esa-sceva/satcom-chunk-collection huggingface dataset:
# A Digital Twin-Based State Monitoring Method of Gear Test Bench
## 7 Conclusions  
In this paper, a digital twin-based state monitoring method of the gear test bench is proposed, and the state monitoring system of the gear test bench based on a digital twin model is constructed. By comparing the test and simulation results, the correctness and practicability of the proposed state monitoring system is verified, which really realized the state monitoring of the gear test bench running process based on a digital twin model. The main conclusions are as follows:  
In this paper, digital twin technology is first applied to timely and effectively monitor the running state of the gear test bench, which makes the digital condition monitoring of the gear test bench possible, as well as improving the equipment monitoring efficiency and reducing the running monitoring cost.  
Normal operation data of the gear test bench can be accurately and timely collected by the proposed state monitoring system. At the same time, the running law of the gear test bench can be reflected by the analysis and processing of the collected data, and the state monitoring for the gear test bench operation is achieved.  
The temperature and noise variation trends of the gear test bench equipment operation are basically identical to that of the proposed state monitoring system simulation. The temperature and noise of the gearbox gradually increase from the beginning of the test. When the temperature reaches about 75 \({}^{\circ}\)C, the noise reaches about 82 dB, the running state of the gearbox appears abnormal, and the tooth broken phenomenon occurs.  
The vibration variation trends of the gear test bench equipment operation are basically identical to that of the proposed state monitoring system simulation. Under the normal running state with fixed load and speed, the vibration time domain diagram of the equipment is a stable waveform from the beginning to the tooth broken. The vibration frequency domain diagram is approximately a normal distribution, which reaches its peak at the 3 rd frequency doubling and then gradually declines.  
The proposed state monitoring system provides a new idea and reliable solution for the dynamic monitoring, further fault diagnosis, and performance prediction of the gear test bench.  
Conceptualization, J.L.; investigation, S.W. and J.L.; methodology, S.W. and J.L.; software, S.W.; validation, H.Z. ([PERSON]), H.Z. ([PERSON]), and S.W.; writing--original draft preparation, S.W.; writing--review and editing, J.Y. and J.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant nos. 51405135, 52275054).  
Not applicable.  
The authors would like to thank all the authors of the references that gave us inspiration and help. The authors are grateful to the editors and anonymous reviewers for their valuable comments that improved the quality of this paper.  
The authors declare no conflict of interest.
# Evaluation of Green Strategies for Prolonging the Lifespan of Linear Wireless Sensor Networks
## 7 Discussion
#### 7.1.4 Environmental Constraints and Fault Tolerance  
Linear WSN monitoring applications often operate in harsh and environmentally challenging conditions, where factors such as terrain, weather, and interference can impact a network's performance and energy efficiency. Energy-saving techniques that rely on idealized assumptions about network topology and environmental conditions may fail to account for the real-world constraints encountered in LWSNs [30].  
The linear topology inherent in LWSNs makes them more vulnerable to data delivery failures compared to traditional WSNs [27]. In LWSNs, a single node failure has the potential to disrupt the entire communication process, representing a significant weakness. Node failures can occur due to various factors, such as battery depletion, hardware malfunctions, or external damage. Additionally, the occurrence of consecutive faulty nodes can create gaps in the network, leading to the fragmentation of the LWSN into multiple disjointed segments [28]. Moreover, the failure of nodes located closer to the sink node, especially if they are overloaded, can expedite the depletion of the network's energy resources, thereby shortening its operational lifespan. Such failures can have severe repercussions, emphasizing the critical importance of robust MAC and network layer protocols in LWSNs [26]. These protocols must be equipped to handle node failures effectively, as there are limited alternative routing options available, particularly towards the sink node.
# Handover-Driven Interference Management for Co-Channel Deployment of Femto- and Macro-Cells
## 4 The HO-FIM Architecture
### Overview of HO-FIM
#### 4.1.3 Radio Resource Controller  
This controller schedules the frequency and handles time and power resources to meet the users' target SINRs. It is assumed that the allocation of frequency (resource block, RB) and time resources is done by conventional schedulers. It is also assumed that the target SINR of a user is given on a per-RB basis. Transmit power is controlled to meet the per-RB target SINR.
