esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methodology
### Dataset  
In the field of pothole detection, it is of the utmost importance to use datasets that closely simulate real-world scenarios. This typically involves a camera mounted on a moving vehicle that captures images while on the road in adverse weather conditions. Unfortunately, the existing datasets for pothole recognition fall short of replicating these conditions. To tackle this issue, we created a database that is accessible in [47].  
Our image data collection showcases a wide range of potholes, including those that pose significant road hazards. The images with a resolution of 1920 \(\times\) 1080 were captured from a single stretch of road located in an industrial area of a city, known for its inadequate road conditions, over a period of three months (May, June, and July). This timeline allows for the capture of the evolving surroundings, such as the presence of pedestrians, passing or stationary vehicles, and changes in road conditions. Each image is stamped with an exact date and time of capture.  
The dataset also includes images of manhole covers, which can pose a challenge to the model as they can be easily mistaken for potholes due to their circular shape. This step helps to improve the generalization capability of the computer vision model and reduce the likelihood of false positives. The annotated collection of 1052 photos was captured during clear weather conditions, but the dataset also covers four unfavourable weather conditions: rain, sunset, evening, and night. These conditions pose a challenge to the detection models; thus, the availability of a realistic dataset is crucial for the development of robust and reliable pothole detection systems. Figure 3 provides a visual representation of the real-world scenarios, while Table 5 summarizes the statistics of the dataset. In addition to the number of images in each subset, statistics include the number of occurrences of potholes and manhole covers in each of the two categories. For training purposes, the clear dataset was split into training, test, and validation subsets in a 70:15:15 ratio. The data from the remaining adverse conditions were then used for testing only.
# Review on Free-Space Optical Communications for Delay and Disruption Tolerant Networks
## 3 Delay-/Disruption-Tolerant Networking
### Licklider Transmission Protocol (LTP)  
The Licklider Transmission Protocol (LTP) was first proposed by NASA for reliable data delivery in challenging space environments, as part of DTN [41]. This protocol was motivated from the need of a retransmission-based protocol that would be reliable over \"links characterized by extremely long round-trip times (RTTs) and/or frequent interruptions in connectivity\" [42]. It should be used as Convergence Layer of choice in space links, immediately below the Bundle Protocol.  
The LTP is applied on DTN, and it supports long-haul transmissions. The bundles are aggregated in LTP blocks, which are split into LTP segments. The blocks are divided in green and red parts, where the green part is unreliably transmitted, and the red transmission is reliable, i.e., the red part requires acknowledgements to ensure its reception [35].  
Figure 3: Scheme of the DTN architecture (adapted from Reference [36]). The BP overlays the Transport Protocol, interfacing through a CLA.
## 1 Introduction
While Fe-based systems tend to be more effective in acidic conditions, Cu-based systems maintain high catalytic activity in neutral and even alkaline environments. This versatility makes Cu a more suitable choice for the various environmental conditions encountered in real-world applications. In addition, Cu exhibits lower toxicity and environmental risk compared to other transition metals such as Co. Although Fe is less toxic and more abundant, the efficiency of Cu in activating PMS often exceeds that of Fe, particularly in generating a higher reactive oxygen species (ROS) yield. In addition, the lower cost of Cu and its effectiveness in removing organic compounds and disinfecting pathogens present an economical and safe alternative, especially when considering the potential risks associated with Fe-based systems, such as the formation of iron sludge. Recent advances have also focused on improving the performance of Cu-based catalysts by developing heterogeneous systems, which further mitigate the problems related to metal leaching and secondary contamination. These innovations have made Cu not only a practical but also an environmentally responsible option for PMS activation. Different forms of heterogeneous Cu-based catalysts have been widely investigated thus far, encompassing zero-valent copper (ZVCu) [23], copper oxides [24; 25], and magnetic copper ferrite [26]. Table 1 summarizes the removal performance of emerging contaminants through the catalytic activation of PMS by ZVCu, copper oxide, and copper ferrite. In addition to these heterogeneous Cu-based catalysts, other catalysts with more unique and specific properties have also been developed, and they are referred to as Cu-based designed catalyst materials. Advances in the design of Cu-based catalysts, such as the development of core-shell structures, layered double hydroxides (LDHs), and metal-organic frameworks (MOFs), have shown promise in enhancing the stability of the catalyst and preventing copper leaching. These materials are engineered to maintain their catalytic activity while minimizing dissolution into the water, thereby mitigating secondary pollution. All these mentioned catalysts are explained in more detail in the respective subsections of Section 2. Therefore, this review intends to provide the most recent overview of multiple Cu-based catalysts for activating PMS. Moreover, methods used to assess the activation of PMS with copper-based catalysts are outlined. Afterwards, diverse variables influencing reactivity are examined. Then, the concise introduction of combining Cu-based catalyst/PMS systems with other sophisticated oxidation approaches is provided. The degradation mechanism of the Cu-based catalyst-activated PMS system is summarized and discussed. Finally, we present conclusions and future outlooks.
