esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Design and Methods
### Vibration Test
#### 2.3.2 Test Index  
According to the literature [27; 28], the working vibration amplitude of machinery, such as a tractor, can be represented by the vibration severity. The tractor is classed as agricultural power machinery, so the vibration amplitude of the tractor can be expressed by the vibration severity. Vibration intensity is a comprehensive characteristic quantity that can adeptly reflect the vibrational state of a machine in a succinct, pragmatic, and efficacious way. Therefore, vibration severity was used to characterize the vibration characteristics of the tractor at idle speed during the present study. According to the literature [27], the vibration severity can be calculated by Formula (1):  
\[v_{rms}=\sqrt{\left(\frac{\sum v_{x}}{N_{x}}\right)^{2}+\left(\frac{\sum v_{y }}{N_{y}}\right)^{2}+\left(\frac{\sum v_{y}}{N_{x}}\right)^{2}} \tag{1}\]  
where \(v_{rms}\) is vibration severity (mm/s); \(v_{x}\), \(v_{y}\), and \(v_{z}\) are the vibration velocities in X, Y, and \(Z\) directions (mm/s); and \(N_{x}\), \(N_{y}\), \(N_{z}\) are the test points in the X, Y, and \(Z\) directions.
# Graph Neural Networks for Routing Optimization: Challenges and Opportunities
## 8 Conclusions  
In conclusion, this paper presents a comprehensive overview of the application of GNNs for routing optimization in modern communication networks, emphasizing their advantages over traditional routing algorithms. By systematically categorizing existingGNN-based approaches, we have highlighted their effectiveness in adapting to dynamic network conditions while improving key performance metrics such as latency and throughput. Notably, our review showcases numerical indicators of model accuracy, with several studies achieving mean relative errors as low as 1.27% and significantly enhancing routing decisions compared to conventional methods. Additionally, we provide a curated collection of data resources and tools for practitioners in the field, addressing a critical gap in the literature. This contribution not only underscores the importance of GNNs in evolving network architectures but also offers valuable insights and practical resources for researchers and engineers seeking to implement these advanced techniques. By addressing the challenges and opportunities inherent in this domain, we hope to inspire further exploration and innovation in GNN-based routing solutions, ultimately contributing to the development of more efficient and resilient communication networks.  
Conceptualization, [PERSON], H.H., Y.Z., [PERSON], [PERSON] and W.G.; methodology, [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and W.G.; software, [PERSON], [PERSON], [PERSON], [PERSON], M.H. and W.G.; validation, [PERSON], [PERSON], [PERSON], [PERSON], M.H. and W.G.; formal analysis, [PERSON], [PERSON], [PERSON], [PERSON], M.H. and W.G.; investigation, [PERSON], [PERSON], [PERSON], [PERSON], M.H. and W.G.; resources, [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and W.G.; data curation, [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]; writing--original draft preparation, [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]; writing--review and editing, [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]; visualization, [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and W.G.; supervision, [PERSON] and X.C.; project administration, [PERSON] and X.C.; funding acquisition, [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Social Science Foundation Youth Program under Grant 24 CTJ011; in part by the National Natural Science Foundation of China under Grant 62401070; in part by the Fundamental Research Funds for the Central Universities under Grant 2023 RC16; in part by Zhejiang Provincial Natural Science Foundation of China under Grant No. LQ24F030023.  
Not applicable.  
The authors declare no conflicts of interest.
# Maintenance Prediction through Sensing Using Hidden Markov Models--A Case Study
## 4 Case Study
### HMM--Equipment Diagnostics
Now, once the parameters of the HMM model are known and embedded in the characteristic matrices, the conditions for using the \"Viterbi\" algorithm are met, which will allow the characterization of the most likely sequence of hidden states over time, which will give the diagnosing of the equipment's health. Then, based on Figure 9, it is possible to see the evolution of the failure state of the drying press over time. To better understand the equipment diagnosis (Hidden States), the observable states extracted from the Clustering and the \"raw\" signal of each sensor is also represented in Figure 9.
