esa-sceva/satcom-chunk-collection huggingface dataset:
# Fault-Diagnosis Method for Rotating Machinery Based on SVMD Entropy and Machine Learning
## 4 Experimental Analysis
### Additional Dataset Validation  
In addition to Southeast University experimental dataset introduced above, to further validate the proposed method, we also used the experimental data collected by the GDS3000 (SpectraQuest, Inc., Richmond, VA, USA) experimental platform. The platform is equipped with switchable normal bearings, inner fault bearings, outer fault bearings, rolling-element fault bearings, and compound fault bearings (with inner and outer fault), as shown in Figure 14.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multirow{2}{*}{**Algorithm**} & \multicolumn{2}{c}{**Component: Bearing**} & \multicolumn{2}{c}{**Component: Gear**} \\ \cline{3-5}  & & **20-0** & **30-2** & **20-0** & **30-2** \\ \hline \multirow{2}{*}{Literature [36]} & K-NN & 80.80\% & 86.40\% & 93.20\% & 89.20\% \\  & SVM & 83.30\% & 88.60\% & 94.40\% & 90.10\% \\ \hline \multirow{4}{*}{Our paper} & SVMD fuzzy entropy + k-NN & 99.22\% & 97.25\% & 94.42\% & 90.98\% \\  & SVMD energy entropy + SVM & 97.25\% & 94.12\% & 91.27\% & 90.20\% \\  & SVMD fuzzy entropy + RF & 99.22\% & 95.69\% & 96.02\% & 93.33\% \\  & SVMD fuzzy entropy + GBDT & 99.22\% & 95.29\% & 95.22\% & 89.41\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of SVMD feature extraction and classification results and those from other literature.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Data**} & \multicolumn{6}{c}{**Gearbox Bearing Data of SEU**} \\ \hline
**Operation Condition** & \multicolumn{3}{c}{**Operation Condition I**} & \multicolumn{3}{c}{**Operation Condition II**} \\ \hline
**Model** & **SVM** & **k-NN** & **RF** & **GBDT** & **SVM** & **k-NN** & **RF** & **GBDT** \\ \hline VMD energy entropy & 97.57\% & 97.34\% & 98.04\% & 98.04\% & 97.25\% & 96.86\% & 96.08\% & 97.25\% \\ SVMD energy entropy & 97.25\% & 96.86\% & 96.47\% & 97.25\% & 94.12\% & 93.33\% & 91.76\% & 94.51\% \\ VMD fuzzy entropy & 99.13\% & 98.98\% & 99.22\% & 99.22\% & 99.61\% & 92.16\% & 99.61\% & 99.22\% \\ SVMD fuzzy entropy & 93.96\% & 99.22\% & 99.22\% & 99.22\% & 94.90\% & 97.25\% & 95.69\% & 95.29\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Accuracy of gearbox bearing data at SEU.  
Using the GDS3000 experimental platform, data were collected based on two working conditions: medium-high speed (1540 r/min) and high speed (1788 r/min). The experimental results in Table 7 show that the fuzzy entropy was superior to the energy entropy.
# Online Estimation of Short-Circuit Fault Level in Active Distribution Network
## 2 Short-Circuit Fault Level Estimation for Active Distribution Networks
### The Fuzzy Logic Identifier to Distinguish Useful Disturbance
#### Fuzzy Logic Rules and Membership Functions  
Fuzzy logic provides a computational representation of heuristic knowledge about a specific problem [32]. The purpose is to find out how the input variables influence the decisions and to quantify their values in relevance levels. Membership functions establish the profile of the variables by expressing the compatibility degree of each one with previous experiential information. Then the fuzzy logic rules can be built, and the evaluation of the input signals can be acquired via defuzzification, which consists of all variables' interaction through stochastic techniques [32].  
The values of the membership function are all in the interval \([0,\ 1]\), where the value of 1 means that the object entirely belongs to the member of the set, while the value of 0 represents complete irrelevance. When designing the fuzzy logic identifier, the triangle and trapezoidal memberships are used in this paper. The linguistic terms of two input variables and the universes are identical. Therefore, they are allocated with the same membership function, as shown in Figure 4a.  
Take the four cases in Table 1 (O, PVS, PML, and PL in bold) as the examples to explain the rules in detail. The rules are listed as follows:  
IF \(\Delta\hat{U}\) ranges within O, and \(\Delta\hat{I}\) ranges within O, THEN the output is O;  
IF \(\Delta\hat{U}\) ranges within O, and \(\Delta\hat{I}\) ranges within PS, THEN the output is PVS;  
......  
