esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Research Methods
### Security Constraints and Requirements  
Incorporating security measures into resource-constrained devices poses significant challenges since the security mechanisms are influenced by computational limitations, memory constraints, and network restrictions. The authors of [69] also suggest that the IoT grapples with processing, storage, and network constraints. While solutions like the cloud could potentially address these constraints, they introduce additional security and privacy concerns since the majority of the storage or computation is carried out with third-party service providers. While balancing the above constraints, it is critical to maintain a balance between the minimum necessary security requirements, performance, and device constraints. It is critical to maintain the following key aspects in the process of developing secure IoMT infrastructure.  
Data Confidentiality: Ensure all patient data are protected to prevent privacy violations or exposure to unauthorised third parties.  
Integrity: Ensure that patient medical data are tamper-proof during the communication from the IoMT device to the server.  
Authentication, Authorisation, and Access Control: Ensure that only authorised devices and users can join the network or begin communication to and from the server while participation is authorised and access to information is controlled.  
Freshness: Ensure that real-time communication is achieved.  
Non-Repudiation: Ensure data signing to validate where the data are originating from to ensure non-repudiation in the process of an identity attack.  
Isolated Network: The system should be allowed to withstand network failure and be able to conduct self-healing in the process of data recovery when the network fails.
## 4 New Technologies
In such remote areas, where other means of seafloor mapping are not easily feasible, bathymetry derived from satellite imagery is very promising. Freely available imagery, such as Landsat 8, as well as commercial higher-resolution satellite images, comprise vast sources of data with global coverage. Conventional \"water penetrating\" satellite-derived bathymetry also requires relatively clear water, but the development of satellite-derived bathymetry methods that are not solely based on the optical spectrum may overcome turbid water issues, though their accuracy and resolution are considerably lower and the method will still be depth limited. Whether from the identification of existing data sets or the collection of new data, the Nippon Foundation GEBCO Seabed 2030 Project will be responsible for bringing disparate depth measurements together into the compilation of a coherent bathymetric portrayal of the world ocean floor. Therefore, advances in bathymetric post-processing and analysis software, database technology, computing infrastructure, and gridding techniques are also important technological considerations. The present GEBCO central bathymetric data compilation, as well as compilations of the existing Regional Mapping Projects under GEBCO, reside on distributed servers at their respective host organizations. Moving towards establishing the RDACCs suggests that there may be potential benefits from establishing a shared cloud-based infrastructure for data storage as well as for gridding and processing routines (Virtual Research Environments and Infrastructures) that can also become part of new e-learning processes. The overall coordination of efforts to make use of emerging technologies will be the responsibility of the GDACC in coordination with the RDACCs.
## 3 Watermarking Approaches
### Watermarking Methods  
This section provides an overview of watermarking methods in the context of latent diffusion models, analyzing their techniques, characteristics, and pros and cons. The section is structured to include different watermarking approaches, categorized based on their embedding locations and mechanisms, followed by an analysis of their strengths, and limitations.  
Table 1 provides an overview of various watermarking techniques applied in latent diffusion models. The _Embedding Type_ refers to the point where the watermark is embedded (see Section 3.1 for more details). _Requires Training_ indicates whether additional training is required for the model or adapter to insert the watermark. _Controllable_ describes whether the insertion of the watermark can be controlled. Specifically, _Prompt_ allows control over watermark insertion using a prompt trigger, while _Adaptor_ enables control through the plug-and-play capability of an adaptor. _Controllable_ means the watermark can be selectively applied based on whether the method is used, whereas _Uncontrollable_ means that the watermark is always embedded in the generated image without the possibility of control. The _Task_ is divided into two categories: _Verification_, which checks whether an image was generated by the model, and Identification, which embeds a message of a certain length for identifying the user who generated the image. _fine-tuning_ refers to whether additional fine-tuning of the generative model is required.  
Figure 4: Research trends in watermarking approaches within LDMs, illustrating the evolution and preference of various watermarking techniques.
