esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Linear Measurements on the X-, Y-, and Z-Axes  
Linear measurements were obtained in all directions along the X- (width), Y- (length), and Z-axes (height). Four linear distances on each axis, resulting in a total of 12 measurements, were measured between the centers of the selected reference balls (Figure 3).  
Figure 3: Linear measurements according to X-, Y-, and Z-axes. (**a**) Linear measurements on X-axis: X\({}_{\text{PM1}}\), between right and left first premolars; X\({}_{\text{PM2}}\), between right and left second premolars; X\({}_{\text{M1}}\), between right and left first molars; and X\({}_{\text{M2}}\), between right and left second molars. (**b**) Measurements on Y-axis: Y\({}_{\text{PM1}}\), from the incisor center to the virtual line between right and left first premolars; Y\({}_{\text{PM2}}\), from the incisor center to the virtual line between right and left second premolars; Y\({}_{\text{M1}}\), from the incisor center to the virtual line between right and left first molars; and Y\({}_{\text{M2}}\), from the incisor center to the virtual line between right and left first molars; and Y\({}_{\text{M2}}\), from the incisor center to the virtual line between right and left second molars. (**c**) Measurements on Z-axis: Z\({}_{\text{MR}}\), between the balls on right first molar; Z\({}_{\text{IR}}\), between the balls on right incisor; Z\({}_{\text{IL}}\), between the balls on left incisors; and Z\({}_{\text{ML}}\), between the balls on left first molars. Note that the center of reference ball is marked with cross sign and the measurements are marked with double-headed arrows.  
On the X-axis, the distances between the balls on the first premolars (X\({}_{\rm{PM1}}\)), second premolars (X\({}_{\rm{PM2}}\)), first molars (X\({}_{\rm{M1}}\)), and second molars (X\({}_{\rm{M2}}\)) were measured. On the Y-axis (length), the straight linear distances from the ball on the incisor edge to the line connecting the balls on the premolars (Y\({}_{\rm{PM1}}\), Y\({}_{\rm{PM2}}\)) and molars (Y\({}_{\rm{M1}}\), Y\({}_{\rm{M2}}\)) were measured. On the Z-axis (height), linear measurements were made between the balls on the occlusal and cervical sides of the right and left incisors (Z\({}_{\rm{IR}}\), Z\({}_{\rm{IL}}\)) and the first molars (Z\({}_{\rm{MR}}\), Z\({}_{\rm{ML}}\)).  
The measurement procedures were performed in a way designed to eliminate experimenter-dependent error. For the true model, a CMM (Victor 101208; Dukin Co., Ltd., Daejeon, Seoul) with 0.1 \(\upmu\)m resolution and 2.0 + L/300 maximum permissible error per ISO 10360-2 (\(\upmu\)m, Lmm) was used, and the resulting values were considered the gold standard values. For the digital models, 3-dimensional analysis software (Geomagic Control X; 3D systems, Braunschweig, Germany) was used to automatically detect the centers of the reference balls and to automatically measure the shortest distance between the centers of the balls (Figure 3). This measurement process was repeatedly done for all 80 digital models obtained via the four different scanning methods (intraoral scans, impression CBCT scans, extraoral scans, and cast CBCT scans).
## 5 Conclusions  
A UVP method for measuring the instantaneous velocity profiles of liquid and bubbles in two-phase bubbly flow has been developed. Instantaneous velocity profiles of single-phase and two-phase bubbly flow have been measured experimentally. The developed system employs only a single resonant frequency. Therefore, extra equipment is not needed. Signal processing, based on the Doppler pulse repetition technique, has been modified by applying the integration of Short-Time Fourier Transform and Doppler Amplitude Classification. The Developed-UVP can decompose the Doppler frequency of a particle (liquid phase) and bubble. Hence, the velocity distribution of the liquid and bubble can be measured and separated clearly. The experiment was conducted on vertical pipe flow apparatus, and measurement comparison was performed using PIV method. The measurement  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Error Sources** & **Error Magnitude** & **Percentage of Error** \\ \hline Basic frequency \(f_{0}\) at 4 MHz of the emitted pulse & \(\pm 0.065\) MHz & \(\pm 1.6\%\) \\ \hline Sound velocity \(c\) due to temperature variation & \(\pm 10.42\) m/s & \(\pm 0.69\%\) \\ (1493 m/s at 25 \({}^{\circ}\)C \(\pm\) 2 \({}^{\circ}\)C) & \(\pm 10.42\) m/s & \(\pm 2.46\%\) \\ \hline Incident angle \(\theta\) at 45\({}^{\circ}\) & \(\pm 1\) degree & \(\pm 2.46\%\) \\ \hline Doppler frequency \(f_{Di}\) at 4000 Hz (maximum value)(at \(S_{n}\):1, \(W_{n}\):24 and \(f_{PRF}\):8 kHz) & \(\pm 8\) Hz & \(\pm 0.2\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Systematic error.  
