RSA Performance  
Significant main time effects were observed for the kinematic variables measured during the 30 m RSA (Figure 2): sprint times (F3.65 = 11.55, \(p\) < 0.001, \(\eta\)2 = 0.33); SF (F4.60 = 14.05, \(p\) < 0.001, \(\eta\)2 = 0.38); and SL (F4.43 = 2.84, \(p\) = 0.024, \(\eta\)2 = 0.11).  
Figure 2: Evolution of step kinematics during the six repetitions in (**a**) 0–30 m, (**b**) 0–10 m, (**c**) 10–20 m, and (**d**) 20–30 m sections for all the players during the RSA test. (*) Different from the first repetition at \(p\) < 0.05.  
When studying the 10 m intervals of the test (Figure 2b-d), a small effect of the repetitions was detected for the sprint times in the 20-30 m section (F4.44 = 12.49, \(p<0.001\), \(\eta\)2 = 0.35) and for the SF of the first (F5.00 = 12.85, \(p<0.001\), \(\eta\)2 = 0.36) and last (F4.53 = 5.70, \(p<0.001\), \(\eta\)2 = 0.20) 10 m sections. Trivial effects for SL were observed in all the sections (0-10 m: F4.38 = 2.84, \(p=0.024\), \(\eta\)2 = 0.11, 10-20 m: F3.44 = 0.96, \(p=0.43\), \(\eta\)2 = 0.04 and 20-30 m: F4.61 = 2.58, \(p=0.034\), \(\eta\)2 = 0.10).  
Pairwise comparisons (Figure 2) revealed statistical differences in sprint times (\(p=0.42\)-0.005) and SF (\(p=0.005\)-0.001) between the first repetition and the third, fourth, fifth, and sixth repetitions during the 30 m sprint times. In the case of the SL, there were only differences (\(p=0.007\)) between the first and fifth repetition of the 30 m sprint. When comparing the first and the last repetition of the 30 m test, the sprint time increased by 2.1% (\(p=0.017\)), the SF decreased by 2.4% (\(p=0.004\)), whereas there were no differences (\(p>0.05\)) in the SL.
# Photodegradation Assessment of Calicotriol in the Presence of UV Absorbers by UHPLC/MSE
## 3 Results and Discussion
### Molecular Modeling  
Molecular docking studies on a model of the VDR ligand-binding domain resulted in reasonable binding modes for two photodegradation products--CP-1 and one of the CP-2--CP-5 isomers (specifically, the E isomer with R-configuration at the spiro carbon atom, referred to as CP-ER). To some extent, the compounds mimicked the interactions of calcipotriol, suggesting potential biological activity, as reflected by their MM-GBSA score values.  
CP-1 interacted in a similar manner to calcipotriol; however, its predicted binding free energy was higher, suggesting weaker pharmacological activity of the Z isomer (\(-129.5\) kcal/mol, compared with \(-139.4\) kcal/mol predicted for calcipotriol). Both molecules formed favorable hydrogen bonds with polar amino acid residues. The 4-methylidencyclohexane-1,3-diol moieties interacted with Ser237 and Arg274, as well as with Ser278 and Tyr143. The differing interactions of the 5-cyclopropyl-5-hydroxypent-3-en-2-yl fragment explain the poorer performance of the Z isomer--instead of forming a hydrogen bond with His397, it formed a weaker aromatic hydrogen bond with His305 (Figure 2A).  
Figure 2: Predicted binding modes of **(A)** CP-1 (green) and **(B)** CP-ER (pink), in comparison with the experimentally resolved calcipotriol (grey) interactions in the VDR ligand-binding domain (LBD). The protein model used for docking was based on PDB entry 1S19—the VDR LBD co-crystallized with calcipotriol [30]. The compounds formed hydrogen bonds (yellow dashed lines) and aromatic hydrogen bonds (cyan dashed lines).  
The second degradation product that succeeded in the docking study was CP-ER. Its spiro[indene-4,2\({}^{\prime}\)-naphthalene]-6\({}^{\prime}\),7\({}^{\prime}\)-diol moiety was able to interact solely with Ser237 and Arg274, while the second terminal fragment of the molecule retained interaction with His397, characteristic for calcipotriol (Figure 2B). Nevertheless, the predicted binding free energy for CP-ER was even higher, reaching \(-108.2\) kcal/mol.