# Pre-Training Multi-Concept Question Embeddings for Knowledge Tracing
## 3 Problem Formulation  
For a student \(s\in S\), we define their historical learning interaction sequence as \(s=\left\{e_{j}\right\}_{j=1}^{n}\), where \(e_{j}\) represents the \(j\)-th interaction, and \(S\) is a set of all students' sequences. Each interaction is a tuple \(e=\left\langle q,r\right\rangle\), where \(q\) denotes the question involved in this interaction, and \(r\in\left\{0,1\right\}\) is a binary value indicating whether the student's response was correct. Each question \(q\) includes a set of concepts and auxiliary information, represented as \(q=\left\langle\left\{c\right\},a\right.\right\rangle\). A question may encompass one or more concepts. The set of all concepts is denoted as \(\text{C}=\left\{c\right\}\), and the set of all questions is denoted as \(\text{Q}=\left\{q\right\}\).  
The auxiliary information for a question is derived from statistical data within the dataset. Different datasets provide varying types of auxiliary information. For instance, in the Assist09 dataset, we can derive statistics such as the question error rate \(d\), average number of hints \(h_{mag}\), maximum number of hints \(h_{max}\), average first response time \(t_{avgfirst}\), average response time \(t_{avgresp}\), and average number of attempts \(t_{attempt}\).  
The set of concepts associated with a question indicates which concepts are relevant to that question, while the auxiliary information reflects aspects related to the question's difficulty.  
The knowledge tracing task aims to determine a student's knowledge state given their past interaction sequence \(s\). Due to the nature of real-world data collection, explicit knowledge states cannot be directly used as targets for the task. Therefore, the primary objective of the knowledge tracing task is to predict the probability that the student will answer the next question correctly. Specifically, given the current sequence \(s=\{e_{1},e_{2},...,e_{j}\}\), the task is to predict the probability of correctness for the next interaction \(e_{j+1}\), denoted as \(P_{j+1}\).
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Measurement**} & \multicolumn{2}{c}{**N**} & \multicolumn{2}{c}{**Mean**} & \multicolumn{2}{c}{**SD**} & \multicolumn{2}{c}{**Min**} & \multicolumn{2}{c}{**Med**} & \multicolumn{2}{c}{**Max**} & \multicolumn{1}{c}{\(p^{1}\)**} & \multicolumn{1}{c}{\(p^{2}\)**} \\ \cline{2-13}  & **T** & **T1** & **T2** & **T3** & **T1** & **T2** & **T3** & **T1** & **T2** & **T3** & **T1** & **T2** & **T3** & **T1** & **T2** & **T3** & \\ \hline \multirow{4}{*}{N-ANS (mm)} & & 50.29 & 51.34 & 51.55 & 4.10 & 3.81 & 3.97 & 42.70 & 42.30 & 42.60 & 49.70 & 50.85 & 50.95 & 57.70 & 58.20 & 58.80 & \\ \cline{2-13}  & 2-1 & 34 & 1.05 & & 1.28 & & \(-1.20\) & & 1.35 & & 3.10 & & & 0.0001 \\ \cline{2-13}  & 3-2 & 34 & 0.22 & & 0.81 & & \(-1.30\) & & 0.20 & & 2.10 & & 40.001 & **0.52** \\ \cline{2-13}  & 3-1 & 34 & 1.27 & & 1.35 & & \(-1.00\) & & 1.55 & & 4.10 & & & 0.0001 \\ \cline{2-13}  & & 45.14 & 45.39 & 45.68 & 4.01 & 4.15 & 4.13 & 37.10 & 37.30 & 37.80 & 46.05 & 46.60 & 46.15 & 51.70 & 52.20 & 52.60 & \\ \cline{2-13}  & 2-1 & 34 & 0.24 & & 0.56 & & \(-1.10\) & & 0.25 & & 1.40 & & & 0.0001 \\ \cline{2-13}  & 3-2 & 34 & 0.30 & & 0.52 & & \(-0.90\) & & 0.30 & & 1.60 & & & 0.0001 \\ \cline{2-13}  & 3-1 & 34 & 0.54 & & 0.68 & & \(-0.90\) & & 0.60 & & 1.80 & & & 0.045 \\ \hline \multirow{4}{*}{ARMR-ARML (mm)} & & 51.87 & 57.82 & 56.68 & 4.69 & 4.88 & 4.47 & 41.90 & 47.00 & 47.30 & 51.40 & 56.80 & 56.25 & 64.00 & 68.50 & 67.50 & \\ \cline{2-13}  & 2-1 & 34 & 5.95 & & 2.15 & & 3.10 & & 5.30 & & 12.20 & & & 0.001 \\ \cline{2-13}  & 3-2 & 34 & & \(-1.14\) & & 1.11 & & \(-3.60\) & & \(-0.95\) & & & 0.30 & & **+0.001** \\ \cline{2-13}  & 3-1 & 34 & 4.81 & & 2.41 & & 1.00 & & 5.00 & & 12.10 & & & **+0.001** \\ \hline \multirow{4}{*}{ARCR-ARCL (mm)} & & 35.02 & 43.45 & 41.41 & 3.58 & 3.20 & 3.92 & 25.82 & 37.60 & 34.70 & 34.15 & 43.00 & 41.80 & 42.20 & 51.30 & 50.60 & \\ \cline{2-13}  & 2-1 & 34 & & 8.43 & & 2.38 & & 3.90 & & 8.25 & & & 17.40 & & **+0.001** \\ \cline{2-13}  & 3-2 & 34 & & \(-2.04\) & & 1.45 & & \(-4.80\) & & \(-2.15\) & & & 0.50 & & **+0.001** \\ \cline{2-13}  & 3-1 & 34 & & 6.39 & & 2.85 & & & 0.20 & & 5.55 & & & 16.70 & & **+0.001** \\ \cline{2-13}  & & 82.12 & 82.06 & 82.15 & 3.19 & 3.22 & 3.20 & 77.11 & 76.92 & 77.08 & 82.10 & 82.14 & 82.16 & 90.00 & 90.30 & 90.40 & \\ \cline{2-13}  & 2-1 & 34 & & 0.06 & & 0.65 & & \(-2\) & & 0.23 & & & 1.17 & & \\ \cline{2-13}  & 3-2 & 34 & & 0.09 & & 0.29 & & \(-0.3\) & & 0.01 & & 1.01 & & 0.23 & \\ \cline{2-13}  & 3-1 & 34 & 0.15 & & 0.50 & & \(-1.12\) & & & 0.12 & & & 1.06 & & \\ \hline \multirow{4}{*}{SNB (\(\circ\))} & & 80.77 & 80.53 & 80.62 & 4.80 & 4.98 & 4.95 & 72.06 & 72.1 & 72.14 & 80.12 & 80.06 & 79.95 & 92.95 & 93.12 & 93.17 & \\ \cline{2-13}  & 2-1 & 34 & & \(-0.24\) & & 0.96 & & \(-4.36\) & & & 0.02 & & 0.12 & & \\ \cline{2-13}  & 3-2 & 34 & & 0.09 & & 0.29 & & 0.12 & & \(-0.01\) & & 1.32 & & 0.16 \\ \cline{2-13}  & 3-1 & 34 & & \(-0.15\) & & 0.74 & & \(-3.11\) & & & 0.11 & & 1.24 & & \\ \hline \end{tabular}
\end{table} 
Table 1: Descriptive statistics of the linear and angular skeletal measurements.
# Performance Optimization of Underwater Communication Links at Different Ranges for AIS Relay to AUV
## 8 Conclusions  
In this paper, a modem designed for AUVs applications is proposed. This modem combines multi-carrier signal and 2D spreading. A variable length spreading sequence combined with OFDM creates a flexible design that uses frequency diversity, time diversity, and adds a processing gain to transmit at various distances. The modem was tested using realistic channels of the watermark benchmark. Results shows the effectiveness of SF to improve the error rate at low \(\frac{\text{SF}}{\text{SF}}\). In addition, by choosing a SF according to the coherence bandwidth and the coherence time of the channel the BER can be further improved. A method to choose the time/frequency allocation of the SF is also proposed. Robustness and adaptability of the modem is interesting for AUVs applications where the communication needs to be reliable even if the communication range varies. A possible application where the modem is used to relay AIS data to AUVs is presented. Further potential improvements for this modem, such as short length channel coding and reducing the number of pilots are being looked upon. Furthermore, using a platform like IROMI [34], the modem will be tested at sea.  
