esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Swelling Studies  
Swellability is a factor that defines accessibility of the internal chemical structure in the crosslinked polymers for penetration by solvent molecules. The swellability coefficients in tetrahydrofuran (THF), acetone (ACE), acetonitrile (ACN), toluene (TOL), chloroform (TCM), methanol (MeOH), and water were studied. The results are summarized in Table 4. Based on the results presented below, one can conclude that the highest values of swelling coefficients were obtained for toluene and acetone. Water has the lowest tendency to swell which proves that the obtained copolymers swell more easily under the influence of organic solvents than in water. All of the copolymers with lignin reached the highest swellability coefficient values in toluene. Microspheres swell in toluene, tetrahydrofuran, and acetone. Toluene has the highest swelling coefficient for each copolymer.  
Microspheres swell in toluene, tetrahydrofuran, and acetone. Toluene has the highest swelling coefficient for each copolymer except EGDMA-St copolymers without the addition of lignin. This copolymer generally shows very low swelling in all solvents. This is due to the low porosity of this system. Six percent swelling for ACN and THF results from their greater polar nature and greater interaction of these solvents with the polar EGDMA.  
Microspheres have higher affinities for hydrocarbons solvents. The lowest swelling coefficients were shown for the EGDMA + St polymer without lignin, which indicates an increase in swelling after the lignin presence. As the amount of lignin in the microspheres increases, the swelling coefficient increases. The copolymer EGDMA + St + 5L was characterized by the highest susceptibility to swelling. Generally, materials based on EGDMA have a low tendency to swell due to their low porosity. The addition of polar groups from lignin causes an increasing in the interaction of solvents with the microspheres. After carbonization the resulting materials do not indicate a tendency to swell.  
Figure 7: Impact of lignin content on the diameter distribution of microspheres.
# Knowledge Tracing Model and Student Profile Based on Clustering-Neural-Network
## 3 Experiment
### Results
#### 3.2.2 Training Efficiency  
This experiment aims to verify the performance improvement of the clustering-based neural network model.  
Based on the results in Table 4, several conclusions can be gained. Firstly, compared with the traditional B-KT, EERNN, and DKT models, the C-B-KT, C-ERNN, and C-DKT models have an average time reduction of 67.28%. The EERNN model has the highest performance improvement, up to 75.75%. Although the R-DKT model has the highest training efficiency, its prediction accuracy did not improve compared to the DKT model. This indicates that the clustering-based neural network model can improve the training efficiency of the model through parallel training. Then, the training efficiency of the C-DKT model is higher than that of the DKT model but lower than that of the R-DKT model (random clustering makes the data amount more average). The DKT model has not undergone the processing embedded in this article and still has the same experimental results. This shows that the improvement of the model performance is mainly related to the data volume and not to the text information embedding.  
After applying the clustering idea to all models, it performs very well in terms of both prediction accuracy and training efficiency, with good versatility.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Classification Indicators (\%)**} & \multicolumn{4}{c}{**Regression Indicators**} \\ \cline{2-9}  & **AUC** & **F1** & **Recall** & **Precision** & **MAE** & **RMSE** & **MSE** & **R2** \\ \hline DKT & 81.10 & 72.89 & 74.29 & 71.54 & 0.36 & 0.42 & 0.18 & 0.29 \\ R-DKT & 81.21 & 72.22 & 70.69 & 73.81 & 0.34 & 0.42 & 0.18 & 0.29 \\ EERNN & 85.73 & 80.27 & 79.36 & 81.20 & 0.25 & 0.36 & 0.13 & 0.49 \\ B-KT & 89.94 & 81.89 & 81.26 & 82.52 & 0.26 & 0.36 & 0.13 & 0.49 \\ C-DKT & 84.55 & 75.70 & 74.52 & 76.92 & 0.33 & 0.40 & 0.16 & 0.36 \\ C-EERNN & 88.49 & 83.41 & 80.90 & 86.07 & 0.24 & 0.35 & 0.12 & 0.50 \\ C-B-KT & 91.01 & 83.15 & 81.26 & 85.14 & 0.24 & 0.35 & 0.12 & 0.52 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Model result analysis.