# Multistage Quality Prediction Using Neural Networks in Discrete Manufacturing Systems
## 2 Materials and Methods  
### Data Acquisition  
The data collected concerned the production line of the water dispenser of Pray Qixi Intelligent Technology Co., Ltd., (Ningbo, China). Through the production of each testing stage of the interface transmission detection and data collection processes, the collected data stored on the server could be viewed as real-time data of the product ID on a mobile terminal.  
Each subset of the sampling data that was attained by the quality inspector is the same manufacturer's batch of products according to a certain proportion of the function of the conformity check. Usually, taking the sample data and sampling them further as the batch of parts' quality data in order to prevent the generation of quality differences can increase the sampling time required to increase the reliability of detection. Semi-finished product testing, according to a certain degree of product assembly after testing a step water dispenser product, can be divided into electrical testing and water testing. Electrical testing can be performed using a series of measurement tools and practices, such as oscilloscopes, specific data testing, water testing using a flow sensor, temperature sensors, and determining the relationship between the specific values of the time of detection. A water dispenser can be tested via the hot-and-cold shock test, the fatigue test, and, finally, the smoke test, with the third listed item serving as the final test link to ensure the product is of excellent quality when in the hands of consumers.  
The processes for each detection stage are displayed in Figure 1, the information for which was obtained by navigating the gateway data of the water dispenser manufactured by QIXI Intelligent Technology Co., Ltd., (Ningbo, China). The quality level was assessed with 500 drinking fountains manufactured continuously on the same production line, and the quality divergence points were determined by extracting the semi-finished product stage data from the supply chain and manufacturing execution system of the production line. Generally speaking, the LSTM model is only capable of reflecting changeable time-series dynamic characteristics; it cannot extract quality features. Fuzzy theory is used to extract the quality features at this stage. Since the export of real-time data begins with a starting file that is primarily written in JSON format, we must process each variable to ease the process for a later input model. Once the data had been traversed, we tested the semi-finished water dispenser, including with respect to power, current (using the PCA Current test), voltage (using the PCA Voltage test), flow output, and leakage current. Missing defaults were assessed using the interpolation method for completion. There are many reasons for missing data, which may be caused by human or sensor failure, and the specific methods can be found in [27].
## 3 Experimental Results
### Image Pixel Correlation
Here, we select the horizontal, vertical, diagonal, and anti-diagonal directions as examples and demonstrate them in Figures 6 and 7. By observing the distribution of neighboring pixels in the original and encrypted images, we can see significant differences. In the original image, neighboring pixels exhibit a clear linear correlation, as illustrated in Figure 6. In contrast, as shown in Figure 7, the distribution of neighboring pixels in the encrypted image is relatively uniform, indicating a lower correlation among them within the coordinate system. It is concluded that the encryption function works well in our proposed scheme. The results presented in Figure 6 indicate that the neighboring pixels in the four directions of the original image are linearly correlated, reflecting the characteristic that adjacent pixels show a strong linear correlation. In contrast, the neighboring pixels in the four directions of the encrypted image, shown in Figure 7, are distributed more uniformly across the coordinate system. The findings in Figures 6 and 7 confirm that the image encryption operations outlined in Section 2.1 are effective. Figure 6: The pixel correlation of the original image ‘Peppers’: (**a**) horizontal direction; (**b**) vertical direction; (**c**) diagonal direction; (**d**) anti-diagonal direction.
# Faults and Diagnosis Methods of Permanent Magnet Synchronous Motors: A Review
## 3 Fault Diagnosis Methods
### Signal Processing Methods
#### 3.2.3 Wavelet Transform  
Wavelet transform (WT) includes discrete wavelet transform (DWT), continuous wavelet transform (CWT) and so on. Its process is to generate wavelet coefficients by convolving the input signal \(f(t)\) with the mother wavelet function \(\psi(t)\)[69], which is the window function. The scaling factor \(a\) and displacement factor \(b\) of the mother wavelet function are related to frequency and time, respectively. Thus, its expression is as follows:  
\[W_{\psi}f(a,b)=\int f(t)\overline{|a|^{-\frac{1}{2}}\psi(\frac{t-b}{a})}dt \tag{10}\]When is compared with STFT, the window size \(a\) and \(b\) of WT can vary with the frequency. Therefore, with WT, the signal can be decomposed into signals at different frequency bands with different resolutions, which makes it suitable for accurate and flexible fault detection of PMSM [9]. [PERSON] et al. have evaluated the degree of demagnetization of PMSM by calculating the energy in the detail of DWT [61]. And they also focused on mechanical faults [70]. [PERSON] et al. used the method based on WT to identify the short circuit winding phase in PMSM [71].  