## 5 Performance Evaluation
### Evaluation Environment  
We utilized MATLAB version R2021a and employed the following toolboxes: Neural Network Toolbox, Deep Learning Toolbox, Signal Processing Toolbox, and Statistics and Machine Learning Toolbox. We conducted the computations on a system equipped with an Intel Core i7 processor, running at 2.6 GHz. The system also had 12 GB of RAM and was running on the Windows 10 operating system. The data selected in this experiment are open source data taken by [PERSON] et al. from Fudan University using video detection method [31]. The data are traffic flow from 7:40 to 16:50 with a total of 28,044 data. These data are open source data based on real maps. The traffic flow data in the dataset represents the number of vehicles passing through a specific point on a road within a certain time period, while the vehicle speed data represents the average speed of vehicles passing through a specific road segment within a specific time interval. As shown in Figure 3, the road is the Yan' an Viaduct near the Shanxi Road in front of the Shanghai Exhibition Hall.
###### Abstract
The addition of Ar to the DBD plasma by [PERSON] et al. resulted in an increase in the conversion of N\({}_{2}\) of 4.53-8.13%, indicating that the addition of Ar favors the generation of NH\({}_{3}\)[26]. [PERSON] et al. used a packed-bed DBD plasma to synthesize ammonia by adding a small amount of Ar to the gas feed, which resulted in a 2% increase in ammonia concentration and energy yield, but the addition of excess Ar reduced ammonia production by 5.6% [27]. [PERSON] et al. modified the plasma characteristics and increased ammonia production by adding He to various configurations of DBD reactors [28]. For different types of plasma, the addition of noble gases under different conditions produces different effects, so it is crucial to study the optimal reaction conditions and synthesis mechanism for the interaction between the reactor and the plasma. From previous work, it can be seen that DBD ammonia synthesis is mostly plasma generated by AC power sources and nanosecond power sources are rarely used for ammonia synthesis. Optimizing the plasma source is one way to improve the use of energy in the process. Nanosecond pulses enable controlled discharges on shorter time scales than AC power sources, allowing electrons to gain high energy in the free range, avoiding energy loss in the gas heating process and providing high efficiency in terms of energy consumption and energy yield. Second, nanosecond pulsed power supplies have more adjustable parameters for better control of plasma properties [29]. In this work, we integrated a nanosecond pulsed power supply with a DBD system to enhance ammonia synthesis by incorporating noble gases, specifically Ar and He. This study thoroughly explored how various gas conditions, noble gas additions, and pulse parameters influenced the rate of ammonia synthesis and its energy yield. Additionally, the impacts of Ar and He additions on plasma characteristics were examined using electrical characterization diagnostics. The analysis of the influence of noble gases on the ammonia synthesis mechanism was conducted using optical emission spectra (OES) and 0D plasma chemical kinetic modeling.
## 3 Background
### Threat Model--Adversary Goals  
Adversaries aim to subvert a model through attacking its confidentiality, integrity, or availability. Confidentiality attacks attempt to expose the model or the data encapsulated within. Integrity attacks occur when an adversary attempts to control the output of the model, for example, to misclassify some adversarial traffic and therefore allow it to pass a detection process. Availability attacks could misclassify all traffic types, or deteriorate a model's confidence, consistency, performance, and access. In this way, an integrity attack resembles a subset of availability attack, since an incorrect response is similar in nature to a correct response being unavailable; however, the complete unavailability of a response would likely be more easily noticed than decreases in confidence, consistency, or performance. The goals of an adversary may be different but are often achieved with similar methods.
## 3 Results  
### Instrumentation  
Data acquisition hardware consisted of (a) an inertial sensor mounted on a shoe by a foot-strap (Figure 7a) and (b) a data acquisition unit (Figure 7b). Inertial data was gathered by a LSM6 DS33--3-axial MEMS gyroscope and accelerometer in special casing, tightly mounted by a foot-strap on the shoe counter. Dynamic bandwidth was \(\pm\)8 g and \(\pm\)2000 deg with 16-bit resolution for each axis. Raw data was sampled with 200 Hz and calibration of the sensors was performed beforehand with a linear error model with additional axis orthogonality correction. The Data Acquisition Unit (DAU) was constructed on Intel Edison Platform with a 32-bit Intel Atom processor on board. The INS algorithm was running on DAU and inter-step data was sent over a 868-MHz radio to the base station for storing and visualization.  