# EC-Kad: An Efficient Data Redundancy Scheme for Cloud Storage
## 3 Cloud Storage System Model Based on Erasure Code
### Data Encoding Storage and Recovery
#### 3.2.5 Verify the Correctness of the Decoding Results  
To verify the correctness of the decoding results, we can recompute the redundant data blocks and compare them with the received redundant data blocks to validate the decoding results. When any data in the redundant blocks stored in the cloud change, the relationship between the encoding matrix \(G\) and the data blocks will no longer hold; that is, Equation (4) will not be valid. Therefore, this method can be used to verify the integrity of the data stored in the cloud. The purpose of verifying the integrity is to ensure that the received data block has not been tampered or lost during transmission, thereby enhancing the security of the storage.  
During the data integrity verification process, we need to extract the rows corresponding to the redundant data blocks from the generator matrix \(G\) to form a submatrix \(G^{{}^{\prime\prime}}\). This submatrix \(G^{{}^{\prime\prime}}\) is used to recalculate the redundant data blocks and compare them with the received redundant data blocks to verify the integrity of the data.  
For the above \((k+m)\times k\) generator matrix, the first \(k\) rows correspond to the original data blocks, while the last \(m\) rows correspond to the redundant data blocks. The submatrix \(G^{{}^{\prime\prime}}\) is represented as shown in Equation (11):  
\[G^{{}^{\prime\prime}}=\begin{bmatrix}G_{k+1,1}&G_{k+1,2}&\cdots&G_{k+1,k}\\ G_{k+2,1}&G_{k+2,2}&\cdots&G_{k+2,k}\\ \vdots&\vdots&\ddots&\vdots\\ G_{k+m,1}&G_{k+m,2}&\cdots&G_{k+m,k}\end{bmatrix} \tag{11}\]  
Using submatrix \(G^{{}^{\prime\prime}}\) and the recovered original data block \(F_{1},F_{2},\ldots,F_{k}\), we can recalculate the redundant data block \(D_{1},D_{2},\ldots,D_{m}\):  
\[(D_{1},D_{2},\ldots,D_{m})^{T}=G^{{}^{\prime\prime}}\times(F_{1},F_{2},\ldots,F_{k})^{T} \tag{12}\]  
When comparing the recalculated redundant data blocks with the received redundant data blocks, if they are consistent, then this indicates that the data are intact. Otherwise, it is suggested that the data have been tampered with or lost. This method ensures the reliability and integrity of the data in the cloud storage system.
# MultiAspect Graphs: Algebraic Representation and Algorithms
## 4 MAG Algorithms
### Depth-First Search (DFS)
#### 4.5.2 Sub-Determined DFS
```
input :J(\(H\)), \(\tau(H)\),\(\zeta\) output :\(discTime\), \(finTime\), pred DFS-Sub(J(\(H\)), \(\tau(H)\),\(\zeta\)) \(T_{\zeta}\leftarrow\tau_{\zeta}(H)\)// \(\zeta\) sub-determined companion tuple \(\mathbf{M}_{\zeta}\leftarrow\mathit{SubDetMatrix}(H,\zeta)\) \(\mathbf{J}_{\zeta}=\mathbf{M}_{\zeta}\;\mathbf{J}(H)\;\mathbf{M}_{\zeta}^{T}\)// sub-determined adjacency matrix \(n\leftarrow[\mathbb{V}_{\zeta}(H)]\)// number of sub-determined vertices for\(u=1\)to\(n\)do \(color[u]\gets 0\)// set all vertices to white (unvisited) \(discTime[u]\leftarrow-1\)// set discovery times to nil \(finTime[u]\leftarrow-1\)// set finish times to nil \(pred[u]\leftarrow-1\)// set predecessors to nil
1 end for time \(\leftarrow 0\) for\(u=1\)to\(n\)do if\(color[u]=0\)then \(vertices=BFS\)-Sub\(\mathbf{J}(H),\tau(H),\zeta,T_{\zeta})\) \(DFS\)-Visit-Sub\((u,vertices)\)  end if
2
3 end for
4
5 end for
6
7 return\(discTime\), \(finTime\), pred
8
9 Procedure DFS-Visit-Sub(\(u\), vertices) \(color[u]\gets 1\)// set vertex u to gray (visited) \(discTime[u]\gets time\gets time+1\) for each\(v\)successor of \(u\)do if\(color[v]=0\)and\(vertices[v]\
eq 0\)then \(pred[v]\gets u\) \(DFS\)-Visit-Sub\((v,vertices)\)  end if
10
11 end for
12\(color[u]\gets 2\)// set vertex u to black (closed) \(finTime[u]\gets time+1\)
```  
