esa-sceva/satcom-chunk-collection huggingface dataset:
# Determining Rotor Blade Multi-Mode Vibration Components +
## 4 Experimental Results of the 1 st-Stage SO-3 Engine Compressor During Run-Up  
Rotor blade vibrations in the 1 st-stage compressor of an ISKRA trainer aircraft SO-3 one-pass engine were measured at Air Force Institute of Technology in Warsaw [14; 15] using a OPR inductive sensor and two inductive sensors in the casing. Each sensor's signal frequency range was 0.025-20 kHz. The casing sensors were installed at 12.9 deg (see Figure A1, \(\chi_{1+1}-\chi_{1}\) = 12.9 deg) over the 1 st stage, radius of a blade tip is R = 0.314 m (the same as in simulation Section 3).  
The SO-3 engine stand consisted of the compressor with seven stages, combustion chamber and one-stage turbine (Figure 5). Two tip-timing signal processing systems were used to analyzed blade vibration: the SAD-2 [14; 15] system for laboratory tests and an SNDL [14; 15] system for on-line blade vibration measurement in trainer aircraft. These systems were developed at the Air Force Institute of Technology in Warsaw [14; 15].  
SAD-2 [15], developed in 1986, measured blade tip displacement in relation to the blade root with two sensors placed, respectively, at the blade tip and root. The sensors on the stand were permanently fixed to the compressor casing or on a special steel frame in the turbine inlet. The system was designed to measure of rotor blades in an SO-3 1 st-stage compressor. The number of blades in this stage was 28. This system could monitor and record the displacements of up to 32 blades at various rotation speeds. This technique was next upgraded to the SNDL system, which is used in ISKRA trainer aircraft to measure blade vibrations during flights. The SNDL system uses two sensors in the compressor casing and indication lamps in the cockpit that switch on when the rotor blade vibration amplitude exceeds 2 mm. In 1988, the SNDL system measured 1 st compressor-stage blade vibrations in 210 turbojet engines during flight, warning pilots of excessive rotor blade amplitudes.  
Figure 6 shows the measured displacements of each of the 28 compressor rotor blades rotating from 7500 to 16,000 rpm after de-noising, using Median Absolute Deviation [16]. The number of blades is on axis x and their rotation speeds are on axis y. The distance between two adjacent blades on axis x (Figure 6) corresponds to 3 mm blade displacement. This distance is the same between all the adjacent blades. The highest amplitude, 2.5 mm at  
Figure 5: SO-3 engine stand view.  
12,000 to 12,500 rpm, is caused by rotating stall [15]. The second highest amplitude, 1.5 mm (15,000 rpm), was caused by forced vibrations in 2 Engine Order (2 EO) [15]. The EO refers to the frequency of oscillations that is a direct multiple of the engine's rotation speed.
## 2 Materials and Methods
### Data
#### 2.2.2 Satellite Data  
Twelve images obtained from the Sentinel-2 sensor of the European Space Agency ([[https://sentinel.esa.int/web/sentinel/sentinel-data-access](https://sentinel.esa.int/web/sentinel/sentinel-data-access)]([https://sentinel.esa.int/web/sentinel/sentinel-data-access](https://sentinel.esa.int/web/sentinel/sentinel-data-access)) (accessed on 10 August 2021)) were used in this study. These images are characterized by a high spatial resolution of 10 to 60 m from the visible to mid-infrared range and a revisit time of 10 days (Table 2). The March image was used for calibration with field data and model validation, while the other images were used for spatiotemporal monitoring of different parameters. These images have been  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Sampling** \\ **Data ID** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Dissolved** \\ **Oxygen** \\ **(mg/L)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Nitrates** \\ **(mg/L)** \\ \end{tabular} } & \multirow{2}{*}{**Chl-a (\(\upmu\)/L)**} & \multirow{2}{*}{\begin{tabular}{c} **Sampling** \\ **Data ID** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Dissolved** \\ **Oxygen** \\ **(mg/L)** \\ \end{tabular} } & \multirow{2}{*}{**Nitrates**} & \multirow{2}{*}{**Chl-a (\(\upmu\)/L)**} & \multirow{2}{*}{
\begin{tabular}{c} **Oxygen** \\ **(mg/L)** \\ \end{tabular} } & \multirow{2}{*}{**Chl-a (\(\upmu\)/L)**} \\ \cline{1-1}  & 7.4 & 1.82 & 0.55 & 11 & 7.5 & 1.57 & 0.47 \\
02 & 9 & 1.57 & 0.77 & 12 & 9 & 1.56 & 0.55 \\
03 & 7.6 & 1.56 & 0.51 & 13 & 9.1 & 1.6 & 0.69 \\
04 & 8.2 & 1.65 & 0.61 & 14 & 9 & 1.82 & 0.57 \\
05 & 7.9 & 1.31 & 0.48 & 15 & 8.6 & 1.54 & 0.64 \\
06 & 5.8 & 1.45 & 0.77 & 16 & 9.7 & 1.78 & 0.52 \\
07 & 8.1 & 1.5 & 0.54 & 17 & 7.4 & 1.74 & 0.72 \\
08 & 7.4 & 1.42 & 0.60 & 18 & 7.5 & 1.96 & 0.68 \\
09 & 7.4 & 1.6 & 0.49 & 19 & 7.2 & 1.71 & 0.77 \\
10 & 7.2 & 0.8 & 0.53 & 20 & 8.9 & 1.85 & 0.55 \\ \hline \hline \end{tabular}
\end{table}
Table 1: On-ground data sample characterizations.  