results clarify that the Developed-UVP is in good agreement with the PIV. Moreover, the effect of void fraction against velocity measurement of both phases was demonstrated. Therefore, it can be concluded that the Developed-UVP can measure liquid and bubble velocity simultaneously in the two-phase bubbly flow.  
The Developed-UVP has the following main advantages.  
Instantaneous velocity profile of liquid and bubble can be obtained simultaneously.  
The velocity profile of liquid and bubble can be separately obtained even if the velocity data of both phases occur in the same measurement channel.  
The developed phase separation can distinguish the velocity of both phases, although similarities occur with both phases.  
The system requires only a single resonant frequency transducer, a single channel pulser/receiver, and basic data processing equipment.  
Nevertheless, the developed technique is promising. Consequently, the technique can be applied to scale-up to industrial units. The further study on the applying of developed technique to industrial scale will be our future study.  
The work presented in this paper was a collaboration of all authors. W.W., A.H., N.T.-u., H.T. and H.K. Methodology; W.W., A.H. and N.T.-u., Project administration; H.T. and H.K., Supervision; H.K. Analyzing the data; W.W., A.H. and N.T.-u., Writing the paper; W.W., which was discussed and revised by all authors.  
The authors are grateful for the support of Ministry of Education, Culture, Sports, Science and Technology (MEXT).  
The authors declare no conflicts of interest.
# Enhanced Multicast Repair Fast Reroute Mechanism for Smart Sensors IoT and Network Infrastructure
## 2 The Fast ReRoute
### The Principle of Fast ReRoute  
For the proper understanding of the FRR technology, there is a need for terminology that denotes routers with special meaning for FRR mechanisms [21; 22; 23; 24; 25]. Here, we introduce the terms using the following simplified network topology described in Figure 1. The source router (S) is a router that has detected a link or neighboring router failure and then activates a locally implemented FRR repair mechanism. In other words, router S is actively involved in FRR repair (Figure 1). This router is also called Point of Local Repair (PLR). The destination router (D) is the destination router of the original data flow. Routers N1, N2, N3 and others are specific routers that are used as an alternative next router (hereafter referred to as next-hop router) for a specific FRR alternative path. R (R1) is a router that is not actively involved in FRR repair.  
However, before starting the main FRR process, an administrator must set up protected links or prefixes that are managed by the router. Subsequently, the FRR mechanism pre-calculates an alternate next-hop router to be used in the event of a protected link or prefix failure. This is called Phase Zero (preparation). FRR can then proceed further through the following phases [26; 27; 28]:  
* Phase One: Detection of a link failure by the specialized FRR technology. This phase activates the FRR mechanism. In the Figure 1, the Fast ReRoute process starts after a failure of the link between routers S and E has been detected. Here, following the terminology, the router S detects a link failure.
* Phase Two: Temporary modification of affected routing records by the FRR mechanism. During this phase, precalculated alternative routes are being installed (the FRR mechanism is active).
* Phase Three: Performing background routing protocol update. Routes installed using the FRR mechanism are used to route packets until the network convergence is completed (the FRR mechanism is active).  
Figure 1: The principle of Fast ReRoute (FRR).  
* Phase Four: The routing protocol completes the necessary routing information update. As the next step, the FRR mechanism is deactivated and the routing process is taken over by the routing protocol.  
Once the update of routing information is completed, the deactivation of the FRR mechanism can be accomplished in several ways. One method used is to apply a hold-down timer. This timer should be set to a minimum time necessary to complete the network convergence process. After this timer expires, the temporary routing information installed by the FRR mechanism is removed and the FRR mechanism is subsequently deactivated [26].  
The main advantage of the Fast ReRoute mechanism is that it offers several times faster network transmission recovery than a traditional routing protocol may achieve (OSPF). The average repair time of actual FRR mechanisms is up to 50 ms [26; 29; 30; 31].