**Algorithm 14**Sub-determined DFS. When applying the sub-determined DFS algorithm to the example MAG \(T\) shown in Figure 3 with a sub-determination \(\zeta_{t}=011_{2}\), which drops the time aspect, the obtained result is  
\[ \begin{split} d&=[0,2,3,6,5,10]\\ f&=[1,9,4,7,8,11]\\ pred&=[Nil,Nil,2,5,2,Nil],\end{split}  \tag{70}\]  
where the list \(d\) carries the discovery time of each sub-determined composite vertex, the list \(f\) its finish time and \(pred\) its predecessor. Considering the MAG \(R\) shown in Figure 5(a) with a sub-determination \(\zeta_{R}=01_{2}\), the result obtained by Algorithm 14 is  
\[ \begin{split} d&=[0,1,4]\\ f&=[3,2,5]\\ pred&=[Nil,Nil].\end{split}  \tag{71}\]It can be seen that even though in the MAG \(R\) sub-determined by \(\zeta_{R}=01_{2}\) (see Figure 6b) there is a path from vertex 1 to 3, vertex 3 is not in the same DFS tree as vertices 1 and 2, even with the DFS starting at vertex 1, as can be seen in \(d[0]\). This occurs because in MAG \(R\) (with no sub-determination) there is no path connecting the composite vertex 1 to the composite vertex 3.
# A New Lightweight Watchdog-Based Algorithm for Detecting Sybil Nodes in Mobile WSNs
###### Abstract  
Wide-spread deployment of Wireless Sensor Networks (WSN) necessitates special attention to security issues, amongst which Sybil attacks are the most important ones. As a core to Sybil attacks, malicious nodes try to disrupt network operations by creating several fabricated IDs. Due to energy consumption concerns in WSNs, devising detection algorithms which release the sensor nodes from high computational and communicational loads are of great importance. In this paper, a new computationally lightweight watchdog-based algorithm is proposed for detecting Sybil IDs in mobile WSNs. The proposed algorithm employs watchdog nodes for collecting detection information and a designated watchdog node for detection information processing and the final Sybil list generation. Benefiting from a newly devised co-presence state diagram and adequate detection rules, the new algorithm features low extra communication overhead, as well as a satisfactory compromise between two otherwise contradictory detection measures of performance, True Detection Rate (TDR) and False Detection Rate (FDR). Extensive simulation results illustrate the merits of the new algorithm compared to a couple of recent watchdog-based Sybil detection algorithms.  
mobile WSN; security; watchdog node; Sybil attack
# Improved Defect Detection Using Adaptive Leaky NLMS Filter in Guided-Wave Testing of Pipelines
## 5 Results
### Example Test Case  
This technique is initially developed and validated on synthesized data generated by FEM. The used excitation sequence for generating this data set is 30 kHz and variable transfer functions were used for each of the source points. Figure 11 shows the two sets of signals achieved after applying the phase delays of general propagation routine (blue and red). Four regions are of main interest in this figure, which are (a) backward leakage, (b) flexural noise, (c) Defect-1 and (d) Defect-2. Since both Defect-1 and Defect-2 signals are received from the forward testing direction, they have similar characteristics and thus both should be enhanced by this algorithm. As can be seen, noise region (b) is significantly less correlated than the defect signals (c and d). Furthermore, although the amplitudes of two sets of signals received in the defect regions are not the same, their phases and polarity are in perfect correlation. Therefore, while in the propagation routine their characteristic might be similar but, in this space, the defect and flexural signals are distinguishable.  
Figure 11: FEM signals (30 kHz) where two defect regions and the flexural noises are marked where blue and red lines show the first and second sets of the signals respectively.