# Airport Field Path Optimization Method Based on Conflict Hotspot Avoidance Mechanism
###### Abstract  
The state path optimization model, alongside strategies like slowing down and waiting, aims to identify optimal aircraft routes that minimize the total taxi time and prevent conflicts. Optimization reduces taxing times for aircraft YZR7537, CES2558, and CSZ9806, while slightly increasing the times for CSN6310 and CSN3210 due to conflict hotspot avoidance measures. This approach also decreases the number of aircraft passing through key conflict hotspots, effectively reducing both conflicts and risk levels in these areas. Consequently, the total taxing time for the optimized aircraft is cut by 53 s, enhancing airport operational efficiency. The proposed model serves as a theoretical foundation for developing an intelligent airport operation management system.  
air transport; path optimization; genetic algorithm; aircraft taxi path; conflict hotspot identification; conflict avoidance
# Bearing Capacity of a Concrete Grouting Pad on the Working Surface of a Highway Tunnel Shaft
## 5 Design Method and Engineering Application of Grouting Pad Thickness
### Engineering Applications
#### 5.2.2 Calculation of Load Capacity of the Grouting Pad  
The radius of the 2-2 shaft radius is \(r_{2}=5.6\) m, the original design thickness of the grouting pad 2.1 m, and C25 concrete was used, with \(M_{\text{P}}=11.5445\) kN/m. There are 25 grouting holes arranged in a circular direction with a radial distance of about 1.0 m from the initial support. By substituting the lining radius \(r_{2}=5.6\) m, \(c=2.8\) m, and \(b=1\) into Equation (8) and iteratively solving for \(r_{1}=4.35\) m, and then substituting it into Equation (15), it can be given as:  
\[q_{\text{u}}=\frac{(2\times 1^{2}+6+8\times 1)\times 11.5445}{(2+1)\times\left[ 2\times 2.8^{2}(2.8/4.35)^{1+1}-(3+1)\times 2.8^{2}+4.35^{2}\times(1+1) \right]}=4.745\text{ MPa}\]  
The water pressure of the shaft working face is 1.6 MPa, and the average injection pressure of the injection hole is 5 MPa. This means that P = 6.6 MPa\(<\)4.745 MPa. Insufficient bearing capacity caused the punching shear failure of the 2-2 shaft grouting pad.  
Figure 15: Overview of the shaft’s engineering.
## 2 Related Work
### ERT and Students with FD  
Due to the Covid-19 crisis, approximately 100 countries closed their schools leaving 1.5 billion students without traditional classroom learning [15]. Most of the educational institutions transitioned to urgent distance education through various methods [16].  
[PERSON] et al. [8] termed this as ERT, distinguishing it from structured online education. Transition to ERT was not smooth [17]. In countries where the educational sector was already inclined towards technological integration, the pandemic merely accelerated the move towards digital education [18]. The success of online instruction was contingent upon several factors, namely the educators' familiarity with technology and the accessibility of Internet and digital tools [19]. However, in Greece the pre-pandemic plan for K-12 distance education was limited. ERT was hastily implemented to ensure educational continuity, despite general unpreparedness and lack of thorough planning [20,21]. Certain researchers highlighted the limited preparedness in using digital tools and the notable lack of official support; students faced interrupted learning and social isolation, and teachers had to rapidly acclimate to new online platforms and methodologies [22,23]. They employed quantitative, qualitative, and mixed methods to analyze data of surveys from university students, interviews with teachers, administration, students, and parents. Students with FD faced difficulty in adaptation, technical issues, emotional and psychological stress, and lack of personalized support [1,24]. Additionally, due to large online classes and the students' young age, the presence and support of parents during remote teaching became essential [25,26]. Their collaboration with the teachers, combined with their hands-on guidance at home, improved the efficacy of ERT. This underscored the need for tailored strategies to cater to the diverse needs of all students. Investigating and understanding ERT's challenges are important, offering a valuable research contribution towards better preparedness in education [27].
## 4 System Model
### Energy Model  
Sending and receiving data have maximum consumed energy in the network. For managing energy consumption, two free space and multi-path models are applied to  
Figure 2: Network model in CTSRD.  
calculate the energy consumed by the receiver and transmitter. The total energy used in the network is the sum of the energy required by receivers and transmitters:  
\[E_{Total}=\sum_{i=1}^{N}\left(E_{tx}^{i}+E_{rx}^{i}\right) \tag{3}\]  
where \(E_{tx}^{i}\) and \(E_{rx}^{i}\) indicate the energy used for sending and receiving data by the IoT node \(i\), respectively.  
