esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results and Discussion
### Performance Evaluation Using K-Fold Cross-Validation Training and Subsequent Compression
** & **Test Acc. ** & **F1 Score** \\ \hline Basic RNN & 0.49 & 0.49 & 0.44 & 0.27 & 2.05 & 0.51 & 0.51 & 0.50 & 0.30 \\ \hline FastGRNN & 0.49 & 0.50 & 0.45 & 0.27 & 2.12 & 0.99 & 0.59 & 0.55 & 0.36 \\ \hline FastRNN & 0.57 & 0.59 & 0.55 & 0.43 & 2.05 & 0.63 & 0.63 & 0.61 & 0.43 \\ \hline UGRNN & 0.58 & 0.57 & 0.53 & 0.34 & 3.30 & 0.66 & 0.67 & 0.67 & 0.49 \\ \hline GRU & 0.57 & 0.56 & 0.50 & 0.34 & 4.55 & 0.58 & 0.56 & 0.53 & 0.36 \\ \hline LSTM & 0.53 & 0.52 & 0.48 & 0.31 & 5.80 & 0.59 & 0.59 & 0.59 & 0.37 \\ \hline Best PerformanceFor MHEALTH-FNOW, GRU and FastRNN were the best performers, and FastGate and Vanilla RNN were the smallest. For MHEALTH-SNOW, FastRNN, GRU, and LSTM were the best performers. For USCHAD-FNOW, UGRNN obtained a normalized F1 score of 0.883, and FastGate and Vanilla RNN were the smallest at around 2.3 KB. For USCHAD-SNOW, FastRNN performed best. For WHARF-FNOW, UGRNN had the best training accuracy, and FastRNN had the best test accuracy and F1 score. For USCHAD-SNOW, UGRNN had the best performance and was the smallest. For WISDM-FNOW, UGRNN performed best with a 0.89 normalized F1 score, and, for WISDM-SNOW, GRU performed best with a 0.95 normalized F1 score.
## 4 Experiments
### Model Comparisons
#### 4.2.1 Attention-Based Models  
**ATAE-LSTM**: They proposed to use LSTM and attention mechanism to obtain a vector representation for sentiment prediction, and add aspect embedding to each context word embedding.  
**MemNet**: They proposed to use external memory to model the context representation, using a multi-hop attention architecture.  
**IAN**[39]: An interactive modeling model of aspects and context is designed, using BiRNN and attention mechanism to interactively learn aspects and context representation.  
**AOA**: They proposed an attention-over-attention neural network to model aspects and sentences in a joint way and explicitly capture the interaction between aspects and context sentences.  
**T-MGAN**: They proposed a transformer-based, multi-granularity attention network (T-MGAN), which uses a tree transformer module to obtain a phrase-level representation, using a dual-pool operation and a multi-granularity attention network to extract high-quality feature representations.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Category** & **Positive** & **Neural** & **Negative** \\ \hline \multirow{3}{*}{Twitter} & Train & 1561 & 3127 & 1560 \\  & Test & 173 & 346 & 173 \\ \multirow{2}{*}{Lap14} & Train & 994 & 464 & 870 \\  & Test & 341 & 169 & 128 \\ \multirow{2}{*}{Rest14} & Train & 2164 & 637 & 807 \\  & Test & 728 & 196 & 196 \\ \multirow{2}{*}{Rest15} & Train & 912 & 36 & 256 \\  & Test & 326 & 34 & 182 \\ \multirow{2}{*}{Rest16} & Train & 1240 & 69 & 439 \\  & Test & 469 & 30 & 117 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detailed statistical of five datasets in our experiments.  
**IMAN**: They made improvements based on the AEN model, adding a multi-head interactive attention mechanism to the last layer to interact with context information and aspect information to obtain the final feature representation.  
**AEN-Glove**: They proposed an attention encoder network to model the relationship between context and specific aspects, and the embedding layer uses Glove static embedding.  