## 2 Materials and Methods
### System Definition
#### 2.4.4 Thermodynamics  
Lastly, to derive the Kantrowitz limit main expression, three basic thermodynamics equations were utilized: Mass flow conservation, Mach number definition, sound speed in an ideal gas, ideal gas law, and isentropic relations for pressure and temperature. The subscript 1 represents the air state or associated variables before the air flows into the annulus and the subscript 2 represents the contrary. In reference [23], most of the aforementioned formulae may be consulted. The main expression to analyze the Kantrowitz limit phenomenon is derived by combining Equations (26)-(32) (subscripts for i = 1 and 2). The complete process can be found in reference [22] and its outcome is Equation (33):  
\[\dot{m}_{i}=\rho_{i}A_{i}v_{i} \tag{26}\]  
\[\dot{m}_{i}=constant \tag{27}\]  
Figure 2: (a) Cross\(-\)sectional drawing of the tube in front of the vehicle and its profile; (b) Cross\(-\)sectional drawing of the tube and the vehicle, near the rear, and its axial section; (c) Electrical model for the considered EDS. Source: Own elaboration.  
\[M_{i}=\frac{v_{i}}{a_{s_{i}}} \tag{28}\] \[a_{s_{i}}=\sqrt{\gamma RT_{i}}\] (29) \[\rho_{i}=\frac{p_{i}}{RT_{i}}\] (30) \[\frac{p_{0_{t}}}{p_{i}}=\left(1+\left(\frac{\gamma-1}{2}\right)M_{i }\right)^{\frac{\gamma}{\gamma-1}}\] (31) \[\frac{T_{0_{t}}}{T_{i}}=\left(1+\left(\frac{\gamma-1}{2}\right)M_{ i}\right)^{\frac{\gamma}{\gamma-1}}\] (32) \[\dot{m}_{cc_{mix}}=A_{cc}\frac{p_{0_{t}}}{\sqrt{T_{0_{t}}}}\sqrt{ \frac{\gamma}{R}}\left(1+\left(\frac{\gamma-1}{2}\right)\right)^{-\left(\frac{ \gamma+1}{2(\gamma-1)}\right)} \tag{33}\]  
Note: \(\rho_{1}=\rho_{t},\ A_{1}=A_{t},\ A_{2}=A_{cc},\ v_{1}=v\). See also Figure 2a,b.
## 5 Application of the Proposed Method
### Simulation Analysis  
#### 5.1.1 The Construction of Compound Fault Simulation Signal  
The compound fault signal was simulated by the simulation signal of the inner race and outer race. The outer race fault signal simulation model was defined as in the following equation [46]:  
\[x_{0}(t)=y_{0}e(-2\zeta\pi f_{n}t_{0})\cdot\sin(2\pi f_{n}\sqrt{1-\zeta^{2}} \cdot t_{0}) \tag{15}\]  
where \(y_{0}\) is the possible amplitude modulator; \(f_{n}\) is the natural frequency; \(t_{0}\) is a single cycle sampling time; \(\zeta\) is the damping factor. The parameters of the outer race simulation signal are shown in Table 1.  
Figure 5: The flow chart of the proposed method.  
The inner race fault simulation signal is defined as:  
\[x_{i}(t)=s(t)+n(t)=\sum{{}_{i}A_{i}}h(t-iT-\tau_{i})+n(t) \tag{16}\]  
where  
\[\begin{array}{l}A_{i}=A_{0}\cos 2(2\pi f_{r}t+\phi_{A})+\text{C}_{A}\\ h(t)=e^{-Bt}\cos(2\pi f_{n}t+\phi_{\omega})\\ 1/f_{r}\end{array} \tag{17}\]  
where \(\tau_{i}\) represents the minor fluctuation of the \(i\) shock relative to the period \(T\); \(A_{i}\) is the amplitude modulation with \(1/f_{r}\) as a cycle; \(h(t)\) is an exponential decay pulse; \(B\) is the attenuation coefficient of the system; \(\phi_{A}\), \(\phi_{\omega}\) are the initial phase. The parameters of the inner race simulation signal are shown in Table 2.  