uploaded for the period April 2020-March 2021 (Table 3). The QGIS software was used to process the Sentinel-2 satellite images through the interface (Semi-Automatic Classification Plugin) developed by [PERSON] [42]. There were several preprocessing steps, including the conversion of digital number (DN) to top of atmosphere reflectance (TOA) and the subsequent atmospheric correction by the dark object subtraction (DOS) algorithm [43].
# A New Lossless DNA Compression Algorithm Based on A Single-Block Encoding Scheme
## 3 Performance Evaluation
### Compression Performance

Table 4: Compression ratio of the DNAC-SEB algorithm and other DNA compression methods. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sequence** & **HEHCMVCG** & **HUMHBB** & **VACCG** \\ \hline gzip & 1.85 & 2.23 & 2.19 \\ Bzip2 & 2.17 & 2.15 & 2.10 \\ Arithm. & 2.05 & 2.12 & 1.86 \\ CTW & 1.84 & 1.92 & 1.86 \\ BWT+MTF+RLE +arithm. & 2.24 & 2.21 & 2.18 \\ BWT+MTF+ arithm. & 2.03 & 2.02 & 2.07 \\ BWT+MTF & 2.02 & 2.00 & 1.95 \\ DNAC-SBE & **1.74** & **1.72** & **1.67** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison between various text compression algorithms and some of Burrows–Wheeler transform (BWT) based algorithms in terms of compression ratio (CR). From the result shown in Table 7, in all cases, the proposed approach generated a small file size as compared to other methods and hence provided the best results in terms of compression ratio. This was due to the modeling step and SEB method, which made the input files more suitable for the final compression. However, generally Deliminate uses less memory in both compression and decompression rather than other compressors. Since Bind was the fastest one in compression and decompression, it yielded a worse compression ratio in comparison to other competitors. To assess the efficiency of the proposed approach, its compression performance was also compared to some of FASTQ compression methods in terms of CR, execution time and memory usage. The obtained results are tabulated in Tables 8 and 9. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Original Size**} & \multirow{2}{*}{**7z**} & \multirow{2}{*}{**Bind**} & \multirow{2}{*}{**Deliminate**} & \multicolumn{2}{c}{**MF- compress**} & \multicolumn{1}{c}{**Seq- compress**} & \multicolumn{1}{c}{**DNAC-**} \\  & & & & & & & & \\ \hline NC\_017526 & 2.59 & 0.72 & 11.11\% & 12.5\% & 9.72\% & 16.67\% & **23.61\%** \\ NC\_017652 & 4.87 & 1.36 & 11.76\% & 12.5\% & 10.29\% & 16.91\% & **22.06\%** \\ Ce10 & 97.55 & 27.81 & 5.11\% & 14.42\% & 17.12\% & 18.62\% & **28.98\%** \\ sacCer3 & 11.82 & 3.33 & 16.68\% & 19.6\% & 22.08\% & 23.44\% & **25.53\%** \\ Eukaryotic & 2139.98 & 527.27 & 11.31\% & 15.44\% & 17.35\% & 17.11\% & **19.71\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Percentage reduction attained by Bind, Deliminate, MFCompress and SeqCompress in comparison to 7z. The sizes of compressed data using 7Z are shown in (MB).
