esa-sceva/satcom-chunk-collection huggingface dataset:
# Think Twice: A Post-Processing Approach for the Chinese Spelling Error Correction
## 6 Discussion  
After introducing post-processing operations in the two models, the precision in all datasets was improved. Firstly, the basic model determines replacement candidates for each character in the sentence. Then, the post-processing operation combines the features and classifiers to filter out unnecessary corrections effectively. Because of this, the model's False Positive Rate is also significantly reduced.  
Compared with the previous error correction algorithm, although the post-processing operation improves the model's precision, it comes at the cost of slightly reducing the recall because the post-processing filters out correct replacements when screening competitor replacements, which is disadvantageous for high recall tasks. However, in the Chinese Spelling Error Correction task, the corrections' preciseness is more critical than correcting all potential errors. Therefore, a model with higher precision rates is more suitable than one with a higher recall rate.  
We also found that the performance of the post-processing operation on the three different test sets was quite different. For example, all indicators except recall were improved in SIGHAN2013, while the F1 score dropped on the other test sets. We believe that the uneven distribution of errors in the three datasets causes this phenomenon. As evidence, there are a large number of pronoun errors ( #--he, #--she) in the SIGHAN2014 test set. This type of error is challenging to identify for error correction models and post-processing operations.  
Our approach is based on the observation that the distribution between the model's training text and its actual application text is inconsistent, hence existing algorithms produce many fault corrections. In our two-stage process of introducing post-processing operations, the basic model generates replacement suggestions in the first stage, and then the post-processing operation is taken as the second stage. One of the advantages of this approach is that without modifying a ready-to-use Chinese Spelling Error Correction model, it is possible to reduce incorrect error correction caused by the mismatch of the training text and the actual application text. The post-processing operation has a high generalization capability. Thus, in addition to BERT and SpellGCN, which we mentioned in this paper, researchers can select a greater variety of base models, according to different tasks.
# Research of Security Routing Protocol for UAV Communication Network Based on AODV
## 3 Improved Security AODV Routing Protocol
### Secure Routing Scheme Based on ECC
#### 3.2.3 The Process of Routing Maintenance  
When a link is interrupted due to node movement or node energy exhaustion in the routing path, the upstream node of the link will send a routing error message (RERR) to notify the upstream node containing this path to delete the corresponding routing table entry. In order to prevent malicious nodes from publishing false routing error information by forging RERR messages, it is necessary to perform identity authentication on the nodes that send RERR messages. Therefore, the intermediate node must sign the RERR message with its own private key. Assume that in Figure 3, the link between nodes \(A\) and \(B\) is interrupted, and node \(A\) will send a routing error message along the reverse path to notify source node \(S\) to delete the corresponding routing table entry.  
\[A\to S:[RERR,RERR\_Sig_{A}]\]  
After receiving the routing error message, the upstream node authenticates the source node of the message. Only after the source node of RERR message is authenticated can the corresponding routing table entries be deleted from the routing table. This prevents the illegal node from destroying the network operation by forging the RERR message.
###### Abstract  
Inductive thermography is an NDT method, which can be excellently used to inspect long metallic specimens (such as railway tracks) to detect surface defects. Aiming at the inspection of railway tracks in service with a movable setup, the method had to be advanced from a stationary application to a scanning setup. This work presents methods for using calibration targets for rectification, in order to improve the quality of the resulting images. Two scanning techniques are presented for detecting different types of rolling contact fatigue (RCF) defects on rail pieces. In the case of the first method, separate stationary inductive pulsed measurements are carried out for the segments of a long sample and the results are stitched together to one panoramic image of the whole specimen (\"stop-and-go\"). Since the surface of the rail piece is curved, rectification of the surface with a flexible grid is necessary to generate seamless panoramic images. In the case of the second method, a specimen is moved with constant speed underneath the induction coil. For the detection of shallow surface cracks, the infrared camera has to have a view of the surface during the heating; therefore, the camera is placed behind the coil but tilted towards a position below the induction coil. In order to be able to evaluate phase images from the temporal temperature change, a checkerboard grid as a rectification target is used. It is also analyzed how the chosen IR camera frame rate and the motion speed affect the scanning result.  