###### Abstract  
In underwater acoustic communication (UAC) systems, the channel characteristics are mainly affected by spatiotemporal changes, which are specifically manifested by two factors: the effects of refraction and scattering caused by seawater layered media on the sound field and the random fluctuations from the sea floor and surface. Due to the time-varying and space-varying characteristics of a channel, the communication signals have significant variations in time and space. Furthermore, the signal shows frequency-selective fading in the frequency domain and signal waveform distortion in the time domain, which seriously affect the performance of a UAC system. Techniques such as error correction coding or space diversity are usually adopted by UAC systems to neutralize or eliminate the effects of deep fading and signal distortion, which results in a significant waste of limited communication resources. From the perspective of the sound field, this study used experimental data to analyze the spatiotemporal fluctuation characteristics of the signal and noise fields and then summarized the temporal and spatial variation rules. The influence of the system then guided the parameter configuration and network protocol optimization of the underwater acoustic communication system by reasonably selecting the communication signal parameters, such as frequency, bandwidth, equipment deployment depth, and horizontal distance.  
Keywords:underwater acoustic communication; channel characteristics; spatiotemporal fluctuation +
Footnote †: journal: Sensors
# Fault Diagnosis Method for Aircraft EHA Based on FCNN and MSPSO Hyperparameter Optimization
## 4 Fault Diagnosis Algorithm Based on MSPSO and FCNN
### The Process of MSPSO-FCNN Fault Diagnosis Method  
The process of proposed MSPSO-FCNN fault diagnosis method for aircraft EHA fault diagnosis is as follows:  
First, obtain the EHA failure sample dataset and encode the original one-dimensional data signals as two-dimensional feature images by GADF. After that, the data signals and feature images are input into 1 DCNN and 2 DCNN of the FCNN model, respectively. Then their features are extracted by multiple convolutional and pooling layers. These features are stretched into feature vectors by a fully connected layer and subsequently fused in the convergence layer and sent to the Softmax layer for classification after the fully connected layer. The accuracy of the fault classification will be used as the output of the MSPSO-FCNN model.  
The position information of each particle in MSPSO will represent a combination of hyperparameters in FCNN, and the hyperparameters in FCNN will be set according to the decoding results of the particle position information. The fault classification accuracy  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Particle** & **Hyperparameters** & **Particle** & **Hyperparameters** \\ \hline \(x_{1}\) & Filter quantity of \(C_{2}^{2}\) & \(x_{11}\) & Activation function of \(C_{2}^{2}\), \(C_{2}^{2}\), \(C_{3}^{2}\) \\ \(x_{2}\) & Filter quantity of \(C_{2}^{2}\) & \(x_{12}\) & Activation function of \(C_{1}^{2}\), \(C_{1}^{2}\) \\ \(x_{3}\) & Filter quantity of \(C_{3}^{2}\) & \(x_{13}\) & Activation function of \(F^{1}\), \(F^{2}\) \\ \(x_{4}\) & Filter quantity of \(C_{1}^{1}\) & \(x_{14}\) & Size of \(F\) \\ \(x_{5}\) & Filter quantity of \(C_{2}^{1}\) & \(x_{15}\) & Activation function of \(F\) \\ \(x_{6}\) & Filter size of \(C_{2}^{2}\) & \(x_{16}\) & Optimizer \\ \(x_{7}\) & Filter size of \(C_{2}^{2}\) & \(x_{17}\) & Learning rate \\ \(x_{8}\) & Filter size of \(C_{3}^{2}\) & \(x_{18}\) & Dropout \\ \(x_{9}\) & Filter size of \(C_{1}^{1}\) & \(x_{19}\) & Batchsize \\ \(x_{10}\) & Filter size of \(C_{2}^{1}\) & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hyperparameters of FCNN.  