## 4 Discussion
### Limitations  
This study focused on the literature with \"Safety management\" and \"Smart\", \"AI\", or \"Artificial Intelligence\" in the titles, potentially overlooking relevant studies without these keywords. Consequently, the analysis may not fully represent the breadth of AI applications in safety management. Future research should employ broader search strategies, including keywords like \"Machine Learning\", \"Deep Learning\", and \"Data-Driven\", and consider full-text searches to capture a more comprehensive range of studies.
# Parallel Complex Quadrature Spatial Modulation
## 2 System Model and Related Works
### Parallel Quadrature Spatial Modulation  
To increase the spectral efficiency of the QSM, the PQSM system transmits the real part \(s_{a_{\rm B}}\) and the imaginary part \(s_{a_{\rm 2}}\) of one signal symbol from independent antenna subgroups of the transmit antennas set. Each group is of size \(n_{T}=N_{t}/P\) and \(P\) is the number of parallel subgroups. Accordingly, the received vector is given as follows,  
\[\mathbf{y}=\sum_{i=1}^{P}\mathbf{h}_{l_{i}}s_{a_{\rm B}}+j\sum_{i=1}^{P} \mathbf{h}_{k_{i}}s_{a_{\rm 2}}+\mathbf{n}, \tag{3}\]  
where \(l_{i}\) and \(k_{i}\) are the in-phase and quadrature antenna indices from which the real and imaginary parts are transmitted, respectively, in the \(i\)-th subgroup. The spectral efficiency of the PQSM is \(P\log_{2}(n_{T}^{2})+\log_{2}(M)\) bits\(/\mathrm{s}/\mathrm{Hz}\).
## 4 Results and Discussion
### Detailed Mechanical Characterization of the 100 mm Diameter Membrane
#### 4.3.2 Solution of the Inverse Problem: FE Analysis and Metaheuristic Optimization
Remarkably, HS-JAYA solved variants 1 and 2 of the identification problem including, respectively, 324 and 110 unknown material/structural parameters, a significantly larger number of unknowns than those usually reported in the literature for elastomers and rubberlike materials [66; 67; 68; 69; 70; 71; 72] (from two to six unknown material parameters or neural networks depending on three input characteristics), visco-hyperelastic materials [73; 74; 75] (from six to nine unknown material parameters including tangent modulus and softening index, Prony constants and relaxation times), biological tissues [76; 77; 78; 79; 80; 81] (from five to sixteen unknowns accounting also for visco-elastic effects and stochastic variation of material properties), non-homogeneous hyperelastic structures [82; 83; 37; 63; 84] (from four to sixteen unknown material parameters for the global model, or two unknown material parameters for each local inverse problem at the element level) or anisotropic hyperelastic materials modeled with much more complicated constitutive equations [27; 34; 85; 86] (from three to seventeen unknown material parameters). A very recent study by [PERSON] et al. [38] considered identification problems of nonlinear shells subject to various loading conditions (i.e., uniaxial tension, pure bending, sheet inflation and abdominal wall pressurization). Similar to the present study, thickness values were included as design variables in some test problems of [38]. However, target properties in [38] either were defined analytically or some level of noise was artificially added to the analytical fields. Target displacements and reaction force were hence computed by a finite element model and not measured experimentally. The number of design variables (including shear modulus of the neo-Hookean hyperelastic model and bending stiffness or shell-wall thickness) considered in the identification problems including artificial noise was at most 289, yet lower than the 324 variables considered in this study.