Figure 6: A path corrected by the map matching algorithm. The axes are defined in meters [m].  
Figure 7: Experimental INS system. (**a**) IMU mounted on a shoe with a foot-strap; (**b**) Data Acquisition Unit.  
An additional pair of Ultra Wide Band (UWB) radios was exploited to perform indoor ranging between a fixed point (an anchor node) and mobile agent. The anchor node depicts the 'fall back' place in firefighters' tactics. Indoor Ranging (with wall obstacles) is problematic due to the phenomena called multipath effect [40], which means that little change in position can result in a very different path (which in the end results in a different time of flight). Analytical models suggest that a different path can result in different signal propagation, but a change of frequency can have a similar influence [40]. Therefore, UWB ranging means that measurements are taken consecutively at a number of different frequencies and the smallest ToF is taken into consideration.  
The main component for performing UWB ranging was DW1000 from Decawave with third-party open source firmware. The module that we used was radino32 DW1000, additionally calibrated for bias. The operating frequency was 3993.6 MHz \(\pm\) 665.6 MHz with 64 MHz Pulse Frequency.
## 3 The Proposed Hybrid Demodulation Technique
### A Hybrid Demodulation Technique via MID and VMD
MID\({}_{\Delta f}^{\text{SCoh}}\) of the BLIMF2 is shown in Figure 9b, where it can be clearly seen that the carrier frequency is 3000 Hz and the cycle frequency (modulation frequency) is about 100 Hz. This demonstrates the noise robustness of the proposed technique. In addition, we will also investigate the difference in the MID\({}_{\Delta f}^{\text{SCor}}\) and MID\({}_{\Delta f}^{\text{SCoh}}\) algorithms in signal demodulation. Thus, the MID\({}_{\Delta f}^{\text{SCor}}\) algorithm is also used to analyze the BLIMF2 component and the IMF3 component, respectively. It can be found in Figure 10a,b that the carrier frequency and the cycle frequency are both detected, but the multipliers of the cycle frequencies are not very clearly detected compared with Figure 8a,b. The reason is that the spectral correlation indicator cannot fully play the role of a modulation intensity factor. Thus, spectral coherence analysis will be used in the MID in the following application. The effectiveness of the proposed hybrid demodulation technique will be further investigated using practical gear vibration signals. \[s(t)=e^{-1000t}\cos(2\pi\cdot 3000t) \tag{18}\]0 The MID\({}_{\Delta f}^{\text{SCoh}}\) of the decomposed BLIMF2 using VMD (**a**) and IMF3 using EMD (**b**). \[s(t)=e^{-1000t}\cos(2\pi\cdot 3000t) \tag{18}\]1 The MID\({}_{\Delta f}^{\text{SCoh}}\) of the simulated vibration signal (the noise variance is 0.7) (**a**) and the decomposed BLIMF2 using VMD (**b**).
## 3 Results and Discussion
### Aromatic Profile  
It is important to note that, to our knowledge, there is no information or study in the literature on the aromatic profile of this fruit. The HS-SPME-GC/MS analyses carried out on the fruit samples allowed for the detection of a total of 80 aroma compounds, of which 57 were identified (match factor > 80%) (Table 4). An example of chromatogram obtained is given in Figure S4. Among these compounds, 14 of them presented peak areas greater than 0.5% of the total relative peak area, and so they were selected for quantification (Table 5). The Venn diagram in Figure 8 provides a synthetic visualization of how the aromatic compounds identified were represented in fruits from the different regions/years of this study. Among the 57 aroma compounds, only 25% (marked with an asterisk in Table 4) were identified in all the samples, regardless of the region or year of harvest. This diagram showed the effect of the harvest area on the aroma profile. In fact, it was found that in addition to the 14 aroma compounds common to all samples, 25 compounds were common to the 3 samples collected in 2022. Together, these two classes represented 70% of the total number of compounds. Figure 7: Total carotenoid content (TCC) of the samples of loengo fruit (mean with \(n=3\)). Different letters for significant difference, according to the Tukey test (\(p<0.05\)).