Suppose that the distance from the transmitter to receiver is equal to \(d\), and the transmitter wants to transfer \(l\) bits to the receiver. In this case, the energy consumed by the transmitter node is expressed with regard to Equation (4):  
\[E_{tx}(l,d)=\begin{cases}1\times E_{elec}+l\times\varepsilon_{fs}\times d^{2},&d<d_{0}\\ l\times E_{elec}+l\times\varepsilon_{mp}\times d^{4},&d\geq d_{0}\end{cases} \tag{4}\]  
where \(E_{elec}\) means the energy consumed by the electrical circuit of receiver/transmitter to receive/send each bit. \(\varepsilon_{fs}\) indicates the signal amplifier coefficient in the free space and \(\varepsilon_{mp}\) represents this coefficient in the multi-path space. \(d_{0}\) reflects the threshold (border condition) for the data transfer model used by IoT nodes:  
\[d_{0}=\sqrt{\frac{\varepsilon_{fs}}{\varepsilon_{mp}}} \tag{5}\]  
Finally, the energy consumed by the receiver node for receiving \(l\) bits is calculated based on Equation (6):  
\[E_{rx}(l)=l\times E_{elec} \tag{6}\]
## 4 Asymptotic Analysis  
In this section, we perform the asymptotic analysis for system capacity outage probability in the high SNR region, as the expression of (29) is too complex and reveals limited engineering insights. Since we tend to focus on the performance of parallel FSO/RF links, a fixed SNR is assumed for the dual-hop FSO link from OGS to HAP, ensuring an outage probability less than \(1\times 10^{-3}\). Thus, the asymptotic capacity outage probability in (29) is mainly determined by \(P_{Out2}\). Applying ([28], Equation (07.34.06.0040.01)) and ignoring the high-order terms, the asymptotic capacity outage probability can be expressed by  
\[\begin{split} P_{out}&\overset{\mu_{3}\gamma_{R} \gg 1}{\approx}\frac{W_{f}\xi_{3}^{2}b^{\mu_{3}+\beta_{3}-2}}{W_{r}\Gamma(mN_{t}) \Gamma(\alpha_{3})\Gamma(\beta_{3})(2\pi)^{b-1}}\bigg{(}\frac{m}{\tau_{RF}} \bigg{)}^{mN_{t}}\bigg{(}2^{c/W_{f}}-1\bigg{)}\\ &\times\sum\limits_{k_{0}=1}^{3b}\frac{\prod\limits_{j_{0}=1,j_{ 0}\
eq k_{0}}^{3b}\Gamma\big{(}E_{6,j_{0}}-E_{6,k_{0}}\big{)}}{\prod\limits_{j _{0}=3}^{b+2}\Gamma\big{(}E_{5,j_{0}}-E_{6,k_{0}}\big{)}E_{6,k_{0}}\big{(}1+E_{ 6,k_{0}}\big{)}}\bigg{[}\frac{D_{3}^{b}\Big{(}2^{c/W_{f}}-1\Big{)}}{b^{2b}\mu_ {3}\tau}\bigg{]}^{E_{6,k_{0}}}\end{split} \tag{45}\]  
where \(E_{6,j_{0}}\) and \(E_{6,k_{0}}\) are the \(j_{0}\)th and \(k_{0}\)th terms of \(E_{6}\); \(E_{5,j_{0}}\) means the \(j_{0}\)th term of \(E_{5}\). From (45), we can find that \(P_{out}\propto(\overline{\tau}_{RF})^{-mN_{t}}(\mu_{3})^{-E_{6,k_{0}}}\). Hence, the diversity order of system capacity outage probability is given by  
\[G_{d}=mN_{t}+\min\Bigg{\{}\frac{\alpha_{3}}{b},\frac{\beta_{3}}{b},\frac{\xi_ {3}^{2}}{b}\Bigg{\}} \tag{46}\]  
It is to be noted from (46) that the proposed parallel FSO/RF links can obtain full gain value from both FSO and RF links. Coding gain (\(G_{c}\)) can be obtained from (45) and (46) when the asymptotic expression is in the form of \(P_{out}\approx(G_{c}\overline{\tau})^{-G_{d}}\). By comparing (45) to \(P_{out}\approx(G_{c}\overline{\tau})^{-G_{d}}\), we obtain the expression of coding gain as  
\[\begin{split} G_{c}&=\sum\limits_{k_{0}=1}^{3b} \Bigg{[}\frac{W_{f}\xi_{3}^{2}b^{\mu_{3}+\beta_{3}-2}m^{mN_{t}}}{W_{r}\Gamma(mN _{t})\Gamma(\alpha_{3})\Gamma(\beta_{3})(2\pi)^{b-1}}\Big{(}2^{c/W_{f}}-1\Big{)} \\ &\times\frac{\prod\limits_{j_{0}=1,j_{0}\
eq k_{0}}^{3b}\Gamma \big{(}E_{6,j_{0}}-E_{6,k_{0}}\big{)}}{\prod\limits_{j_{0}=3}^{b+2}\Gamma \big{(}E_{5,j_{0}}-E_{6,k_{0}}\big{)}E_{6,k_{0}}\big{(}1+E_{6,k_{0}}\big{)}} \Bigg{]}^{-\frac{1}{G_{d}}}{b^{2b}\tau}\Bigg{[}\frac{D_{3}^{b}\Big{(}2^{c/W_{f} }-1\Big{)}}{b^{2b}\tau}\Bigg{]}^{-\frac{E_{6,k_{0}}}{G_{d}}}\end{split} \tag{47}\]