Conceptualization, S.I.; C.L.; A.-O.B.; J.-S.; methodology, S.I.; C.L.; A.-O.B.; J.-S.; software, S.I.; validation, S.I.; C.L.; A.-O.B.; J.-S.; formal analysis, S.I.; C.L.; A.-O.B.; J.-J.S.; investigation, S.I.; C.L.; A.-O.B.; J.-J.S.; writing--original draft preparation, S.I.; C.L.; A.-O.B.; J.-J.S.; writing--review and editing, S.I.; C.L.; A.-O.B.; J.-J.S.; visualization, S.I.; supervision, C.L.; A.-O.B.; J.-J.S. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \(Q\times M\) & \(4\times 4\) & \(4\times 8\) & \(8\times 4\) & \(8\times 8\) & OFDM \\ \hline NOF1 channel: & 0.058 & 0.006 & 0.0003 & 0 & 1 \\ NCS1 channel: & 0.753 & 0.302 & 0.237 & 0.031 & 1 \\ \hline \end{tabular}
\end{table}
Table 2: Packet error rate at an SNR of 3 dB.  
**Funding:** This work was supported in part by Region Bretagne and THALES, in the context of the WAVES laboratory and the SAAM project.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 2 Materials and Methods
### Assessment and Monitoring of the Pressure Intensity during DFM  
In order to quantify the amount of pressure performed by the physiotherapist while executing DFM, an instrument that monitored and recorded the pressure applied was used (Figure 1). This consisted of a piezo-resistive sensor provided by TekscanTM A201 FlexiForceTM (TekscanTM, Inc., South Boston, MA, USA) with an active area of 9.53 mm\({}^{2}\) in a pressure range of 0-445 N. The sensor was connected according to the corresponding datasheet specifications to achieve the stated linear error of 3% at full scale and a repeatability error of 2.5%. To acquire the pressure data, a microcontroller-based platform (Teensy 3.2, PIJ RCTM, OR, USA) was programmed at a 100 samples per second (sps) sampling frequency. This device ran on a 96 MHz clock with a 16-bit resolution for analog-to-digital conversion. The microcontroller was coupled with a Bluetooth 2.1 + EDR module for wireless data transmission at 230,400 bps. Under these specifications, the sensor full-scale bandwidth from 0 to 3.3 V achieved a resolution of 0.0068 N (680 micrograms). For data recording and real-time visualization, a WindowsTM (MicrosoftTM, Albuquerque, NM, USA)-based application was used (Figure 1). Before each acquisition, the sensor was properly calibrated with two standard weights. The recorded data were processed under Matlab_R2016a (MathWorksTM, Inc., Natick, MA, USA). Data were first filtered by a low-pass filter with a cut-off frequency of 5 Hz through a 4 th-order Hamming window finite impulse response (FIR).
# A Spatial Prediction-Based Motion-Compensated Frame Rate Up-Conversion
## 1 Introduction
Similar to natural images, the MVF of video frames also has local stationary statistics [22], which can help MCFI to reduce the computational complexity. The existing works throw a lot of computations to suppress MV outliers, but the improvement of MV precision is far from satisfactory. We expect a good balance between computations and MVF accuracy, so a Spatial Prediction-based Motion-Compensated Frame Interpolation (SP-MCFI) is proposed in this paper. The contributions of SP-MCFI are listed as follows:  
1. A predictive model defines two kinds of blocks: basic block and absent block. BME is directly performed to compute the MV of basic blocks. 2. To speed up BME, the Successive Elimination Algorithm (SEA) [23] is used to reduce search candidates, and the block matching is performed with a subsampling pattern. 3. The MV of absent blocks will be accurately recovered from MVs of its neighboring basic blocks. Experimental results demonstrate that the proposed SP-MCFI algorithm generates a pleasant up-converted video, and meanwhile, it has a low computational complexity.
## 3 Experimental Results and Analysis
### Ablation Study  
The SSCA and DMF modules are the core components of the proposed method. To evaluate their effectiveness, three network variants were tested on the Pavia University dataset: (a) basic network with SSCA module, (b) basic network with DMF module, and (c) the full network with both the SSCA and DMF modules (i.e., the proposed method). The results in Table 5 show that the network with both SSCA and DMF modules achieves the best denoising performance, further validating the effectiveness and complementarity of these two modules in hyperspectral image denoising tasks.  