## 3 Environmental Applications of Graphene-Based Photocatalysts
### Magnetically Separable Graphene-Based Composite Photocatalysts
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Photocatalyst** & **Pollutants** & **Experimental Conditions** & **Findings** & **Ref.** \\ \hline Co\({}_{0.7}\)Zn\({}_{0.3}\)Er\({}_{x}\)Fe\({}_{2-\lambda}\)O\({}_{4}\)+GO & MB & 1-h vis (Xe lamp) & 100\% dec.; 55\% deg. & [173] \\ (x = 0.01–0.05) & & & & \\ \hline AFG@MIL-101(Fe) & & & & \\ (amine-func. Fe\({}_{3}\)O\({}_{4}\) wrapped with rGO) & DIZ, ATZ pesticides & HPHg (400 W, \(\lambda\) = 546.8); & 100\% rem. (ATZ, aci. pH); & \\  & & 30 ppm; 105 min & TOC rem.; 4 cycles—almost & \\  & & & same act. & \\ \hline ZnO/ZnFe\({}_{2}\)O\({}_{4}\)/N-doped G & MO & 10-W LED (400–700 nm); & 99\% rem. & \\ ZnO/CoFe\({}_{2}\)O\({}_{4}\)/N-doped G & MG & 10 ppm; 70 min (MG), & ZnO/CoFe\({}_{2}\)O\({}_{4}\)/N-doped & \\  & & 140 min (MO) & G (pH 4–6). & \\ \hline BiFeO\({}_{3}\)-G & & & & \\ (G: 1–9 wt\%) & MO & SSol (200-W Xe lamp); & on BiFeO\({}_{3}\)-5\%G; & \\  & & 10 ppm; 6 h & e\({}^{-}\) captured by G, h\({}^{+}\) & \\  & & & oxidative species & \\ \hline Fe\({}_{2}\)O\({}_{3}\)-Fe\({}_{3}\)O\({}_{4}\)-M-G & & & & 100\% MO deg. (175 min) & \\ (M—montmorillonite) & MO & vis (300-W Xe lamp); & on Fe\({}_{2}\)O\({}_{3}\)-Fe\({}_{3}\)O\({}_{4}\)-M-G; & \\  & & 20 ppm & 98.8\% recycling with & \\  & & & magnetic field & \\ \hline BiFeO\({}_{3}\)-g-GNS & MOMB & vis (\(\lambda\)\(\geq\) 420 nm, 300-W halogen lamps); 20 ppm; & 87\% deg. MB, 35.9\% deg. & \\  & & & halogen lamps); 20 ppm; & MO; activity increase with & \\  & & 60 min & increase of BiFeO\({}_{3}\) content & \\ \hline CoFe\({}_{2}\)O\({}_{4}\)-G & MB, RhB, MO, BL-G, & vis (500-W Xe lamp, & \\ (G = 15–50\%) & RGB & JB450 cut-off filter; 20 ppm; & 95\% deg. MB on & \\  & & 240 min & CoFe\({}_{2}\)O\({}_{4}\)-G(40\%) & \\ \hline MgFe\({}_{2}\)O\({}_{4}\)-G & MB & UV/vis; 40 min & \(\sim\)100\% deg. MB & [179] \\ \hline GO/magnetic/Ce-doped & & vis (300-W Xe lamp, & 89.92\% deg. & \\  & & 400-nm cut-off filter); & on MGO-CeTiO\({}_{2}\); & \\  & & 25 ppm; 60 min. & 4 cycles-decrease & \\  & & & activity to 60\% & \\ \hline Fe\({}_{2}\)TiO\({}_{5}\)/rGO\({}_{\rm x}\) & MB & vis (50-W LED); 10 ppm; & 40\% deg. & \\ (x = 0–15\%) & MB & 150 min & on Fe\({}_{2}\)TiO\({}_{5}\)/rGO\({}_{\rm x}\) & \\  & & & (x = 10\%) & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of selected graphene-magnetic NPs photocatalysts.