Keywords:inductive thermography; image registration; image rectification; scanning methods; rolling contact fatigue; defect detection +
Footnote †: journal: Applied
## 3 Results and Discussion
### Quantitative Analysis of Ovarian Cancer Cell Apoptosis Induced by GEs  
The ability of both FAMEs and GEs produced in this study to induce SKOV-3 cell apoptosis was assessed by flow cytometry after Annexin FITC staining of cells (Figure 9). The results show that all GEs induced apoptosis of the SKOV-3 ovarian cancer cell line compared with untreated cells, with the apoptotic rate increasing significantly after 48 h. A higher percentage of apoptosis was observed in the cells treated with EPA-GEs (i.e., 43.1%), followed by _C. echimulata_-GEs, _U. isabellina_-GEs and olive oil-GEs (i.e., 39.2%, 34.0% and 33.5%, respectively). Similarly, FAAs containing EPA in their structures in high percentages displayed a strong anticancer activity against the SKOV-3 ovarian cancer cell line [36]. In the current paper, similar and in some cases higher apoptosis was observed in the SKOV-3 cells treated with FAMEs instead of GEs. Likewise, FA lithium salts derived from _C. echimulata_ lipids were proved effective against HL-60 human leukemia cells [13].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Source of GEs** & **LC50** & **Lower** & **Upper** & **RR** \\ \hline _Cunninghammellachinulata_ & 0.54 & 0.47 & 0.64 & 1.00 \\ _Umbelopsis isabellina_ & 39.62 & 33.75 & 46.10 & 73.23 \\ _Nannochloropsis gaditana_ & 16.92 & 12.60 & 25.82 & 31.27 \\ Olive oil & 12.88 & 10.08 & 17.54 & 23.81 \\ EPA concentrate & 10.24 & 7.84 & 14.00 & 18.93 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Susceptibility of _Aedes aegypti_ larvae to GEs under laboratory conditions by using dipping methods. Data represent the mean values of six replicates.  
Figure 9: Effect of FAMEs and GEs on the SKOV-3 cancer cell line: flow cytometry analysis of apoptosis in SKOV-3 cells either untreated or treated with 10 \(\upmu\)g/mL of every compound for 48 h. After the treatment period, the cells were stained with Annexin FITC and subsequently analyzed by flow cytometry. Abbreviations: _C. e., C. echimulata_; _U. i., U. isabellina_; _N. g., N. gaditana_; O. o., Olive oil; EPA, EPA concentrate.  
[PERSON] et al. [67] and [PERSON] et al. [68] reported that a variety of modified FAs are promising molecules in the treatment of cancers. Furthermore, there have been several studies dealing with the anticancer, antimicrobial and anti-inflammatory activities of SE derivatives [69; 70]. Our results correlated with those reported by [PERSON] and [PERSON] [71], who evaluated the antitumor activity of a series of glucosyl ester derivatives against three cancer cells, human breast adenocarcinoma (MCF-7), human colon carcinoma (K562) and human hepatoma (HepG2). They found that the glucosyl esters exhibited significant anticancer activity in a dose- and time-dependent fashion. The structure--activity relationship analysis revealed that lipophilic properties might be an essential parameter affecting their activity. Research to inhibit cancer cell proliferation has shown that SE activity is linked to the nature of both sugar and fatty acyl chains [71].
## 3 Methodology
### Sensitivity Analysis
This approach enables the identification of the TOPSIS variant that is more capable of handling dynamic conditions in satellite networks, ensuring reliable and efficient handover decisions even when network performance metrics experience variations. The TOPSIS algorithm with the smallest rate of change in its scores with respect to the initial score, i.e., the lower sensitivity, is considered the most robust. In addition to sensitivity analysis, we compare the standard deviation of the scores obtained by each of the TOPSIS variants under study. The objective of this analysis is to assess the stability of score distributions for each of the TOPSIS variants across different scenarios. A lower standard deviation indicates higher stability of the handover algorithm, whereas a higher standard deviation reflects greater sensitivity to small changes in criteria values. The algorithm whose standard deviation remains relatively constant in comparison to its counterparts despite small changes in criteria values is therefore considered more robust and reliable for managing satellite handover. The block diagram of the methodology is shown in Figure 2.
## 2 Related Evaluation Methods
### FCE Method  
FCE is a practical application method of fuzzy mathematics. It primarily employs the principle of fuzzy transformation and the maximum membership degree principle to conduct integrated assessments by considering all relevant factors [36; 37; 38; 39; 40]. Its mathematical model usually contains four core elements, which are evaluation indicator set (\(U\)), evaluation level (\(V\)), fuzzy relation matrix (\(\mathbf{R}\)), and evaluation set (\(\mathbf{B}\)). The evaluation flow chart of this evaluation method is shown in Figure 2.  