# Hair Regeneration Effects of _Lespedeza bicolor_ Extract In Vivo and In Vitro
## Materials and Methods
### Quantitative RT-PCR  
Total RNA was purified using RNAisoPlus reagent (Takara, Kusatsu, Japan). Purified total RNA (1 \(\upmu\)g) was synthesized using the PrimeScript II first strand cDNA synthesis kit (Takara, Kusatsu, Japan), and the assays were run using SYBR Premix Ex Taq II (Takara, Kusatsu, Japan) for quantitative real-time PCR. Briefly, the reaction mixture (total volume 20 \(\upmu\)L) consisted of 10 \(\upmu\)L 2\(\times\) master mix solution, 8 \(\upmu\)L DEPC water, 1 \(\upmu\)L cDNA template, and 1 \(\upmu\)L primers (forward/reverse), followed by heat cycling, preincubation at 95 \({}^{\circ}\)C (20 min), 40 cycles were run at 95 \({}^{\circ}\)C (20 s), and 58-61 \({}^{\circ}\)C (40 s). Quantitative real-time PCR was performed with a QuantStudioTM 5 Real-Time PCR system (Thermo Fisher Scientific) using the primers summarized in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Target Gene** & \multicolumn{2}{c}{**Sequences**} & **Accession No.** \\ \hline \multirow{3}{*}{Bax-\(\upalpha\)} & Forward & TGATGGCAGGGCCTGCCGCG & NM\_138763.4 \\  & Reverse & CAAAGGGCCTGGTCCTTICA & \\ \multirow{3}{*}{Bcl-2} & Forward & CGCGACGGTCGATGCCCTTTGT & \\  & Reverse & CAAAGCTCCCACCAGGCCAAA & NM\_000633.3 \\  & Forward & ACTCCAAAGCCTTGACCTG & NM\_002746.3 \\  & Reverse & CTTCTCGCGCCTGTTAGGTA & \\ \multirow{3}{*}{ERK2} & Forward & CCCCTGCTGTTATCC & \\  & Reverse & TACATACTGCCCGAGGTCAC & NM\_002745.5 \\  & Forward & TCACTGTCTTGTACCCTTGTGC & \\ \multirow{3}{*}{p21} & Reverse & GCGCTTGAGCTGAGAAA & NM\_001220777.2 \\  & Forward & CTTGCCGCAGTTGAC
# Addressing ZSM Security Issues with Blockchain Technology
## 3 The ZSM Framework Overview: Architecture and Open Security Issues  
ZSM is expected to become one of the dominating frameworks of NGNs according to ETSI and many articles in the literature such as [31]. The goal is the development of a framework that will include solutions and management services to achieve orchestration and automation of the emerging end-to-end network slicing technology, as well as of the end-to-end, cross-domain service orchestration and automation. Additionally, the ETSI standardization team works on generic enablers and solutions for closed-loop as well as on advanced topics for next-generation closed-loop operations. In this course of action, ZSM has highlighted the use of Artificial Intelligence (AI) and Machine Learning (ML) technologies in NGNs aiming to leverage the benefits they provide towards the automation and optimization of the service management process [33].  
The reference architecture of the framework is presented in [34] and enables the definition of the functionality and the requirements that should be met in any ZSM implementation. For example, in cross-domain services, QoS requirements and service interoperability should be guaranteed across different management domains. In a multi-stakeholder scenario, a Management Domain (MD) is usually the administrative area of an NP that is responsible for the proper functioning of services running in this area. When E2E cross-domain services are deployed, the ZSM framework should guarantee the proper collaboration of MDs in order to support the E2E service with appropriate resources.  
One of the main factors that affect the performance of the service is the time needed for the management tasks to be completed. The management tasks related to the deployment of E2E services should be executed within the limited processing time according to the ZSM reference architecture requirements. Functional and non-functional requirements defined by the ETSI standardization team in their manuscript determine the successful operation of the framework. The satisfaction of those requirements ensures the efficient operation of the network.
## 1 Introduction
### Maps in a Context of Mobility  
The expansion of internet connectivity since the mid-1990s, combined with the rise of portable computing in the early 21 st century, has revolutionised access to digital information. Unlike earlier systems that required dedicated spaces for data consultation, modern technologies enable ubiquitous access to geospatial content from any location [3; 18; 19].  
Mobile maps have emerged as a core component of spatial communication in this evolving landscape, and have been seamlessly integrated with location-based services (LBSs). These tools are now widely incorporated into various applications, including social media platforms, real-time navigation, and digital documentation systems [20]. The availability of mobile devices with geospatial capabilities and wireless internet access allows users to instantly explore localised information, ranging from nearby events to predictive spatial analytics [21]. The widespread use of mobile cartography has driven innovations in data visualisation, emphasising usability and performance optimisation. Given the constraints of device size and interface design, cartographic solutions for portable applications must be adapted to meet hardware capabilities and user cognitive processes, ensuring accessibility and seamless interaction in on-the-go environments.  
Traditional cartographic methods rely on structured learning, standardised conventions, and specialised terminology to interpret geospatial data accurately. However, directly applying these principles to mobile mapping applications presents challenges due to digital platforms' dynamic and interactive nature. Instead of replicating static cartographic models, mobile maps must simulate real-world spatial contexts effectively, prioritiising concise, user-friendly representations that enhance on-the-go accessibility.  