**AEN-BERT**: Different from the AEN-Glove model, a pre-trained BERT-base model is used in the embedding layer.
## 5 Drilling Nozzles Using a Continuous High-Pressure Liquid Stream
These findings underscore the influence of jet pressure and ROP on the efficiency and dimensions of the cracks formed during the fracture-cutting process. An increased rate of penetration has a significant effect on reducing the specific energy of fracture cutting, particularly at higher pressures [26; 30; 36]. For example, at pressures ranging from 1000 to 1100 MPa, the specific energy (specific drilling energy) of slot cutting decreased from 7240 to 1660 J/cm\({}^{2}\) when the ROP was increased from 10 to 40 cm/s. This represents a substantial 77% reduction in power requirements for the slot-cutting tool in this rock. Similarly, at pressures of 200-300 MPa, the specific energy of the slot-cutting tool decreased from 1400 to 300 J/cm\({}^{2}\), resulting in a 79% reduction in power demand. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Stream Pressure,** & **Jetting Velocity,** & **Average Crack Depth,** & **Average Crack Width,** & **Stream Power, kW** & **Specific Energy of jet Drilling,** & **Specific Energy of Jet Drilling per Unit of Crack** \\
**MPa** & **m/s** & **cm** & **cm** & **km** & **j/cm\({}^{3}\)** & **Volume,** \\ \hline
1000–1100 & 10.1 & 4.20 & 0.31 & 307 & 6630 & 7240 \\ \hline
900–1000 & 10.1 & 4.12 & 0.31 & 259 & 5570 & 6220 \\ \hline
800–900 & 10.1 & 4.60 & 0.31 & 230 & 4420 & 4950 \\ \hline
700–800 & 10.1 & 3.75 & 0.31 & 190 & 5240 & 5020 \\ \hline
600–700 & 10.1 & 3.64 & 0.31 & 138 & 6040 & 3750 \\ \hline
500–600 & 10.1 & 3.91 & 0.31 & 117 & 7100 & 2960 \\ \hline
400–500 & 10.1 & 3.30 & 0.31 & 76 & 4910 & 2280 \\ \hline
300–400 & 10.1 & 3.32 & 0.31 & 56 & 5220 & 1670 \\ \hline
200–300 & 10.1 & 2.68 & 0.31 & 38 & 2760 & 1400 \\ \hline
100–200 & 10.1 & 1.67 & 0.31 & 14 & 2620 & 830 \\ \hline
0–100 & 10.1 & 1.11 & 0.31 & 5 & 1490 & 445 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Influence of the jet pressure value on the specific energy of slot cutting at a rate of penetration, v = 10.1 cm/s for Indiana limestone [34]. Figure 7: Results for jet fracture cutting in various rocks [33]. Under specific operating conditions (rock type, nozzle size, pressure drop), there exists an optimal tool rate of penetration that minimizes the specific energy of fracture cutting. This optimal rate corresponds to the speed at which the exposure factor of the fracture surface (ROP \(\times\) fracture depth) is maximized. Figure 8 illustrates that at 4896 bar, the slot-cutting operation with a 0.5 mm diameter jet was optimized at the ROP of 40 cm/s. At 7655 bar, the optimum velocity exceeded 50 cm/s, as evident from the continuing increase in the curve at this rate of penetration. [PERSON] observed that interference between the jet and the walls of the drilled borehole decreased the efficiency of hydraulic jet drilling.
# Research on Dynamic Modeling and Vibration Characterization of Integrated Bearings
## 3 Conclusions  
A dynamic model for an integrated bearing that employs a structure with an inner ring and shaft integration was established. The vibration characteristics of the integrated bearing were analyzed, focusing on the influence of various structural parameters, such as cage pocket clearance and the curvature radius coefficients of the inner and outer raceways, alongside operational parameters, including axial preload, rotational speed, and radial load. A comparative analysis between the integrated bearing and the deep-groove ball bearing was also conducted. The results indicate that, under the same vibration characteristic requirements, the integrated bearing features a more compact structure and is capable of adapting to a wider range of operating conditions. Finally, the model in this paper was validated through finite element analysis. The results show that the maximum error is approximately 13%, which confirms the accuracy of the model.  