However, wavelet analysis cannot further decompose the high-frequency bands of the signal, but the fault characteristics of motors often appear at high frequencies. In order to overcome this shortcoming, wavelet packet transform (WPT) [72] was proposed. [PERSON] et al. have used the wavelet packet band energy analysis method to extract the characteristics of the PMSM inter-turn short circuit fault [73], which is to accurately divide the frequency band of the current signal by WPT, and consider the signal energy of the appropriate frequency bands as the feature vector of the fault. A suitable mother wavelet function will make the WT more sensitive to transient shocks, which is also a limit: the results of the analysis are strongly related to the choice of the mother wavelet function. Besides, like STFT, it is also impossible to have high accuracy in both time and frequency at a certain frequency band.
# LoRaWAN Physical Layer-Based Attacks and Countermeasures, A Review
## 2 LoRaWAN
### Adaptive Data Rate  
With the adaptive-data-rate (ADR) mechanism, a LoRaWAN network is able to optimize the transmission power, air time and data rates of the devices. This optimization step directly translates into a longer device battery lifetime, higher data throughput and also, potentially, into lower latency.  
The ADR is based on three main components: a collection of RF-link statistics, MAC commands and an ADR algorithm.  
* Collection of RF-link statistics: The algorithm receives the RF link information in SNR values. After a sufficient number of SNR values is collected, a margin to the minimum SNR thresholds of the different spreading-factor settings is calculated. Depending on the margin, the network server can request that a device switch the spreading factor and the transmission power settings accordingly.
* MAC Commands: LoRaWAN defines a few MAC command packets that control the ADR. With the 1-bit **ADR** uplink packet, a device can request that the network server control its data rate. The 1-bit **ADRAckReq** uplink packet is periodically sent by devices to request that the network server validate the received uplink messages. Depending on the network servers response to this packet, the end device can optimize its spreading factor and transmission power to find the optimal communication parameters.
* ADR Algorithm: The 4 bytes long **LinkADRReq** downlink packet contains the parameters determined by the ADR algorithm, which are communicated to the device from the network server. A more comprehensive overview of the algorithm can be found, e.g., from [40].  
Typically, ADR is recommended for devices with fixed locations, so that RF-link-quality information can be collected reliably. For mobile devices, additional location sensing (e.g., via GPS localization) is recommended to determine time frames during which the device remains in a fixed location.
# Super Twisted Sliding Mode Observer for Enhancing Ventilation Drive Performance
## 2 System Modelling
### Super-Twisted Slide Mode Observer (ST-SMO) Speed Estimator
#### 2.2.2 Current Observer  
The sliding surface in the SMO is defined by the state trajectory, which consistently adheres to the switching surface \(S=0\). A similar control may verify that the state trajectory remains on the sliding surface by ensuring that \(pS=S=0\).  
The surface of the SMO is defined as  
\[S_{n}\ =\ \big{[}S_{\alpha}\ S_{\beta}\big{]}^{T}, \tag{14}\]  
\[S_{\alpha}\ =\ \tilde{i}_{\alpha}\ =\ \big{(}i_{\alpha}-\hat{i}_{\alpha}\big{)}, \tag{15}\]  
\[S_{\beta}\ =\ \tilde{i}_{\beta}\ =\ \big{(}i_{\beta}-\hat{i}_{\beta}\big{)}, \tag{16}\]  
where \(i_{\alpha}\) and \(i_{\beta}\) are the measured stator current components, while \(\hat{i}_{\alpha}\) and \(\hat{i}_{\beta}\) are their estimated counterparts. The estimated currents congregate to the actual currents (\(i_{\alpha}=\hat{i}_{\alpha}\) and \(i_{\beta}=\hat{i}_{\beta}\)) as the error trajectories reach the sliding surface (\(S_{n}=0\)).  