IF \(\Delta\hat{U}\) ranges within PL, and \(\Delta\hat{I}\) ranges within PM, THEN the output is PML;  
IF \(\Delta\hat{U}\) ranges within PL, and \(\Delta\hat{I}\) ranges within PL, THEN the output is PL.  
As there are two input signals, each of which is with 4 possible linguistic terms, the number of the fuzzy rules is total 16. The tuning rules in detail are explicitly illustrated in Table 1. It implicates that the estimation results derived from a different disturbance will have a different confidence. Those with apparent load changes are allocated high confidence. The membership function of output variable CL is shown in Figure 4b.
# CrossDeFi: A Novel Cross-Chain Communication Protocol
## 6 Analysis
### Security Analysis
#### 6.2.2 Lemma 2 for Sybil Attack  
In CrossDeFi, the Sybil attack poses a significant threat, where a malicious user (MU) may create multiple duplicate miners or fraudulent accounts to manipulate transactions for personal gain. This section outlines two potential cases where a Sybil attack could be executed within CrossDeFi.  
**Case 1:** Fraudulent Account Creation When an MU creates multiple fake accounts attempting to transfer assets illegitimately claimed as their own.  
**Case 2:** Fake Node Creation When an MU establishes a counterfeit node purporting to be a legitimate miner.  
Proof.: In CrossDeFi, assets are uniquely tied to their owners, ensuring that each asset is associated with a single user at any given time. Suppose user [PERSON], [PERSON] \(\in\sum_{i=1}^{n}U\) and user [PERSON] legitimately owns asset Ass X \(\in\sum_{i=1}^{n}\). If an MU from DeFi X attempts to transfer AssX, the transfer is only valid if [PERSON] is the unequivocal owner of AssX. The system checks Owner(MU, Ass X), ensuring that the MU \(\
ot\in\sum_{i=1}^{n}U\) and therefore cannot initiate the transfer. This mechanism ensures that before and after the transfer, AssX is owned by [PERSON] and subsequently UY, with both users being validated members of their respective DeFi systems.  
Proof.: This scenario involves an MU attempting to pose as a legitimate miner. CrossDeFi's miner and bridge selection (MBS) process is designed to select trustworthy miner nodes based on their historical performance and contributions to the blockchain.  
For example, consider three miners M1, M2, and M3, mining blocks B1, B2, and B3, respectively, with the blocks ordered as \(B1\gets B2\gets B3\). Here, B3 is the latest block mined by M3, and B2 is its parent block mined by M2. In this setup, M2 is selected as the miner for its proven reliability and the absence of any malicious activity in its past block mining efforts. This selection process reduces the load on M3 and leverages M2's established trustworthiness, thereby mitigating the risk of a Sybil attack. If an MU falsely claims to be a miner, CrossDeFi evaluates the miner's reliability before designation, ensuring that only legitimate miners are tasked with critical roles.  
Through these proofs, CrossDeFi demonstrates robust defenses against the Sybil attack, leveraging stringent asset ownership verification and meticulous miner selection to maintain the integrity and security of asset transfers within its ecosystem.
# Acoustic Pressure Amplification through In-Duct Sonic Black Holes
## 3 Simulation Analysis
### Parametric Study  
It is found in Section 3.3 that the PG performance brought by the outer SBH is about five times lower than that obtained from the inner SBH. Parametric studies are now being performed to determine which parameters may enhance the PG peak value of the outer silencers when used as sensors. The results are gathered in Figure 10.  
One observes that enlarging the cavity width of the O-SBH up to \(w=7\) mm while keeping a fixed length \(L=0.15\) m and \(N=20\) cavities increases the PG peak value up to 32, which is half the value of the C-SBH. This requires thin rings of sub-millimeter thickness \(w_{p}=0.5\) mm. Increasing the number of cavities in O-SBH to \(N=30\) provides a better spectral resolution of the amplified pulse components. Widening these cavities again increases the PG peak values. But, for a given width, one observes that a greater density of cavities lowers the maximum PG peak values due to a redistribution of the incident energy amongst a greater number of adjacent cavities activated at similar resonance frequencies. Moreover, it sets an upper limit on the cavity width, \(w_{\text{max}}=L/N=\)7.5 mm, to keep up with positive ring thickness. Lastly, raising the axial rate of increase of the O-SBH cavity depths from \(m=2\) to \(m=5\) does not significantly enhance the PG peak value and hinders the O-SBH spectra resolution towards \(f_{c}\) due to a lower number of shallow cavities.  