(1) Our findings indicate that a careful selection of structural parameters can significantly reduce vibration in the design of the integrated bearing. By optimizing the curvature radius coefficients for both the inner and outer raceways, as well as the cage pocket clearances, the vibration levels can be substantially diminished. The optimal curvature radius coefficients for the inner and outer raceways range from 0.52 to 0.53, and the cage pocket clearance is between 0.045 and 0.055 mm.  
(2) Furthermore, our results demonstrate that the application of an appropriate axial preload effectively reduces the vibration of the integrated bearing. Conversely, an increase in rotational speed correlates with a progressive escalation in vibration levels. Similarly, higher radial loads are associated with increased vibration acceleration. Therefore, selecting appropriate operating parameters for the integrated bearing, such as rotational speed and load, can ensure its stable operation.  
Conceptualization, C.Z. and J.Z.; methodology, C.Z.; software, C.Z.; validation, C.Z., N.L. and B.W.; formal analysis, C.Z.; investigation, J.Z.; resources, J.Z., B.W. and N.L.; data curation, C.Z.; writing--original draft preparation, C.Z.; writing--review and editing, J.Z. and N.L.; visualization, C.Z.; supervision, J.Z.; project administration, J.Z.; funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the Key research and development program of Ningbo (2022Z050). The APC was funded by Dalian University of Technology.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
# Recurrence Quantification Analysis as a Form of Postural Control Assessment: A Systematic Review
## 1 Introduction
### Recurrence Quantification Analysis Parameters
#### 1.1.1 Recurrence Rate (REC)  
REC is a nonlinear autocorrelation measure (% recurrence) indicating the degree to which data points repeat themselves in the time series, with repetition defined in terms  
Figure 1: Example recurrence plot for 30 s of CoP data. Black areas represent points that are recurrent in time. White spaces indicate no recurrent points. It denotes intermittent activity, a sudden change in level, and may reflect a state change. The recurrence point density can change quickly when moving along the axis of the graph. It indicates a possible change in the dynamic regime and can be observed after a transition period.  
of proximity in reconstructed multidimensional phase space. Thus, REC describes the percentage of recurrent points lying within a specified radius _r_(_i_). This variable can range from 0% (no recurrent points) to 100% (all points are recurrent) [11; 13]. REC is also known as the density of recurrence points in the recurrence plot.  
\[\text{REC}=\frac{1}{N^{2}}\sum_{i,j=1}^{N}R(i,j)\]  
where \(R(i,j)=\Theta(r(i)-\|x(i)-x(j)\|)\); \(i\), \(j\) = 1, 2,..., \(N\); \(N\)--number of points on the phase space trajectory; \(r(i)\)--specified threshold, \(\|\) --the norm or metric (in this study, the Euclidean norm was calculated), and \(\Theta(\;)\)--is the Heaviside function, defined as:  
\[\Theta(x)=\begin{cases}1,\;x\geq 0\\ 0,\;x<0\end{cases}\]  
The 0 and 1 values of the Heaviside function are represented in white and black, respectively. According to [PERSON] et al. [14], the _r_(_i_) value was 1% of the maximum phase space diameter.  
Selection of the radius threshold is necessary because two points will never lie exactly together in the reconstructed phase space, and thus the threshold permits two locally similar points to be considered recurrent, and any points outside the threshold will not be summed into the percent recurrence of the signal. The radius threshold can have a great impact on the output variables of RQA, so guidelines have been proposed such that the appropriate radius dimension maintains the total percentage of recurrent points under 5% [11; 15]. Moreover, a large radius threshold that saturates determinism or recurrence should be avoided.