Mobile mapping solutions often rely on simplified visual elements, such as points of interest (POIs) and topological schematics, to achieve optimal usability rather than intricate traditional map designs. These streamlined approaches improve information retrieval while ensuring that users can quickly interpret and interact with spatial data in real time. Another crucial aspect is the situational nature of mobile maps, which are typically accessed for immediate reference rather than prolonged analysis. Given this ephemeral usage pattern, mobile cartographic tools must present precise, context-specific data, ensuring that users can obtain relevant insights quickly and efficiently. The computing environment [22; 23; 24] also plays a crucial role in distinguishing mobile cartography from standard cartography, facilitating the emergence of new contexts in the consultation and deployment of territorial information.
# Can We Survive without Labelled Data in NLP? Transfer Learning for Open Information Extraction
## 3 Related Work
### State-of-the-Art Open Information Extraction
#### 3.1.1 Machine Learning Classifiers Approaches  
OIE systems that are built on machine learning classifier techniques require automatically generated data to train the classifier. In 2007, [PERSON] et al. introduced the first OIE system based on shallow syntactic analysis, TextRunner [13]. It implements extraction in three main phases. It starts with a self-supervised learner that depends mainly on a conditional random field (CRF) classifier  
Figure 1: Open information extraction (OIE) transfer learning assessment. A total of four transfer learning experiments were carried out in our work. Left: the two transductive transfer learning experiments (BM1 and RE1). Right: illustration of the two experiments using inductive approaches (BM2 and RE2). Middle: the red dotted line represents the original model (ROIE), in which we tested our proposed neural model by testing and training on OIE news data, which is discussed in Section 4. The experiments’ ID and the section they are discussed in are encapsulated in the yellow rectangles.  
that utilizes unlexicalized features required for relation extraction, followed by a single pass extractor that extracts any potential relation triple and classifies each as either trustworthy or not. Finally, a redundancy-based assessor that re-ranks the extracted relations and assigns a confidence score to each extracted tuple is implemented. Not only did the authors of TextRunner facilitate domain-independent detection of relations from a corpus but their work triggered researchers towards developing OIE systems. For instance, the WOE (Wikipedia-based Open Extractor) [14] system is built on TextRunner, having two modes of operation: WOE\({}^{\text{Pos}}\) and WOE\({}^{\text{Parse}}\). The main hypothesis behind WOE is the automated assembly of training samples by heuristically pairing Wikipedia info box values with corresponding texts, hence improving TextRunner's performance. WOE\({}^{\text{Pos}}\) exploits the CRF classifier trained with shallow syntactic proprieties to extract specific words between two noun phrases that represents a relation.  
An example of an OIE approach that utilizes dependency parsing is WOE\({}^{\text{Parse}}\); it exploits a rich dictionary of dependency path patterns acquired from Wikipedia extractions. While the OLIE (Open Language Learning for Information Extraction) approach [15] relies on the bootstrapping concept, it learns semi-lexicalized pattern templates using dependency parses by bootstrapping a plentiful amount of training data that results in surpassing WOE's performance.
# Exploring the Use of Landmarks to Aid Pedestrian Wayfinding
## 1 Introduction  
Landmarks have distinctive properties that can assist people in finding their way [1; 2; 3]. This study's major goal was to determine if photographs of landmarks can be used by pedestrians to follow a route to aid the development of spatial knowledge of surroundings. If this is the case, subsequent research could then explore their use in mobile maps as problems exist when wayfinding with such maps--users use a very small, constrained map of a region and the route is generated by an automated system, which entails little thinking and planning on behalf of the user [4; 5; 6].  
Little research has been conducted where landmarks have been the principal navigational aid used to follow a route, exceptions being [7; 8]. We investigated the extent to which a static landmark-based wayfinding system may facilitate pedestrian wayfinding and the acquisition of landmark, route, and survey knowledge compared with a static map-based system based on Google Maps and a conventional static paper map. The length of the chosen route that participants were asked to take was similar to that reported in another relevant research [2; 9; 10]. The route was located in a landmark-rich region that included several turns comparable to those of those other studies. Different navigational aids were provided to three participant groups to accomplish the route-following tasks. Sequential images of the landmarks along the route were shown to the first group. The second group used a small book of screenshots of the route as depicted in Google Maps, while the third group relied on a paper map to determine their own paths. A printed booklet version of the route in Google Maps was used by the second group, as it wasthought that real mobile maps would offer an unfair advantage to participants using this aid. Therefore, all three wayfinding aids were paper-based. Typically, GPS-based mobile maps can provide precise location information, step-by-step directions, and augmented reality overlays aligned with the user's perspective. By leveraging these sensors, users can effortlessly navigate unfamiliar environments, find the nearest points of interest, and seamlessly explore their surroundings with confidence.