The mathematical model of the compound inner race and outer race spalling is shown in Equation (18):  
\[x(t)=x_{i}(t)+x_{0}(t). \tag{18}\]  
The time domain waveform of the outer race and inner race fault simulation signals are shown in Figure 6a,b. Figure 7a is the compound signal; Figure 7b is the compound fault simulation signal with gauss noise. It can be seen from Figure 7b that the inner and outer periodic shock characteristics are overwhelmed by noise and it cannot accurately reflect the characteristics of fault impact. Therefore, it is necessary to further extract fault features.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Rotating Frequency\(f_{\text{r}}\)/Hz** & **Natural Frequency** & **Sampling** & **Sampling** & **Fault Frequency** \\
**Frequency\(f_{\text{r}}\)/Hz** & \(f_{\text{n}}\)/Hz** & **Frequency\(f_{\text{s}}\)/Hz** & **Point\(N\)** & **\(f_{\text{o}}\)/Hz** \\ \hline
24 & 2000 & 15360 & 8192 & 62 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameters of outer fault simulation signal.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Rotating Frequency\(f_{\text{r}}\)/Hz** & **Natural Frequency** & **Sampling** & **Sampling** & **Fault Frequency** \\
**Frequency\(f_{\text{r}}\)/Hz** & \(f_{\text{n}}\)/Hz** & **Frequency\(f_{\text{s}}\)/Hz** & **Point\(N\)** & **Point\(N\)** & **\(f_{\text{j}}\)/Hz** \\ \hline
24 & 2000 & 15360 & 8192 & 300 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The parameters of inner fault simulation signal.  
Figure 6: The time-domain waveform of the single fault simulation signal: (**a**) Outer race; (**b**) Inner race.
# Abstractive Sentence Compression with Event Attention
## 3 Sentence Compression by a Sequence-to-Sequence Model  
Given a source sentence \(X=(x_{1},x_{2},x_{3},...,x_{n})\), sentence compression aims to generate a target sentence \(Y=(y_{1},y_{2},y_{3},...,y_{m})\), where \(n\) and \(m\) are sentence lengths and \(n>m\). Thus, a sentence compression model can be formulated by a conditional probability \[P(Y|X)=\prod_{t=1}^{m}P(y_{t}|y_{<t},X). \tag{1}\]  
Since optimizing Equation (1) is an objective of standard sequence-to-sequence models [16; 18], a sequence-to-sequence model can be used as a sentence compression model. A sequence-to-sequence model has an encoder and a decoder. The encoder which is usually implemented as a bi-directional RNN [32] takes the source sentence \(\mathbf{X}=(\mathbf{x_{1}},\mathbf{x_{2}},\mathbf{x_{3}},...,\mathbf{x_{n}})\) where \(\mathbf{x_{i}}\) is an embedding vector of the word \(x_{i}\). At each time step \(t\), the encoder outputs a forward hidden state \(\overrightarrow{h_{t}}=f(\mathbf{x_{t}},\overrightarrow{h_{t-1}})\) and a backward hidden state \(\overleftarrow{h_{t}}=f(\mathbf{x_{t}},\overleftarrow{h_{t-1}})\), where the function \(f\) is a recurrent unit such as GRU [16] or LSTM [33]. The hidden state \(h_{t}\) at time \(t\) is expressed as a concatenation of \(\overrightarrow{h_{t}}\) and \(\overleftarrow{h_{t}}\). That is,  
\[h_{t}=[\overrightarrow{h_{t}};\overleftarrow{h_{t}}],\]  
where \([a;b]\) denotes the concatenation of a vector \(a\) and a vector \(b\). The concatenation of the final forward and backward states of the encoder  
\[Q=[\overrightarrow{h_{final}};\overleftarrow{h_{final}}] \tag{2}\]  
is used as a representation of the source sentence \(\mathbf{X}\).  
The decoder which is also implemented as an RNN generates a target compressed sentence \(Y\) with vectors computed by the encoder. It computes a decoder state \(s_{t}\) at each time \(t\) as  
\[s_{t}=f(y_{t-1},s_{t-1},Q). \tag{3}\]  
Then, \(P_{vocab}\), the probabilistic distribution over all words, is obtained by  
\[P_{vocab}=softmax(W_{v}s_{t}+b_{v}), \tag{4}\]  
where \(W_{v}\) and \(b_{v}\) are the parameters to be tuned. That is, the decoder state \(s_{t}\) is fed to the softmax function to produce \(P_{vocab}\). Finally, the word with the highest probability in \(P_{vocab}\) is chosen as the output word at time \(t\). Since the sequence-to-sequence model is trained with a set of pairs of a long source sentence and its short compressed sentence, it eventually learns how to transform a long sentence to a short sentence, which is sentence compression.