## 3 MHT Process Improvement
### Improved MHT Verification
#### 3.2.2 Verifying Node Values in Improved MHT
Moreover, if a segment having an even index \(i\) is valid, the user already has the valid hash value of the next segment having index \(i+1\) because it is transmitted as a witness for \(s[i]\). Hence, if the user adds an internal memory \(m[k]\) and saves the witness to \(m[k]\), then a user can utilize the value of \(m[k]\) to verify the next segment \(s[i+1]\). Algorithm 2 describes this process. ```
1:data = {index, segment, witness = \(\{w[k],\ w[k-1],\ldots,\ w[1]\}\), signature}
2:storage = \(\{m[k],\ m[k-1],m[k-2],\ \ldots,\ m[0]\}\)
3:result = {valid, invalid}, storage = {\(m[k],\ m[k-1],m[k-2],\ \ldots,\ m[0]\}\)
4:ifindex is odd then
5: Compute the hash value of the segment
6: Compare the hash value with \(m[k]\)
7:if the two values are equal then
8: Return valid
9:endif
10:endif
11:else
12: Perform the pseudo code of Algorithm 1
13:if segment is valid then
14: Save \(w[k]\) to \(m[k]\)
15: Return valid
16:endif
17:endif
18:Return invalid;
```  
**Algorithm 2** Pseudo code for verifying a segment (Proposal 2). (1) The process to verify a segment having an even index \(i\) is the same procedure described in Algorithm 1, except for additionally saving the hash value of the next segment \(s[i+1]\) to \(m[k]\). (2) If a segment has an odd index \(i\), the user computes the hash value of the segment and then compares the hash value with the value of \(m[k]\) that was saved in the previous segment having an index \(i-1\) verification process. If these two values are equal, the user regards the received segment as being valid.
## 3 VVCM Evaluation
### Influence of VFA on the Mechanical Properties of LSAM-50 Asphalt Mixture and Design Criteria
#### 4.2.2 Effect of VFA on the Mechanical Properties of Asphalt Mixtures  
Based on the experimental data correlating the volume parameters of the LSAM-50 mixture with the mechanical properties of VVCM specimens, the trend graphs were constructed to illustrate the variation in mixture mechanical properties with respect to VFA, as depicted in Figure 8.  
Figure 7: VFA versus asphalt–aggregate ratios at different gradations.  
Figure 8 shows that with an increase in VFA, each mechanical property of the LSAM-50 asphalt mixture follows a pattern of initially increasing and subsequently decreasing. When the asphalt-aggregate ratio is low, resulting in a reduced VFA, the effective asphalt content is minimal, leading to inadequate adhesion between the asphalt and mineral particles and compromising the mixture's mechanical properties. As the amount of asphalt increases, the effective asphalt content in the mixture rises, enhancing adhesion and subsequently improving the overall mixture properties. Upon reaching the optimal asphalt dosage, the mechanical properties of the mixture peak. However, any subsequent increase in asphalt results in a rise in VFA, but with an increased proportion of unbound asphalt. This excessive asphalt can displace mineral particles, weaken the adhesive bond, and ultimately degrade the mixture's mechanical properties.
_Int. J. Mol. Sci._**2011**, _12_, 5908-5945; doi:10.3390/ijms12095908  
International Journal of  
Molecular Sciences  
**ISSN 1422-0067**  
www.mdpi.com/journal/ijms  
_Review_  
**Molecularly Imprinted Polymers: Present and**  
**Future Prospective**  
**[PERSON] *, [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]**  
Department of Engineering of Innovation, University of Salento, via per Arnesano km 1, Lecce 73100, Italy; E-Mails: [EMAIL_ADDRESS] (R.D.S.); [PERSON]S] (L.M.); [PERSON]R.L.); [PERSON]S] (A.S.); [PERSON]] (S.S.); [EMAIL_ADDRESS] (G.M.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +39-0832-297-252; Fax: +39-0832-297-272.  
_Received: 7 June 2011; in revised form: 3 August 2011 / Accepted: 17 August 2011 / Published: 14 September 2011_  
**Abstract:** Molecular Imprinting Technology (MIT) is a technique to design artificial receptors with a predetermined selectivity and specificity for a given analyte, which can be used as ideal materials in various application fields. Molecularly Imprinted Polymers (MIPs), the polymeric matrices obtained using the imprinting technology, are robust molecular recognition elements able to mimic natural recognition entities, such as antibodies and biological receptors, useful to separate and analyze complicated samples such as biological fluids and environmental samples. The scope of this review is to provide a general overview on MIPs field discussing first general aspects in MIP preparation and then dealing with various application aspects. This review aims to outline the molecularly imprinted process and present a summary of principal application fields of molecularly imprinted polymers, focusing on chemical sensing, separation science, drug delivery and catalysis. Some significant aspects about preparation and application of the molecular imprinting polymers with examples taken from the recent literature will be discussed. Theoretical and experimental parameters for MIPs design in terms of the interaction between template and polymer functionalities will be considered and synthesis methods for the improvement of MIP recognition properties will also be presented.  