The specific evaluation steps are as follows.  
Step 1: Determine the set of evaluation indicators.  
The evaluation indicator set \(U\) is determined by the evaluation objectives, as shown in Equation (11).  
\[U=\{u_{1},u_{2},\ldots,u_{N}\} \tag{11}\]  
where \(u_{i}\) is the \(i\)th evaluation indicator of the impact evaluation objectives.  
Step 2: Determine the evaluation level.  
The evaluation level \(V\) is given by the evaluation objectives, as shown in Equation (12).  
\[V=\{v_{1},v_{2},\ldots,v_{p}\} \tag{12}\]  
where \(v_{i}\) is the \(j\)th evaluation level, and it is usually described in words.  
Step 3: Construct the fuzzy relation matrix.  
The fuzzy relation matrix \(\mathbf{R}\) is constructed as shown in Equation (13).  
\[\mathbf{R}=\left[r_{ij}\right]_{N\times p} \tag{13}\]  
where \(r_{ij}\) is the membership degree of the \(i\)th index relative to the \(j\)th level, and it is the possible degree of \(v_{i}\) evaluation of the \(u_{i}\).  
Step 4: Determine the weight of each evaluation index.  
Each evaluation indicator is assigned \(a_{i}\) to represent its importance, yielding the weight vector \(\mathbf{A}\) for the evaluation.  
\[a_{i}\geq 0,\sum a_{i}=1 \tag{14}\]  
Step 5: Select the synthesis operator and calculate the evaluation result.  
Figure 2: Fuzzy comprehensive evaluation process.  
The final evaluation set **B** can be calculated through the matrix **R** and the weight vector **A**, as shown in Equation (15).  
\[\begin{split}\textbf{B}&=\textbf{A}\bullet\textbf{R} \\ &=[a_{1},a_{2},\dots,a_{N}]\bullet\left[\begin{array}{cccc}r_{11} &r_{12}&\dots&r_{1p}\\ r_{21}&r_{22}&\dots&r_{2p}\\ \vdots&\vdots&\ddots&\vdots\\ r_{N1}&r_{N2}&\dots&r_{Np}\end{array}\right]\\ &=\left[b_{1},b_{2},\dots,b_{p}\right]\end{split} \tag{15}\]  
where **R** is the degree of membership of each evaluation index relative to the evaluation; **A** is the degree of the relevant evaluation index; and \(\bullet\) represents the synthesis operators.  
There are three types of synthesis operators, as shown below.  
\[S_{j}=\min\left(1,\sum\limits_{i=1}^{N}a_{i}r_{ij}\right),j=1,2,\dots,p \tag{16}\]  
\[S_{j}=\mathop{\vee}\limits_{i=1}^{N}\left(a_{i}\wedge r_{ij}\right)=\max_{i} \left\{\min\left(a_{i},r_{ij}\right)\right\},j=1,2,\dots,p \tag{17}\]  
\[S_{j}=\mathop{\vee}\limits_{i=1}^{N}\left(a_{i}\bullet r_{ij}\right)=\max_{i} \left\{a_{i}\bullet r_{ij}\right\},j=1,2,\dots,p \tag{18}\]
## 5 Conclusions  
Based on the research and analysis carried out to verify the neural method of assessing the humidity of saline brick walls, which was previously developed by the authors, the following conclusions can be drawn:  
* It was shown that it is possible to reliably assess the humidity of a saline brick wall using an artificial neural network with a properly selected structure and learning algorithm on the basis of parameters assessed using the nondestructive dielectric and microwave methods.
* An artificial unidirectional multilayer neural network with backward error propagation and the algorithm for learning conjugate gradient (CG) is predisposed for this purpose. The obtained satisfactory value of the linear correlation coefficient R of 0.807 confirms this statement.
* The correct identification of validation data was evidenced by the low average absolute error \(|\Delta\)fi value of 1.16% and the average relative error \(|RE|\) of 19.02% (not very high for an in-situ study). Moreover, the relative error values \(|RE|\) were mostly in the range of 15% to 25%.
* The average \(U_{mc}\) humidity value of 5.59% identified by the ANN was very close to the \(U_{m}\) humidity value of 5.68% obtained by means of a test using the gravimetric method.  
Further verification work is currently underway. This paper presents the results obtained to date, which are based on the results of testing two buildings. Ultimately, the group of buildings that will be used for verification purposes will be more numerous.  