# Experimental Investigation on Flow-Induced Rotation of Two Mechanically Tandem-Coupled Cylinders
###### Abstract  
The flow-induced rotational motion of tandem double cylinders has rarely been studied in existing papers. In order to further study the flow-induced rotation (FIR) of two mechanically tandem-coupled cylinders, an FIR device was designed in this paper, and the theoretical basis of this system was established. On this basis, a series of variable spacing ratio (\(L/D\)) tests were carried out in a recirculating water tunnel. The range of \(L/D\) was \(4.0\leq L/D\leq 9.0\). The main experimental conclusions can be summarized as follows: (1) When \(L/D=4.0\) and \(4.5\), the rotational response was similar to vortex-induced vibration (VIV), which is different from typical VIV, in that the rotational oscillation would appear to be a re-growth region when velocitycontinued to increase after the oscillation entered the lower branch of VIV. Additionally, the oscillation was at a low level and the maximum arc length ratio (\(A^{\star}\)) was less than \(0.55\) in these two cases; (2) For \(L/D=5.0\), \(5.5\) and \(6.0\), the rotational responses all showed typical VIV. When the oscillation reached a high level, the maximum \(A^{\star}\) was more than \(0.85\) for each case; (3) When \(L/D=7.0\), \(8.0\) and \(9.0\), the rotational responses still presented typical VIV. The oscillation was at a medium level, and the maximum \(A^{\star}\) was between \(0.53\) and \(0.72\), but these three cases had a wider synchronization interval than the other cases, and the range showed an increasing trend with the growth of \(L/D\).  
flow-induced rotation; mechanically tandem-coupled cylinders; spacing ratio +
Footnote †: journal:
## 2 Materials and Methods
### UHPLC-TOF/HRMS Analysis of Phenolic Compounds  
Freeze-dried samples were weighed to 0.1 g each, and 5 mL of 80% methanol aqueous solution was used to extract these samples using sonication for 1 h followed by filtering with a 0.2 \(\mu\)m membrane filter. An Ultimate 3000 RSLC UHPLC system (Thermo Fisher Scientific Inc. Waltham, MA, USA) was used to analyze the phenolic compounds for profiling of the ice plant (_Mesembyranthemum crystallinity_ L.) for this study. The system consisted of an autosampler, a column oven, an ultrahigh-pressure solvent delivery pump, and an automatic degasser. Chromatographic separation of the samples was performed using a Waters Corrects T3 (150 \(\times\) 2.1 mm, 1.6 \(\mu\)m, Waters Co. US-MA). The column temperature was set to 45 \({}^{\circ}\)C. The injection volume was 1 \(\mu\)L with a flow rate of 0.4 mL/min. The mobile phases (0.1% formic acid in DW, A; 0.1% formic acid in ACN B) was elutedwith the linear gradient programmed as follows: (i) 0-1 min, 3% B; (ii) 1-8 min, from 3% to 10% B; (iii) 30 min, from 10% to 95% B; (iv) 35-35.1 min, from 95-3% B. MS and MS/MS detection were conducted using a Q-Exactive Orbitrap mass spectrometer (Thermo Scientific, Horsham and Loughborough, England, and Inchinnan, Scotland.) operating in a positive and negative electrospray ionization mode. The mass scan was used in full scan mode and data-dependent mass spectra (DDMS2) mode with the mass range set at 100-1500 m/z. The mass spectrometry conditions were set as follows: ion spray voltage of 3.5 kV in positive mode and 3.0 kV in negative mode; ion source type of H-ESI; sheath gas of 50 (Arb); the aux gas pressure of 10 (Arb); sweep gas of 1 (Arb); and source temperature of 320 \({}^{\circ}\)C. During the profiling analysis, the phenolic compounds were identified based on their accurate mass (m/z) and molecular (m/z) ion fragmentation pattern using Scafford Elements version 2.2.1. Mass spectral libraries were used including the NIST Library 2017 and the MassBank of North America (MoNA) library database [16; 17].
# An Overview on the Latest Nature-Inspired and Metaheuristics-Based Image Registration Algorithms
## 4 Revision of the State-of-the-Art
### [PERSON] et al.'s OLDE-Based Proposal  
[PERSON] et al. [54] proposed an intensity-based IR method for remote sensing. This IR variant made use of the hybridization of both the orthogonal learning (OL) and the DE algorithms (OLDE). It has been demonstrated that the OL strategy provides an efficient alternative when searching for in complex problem spaces. In OLDE, the OL strategy guides the DE algorithm to select promising search directions towards the global optimum. In particular, they provide a crossover component for DE based on OL, named orthogonal crossover. They tested the performance of OLDE against the one from other EA-based IR methods. The experimental section considered 2D images of RADARSAT SAR sensors. The reported results showed that OLDE was the best IR algorithm which achieved robust and efficient outcomes with respect to the initial values of the IR transformation.