**Keywords:** molecularly imprinted polymers (MIPs); molecular imprinting technology (MIT); molecular recognition; solid phase extraction; sensors; HPLC; drug delivery; catalysis; artificial receptors
# Hybrid TOA/RSS Range-Based Localization with Self-Calibration in Asynchronous Wireless Networks
## 5 Conclusions  
In this paper, we have addressed the TOA/RSS hybrid localization problem in presence of synchronization errors among all the nodes in the network and other missing information on the channel parameters. In fact, although many works assume knowledge of such parameters to reduce the high dimensionality of the cost functions involved in the position estimation task, such an information is not available in practice; moreover, parameters change over time due to the presence of dynamic obstacles and other modification of the environment; hence, automatic re-calibration is needed.  
Figure 10: ECDF of the considered algorithms for the case \(\alpha=2\), \(\sigma=6\) and \(N=8\). **(left)** Ideal TOA/RSS parameters and (right) estimated TOA/RSS parameters.  
Figure 9: Empirical cumulative distribution function (ECDF) of the considered algorithms for the case \(\alpha=2\), \(\sigma=4\) and \(N=8\). **(left)** Ideal TOA/RSS parameters and (right) estimated TOA/RSS parameters.  
To circumvent the limitations of joint ML estimation--which is not available in closed-form and is practically unfeasible via numerical optimization due to the large number of nuisance parameters--we have designed a novel two-step algorithm with reduced complexity. In a first calibration phase, nodes in known positions are exploited to estimate the unknown RSS and TOA model parameters; then, in a second localization step, an hybrid TOA/RSS range estimator is combined with an iterative least-squares procedure to finally estimate the unknown target position. The results show that the proposed localization approach can outperform state-of-the-art competitors and, remarkably, achieve almost the same accuracy of the joint ML benchmark, but with significantly lower computational cost.  
All authors contributed equally to this work. This research received no external funding. The authors declare no conflict of interest.
## 5 Conclusions and Recommendations
### Recommendations  
Since the processing time was the most significant parameter for the warning level, it is recommended for a UTM application development to reduce the processing time, such as using a higher computing processor and more efficient programming coding. Moreover, because the query time depends on the internet connection between a cloud server and the computer application, a more stable internet connection and a faster query mechanism are recommended.  
As we believe that the latency time is something that cannot be avoided in some situations, an additional delay warning mechanism or trajectory prediction should be explored in future research.  
Conceptualization, N.R. and C.-Y.L.; methodology, N.R.; software, N.R.; validation, N.R., C.-Y.L. and W.-L.G.; formal analysis, N.R., C.-Y.L. and W.-L.G.; investigation, N.R.; resources, N.R.; data curation, N.R.; writing--original draft preparation, N.R.; writing--review and editing, C.-Y.L. and W.-L.G.; visualization, N.R.; supervision, C.-Y.L. All authors have read and agreed to the published version of the manuscript.
* [1]**Funding:** This research and the paper were supported by the National Science and Technology Council, Taiwan, through the project The System Integrating and Manufacturing of Flight Test Airframe System for Long Endurance Solar UAV (109-2221-E-150-024-MY3, 2020-2023).
* [2]**Data Availability Statement:** The data is available upon request to the correspondence author.
* [3]**Conflicts of Interest:** The authors declare no conflict of interest.
Figure A3: Flight test data from Remote ID module 1 of record number 2: (**a**) flight parameters, (**b**) detection parameters, and (**c**) time parameters.  
Figure A9: Flight test data from Remote ID module 1 of record number 5: (**a**) flight parameters, (**b**) detection parameters, and (**c**) time parameters.