## 2 Overview of the FBN  
### Bayes' Theorem and the BN  
The BN is based on the Bayes theorem presented by [PERSON] in the 18 th century. The main concept of the Bayes theorem revolves around addressing the uncertainty encountered in daily life [29]. It can be used to determine the probability of an event based on the occurrence of a previous event. The Bayes theorem is a fundamental principle of probability theory underpinning the BN to update the probability of a variable based on evidence from its parent node, making it valuable for decision-making and machine-learning applications [22]. The Bayes theorem can be expressed using Equation (1).  
\[\mathrm{P(B/A)}=\frac{\mathrm{P(A/B)\times P(B)}}{\mathrm{P(A)}} \tag{1}\]  
In Equation (1), P(A) is the probability of event A, P(B) is the probability of event B, P(B/A) is the probability of event B given that event A has already occurred, and P(A/B) is the probability of event A given that event B has already occurred.  
BNs are mathematical models representing the causal relationship for different risk variables using CPTs. In a BN, nodes are connected through directed acyclic graphs (DAGs), and edges represent causal relationships between risk variables [30]. A variable's probability can be updated based on the new evidence that the parent's node acquired. The BN approach is a powerful tool for modeling complex systems, decision-making, and machine-learning applications where updating probabilities in response to updated information is crucial to the model's accuracy [31]. A simple description of the BN is shown in Figure 1. Nodes A, B, and C are parent nodes or BEs, Nodes X and Y are child nodes, also known as intermediate events (IEs), and Node Z is the top event (TE) or the outcome of this network.
# A Vibration Based Automatic Fault Detection Scheme for Drilling Process Using Type-2 Fuzzy Logic
## 1 Introduction
The novel concept presented can be implemented effectively for the fault detection in the drilling tool. The paper is organised as follows: In Section 2, the modeling of drilling process involving tool movement is carried out using a type-2 fuzzy technique. In this work, a type-2 fuzzy model is preferred over a type-1 fuzzy model because of its capability to deal with nonlinearities with more degrees of freedom. In Section 3, two theorems are developed. Theorem 1 proves the stability of the fuzzy modeling and Theorem 2 validates the stability of the fault detection estimator. In both theorems, the Lyapunov stability candidate is implemented for proving the stability criteria. For the detection of faults in the drilling process while maintaining stability, a novel fault detection methodology is presented. In Section 4, a numerical analysis is performed to validate the effective implementation of the sophisticated theoretical method. Finally Section 5 summarizes the conclusion of this work.
## 4 Proposed Video Latency Measurement Technique
### Camera Server  
This application node runs on the real-time target and is responsible for communication with the camera, preparing the data for encryption, adding timestamp information to packets and sending encrypted data back to the client. The server accepts connections on a pre-defined TCP port which is used for the RTSP transport initialization procedure [44]. RTSP packets are accepted, parsed and forwarded to the IP camera, while responses from the IP camera are handled in a similar way and are returned to the client, which initiated the RTSP connection in the first place. The following protocol methods are implemented: OPTIONS, DESCRIBE, GET_PARAMETER, SETUP, PLAY and TEARDOWN.  
Once the connection parameters are negotiated, the video data stream is started by sending the PLAY command from the application. It is worth noting that video data are encoded by either H264 or MJPEG encoding algorithms and this process is done by the IP camera before the transmission of the data to server application. Depending on the desired transport protocol for video data transfer (TCP or UDP), the stream can be transferred over the same TCP connection on which RTSP was initiated or it can be transferred over a UDP port, which needs to be negotiated in the RTSP initialization procedure.  
The application node is organized in two threads as can be seen in Figure 5. One thread is responsible for parsing the RTSP protocol while another thread is used for manipulation of video data. Once the stream commences, the data packets sent from the camera are accepted by the application and are reorganized into chunks of 128, 256 or 512 Bytes (Step 1), depending on data length encryption settings. At this point, a timestamp is taken from the real-time operating system (Step 2) for appending to the data after encryption is finished (Step 5). Fixed size chunks are then transferred to the FPGA application node which is responsible for encrypting the data (Step 3). When encryption is finished, data is read from the FPGA (Step 4) and forwarded to the TCP or UDP port (Step 6).  
Figure 4: Timestamp sender application.