## 3 Threat Model  
### Attack Model  
Various anonymous communication systems, such as Tor, Loopix, I2P, etc., can protect user privacy through multi-layer encryption and onion routing. However, adversaries do not need to break the encryption algorithm in order to undermine this privacy protection by employing encrypted traffic analysis techniques. The most typical encryption traffic analysis techniques are website fingerprinting attacks (WFP), and current researchers have demonstrated adversaries' ability to use WFP to recognize which pages users have visited. Traffic passes through the Tor network without any disturbance, like water flowing in and out of a water pipe without any change, which is called the pipeline model. The attacker can use machine/deep learning methods to match client-generated traffic patterns with the fingerprinting of visited websites: (1) The attacker must first monitor a set of known or interested websites; (2) The attacker collects the traffic traces generated by visiting each website page; (3) The attacker extracts the traffic trace features by a feature extractor and uses them as fingerprints to tag the corresponding website. The website fingerprints included the packet size, packet sequence, traffic burst number, inter-packet interval, etc. Although Tor encapsulates user data in a fixed cell, Tor still cannot hide metadata, such as the packet number, direction, and interval. An attacker can still passively exploit this side-channel leakage for attacks; (4) A classifier model can be trained using machine/deep learning algorithms utilizing the extracted features as a dataset. Training a classification model aims to classify traffic patterns by using a probability distribution. Traffic analysis attacks are more successful if the test data are correctly classified; and (5) Finally, the adversary uses the classifier model to determine which specific website a user has visited, as in Figure 1.
# 3D Avatar Approach for Continuous Sign Movement Using Speech/Text
## 2 Related Work
### Sign Language Translation Systems  
Sign movement can be effectively generated from input speech. In [13], the authors have designed a speech-sign translation system for Spanish Sign Language (SSL) using a speech recognizer, a natural language translator, and a 3D avatar animation module. In [11; 14], the authors have implemented the conversion of Arabic Sign Language (ArSL) from Arabic text using an Arabic text-to-sign translation system. The translation system uses the set of translation rules and linguistic language models for detecting different signs from the text. An American Sign Language (ASL)-based animation sequence has been proposed in [15]. The authors' system converts all of the hand symbols and associated movements of the ASL sign box. A speech-to-sign movement translation based on Spanish Sign Language (SSL) has been proposed in [12]. The authors used two types of translation techniques (rule-based and statistical) of the Natural Language Processing (NLP) toolbox to generate SSL. A linguistic model-based 3D avatar (for British Sign Language) has been proposed for implementing the visual realization of sign language [16]. A web-based  
Figure 1: An assistive model for generating sign movements using the 3D avatar from English speech/text.  
interpreter from text to sign language was developed in [17]. The interpreter tool has been created from a large dictionary of ISL such that it can be shared among multilingual communities. An android app-based translation system has been designed to convert sign movements from hand gestures of ISL [18]. In [19], the authors designed a Malayalam text to ISL translation system using a synthetic animation approach. Their model has been used to promote sign language education among the common people of Kerala, India. A Hindi text to the ISL conversion system has been implemented in [20]. Their model used the dependency parser and Part-of-Speech (PoS) tagger, which correctly categorize the input words into their syntactic forms. An interactive 3D avatar-based math learning system of American Sign Language (ASL) has been proposed in [21]. The math-learner model can increase the effectiveness of parents of hearing-impaired children in teaching mathematics to their children. A brief description of existing sign language learning systems is presented in Table 1. It can be observed that some sign language translation models work on speech-to-sign conversion, whereas some models translate the text to signs and represent the signs using a 3D avatar. Our proposed model successfully converts the input speech to corresponding text and then renders the signs movements using the 3D avatar.