As shown in Figure 16, the spatial-spectral attention module guides feature weight distribution via the spatial-spectral heatmap. Spatial attention directs the spatial features, while spectral attention directs the spectral features. Through the parallel branches, the network effectively learns the spatial-spectral feature information from the feature map. The spatial-spectral features, spectral features, and features after multiplying the spatial-spectral heatmap are shown in the figure.  
To validate the sensitivity of the proposed network to the number of layers (i.e., the number of downsampling operations), ablation experiments with network depths of two, three, and four layers were designed. The four-layer network serves as the baseline structure used in this study. The three-layer network was constructed by retaining the core modules of this paper, namely, the SSCA module and the DMF module. The two-layer network only retained the SSCA module. As shown in Table 6 and Figure 17, the experimental results demonstrate a direct correlation between the number of network layers and denoising performance: the four-layer network achieves the best performance. Moreover, when the proposed modules are removed, the network performance significantly drops, further confirming the effectiveness of the modules.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **SSIM** & **PSNR** & **SAM** \\ \hline SSCA & 0.914 & 34.01 & 0.105 \\ DMF & 0.911 & 33.87 & 0.108 \\ SSCA + DMF & **0.917** & **34.14** & **0.104** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation study of SSCA and DMF. Bold values indicate the best results for the corresponding metric.  
Figure 15: Visual results of noise removal on false-color images for bands 30, 110, and 189 of the India Pines dataset compared with competing methods. (**a**) Noise, (**b**) LLRT, (**c**) BM4D, (**d**) MEMNet, (**e**) HSID-CNN, (**f**) SQAD, (**g**) QRNN3D, (**h**) MAFNet, (**i**) Ours.
# Efficient V2V Communications by Clustering-Based Collaborative Caching +
## 2 Related Work
### Collaborative Caching
However, the notification of content information via beacons causes huge overhead and increases the possibility of packet collision. Ref. [21] targets a stationary environment wherein each node requests a time-shifted TV program. Each content router (CR) has a label \(n\) of a positive integer, where \(n<k\). CR \(n\) stores only chunks \(c\), where \(c\mod k=n\), which prevents multiple nodes from caching the same content. It is difficult, however, to apply this method in the V2V environment where nodes move.
## 7 Proportional--Integral--Derivative (PID) Controller Enhanced with Deep Learning (DL)
### System Vibration Control Under Impulse and Random Disturbances  
This section investigates the dynamics of vibration control systems when subjected to impulse and random disturbances. It examines the system's response to each type of disturbance and demonstrates how deep learning techniques can be utilized to tune PID controllers effectively. By integrating these advanced approaches, the study aims to improve vibration mitigation strategies and enhance system stability and performance across diverse operating conditions.
## 4 Experimental Results
### Service Setup and Exhibit Equipment  
Regarding the pipeline setup for both events, we employed two server nodes with public access (services accessible over an internet connection).  
* Analytics hub: this dedicated server hosted the stages described in Section 3.1 (capture) and Section 3.2 (process), under the H2 IOSC project.
* ATON server: this dedicated server hosted the main instance of the ATON framework, providing web applications and 3D content. In this case, this was the \"/imagine\" WebXR application and its generative AI content, as well as the \"Merkhet\" WebXR tool (inspect stage, described in Section 3.3).  
Both servers involved were based on the Linux OS (Ubuntu Server 20.04.6 LTS) with the Node.js and PM2 setups for the cluster deployment of micro-services, located in Rome (main CNR area).  
In terms of equipment, the onsite physical installations were composed of the following:* A single workstation and one HMD (HP Reverb G2 Headset) were used to experience an immersive VR mode for the ArcheoVirtual event;
* A standalone HMD (Meta Quest PRO) was used to experience both the VR and MR modes (see Figure 6) for the TourismA event.  
Indeed, due to the transmission of anonymous data (sessions) to the capture service deployed in the Rome hub, an internet connection was required for both setups.