As for the MPP-SBH, Figure 10 shows that increasing the diameters of the MPP holes shielding the cavities enhances the PG peak value by a factor 1.3 above that of the O-SBH. Similar behavior was observed when increasing the holes pitch \(\Lambda\) or when decreasing the MPP perforation ratio \(\sigma\). Increasing the MPP thickness up to \(2d_{h}\) favors the PG but is a second-order effect with respect to \(d_{h},\sigma\) or \(\Lambda\).
# Intelligently Counting Agricultural Pests by Integrating SAM with FamNet
###### Abstract  
The utilization of the large pretrained model (LPM) based on Transformer has emerged as a prominent research area in various fields, owing to its robust computational capabilities. However, there remains a need to explore how LPM can be effectively employed in the agricultural domain. This research aims to enhance agricultural pest detection with limited samples by leveraging the strong generalization performance of the LPM. Through extensive research, this study has revealed that in tasks involving the counting of a small number of samples, complex agricultural scenes with varying lighting and environmental conditions can significantly impede the accuracy of pest counting. Consequently, accurately counting pests in diverse lighting and environmental conditions with limited samples remains a challenging task. To address this issue, the present research suggests a unique approach that integrates the outstanding performance of the segment anything model in class-agnostic segmentation with the counting network. Moreover, by intelligently utilizing a straightforward TopK matching algorithm to propagate accurate labels, and drawing inspiration from the GPT model while incorporating the forgetting mechanism, a more robust model can be achieved. This approach transforms the problem of matching instances in different scenarios into a problem of matching similar instances within a single image. Experimental results demonstrate that our method enhances the accuracy of the FamNet baseline model by 69.17% on this dataset. Exploring the synergy between large models and agricultural scenes warrants further discussion and consideration.  
agricultural; pests; few-shot; large pretrained model +
Footnote †: journal: Applied Sciences
## 4 Results
### Analysis of Distributions Related to Errors  
A two-sample Kolmogorov-Smirnov test was applied for the analysis of error distributions. There are three pairs related to the combinations of topologies: (global best and random), (random and ring), and (global best and ring). For all considered distributions of errors the test rejected the null hypothesis at the 5% significance level that the data in vector pairs are from the same continuous distribution.  
The test was applied for comparison of errors obtained in cases with noise and without noise. The results are shown in Table 4.  
The obtained distribution could also be compared using the above mentioned test (Table 5).  
\begin{table}
\begin{tabular}{c c c} \hline
**Error Type** & **Topology** & **Kolmogorov–Smirnov Test Result** \\ \hline Mean position & global best & 0 \\ Mean position & ring & 0 \\ Mean position & random & 1 \\ \hline Mean amplitude & global best & 0 \\ Mean amplitude & ring & 0 \\ Mean amplitude & random & 1 \\ \hline Mean angle & global best & 0 \\ Mean angle & ring & 0 \\ Mean angle & random & 0 \\ \hline Mean scale & global best & 0 \\ Mean scale & ring & 0 \\ Mean scale & random & 0 \\ \hline Mean \(\beta\) & global best & 0 \\ Mean \(\beta\) & ring & 0 \\ Mean \(\beta\) & random & 0 \\ \hline \end{tabular}
\end{table}
Table 4: Two-sample Kolmogorov–Smirnov tests: ideal and noised volumes (0—rejection of the null hypothesis).  
Figure 7: The relation of absolute mean amplitude error and absolute mean \(\beta\) error.