\[\hat{i}_{s}^{\alpha}\ =\ \frac{1}{\sigma L_{s}}v_{s}^{\alpha}-\frac{R_{s}}{ \sigma L_{s}}i_{s}^{\alpha}+\frac{L_{m}}{\sigma L_{s}L_{r}}\Psi_{r}^{\alpha}, \tag{17}\]  
\[\hat{i}_{s}^{\alpha}\ =\ \frac{1}{\sigma L_{s}}v_{s}^{\beta}-\frac{R_{s}}{ \sigma L_{s}}i_{s}^{\beta}+\frac{L_{m}}{\sigma L_{s}L_{r}}\Psi_{r}^{\beta}, \tag{18}\]  
\[\Psi_{r}^{\alpha}\ =\ -a_{1}|S_{\alpha}|^{\frac{1}{2}}sign(S_{\alpha})+\Delta_{1} -a_{2}\!\int sign(S_{\alpha}), \tag{19}\]  
\[\Psi_{r}^{\beta}\ =\ -a_{3}|S_{\beta}|^{\frac{1}{2}}sign(S_{\beta})+\Delta_{1} -a_{4}\!\int sign\big{(}S_{\beta}\big{)}, \tag{20}\]  
where \(a_{j}>0\) (j = 1, 2, 3, 4) represent the SM control law gains. These gains improve the observer's robustness by mitigating chattering. These adjustments significantly enhance the observer's robustness and adaptability. However, higher gain values can exacerbate chattering.  
The discontinuous time derivative functions, represented by the first terms of the control law, make \(a_{1}\) and \(a_{3}\) gains more critical than \(a_{2}\) and \(a_{4}\). Among all gains, \(a_{1}\) is more effective in enhancing dynamic performance and reducing chattering. However, the accuracy of estimates depends on the gain \(a_{2}\)[28].
## 3 Simulation and Analysis
### Summary  
Analysis of the simulation results across the four scenarios as shown in Table 2 reveals that the self-balancing control system, operating at a speed of 72 km/h, maintains a tracking error of the desired trajectory within 0.6 m in both the double lane change and curved road scenarios, while keeping a distance from the lane edge of more than 1 m. In the low-adhesion scenario, the self-balancing control system alone could not negotiate a corner at 72 km/h; however, combining the pre-braking control system with the self-balancing control system significantly improved motorcycle maneuverability. In scenarios involving the lateral tilting of the driver's upper body, the controller maintained stable motorcycle passage at the desired speed, confining the absolute value of the maximum lateral deviation to 0.15 m.  
Figure 11: Results on slippery curved road (\(R=60\) m, \(\mu=0.3\)).  
Figure 12: Phantom on slippery curved road (\(\mu=0.3\)).
# Tamper and Clone-Resistant Authentication Scheme for Medical Image Systems
## 2 Background Motivation and State of the Art
### Unclonable Medical Image Transmission System  
In [6], PUF was proposed to provide the Medical Image System (MIS) with a device-intrinsic electronic fingerprint. Here, each medical image device/generator has PUF. Figure 2 illustrates the designed MIS in [6].  
Figure 2: Medical image system deploying the physical unclonable function (PUF) together with an encryption algorithm and RSA system. Adapted from [6].  
In particular, PUF generates a secret key \(K\) for an encryption algorithm. The generated secret key \(K\) is utilized to encrypt the original image. Here, the RSA system as an asymmetric algorithm protects the generated secret key \(K\) and generates a digital envelope as an encrypted \(K\) by the RSA-public key of the receiver. A medical image device/generator as a sender transmits the resulting encrypted image together with the digital envelope to the receiver side. On the receiver side, the receiver recovers the secret key \(K\) from the digital envelope by using its RSA-secret key. Then, the receiver uses \(K\) to decrypt the received encrypted image.  