output from FCNN will be an input to MSPSO as the adaptation value of corresponding particles. MSPSO will get a new particle population after an iteration, reset the hyperparameters in FCNN, and constantly circulate until the optimal hyperparameter combination is obtained.  
Finally, the data in the test set are input to the completed training model for fault diagnosis and output the fault diagnosis results. The specific flow is shown in Figure 8.
## 2 Methods
### SCGH Generation by PC Method  
The process of the SCGH generation by PC method consists of two steps: wavefront recording and phase compensation. In the first step, the diffraction distribution from the object plane to the WRP is recorded. Here, we take the angular spectrum method with FFT to calculate the diffraction distribution. The complex amplitude distribution \(u_{w}(x_{w},y_{w},z_{2})\) of the WRP is expressed as  
\[u_{w}(x_{w},y_{w},z_{2})=F^{-1}\{F\{u_{o}(x_{o},y_{o},z_{1})\}\cdot\exp[j\frac {2\pi}{\lambda}z\sqrt{1-(\lambda f_{x_{o}})^{2}-(\lambda f_{y_{o}})^{2}}]\}, \tag{1}\]  
where \(u_{o}(x_{o},y_{o},z_{1})\) denotes the complex amplitude distribution of object plane; \(z=z_{2}-z_{1}\) shows the propagation distance between object plane and WRP; \((f_{x_{0}},f_{y_{o}})\) is the spacial frequency of the object plane in the directions of x and y axis; \(\lambda\) is the wavelength; and \(F\{\cdot\}\) and \(F^{-1}\{\cdot\}\) are the Fourier and inverse Fourier operators, respectively.  
In the second step, the phase difference caused by the optical path difference between WRP and SCGH is compensated to generate the SCGH, and the schematics of the generation process by PC method are shown in Figure 1c,d.  
The phase difference is compensated point-to-point between the WRP and SCGH. The point \(G\) on the SCGH is the projection of point \(F\) in the direction of \(z\) axis. Note that each point on the WRP should be compensated to generate the SCGH, which means the sampling number of SCGH should be consistent with that of WRP. Based on this principle and the geometric relationship shown in Figure 1, the coordinate relationship between points on WRP and the corresponding projected points on SCGH can be given by  
\[x_{h}=x_{w},\quad y_{h}=y_{w},\quad z_{h}=R\cos\theta_{h}\cos\varphi_{h}, \tag{2}\]  
where  
\[\theta_{h}=\arcsin\left(\frac{x_{h}}{R\cos\varphi_{h}}\right),\quad\varphi_{h }\quad=\arcsin\left(\frac{y_{h}}{R}\right). \tag{3}\]  
The optical path difference between WRP and SCGH can be described as  
\[d_{PC}=z_{h}-z_{2}. \tag{4}\]  
Moreover, the complex amplitude of SCGH can be expressed by  
\[u_{h}(x_{h},y_{h},z_{h})=u_{w}(x_{w},y_{w},z_{2})\cdot\exp(-jkd_{PC}). \tag{5}\]  
Note that the PC method described above is a fast method to approximately calculate the complex amplitude of SCGH in the propagation model between plane and sphere. It compensates the phase difference without the amplitude attenuation. Because of the limited curvature of the SCGH, the distance between WRP and SCGH is relatively small. Therefore, it can be considered that the phase difference is caused entirely by the geometric optical path discrepancy and the amplitude is nearly constant. The error between the PC method and the real propagation situation will be discussed in detail in Section 2.4 and simulated in Section 3.1.3.