# Perioperative Nutritional Support: A Review of Current Literature
## 4 Available Guidelines
### ESPEN Guidelines  
The latest ESPEN recommendations on perioperative nutritional management, published in 2021 [39], are based on the 2017 guidelines [40]. These are the same recommendations (specifically 37), although the presentation has been restructured to make it more accessible. It follows a methodology based on the Scottish Intercollegiate Guidelines Network (SIGN) system, giving a degree of recommendation (A/B/0/Good Practice Points (GPP)) based on the available evidence. A brief summary is available in Table 1. \begin{table}
\begin{tabular}{l c} \hline \hline
**Recommendation** & **Consensus Degree** \\ \hline
1. Preoperative fasting from midnight is unnecessary in most patients. & \\ Patients undergoing surgery, who are considered to have no specific risk of & \\ aspiration, shall drink clear fluids until 2 h before anesthesia. Solids shall & 97\% \\ be allowed until 6 h before anesthesia (A). & \\ \hline
2. In order to reduce perioperative discomfort including anxiety oral & \\ preoperative carbohydrate treatment (instead of overnight fasting, the & \\ night before and 2 h before surgery) should be administered (B). To impact & 100\% \\ postoperative insulin resistance and length of stay (LOS), preoperative & \\ carbohydrates can be considered in patients undergoing major surgery (0). & \\ \hline
3. In most instances, oral nutritional intake shall be continued after surgery & 90\% \\ without interruption (A). & \\ \hline
4. It is recommended to adapt oral intake according to individual tolerance & \\ and to the type of surgery carried out with special caution to elderly & 100\% \\ patients (GPP). & \\ \hline
5. Oral intake, including clear liquids, shall be initiated within hours after & 100\% \\ surgery in most patients. & \\ \hline
6. It is recommended to assess the nutritional status before and after major & 100\% \\ surgery (GPP). & \\ \hline
7. Perioperative nutritional support therapy is indicated in patients with & \\ malnutrition and those at nutritional risk. Perioperative nutritional therapy & \\ should also be initiated if it is anticipated that the patient will be unable to & \\ eat for more than five days perioperatively. It is also indicated in patients & \\ expected to have low oral intake and who cannot maintain above 50\% of & 92\% \\ the recommended intake for more than seven days. In these situations, it is & \\ recommended to initiate nutritional support therapy (preferably by the & \\ enteral route e oral nutritional supplements e tube feeding) without & \\ delay (GPP). & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: ESPEN recommendations on perioperative nutritional management.
# Improved Bilateral Filtering for a Gaussian Pyramid Structure-Based Image Enhancement Algorithm
## 5 Experiments and Results
### Subjective Analysis of Image Enhancement Algorithms
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Gaussian** & **Algorithm** & **PSNR** & **MSE** & **SSIM** & **MSSIM** & **VIF** & **FR-VIF** & **VSNR** \\ \hline \multirow{2}{*}{\(\sigma=0.01\)} & Original algorithm & 22.493 & 376.772 & 0.542 & 0.912 & 0.361 & 0.231 & 18.221 \\  & Modified algorithm & **27.034** & **129.231** & **0.799** & **0.964** & **0.566** & **0.379** & **26.314** \\ \multirow{2}{*}{\(\sigma=0.02\)} & Original algorithm & 20.505 & 587.780 & 0.488 & 0.898 & 0.327 & 0.205 & 17.375 \\  & Modified algorithm & **28.414** & **93.752** & **0.928** & **0.993** & **0.794** & **0.463** & **34.386** \\ \multirow{2}{*}{\(\sigma=0.05\)} & Original algorithm & 18.014 & 1035.936 & 0.451 & 0.881 & 0.284 & 0.182 & 16.101 \\  & Modified algorithm & **27.220** & **123.450** & **0.946** & **0.991** & **0.691** & **0.469** & **30.743** \\ \multirow{2}{*}{\(\sigma=0.1\)} & Original algorithm & 16.222 & 1560.708 & 0.431 & 0.869 & 0.257 & 0.170 & 15.226 \\  & Modified algorithm & **27.507** & **115.525** & **0.970** & **0.995** & **0.713** & **0.502** & **32.404** \\ \multirow{2}{*}{\(\sigma=0.2\)} & Original algorithm & 16.127 & 1610.014 & 0.418 & 0.856 & 0.249 & 0.165 & 14.992 \\  & Modified algorithm & **22.286** & **481.923** & **0.860** & **0.952** & **0.446** & **0.365** & **21.867** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Evaluation of bilateral filtering algorithms based database [29] (24 images). Figure 7: Subjective comparison of the performance of different enhancement algorithms (six images of the dataset). (Column 1) Original images. (Column 2) Images processed using the SSR algorithm. (Column 3) Images processed using the multi-scale Retinex with color restoration (MSRCR) algorithm. (Column 4) Images processed using NLR algorithm. (Column 5) Images processed using INF algorithm. (Column 6) Images processed using our algorithm.