# Multi-Patch Time Series Transformer for Robust Bearing Fault Detection with Varying Noise
## 5 Experiments
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Noise Type** & **Model** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline \multirow{8}{*}{No noise} & LSTM & 0.9961 & 0.9965 & 0.9961 & 0.9963 \\ \cline{2-6}  & GRU & 0.9961 & 0.9961 & 0.9965 & 0.9963 \\ \cline{2-6}  & FCN & 0.9954 & 0.9961 & 0.9954 & 0.9958 \\ \cline{2-6}  & ResNet & 0.9954 & 0.9958 & 0.9961 & 0.996 \\ \cline{2-6}  & InceptionTime & 0.9961 & 0.9968 & 0.9961 & 0.9965 \\ \cline{2-6}  & XceptionTime & 0.9958 & 0.9958 & 0.9965 & 0.9961 \\ \cline{2-6}  & XCM & 0.9961 & 0.9961 & 0.9965 & 0.9963 \\ \cline{2-6}  & LSTMAttention & 0.9965 & 0.9968 & 0.9968 & 0.9968 \\ \cline{2-6}  & GRUAttention & 0.9961 & 0.9965 & 0.9972 & 0.9968 \\ \cline{2-6}  & TSSequencer & 0.9961 & 0.9961 & 0.9965 & 0.9963 \\ \cline{2-6}  & PatchTST & 0.9965 & 0.9972 & 0.9965 & 0.9968 \\ \cline{2-6}  & **MultiPatchTST** & **0.9972** & **0.9972** & **0.9975** & **0.9973** \\ \hline \multirow{8}{*}{Gaussian noise} & LSTM & 0.9789 & 0.9816 & 0.9821 & 0.9815 \\ \cline{2-6}  & GRU & 0.9797 & 0.9821 & 0.9826 & 0.9821 \\ \cline{2-6}  & FCN & 0.9834 & 0.985 & 0.9855 & 0.985 \\ \cline{2-6}  & ResNet & 0.9829 & 0.9848 & 0.9845 & 0.9845 \\ \cline{2-6}  & InceptionTime & 0.9802 & 0.9823 & 0.9823 & 0.9821 \\ \cline{2-6}  & XceptionTime & 0.9818 & 0.9832 & 0.9842 & 0.9834 \\ \cline{2-6}  & XCM & 0.9767 & 0.979 & 0.9789 & 0.9785 \\ \cline{2-6}  & LSTMAttention & 0.9783 & 0.9811 & 0.981 & 0.9806 \\ \cline{2-6}  & GRUAttention & 0.9834 & 0.9847 & 0.9847 & 0.9845 \\ \cline{2-6}  & TSSequencer & 0.9799 & 0.9807 & 0.9834 & 0.9819 \\ \cline{2-6}  & PatchTST & 0.9831 & 0.9844 & **0.9869** & 0.9854 \\ \cline{2-6}  & **MultiPatchTST** & **0.9837** & **0.986** & 0.985 & **0.9854** \\ \hline \multirow{8}{*}{Electrical interference noise} & LSTM & 0.9649 & 0.9658 & 0.9656 & 0.9657 \\ \cline{2-6}  & GRU & 0.9691 & 0.9706 & 0.9698 & 0.9702 \\ \cline{2-6}  & FCN & 0.967 & 0.9682 & 0.9674 & 0.9678 \\ \cline{2-6}  & ResNet & 0.964 & 0.9651 & 0.9649 & 0.965 \\ \cline{2-6}  & InceptionTime & 0.9627 & 0.9629 & 0.9631 & 0.963 \\ \cline{2-6}  & XceptionTime & 0.9667 & 0.9678 & 0.9671 & 0.9674 \\ \cline{2-6}  & XCM & 0.9618 & 0.963 & 0.9621 & 0.9625 \\ \cline{2-6}  & LSTMAttention & 0.9627 & 0.9634 & 0.9635 & 0.9634 \\ \cline{2-6}  & GRUAttention & 0.9615 & 0.9614 & 0.9619 & 0.9616 \\ \cline{2-6}  & TSSequencer & 0.9672 & 0.9681 & 0.9676 & 0.9679 \\ \cline{2-6}  & PatchTST & 0.9663 & 0.967 & 0.9675 & 0.9672 \\ \cline{2-6}  & **MultiPatchTST** & **0.97** & **0.97** & **0.9704** & **0.9702** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental results on MFPT dataset with mixed noise.