Conceptualization, A.H. and L.S.; methodology, L.S.; software, L.S.; validation, A.H. and L.S.; resources, A.H.; data curation, A.H. and L.S.; writing-original draft preparation, L.S.; writing--review and editing, A.H.; visualization, A.H.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.  
Figure 7: Relative error histogram \(|RE|\) of \(U_{m}\) mass humidity obtained using the gravimetric method, as well as \(U_{mc}\) humidity identified by means of an artificial neural network for experimental verification (red curve shows the perfect fit for the normal distribution of the relative error).
## 2 Materials and Methods
### Microbial Composition Analysis  
Microbial community genomic DNA was extracted using the E.Z.N.A.(r) soil DNA Kit (Omega Bio-Tek, Norcross, GA, USA) according to the protocol. The fungal 18S rRNA genes were amplified through polymerase chain reaction (PCR) using the primer pair ITS1 (5'-TCCGGTGGAACCTGCG-3') and ITS2 (5'-GCTGGTTCTTTCATGAC-3'). The bacterial 16S rRNA genes were amplified through PCR with the primer pair 338F (5'-ACTCTCGCGGAGGGCAGC-3') and 806R (5'-GGACTACTACHVGGGWTCTAAT-3'). The PCR products were prepared according to the standard protocols and sequenced using an Illumina HiSeq PE300 platform/NovaSeq PE250 platform (Illumina, San Diego, CA, USA). After quality control of reads, sequencing reads were merged by FLASH version 1.2.7 [9]. Operational Taxonomic Units (OTUs) were clustered using UPARSE version 7.1 with a 97% similarity cutoff value [10]. The taxonomy of the OTU representative sequence was analyzed by blasting the 16S rRNA and 18S rRNA gene databases using the threshold value of 70% [11]. Microbial communities were investigated based on the relative OTUs abundance at the genus level.
# Optimizing HAWK Signature Scheme Performance on ARMv8
### Contributions  
1. We present an optimized implementation by profiling and analyzing the computational process of the HAWK Signature scheme. Through the profiling process, we discovered the overhead occurring during the computation process and optimized it to reduce the overhead. Profiling analysis not only helps in optimization implementation, but also provides higher confidence in the results of this paper by presenting the analysis results. This profiling analysis method can help deepen the understanding of the HAWK algorithm. Additionally, by proposing an analysis method that is not limited to the HAWK algorithm but applicable to any other algorithm, it introduces an analysis method that can be applied to various algorithms.
2. We implemented a parallel version of the NTT, which is frequently used in lattice algorithms, to optimize the HAWK algorithm. However, we found that at lower \(\log n\) values, the additional steps required for parallel implementation led to performance degradation. By analyzing the performance based on \(\log n\), we were able to verify the reliability of the Parallel NTT implementation. As a result, we identified the \(\log n\) values where performance improvements occur. Consequently, we optimized the algorithm by using the Reference NTT for lower \(\log n\) values and applying the parallel implementation for higher \(\log n\) values.
3. We present an optimized implementation of the HAWK Signature scheme on ARMv8. This optimization, based on a precise analysis of the algorithm's operations, leverages relevant instructions provided by the ARMv8 architecture, such as'sbfx' and 'csel', to achieve the same computational results with fewer instructions. These instructions contribute to reducing execution time by efficiently processing data and handling conditional operations without branching. Additionally, by avoiding branches, we ensure security through constant-time implementation.
## 5 The Technologies
### Additive Manufacturing  
Additive manufacturing (AM) technology, also known as 3D printing, is one of the most advanced technologies deployed in current manufacturing contexts. Due to their low cost-effectiveness, the additive manufacturing technologies have not been broadly deployed on factory floors for mass production [124]. AM allows for the fabrication of geometrically complex parts in a single production step [51], which makes AM a perfect technology for applications like rapid prototyping in the aerospace industry [111], medical implants [125], 3D scanning [126], etc.  
Also, new materials have been unlocked to be used for 3D printing technology, which in turn fosters the research in new materials development [127; 128]. Moreover, the deployed 3D printing machines can be networked with other equipment to test and develop the desired interoperability in Industry 4-oriented facilities [51].