Similar to the Pretty Good Privacy (PGP) mechanism for data communication [25], the proposed MIS in Figure 2 provides cryptographic privacy and authentication for digital medical images. The only difference between them is that the proposed MIS utilizes a PUF to generate a secret key \(K\) instead of a pseudorandom number generator in the case of PGP. Furthermore, the proposed MIS is a computationally intensive mechanism, relatively slow, and requires a certificate authority to manage the RSA public- and private- keys. On the other hand, several research efforts were published on PUFs in the last two decades such as ring oscillator PUFs [26], TERO-PUF [27], arbiter PUFs [28], Chaos-based PUF [29], etc. Unfortunately, the noisy and inconsistent responses together with a limited number of PUF- challenge-response pairs are considered as the main PUF vulnerabilities [7]. Any attempt to counteract such vulnerabilities makes the PUF implementation more expensive and complicated.  
To overcome such weaknesses of MIS, a clone-resistant watermarking technique is proposed for medical images based on SUC defined in [8; 30]. SUC is highly consistent and provides each electronic device in the MIS with a clone-resistant unpredictable unique digital signature. The proposed technique combines a watermarking algorithm and a physically clone-resistant identity to generate a clone-resistant watermarking system for medical images. This work introduces a new approach towards constructing a clone-resistant watermarking.
## 4 Literature Review: Other Research on CWSN
### Energy Efficiency
#### 4.2.5 Energy Harvesting  
RF energy harvesting enables the wireless sensor node to operate with a potentially perpetual lifetime. [PERSON] _et al._[84] examined an optimal mode selection policy for CR-WSNs powered by RF energy harvesting. Assuming that the wireless sensor node harvests the RF energy received from the primary network, wireless sensor nodes can take advantage of the spectrum occupancy of the primary network for both idle and occupied states.  
Nevertheless, CR wireless sensors are energy constraints, and the network lifetime basically depends on energy. Most of the energy efficient schemes focus on energy efficiency in a specific operation, such as channel sensing or data transmission, _etc._ To increase the energy efficiency, energy conservation on several aspects of network operation should be considered, including channel sensing scheme, clustering and topology management algorithm, routing algorithm, MAC protocols, channel selection and data reception, _etc._
## 2 Related Works
### Image Processing for Glint Suppression  
Because of the variable trajectory and view of UAVs, inevitably, there are glint areas with high pixel intensity values in maritime UAV images, which can significantly influence the visual saliency of objects.  
Polarizers, the devices conventionally employed for image sensing, can suppress sea surface sun glint noise but can be significantly influenced by the wind speed, wind direction, and solar zenith angle [49]. Moreover, the application of polarizers is generally based on imagery time-domain polarization characteristics [50] and sensor information [51]. Research on glint suppression based on deep learning originates from image restoration, which is used to refine the poor image quality caused by improper exposure of the primary objects in the image; the drawback of this process is the introduction of noise, which can reduce the signal-to-noise ratio and weaken the features and saliency of objects. Therefore, employing image restoration for denoising is not appropriate for tiny human detection in maritime scenarios.  
Despite sun glint and objects in maritime scenarios having high pixel intensity values, based on the difference in their spatial distribution and color characteristics, HDetect-VS can suppress the saliency of sun glint and enhance that of objects.
## 3 Analysis and Results
### Analysis of Coupled Dynamic Model
Figure 2: The results of the unidirectional coupled Henon model using CBTN and CPE for coupling analysis, respectively, at different coupling strengths \(C=0.1,0.3,0.5\) and when the sliding time window width is varied in steps of 200 samples within [200, 5000]. The values of the ordinate are \(H_{1}^{\text{A1-A2}^{surrogate}}-H_{1}^{\text{A1-A2}}\). \(x_{2}^{surrogate}\) can be obtained by surrogating \(x_{2}\) using the iAAFT method. **(a)** The results of coupling analysis using CBTN (30 repeated calculations); **(b)** The results of coupling analysis using CPE, \(d=5\), \(\tau=2\) (30 repeated calculations). coupling strength levels. In the figure, the dashed boxes indicated by the arrows are partial zooms of the analysis results. As can be seen from Figure 4, the greatest difference between the analytical results of CBTN and CPE was mainly in the part where the coupling strength was less than 0.2. In this part, the CBTN method gives correct analysis results, while the CPE calculation results are greater than the values under the random coupling state, which is inconsistent with the theory. The possible reason is the CPE method that is based on the probability distribution statistics of symbols cannot distinguish the interactions between time series under weak coupling conditions. In contrast, the proposed CBTN method has good detection capability of interactions between time series with a weak coupling strength.