## 4 Solution to the Problem of OFN/vOFNs with Non-Convex Shapes
### Mathematical Foundations of the Shape Normalization Operator (SNO)  
As previously noted, the OFN notation, by design, allows for the existence of improper numbers. In general, these are numbers whose coordinates (in the OFN representation) do not follow a uniform directional trend along the \(OX\) axis. Such numbers cannot be readily interpreted using classical fuzzy number notations, and their classification within the OFN framework remains ambiguous. This problem can be resolved through the following definitions.  
Let \(\mathcal{A}\) represent the set of fuzzy numbers, where each fuzzy number \(A\in\mathcal{A}\) is characterized by the following four real values: \(a,b,c,d\) (i.e., \(A[a,b,c,d]\)), which are referred to as the coordinates of \(A\).  
**Definition 18** (Singleton of vOFNs).: _A fuzzy number \(A=[a,b,c,d]\) is called a singleton if all four values are equal, i.e., \(a=b=c=d\). The collection of all such singletons is denoted by \(S\)._  
_Additionally, we define three distinct subsets of the space \(\mathcal{Z}\) as follows:_  
\[\begin{split} P=\{A\in\mathcal{Z}:a\
eq b\},\\ Q=\{A\in\mathcal{Z}:a=b\ \wedge\ a\
eq c\},\\ T=\{A\in\mathcal{Z}:a=b=c\ \wedge\ a\
eq d\}.\end{split} \tag{13}\]  
_The sets \(P\), \(Q\), \(T\), and \(S\) are mutually exclusive, and their union forms a complete partition of the set \(\mathcal{Z}\), i.e., \(\mathcal{Z}=P\cup Q\cup T\cup S\)._  
**Definition 19** (Order of a vOFN).: _Given an ordered fuzzy number \(A\in\mathcal{Z}\) described by coordinates \(A[a,b,c,d]\), its order is determined as follows:_  
* _A number_ \(A\in P\) _has positive order if_ \(a<b\)_, otherwise it has negative order;_
* _A number_ \(A\in Q\) _has positive order if_ \(a<c\)_, otherwise it has negative order;_
* _A number_ \(A\in T\) _has positive order if_ \(a<d\)_, otherwise it has negative order._  
**Definition 20** (Shape Normalization Operator \(\Psi\)).: _Define the operator \(\Psi:\mathcal{A}\rightarrow\mathcal{A}\) such that the following holds:_  
\[\Psi(A)=(\alpha_{1},\alpha_{2},\alpha_{3},\alpha_{4}),\quad\text{if the order of $A$ is positive,} \tag{14}\]  
\[\Psi(A)=(\beta_{1},\beta_{2},\beta_{3},\beta_{4}),\quad\text{if the order of $A$ is negative,} \tag{15}\]  
_where_  
* \((\alpha_{1},\alpha_{2},\alpha_{3},\alpha_{4})\) _is the ascending permutation of_ \((a,b,c,d)\)_;_
* \((\beta_{1},\beta_{2},\beta_{3},\beta_{4})\) _is the descending permutation of_ \((a,b,c,d)\)_._  
For all \(A\in S\), the operator \(\Psi\) acts as the identity, i.e., \(\Psi(A)=A\).
## 2 Fiber-Optic Distribution
### Discussion  
While the increase of the total noise power under external acoustic excitation is as expected, the suppression of the low-frequency phase noise by a single-tone perturbation is somewhat a surprise. The exact mechanism causing this interesting effect is subject to further investigation. One possible scenario is, when an acoustic wave strikes a fiber, the pressure oscillation in the air prompts periodic changes of strain and stress inside the fiber and hence periodic fluctuations of the refractive index. With sufficient amplitude, the resulting periodic phase variation of light can average out the phase noise caused by spontaneous fluctuations of strain and stress at lower frequencies, leading to a suppression of the noise spectral density below the perturbation frequency. This effect can be stronger in anisotropic fiber structures such as PMFs compared to isotropic structures such as SMFs because the fluctuations of strain and stress are more appreciable in anisotropic structures. Nevertheless, the present work demonstrates experimentally the impact of acoustic perturbations to fiber-optic remote clock distribution and characterizes the excess phase noise spectrum for the frequency comb-based scheme. The noise-suppression effect by single-tone acoustic perturbations potentially offers a way to manipulate the phase noise spectrum of transferred clocks at low frequencies without active noise cancellation.