## Materials and Experimental Procedure
### Sensor Coupling  
In order to compare the sensor coupling, [PERSON] et al. [43] has developed a protocol for ceramic matrix composite tests. This protocol is based on the comparison of recorded acoustic energy. A spatial interval of \(\pm\)5 mm around the centre of the gauge length is considered. The propagation distance at each sensor is equivalent and the comparison thus eliminates attenuation effects related to the distance. In order to not integrate attenuation effects related to the damage, the interval studied is reduced at the beginning of the initial loading (strain less than 0.1%). Damage is limited and evenly distributed along the gauge length during this phase associated to the matrix cracking. The energy distribution function, for the signals corresponding to the sources located at the beginning of loading in the \(\pm\)5 mm interval around the centre of the useful zone, makes it possible to evaluate the sensor coupling. For an equivalent coupling, the distribution functions are superimposed. When a discrepancy is observed, this difference is attributed mainly to a difference in coupling between the sensors and the surface of the material. This procedure is applied for the CMC composite in order to check that the coupling of the sensors is equivalent. Moreover, several authors have shown the effect of the coupling of the sensors on its response [44; 45].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Descriptor** & **Symbol** & **Unit** \\ \hline Rise time & RT & \(\upmu\)s \\ Counts & C & - \\ Duration & D & \(\upmu\)s \\ Amplitude & A & dB \\ Average Frequency & AF & kHz \\ Counts to peak & CP & - \\ Decay frequency & DF & kHz \\ Rise frequency & RF & kHz \\ Absolute energy & E & attoj \\ Frequency central & FC & kHz \\ Peak Frequency & FP & kHz \\ Rise time/duration & RT/D & - \\ Duration/Amplitude & D/A & \(\upmu\)s/dB \\ Decay time & D-RT & \(\upmu\)s \\ Rise angle & RA = A/RT & dB/\(\upmu\)s \\ Decay angle & A/(D-RT) & dB/\(\upmu\)s \\ Rise time/Decay time & RT/(D-RT) & - \\ Relative energy & E/A & attoj/dB \\ Counts to peak/Counts & CP/C & - \\ Amplitude/Frequency & A/AF & dB/kHz \\ Weighted Frequency & WF & kHz \\ Partial Power 1 [100–200 kHz] & PP\({}_{1}\) & \% \\ Partial Power 2 [200–400 kHz] & PP\({}_{2}\) & \% \\ Partial Power 3 [400–600 kHz] & PP\({}_{3}\) & \% \\ Partial Power 4 [600–1000 kHz] & PP\({}_{4}\) & \% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Descriptors set of acoustic emission (AE) signals.
## 3 Results and Discussion
### Sensory Evaluation of ISP Colloids and Yuba Films  
A sensory evaluation of the flavor strength, beamy flavor, and sweetness of ISP-based yuba films fabricated with different CD concentrations is shown in Table 2. +CD-1 and -CD-1 were not significantly different from +CD-0 and -CD-0 for all three parameters. The flavor strength and beamy flavor of +CD-2 and -4 and -CD-2 and -4 were notably decreased with increasing concentrations. Sweetness was perceived to be less strong in all samples, as seen in another study on cooked oatmeal [19]. However, sweetness was significantly reduced for the -CD-4 samples. In the comparisons between the +CD and -CD groups, significant sensorial recognition was observed with 4% CD addition.  
Based on the GC-MS results, alkanes and alkenes, which were at higher intensities for the +CD group, may have had little contribution to flavor due to the large threshold values of these compounds in flavor perception [22]. On the other hand, 2-heptanone, hexanal, and 1-octen-3-ol, with low perception thresholds, were related to flavor profiles such as penetrating fruity odor and mushroom-like [23]. It is likely that the decreases in these compounds induced by the addition of CD might have influenced the sensory differentiation of beamy flavor. As shown in the GC-MS profile results, for +CD-2 and -4, most related compounds were significantly reduced with the exception of 2-pentylfuran, which is related to the characteristic beamy flavor. The sensory perception of a mixture of various volatile compounds might be different from that of individual compounds.  
Figure 1: Principal component analysis of the volatile compound natures affected by CD concentrations (0, 1, 2, and 4) and the presence (+) or absence (-) of excessive flavor-entrapped CD (+CD and -CD groups) in ISP colloid.  
According to other studies on the sensory evaluation of soymilk, sensory differences were perceived with the addition of 0.75% \(\beta\)-CD in soymilk during heat treatment [21], while the addition of 0.5% \(\alpha\)-CD or \(\gamma\)-CD did not cause any significant difference [20]. In this study, sensory properties up to 1% CD remained unaffected, regardless of the presence or absence of excessive flavor-entrapped CD. However, a higher concentration of CD (2 or 4%) and the absence (-) of flavor-entrapped CD in ISP colloids could be useful in the reduction of sensorial perception of beamy flavor.