## 3 Typical Weight Models
### Elevation Model  
As with a typical elevation model, one simply assigns a weight according to the satellite elevation angle \(\varepsilon\) by taking a certain sinusoidal function as in Equation (1). In this equation, the variance decreases as the elevation angle increases. In an extended Kalman filter (EKF), a diagonal matrix composed of those \(\sigma_{i}^{2}\) is used as a measurement noise matrix [7].  
\[\sigma_{i}^{2}=\frac{1}{\text{sin}^{2}(\varepsilon_{i})} \tag{1}\]  
An advantage of this model is that it is simple; however, it cannot consider irregular situations where SNR measurements are low, even for high-elevation satellites, owing to multipath interference.
## 5 Case Study of Combined Positioning in Tunnels
### Analysis of Longitudinal Positioning Errors  
For the longitudinal positioning errors inside the tunnel, due to the lack of reference points for dynamic positioning evaluation within the tunnel, the longitudinal accuracy at the 9.2 km tunnel entrance based on RTK positioning results is used to infer the longitudinal accuracy over the 4.6 km tunnel length. Similarly, an analysis was conducted using the experimental results in the reverse direction. When the vehicle first obtains a fixed RTK solution upon exiting the tunnel, a comparison between the positioning points extended via the tunnel's positioning method and the RTK positioning points is shown in Figure 10. At this point, the total horizontal positioning error is 1.43 m, and after subtracting the lateral positioning error of that point (0.86 m), the longitudinal positioning error is calculated to be 1.14 m. Based on the divergence characteristics of INS based on navigation estimation sensors and the cumulative nature of odometer errors with distance, it is known that the positioning error diverges faster with increasing driving distance, and the divergence speed in the latter half of the journey is significantly faster than that in the first half, as indicated in Figure 12, which also shows a similar phenomenon in the lateral positioning results. Therefore, it can be conservatively estimated that the longitudinal positioning error at the 4.6 km mark of the tunnel is no greater than 0.6 m. It should be noted that this longitudinal  
Figure 12: Lateral error of GNSS/INS/OD-integrated positioning with lane constraints in a 9.2 km tunnel.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Statistical Items** & **Numerical Value** \\ \hline Number of data points (200 Hz) & 61,001 \\ Arithmetic mean of errors & 0.013 m \\ Mean absolute error & 0.294 m \\ Root mean square error & 0.332 m \\ Maximum error value & 0.671 m \\ Minimum error value & \(-\)0.865 m \\
95 th percentile of absolute error & 0.612 m \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistical analysis of lateral positioning errors in a 4.6 km midpoint tunnel.  
error is only an inferred value based on the results at the tunnel exit, representing a general accuracy level. The positioning errors at different positions within the tunnel may still fluctuate due to various measurement errors, as illustrated in Figure 12.
## 6 Connection-Aware Scheduling Heuristics
### RelBPA  
BPA [7] is a scheduling algorithm that considers the jobs loaded onto devices and the present hardware capabilities to select the best device to execute an incoming task. The ranking formula for BPA is defined as follows:  
\[BPA=\frac{\Sigma OP\_jobs}{flops\cdot Battery} \tag{4}\]  
where \(OP\_jobs\) is the current job load in a device's queued jobs in terms of how many operations are needed to finish those jobs; \(flops\) is the device capability (processor speed) measured in floating-point operations per second; and \(Battery\) is the remaining battery of the device expressed in values between the range of \(0<Battery\leq 1\)[7].  
The reliability batch processing algorithm (RelBPA) is an adaptation of BPA that incorporates the reliability score introduced in Section 6.1. In the same way as ReleSEAS, the purpose of adding the reliability score is to boost devices that have been connected to the network for more extended periods; the reliability score is added to the denominator to make the metric as small as possible. The ranking formula for RelBPA is defined as follows:  
\[RelBPA=\frac{\Sigma OP\_jobs}{flops\cdot Battery\cdot Reliability} \tag{5}\]  
where the variables used in the formula have the same meaning as in BPA, and the reliability score is calculated using Equation (1).