# Stabilization of Argan Oil Nanoemulsions Using Chitosan Extracted from Pink Shrimp Shells
## 3 Results and Discussion
### Chitosan
#### 3.1.2 Crystallinity and Surface Morphology  
Chitin extracted from pink shrimp is classified as \(\alpha\)-chitin, characterized by polymer chains arranged in sheets with an antiparallel structure. This arrangement facilitates maximum intermolecular hydrogen bonding, contributing to structural stability [20]. X-ray diffraction (XRD) spectra for both chitin and chitosan displayed characteristic patterns of chitin's crystalline structure (Figure 1). Chitin exhibited strong reflections at 20 angles of 9.19\({}^{\circ}\) and 19.02\({}^{\circ}\), corresponding to the lattice planes (020) and (110). Chitosan showed peaks at 10.91\({}^{\circ}\) and 19.81\({}^{\circ}\), typical of its crystalline regions [21]. The peak at approximately 19\({}^{\circ}\) 20 for chitin, the most intense reflection, indicates interchain hydrogen bonding stabilizing the structure. During deacetylation, structural changes resulted in slight peak shifts, broader peaks, and reduced intensity due to increased amorphous scattering [22]. These modifications indicate a more disordered molecular arrangement in chitosan, attributed to its higher levels of deacetylation. This change in crystallinity and surface morphology enhances the functional adaptability of chitosan for various applications.  
The Crystalline Index (CrI), calculated from the XRD data to quantify crystallinity, revealed values of 85.79% for chitin and 66.36% for chitosan. Chitin typically exhibits a higher degree of crystallinity due to its extended, ordered hydrogen-bonding networks. In contrast, chitosan has lower crystallinity, characterized by broader peaks and a more diffuse XRD background. These changes result from structural modifications during the deacetylation process that converts chitin into chitosan [22]. As a semi-crystalline biopolymer, chitosan's crystallinity index is a key parameter reflecting its structural properties and influencing its behavior. Higher molecular weight chitosan tends to have greater crystallinity, while lower crystallinity is often associated with increased solubility. Additionally, crystallinity inversely affects chitosan's ability to absorb water or other substances.  
Figure 1: X-ray diffraction patterns of chitin and its corresponding chitosan.  
Using SEM, high-resolution images of chitin and chitosan powders were recorded at magnifications of \(\times\)100, \(\times\)200, \(\times\)500, and \(\times\)2000 (Figures 2 and 3). Chitin displayed a smooth, flake-like structure composed of stacked parallel sheets, whereas chitosan exhibited a near-spherical morphology with a rough surface and higher porosity, making it advantageous for applications such as drug delivery. The morphology of chitosan was notably more complex and diverse than that of chitin. This distinction results from the deacetylation process, which alters chitosan's polymer structure and properties. Additionally, the specific morphology can vary depending on the source of chitin/chitosan and the processing methods used.
# Tourism Demand Forecasting Based on an LSTM Network and Its Variants
## 4 Results and Discussion
### Series 1
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Month** & **Actual** & **Huarng et al.