# Matched Filtering for MIMO Coherent Optical Communications with Mode-Dependent Loss Channels
## 4 SDM Communication System Model
### Transmitter  
The transmitted data in each of the \(D\) modes are modulated using a PAM with a square-root raised cosine pulse \(p(t)\) with roll-off factor equal to \(\alpha\) and normalized power, which can be expressed as [38]:  
\[p(t)=\frac{4\alpha}{\pi\sqrt{T}}\cdot\frac{\cos\left([1+\alpha]\frac{\pi t}{T} \right)+\frac{T\cdot\sin\left([1-\alpha]\frac{\pi t}{T}\right)}{4\alpha t}}{1- \left(\frac{4\alpha t}{T}\right)^{2}}. \tag{12}\]  
Hence, we can write the sequence of PAM pulses for a given mode \(i\) as:  
\[x_{i}(t)=\sum_{n=-\infty}^{\infty}s_{i}[n]p(t-nT), \tag{13}\]  
where \(s_{i}[n]\) is a random variable with values taken from the set defined by the PAM modulation scheme. Let us define the global impulse response \(\underline{q}(t)\) as the convolution of the transmitting pulse \(p(t)\) and the optical channel impulse response matrix \(\underline{h}_{tot}(t)\) as:  
\[\underline{q}(t)=\underline{h}_{tot}(t)*p(t), \tag{14}\]  
so that \(\underline{q}(t)\) is a \(D\times D\) matrix of impulse responses. This way, \(q_{ij}(t)\) describes the impulse response between the transmitter mode \(i\) and the receiver mode \(j\). Therefore, we can write the relationship between the transmitted symbols \(s_{j}[n]\) and the received signal in mode \(i\), \(y_{i}(t)\), as:  
\[y_{i}(t)=\sum_{n}\sum_{j=1}^{D}s_{j}[n]q_{ij}(t-nT)+n_{i}(t) \tag{15}\]  
with \(n_{i}(t)\) as the noise in the \(i\)-th receiver mode.  
Figure 1: Spatial division multiplexing (SDM) communication system model with linear multiple-input multiple-output (MIMO) receiver.
# Advanced Smartphone-Based Identification of Transport Modes: Resilience under GNSS-Based Attacks
## 2 Methodology
### Step 2: Generation of the Attack  
The spoofing attacks we generated for this work were of the receiver-based spoofer type (see Section 1). We used a software synchronized by the real satellite constellation and a Spirent GSS6560 (in the following Spirent) connected to a passive GPS L1 antenna to generate the faulty navigation signal. The legitimate satellites' constellation has been taken from the LabSat.  
The attacks werecarried out in a post-processing step in an anechoic chamber. An anechoic chamber is an isolated room prepared to avoid the reflection of the electromagnetic waves inside the room (see Figure 5). We used an anechoic chamber because it is forbidden to radiate in the GNSS band outdoors.  
Figure 4: The volunteer carries a smartphone in their hand and a case with the GNSS equipment, the u-blox receiver and LabSat. (Original picture restored.)  
Figure 5: Setup of the spoofing attack carried out in the anechoic chamber. The smartphone is hanging on the mannequin. The transmitter GPS antenna (orange; down, right) and the smartphone are separated by 1 m.  
We introduced the attacks during the journeys; that means the journeys contain both legitimate and faulty navigation signals. The legitimate navigation signal was recorded by the LabSat during the data collection, and it is synchronized with the signals recorded by smartphone, e.g., accelerometer, magnetometer and barometer signals. The faulty navigation signal has been synchronized using the legitimate navigation signal and modified with the Spirent.  
The faulty and the legitimate navigation signals are combined with a splitter (see Figure 1). As a control measure, during the attack in the anechoic chamber we measured the carrier-to-noise ratio detected at the smartphone's place using a u-blox EVK-M8T placed near the smartphone. The carrier-to-noise ratio value is between 20 dB/Hz and 40 dB/Hz, which recreates realistic conditions for the attack.
# Time Series Forecasting Method Based on Multi-Scale Feature Fusion and Autoformer
###### Abstract  
Accurate time series forecasting is crucial in fields such as business, finance, and meteorology. To achieve more precise predictions and effectively capture the potential cycles and stochastic characteristics at different scales in time series, this paper optimizes the network structure of the Autoformer model. Based on multi-scale convolutional operations, a multi-scale feature fusion network is proposed, combined with date-time encoding to build the MD-Autoformer time series forecasting model, which enhances the model's ability to capture information at different scales. In forecasting tasks across four fields--apparel sales, meteorology, finance, and disease--the proposed method achieved the lowest RMSE and MAE. Additionally, ablation experiments demonstrated the effectiveness and reliability of the proposed method. Combined with the TPE Bayesian optimization algorithm, the prediction error was further reduced, providing a reference for future research on time series forecasting methods.  
Keywords:time series forecasting; multi-scale feature fusion network; date-time encoding; Autoformer; Bayesian optimization algorithm +
Footnote †: journal: Applied sciences