###### Abstract  
To accurately detect and monitor the internal temperature of an operating power transformer, the distributed optical fiber sensor (DOFS) was creatively applied inside an oil-immersed 35 kV transformer through high integration with the winding wire. On the former basis, the power transformer prototype with a completely global internal temperature sensing capability was successfully developed and it was also qualified for power grid operation through the ex-factory type tests. The internal spatially continuous temperature distribution of the operating transformer was then revealed through a heat-run test and the numerical simulation was also applied for further analysis. Hotspots of windings were continuously located and monitored (emerging at about 89%/90% height of low/high voltage winding), which were furtherly compared with the IEC calculation results. This new nondestructive internal sensing method shows a broad application prospect in the electrical equipment field. Also, the revelation of transformer internal distributed temperature can offer a solid reference for both researchers and field operation staff.  
condition monitoring; distributed optical fiber sensing; hotspot location; power transformer; Raman scattering; temperature detection +
Footnote †: journal: Applied Sciences
## 5 Conclusions  
Aiming at the characteristics of resource periodicity in massive MIMO system and bandwidth allocation without comprehensive consideration of user service QoS and channel state information, resulting in poor user satisfaction and low bandwidth utilization, an adaptive bandwidth allocation method based on user service is proposed. The method is mainly divided into the following steps. Firstly, dynamic priorities are created for different users according to factors, such as user service priority, channel state information, and continuous periodicity of allocation, and user scheduling is performed according to the priority. Then, the minimum guaranteed time slot is set for the selected users, and the objective function is established. Finally, the improved bald eagle algorithm is used to solve the problem. The simulation results show that the algorithm proposed in this paper has improved system utility value, bandwidth utilization, system throughput, and user satisfaction by 23.70%, 4.22%, 6.55%, and 4.28%, respectively, compared to the algorithms proposed in other literature. The algorithm in this article can improve the utility value, bandwidth utilization, and throughput to a certain extent while ensuring user QoS requirements and improving user satisfaction.  
This article only focuses on the allocation of bandwidth resources in massive MIMO systems. In the future, multiple resources will be jointly allocated, such as bandwidth, power, number of antennas, etc., to optimize them from both user and system perspectives.  
Conceptualization, Q.L. and R.L.; methodology, R.L. and Y.L.; software, R.L.; validation, Y.L., P.W. and J.S.; formal analysis, R.L.; investigation, R.L. and P.W.; resources, J.S.; data curation, R.L.; writing--original draft preparation, R.L.; writing--review and editing, R.L.; visualization, R.L. and Y.L.; supervision, Q.L.; project administration, R.L.; funding acquisition, Q.L. All authors have read and agreed to the published version of the manuscript.  
Research was funded by the National Natural Science Foundation of China, grant number 61931004, and the APC was funded by Dalian University.  
The processed data required to reproduce these findings cannot be shared as the data also form part of an ongoing study.  
The authors declare no conflict of interest.