**[39] & **LSTM** & **Bi-LSTM** & **GRU** \\ \hline Jun-03 & 57,131 & 41,693 & 61,741 & 68,211 & 68,356 \\ Jul-03 & 154,174 & 55,717 & 75,754 & 81,435 & 81,375 \\ Aug-03 & 200,614 & 155,234 & 156,490 & 157,801 & 157,099 \\ Sep-03 & 218,594 & 198,470 & 195,112 & 194,353 & 193,627 \\ Oct-03 & 223,552 & 217,083 & 210,043 & 208,473 & 207,780 \\ Nov-03 & 241,349 & 223,489 & 214,158 & 212,362 & 211,682 \\ Dec-03 & 245,682 & 239,859 & 228,915 & 226,304 & 225,682 \\ Jan-04 & 212,854 & 245,725 & 232,505 & 229,693 & 229,089 \\ Feb-04 & 221,020 & 235,124 & 205,278 & 203,968 & 203,262 \\ Mar-04 & 239,575 & 220,528 & 212,057 & 210,376 & 209,689 \\ Apr-04 & 229,061 & 238,021 & 227,445 & 224,915 & 224,287 \\ May-04 & 232,293 & 231,267 & 218,728 & 216,681 & 216,017 \\ Jun-04 & 258,861 & 232,482 & 221,409 & 219,213 & 218,559 \\ Jul-04 & 243,396 & 256,818 & 243,416 & 239,989 & 239,444 \\ Aug-04 & 253,544 & 246,198 & 230,611 & 227,905 & 227,292 \\ Sep-04 & 245,915 & 252,812 & 239,016 & 235,838 & 235,268 \\ Oct-04 & 266,590 & 247,735 & 232,698 & 229,875 & 229,272 \\ Nov-04 & 270,553 & 264,855 & 249,808 & 246,017 & 245,512 \\ Dec-04 & 276,680 & 270,632 & 253,084 & 249,105 & 248,621 \\ Jan-05 & 244,252 & 276,447 & 258,146 & 253,875 & 253,425 \\ Feb-05 & 257,340 & 266,528 & 231,321 & 228,575 & 227,965 \\ Mar-05 & 298,282 & 256,305 & 242,157 & 238,802 & 238,249 \\ Apr-05 & 269,513 & 296,152 & 275,967 & 270,648 & 270,336 \\ May-05 & 284,049 & 291,862 & 252,225 & 248,295 & 247,805 \\ Jun-05 & 293,044 & 282,861 & 264,230 & 259,605 & 259,199 \\ Jul-05 & 268,269 & 292,460 & 271,650 & 266,588 & 266,240 \\ Aug-05 & 281,693 & 290,673 & 251,196 & 247,326 & 246,829 \\ Sep-05 & 270,700 & 280,606 & 262,286 & 257,774 & 257,354 \\ RMSE (May-00–Sep-05) & 30,789 & 31,182 & 34,872 & 34,770 \\ RMSE (Nov-02–Jun-03) & 61,863 & 59,276 & 59,480 & 59,369 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._Figure 4: The actual and predicted tourist number of Taiwan from May 2000 to September 2005.
###### Abstract  
Unmanned aerial vehicles (UAVs) are widely used in our modern society and their development is rapidly accelerating. Flying Ad Hoc Networks (FANETs) have opened a new window of opportunity to create new value-added services. However, the characteristics that make FANETs unique, such as node mobility, node distance, energy constraints, etc., imply that several guidelines need to be considered for their successful deployment. Although numerous routing protocols have been proposed for FANETs, due to the wide range of applications in which FANETs can be applied, not all routing protocols can be used. Due to this challenge, after breaking down and classifying the different types of existing routing protocols for FANET, this paper analyzes and compares the performance of several routing protocols (Babel, BATMAN-ADV, and OLSR) in terms of throughput and packet loss in a real deployment composed of several UAV nodes using 2.4 and 5 GHz WiFi networks. The results show that Babel achieves better performance in the studied metrics than OLSR and BATMAN-ADV, while BATMAN-ADV delivers significantly lower performance. This experimental study confirms the importance of choosing the proper routing protocol for FANETs and their performance evaluation, something that will be extremely important in a few years when this type of network will be common in our day-to-day life.  
ad hoc networks; experimental study; Flying Ad Hoc Networks; FANET; practical case; routing protocols; testbed; unmanned aerial vehicles; UAV; WiFi +
Footnote †: journal: Applied  
11 May 2021  
**A Comparative Performance Evaluation of Routing Protocols for Flying Ad-Hoc Networks in Real Conditions**  
**Antonio Guillen-Perez \({}^{\star}\), Ana-Maria Montoya, Juan-Carlos Sanchez-Aaronutes and Maria-Dolores Cano \({}^{\star}\)**  
**Construction:** Guillen-Perez, A.; Montoya, A.-M.; Sanchez-Aaronutes, J.-C.; Cano, M.-D. A Comparative Performance Evaluation of Routing Protocols for Flying Ad-Hoc Networks in Real Conditions.  
**Keywords:** ad hoc networks; experimental study; Flying Ad Hoc Networks; FANET; practical case; routing protocols; testbed; unmanned aerial vehicles; UAV; WiFi