# SQL Injection Detection Based on Lightweight Multi-Head Self-Attention
## 4 Experimental Results
### Numerical Evaluations
As a result, it can be accommodated in the Up Board for realtime inference operations. By contrast, although ALBERT [24], DistilBERT [25], and ELECTRA [27] are lightweight BERT models, their weight sizes still exceed the memory capacity of Up Board. Therefore, they can only be deployed in the PC with sufficient memory size for online inference. This limits the flexibility of the existing lightweight BERT models for SQL injection detection applications. All these facts demonstrate the effectiveness of the proposed model. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Proposed Model**} & \multicolumn{2}{c}{**Multi-Model**} & \multicolumn{1}{c}{**ALBERT**} & \multicolumn{1}{c}{**DistilBERT**} & \multicolumn{1}{c}{**ELECTRA**} \\  & \multicolumn{1}{c}{**Model**} & \multicolumn{1}{c}{[22]} & \multicolumn{1}{c}{[24]} & \multicolumn{1}{c}{[25]} & \multicolumn{1}{c}{[27]} \\ \hline \multirow{3}{*}{UP Board} & Average Time & 175.92 ms & 187.47 ms & NA & NA & NA \\ \cline{2-7}  & Standard & \multirow{2}{*}{0.006} & \multirow{2}{*}{0.008} & \multirow{2}{*}{NA} & \multirow{2}{*}{NA} & \multirow{2}{*}{NA} \\  & Deviation & & & & & \\ \hline \multirow{3}{*}{PC} & Average Time & 51.55 ms & 76.82 ms & 148.23 ms & 123.51 ms & 121.26 ms \\ \cline{2-7}  & Standard & \multirow{2}{*}{0.010} & \multirow{2}{*}{0.010} & \multirow{2}{*}{0.097} & \multirow{2}{*}{0.121} & \multirow{2}{*}{0.062} \\ \cline{1-1}  & Deviation & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Average inference time of various models over the test set \(\mathcal{S}\) on different edge devices for SQL injection detection. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Proposed Model** & **Multi-Model** & **ALBERT** & **DistilBERT** & **ELECTRA** \\  & **Model** & **[22]** & **[24]** & **[25]** & **[27]** \\ \hline Accuracy & 96.60\% & 82.40\% & 96.40\% & 96.60\% & 97.40\% \\ Precision & 99.23\% & 98.75\% & 99.74\% & 96.59\% & 98.50\% \\ Recall & 96.50\% & 79.00\% & 95.75\% & 99.25\% & 98.25\% \\ F1 Score & 97.85\% & 87.78\% & 97.70\% & 97.90\% & 98.37\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance of various models for SQL injection detection for the test set \(\mathcal{U}\). There are 500 queries in the test set. The length of each query is above 1000 characters.
## 3 Experiment and Analysis
### Sensitivity Analysis of the Proposed Model's Parameters  
#### 3.2.1 Sensitivity Analysis of the Directional Proximity Thresholds  
The sensitivity of the proposed model to DENC's directional proximity thresholds \(\varepsilon_{\mathrm{x}}\) and \(\varepsilon_{\mathrm{y}}\) is evaluated using a uniformly scaled combination of these thresholds, with \(\lambda=0.250\) m, \(\delta=0.110\) m, \(\alpha=0.080\) m as estimated in Section 3.1.1, and \(\tau=0.001\). Figure 19 shows that the model achieves recall exceeding 85% when \(\varepsilon_{\mathrm{x}}\) is between 0.140 and  
Figure 17: Sensitivity of the Voronoi model [7] to outlier proportion parameter \(\alpha\) using \(\delta=0.110\) m and \(\tau=0.001\)—case study.  
Figure 18: Sensitivity of the Voronoi model [7] to defect’s position uncertainty threshold \(\delta\) using \(\alpha=0.080\) and \(\tau=0.001\)—case study.  
0.490 m and \(\varepsilon_{y}\) is between 0.070 and 0.245 m. The highest recall of 90.7% is achieved when \(\varepsilon_{x}\) is between 0.220 and 0.250 m and \(\varepsilon_{y}\) is between 0.110 and 0.125 m, while the highest precision of 97.2% is obtained when \(\varepsilon_{x}=0.220\) m and \(\varepsilon_{y}=0.110\) m.  
When \(\varepsilon_{x}\) and \(\varepsilon_{y}\) are small, the model delivers low performance, as many actual matches are classified as outliers by DENC. Once \(\varepsilon_{x}=0.080\) m and \(\varepsilon_{y}=0.040\) m, the model achieves a recall above 70%. The model maintains a recall of at least 81.1% for \(\varepsilon_{x}\geq 0.110\) m and \(\varepsilon_{y}\geq 0.055\) m, and it stabilizes at 84.4% when \(\varepsilon_{x}\geq 0.770\) m and \(\varepsilon_{x}\geq 0.385\) m.