## Appendix A
As a consequence, when both social relationships (\(ORI_{T}\)) and communication costs are taken into consideration, the path \(\{A,C,B\}\) is shown to be the best option for moving from device \(A\) to device \(B\). Therefore, to obtain the ranking based on \(CLO_{opp}\), one must compute the \(CLO_{opp}\) of each node with respect to the rest:  
\[ \begin{array}{l}CLO_{opp}(A)=\frac{1}{6-1}\bigg{(}\frac{1}{\min(Cost_{opp}( A,B))}+\frac{1}{\min(Cost_{opp}(A,C))}+\frac{1}{\min(Cost_{opp}(A,D))} \\ \hskip 14.226378 pt+\frac{1}{\min(Cost_{opp}(A,E))}+\frac{1}{\min(Cost_{opp}(A, F))}\\ \hskip 14.226378 pt=\frac{1}{6-1}\bigg{(}\frac{1}{\frac{1}{0.5584}+\frac{1}{0.2 }+\frac{1}{0.1429}+\frac{1}{0.6934}+\frac{1}{1.4289}\bigg{)}}=3.5343\\ CLO_{opp}(B)=\frac{1}{6-1}\bigg{(}\frac{1}{\min(Cost_{opp}(B,A))}+\frac{1}{ \min(Cost_{opp}(B,C))}+\frac{1}{\min(Cost_{opp}(B,D))}\\ \hskip 14.226378 pt+\frac{1}{\min(Cost_{opp}(B,E))}+\frac{1}{\min(Cost_{opp}(B, F))}\\ \hskip 14.226378 pt=\frac{1}{6-1}\bigg{(}\frac{1}{0.5584}+\frac{1}{0.25}+\frac{1}{0.5 5}+\frac{1}{0.4378}+\frac{1}{0.071}\bigg{)}=2.0019\\ CLO_{opp}(C)=\frac{1}{6-1}\bigg{(}\frac{1}{\min(Cost_{opp}(C,A))}+\frac{1}{ \min(Cost_{opp}(C,B))}+\frac{1}{\min(Cost_{opp}(C,D))}\\ \hskip 14.226378 pt+\frac{1}{\min(Cost_{opp}(C,E))}+\frac{1}{\min(Cost_{opp}(C, F))}\\ \hskip 14.226378 pt=\frac{1}{6-1}\bigg{(}\frac{1}{0.2}+\frac{1}{0.25}+\frac{1}{0.4497}+ \frac{1}{1.4288}+\frac{1}{1.2523}\bigg{)}=2.5654\\ CLO_{opp}(D)=\frac{1}{6-1}\bigg{(}\frac{1}{\min(Cost_{opp}(D,A))}+\frac{1}{ \min(Cost_{opp}(D,B))}+\frac{1}{\min(Cost_{opp}(D,C))}\\ \hskip 14.226378 pt+\frac{1}{\min(Cost_{opp}(D,E))}+\frac{1}{\min(Cost_{opp}(D, F))}\\ \hskip 14.226378 pt=\frac{1}{6-1}\bigg{(}\frac{1}{0.1429}+\frac{1}{0.5}+\frac{1}{0.4497}+ \frac{1}{1.2603}+\frac{1}{1.3055}\bigg{)}=2.5562\\ CLO_{opp}(E)=\frac{1}{6-1}\bigg{(}\frac{1}{\min(Cost_{opp}(E,A))}+\frac{1}{ \min(Cost_{opp}(E,B))}+\frac{1}{\min(Cost_{opp}(E,C))}\\ \hskip 14.226378 pt+\frac{1}{\min(Cost_{opp}(E,D))}+\frac{1}{\min(Cost_{opp}(E, F))}\\ \hskip 14.226378 pt=\frac{1}{6-1}\bigg{(}\frac{1}{0.6394}+\frac{1}{1.4378}+ \frac{1}{1.2428}+\frac{1}{1.2603}+\frac{1}{0.7248}\bigg{)}=1.0474\\ CLO_{opp}(F)=\frac{1}{6-1}\bigg{(}\frac{1}{\min(Cost_{opp}(F,A))}+\frac{1}{ \min(Cost_{opp}(F,B))}+\frac{1}{\min(Cost_{opp}(F,C))}\\ \hskip 14.226378 pt+\frac{1}{\min(Cost_{opp}(F,D))}+\frac{1}{\min(Cost_{opp}(F, E))}\\ \hskip 14.226378 pt=\frac{1}{6-1}\bigg{(}\frac{1}{1.4289}+\frac{1}{0.7071}+ \frac{1}{1.2523}+\frac{1}{1.3055}+\frac{1}{0.2248}\bigg{)}=1.0116\\ \end{array} \]  
So, the new ordered ranking of nodes based on their Social Closeness index is as follows:  
\[Ranking_{CLO_{opp}}=\{A,C,D,B,F,E\}\]