esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved YOLOv5-Based Real-Time Road pavement Damage Detection in Road Infrastructure Management
## 4 Result Analysis
### Comparison with Existing Methods Regarding Road Placement Damage Detection and Classification  
Table 6 shows the drastic performance gap between our improved YOLOv5s and other object detection methods on the RDD 2018, RDD 2020, and RDD 2022 datasets. [PERSON] et al. used Mask RCNN with the RDD 2018 dataset to acquire an F1 score of 52.8% [47]. Numerous studies utilized RDD 2020, the successor to RDD 2018, and produced a higher F1 score than previous research [48, 49]. Using the RDD 2020 dataset and other object detection algorithms, the researcher discovered that the YOLO-based approach outperformed most of the other studies [27]. An average F1 score of 66.46% was obtained by the only research found using RDD 2022, with 28.46 million parameters [31]. Their average F1 score was exceeded by 0.05% with 20 million fewer parameters. Moreover, all previous studies that utilized RDD 2018 and RDD 2020 datasets were outperformed by a significant margin in this study. Figure 11 represents the precision-recall curve of our proposed improved YOLOv5s model.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Model** & **F1 Score (\%)** \\ \hline Mask R-CNN with RDD 2018 [47] & 52.80 \\ Multi-stage Faster R-CNN with Resnet-101 backbones with RDD 2020 [48] & 54.26 \\ Ensemble(YOLOv-v4+Faster-RCNN) with RDD 2020 [49] & 57.07 \\ YOLOv5s with RDD 2020 [27] & 57.10 \\ Improved YOLOv5s with RDD 2020 [29] & 60.92 \\ YOLOv7 with RDD 2022 [31] & 66.30 \\ YOLOv5s with RDD 2022 (ours) & 65.22 \\ YOLOv8s with RDD 2022 (ours) & 66.46 \\ Improved YOLOv5s with RDD 2022 ( Ours proposed model) & **66.51** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison with existing methods regarding road pavement damage detection and classification.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **F1 Score** & **[EMAIL_ADDRESS]** & **No. of** & & \\  & **(\%)** & **(\%)** & **Params. (M)** & **GFlops** & **FPS** \\ \hline YOLOv8l & 69.06 & 71.50 & 43.70 & 165.2 & 23 \\ YOLOv8m & 68.63 & 70.79 & 25.80 & 78.70 & 32 \\ YOLOv5l & 68.78 & 70.90 & 46.50 & 109.5 & 26 \\ YOLOv5m & 66.55 & 69.23 & 21.20 & 49.00 & 36 \\ YOLOv7-tiny & 63.68 & 65.67 & 6.27 & 13.80 & 65 \\ YOLOv5s & 65.22 & 65.91 & 7.01 & 16.00 & 63 \\ YOLOv8s & 66.46 & 67.70 & 11.21 & 28.40 & 53 \\ Ours Improved YOLOv5s & 66.51 & 67.81 & 8.11 & 16.30 & 58 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison among different object detectors.
# Automatic Spatial Audio Scene Classification in Binaural Recordings of Music +
## 1 Introduction
### Aims of the Study  
From the above background, the current study proposes a novel method that automatically categorizes binaural recordings of music according to the horizontal distribution of foreground audio content (ensemble of musicians) and background audio components (room reflections) around a listener. The study also compares the influence of several groups of features, extracted from binaural recordings, on the classification accuracy of the proposed method. The following groups of metrics were incorporated in this work: binaural, spectro-temporal, Mel-frequency cepstral coefficients (MFCCs), and descriptors based on the root mean square (RMS) values of the signals.  
The proposed method could be incorporated in the algorithms used for semantic search and retrieval of audio content. It might also be used as a spatial scene-detector in audio quality expert systems (the performance of spatial quality expert systems could be improved by providing them with information on spatial audio scene characteristics [22]). Moreover, it might be embedded to environment-aware signal processing algorithms, including binaural hearing aids, adapting their parameters depending on the surrounding audio scenes. It could also be used as a preselection tool for gathering audio material for subjective audio quality assessment tests.  
This study builds on the work on the spatial audio scene characterization of five-channel surround sound recordings undertaken by [PERSON] [23; 24]. As mentioned above, it constitutes an extension of the preliminary work concerning the automatic categorization of binaural recordings, carried out by the present authors [20].  
The paper is organized as follows: in Section 2, the topology of the algorithm, the audio corpus, and the classification algorithm are described; Section 3 presents the results; and Sections 4 and 5 include discussion and conclusions, respectively.
# Evaluation of the Effects of Hidden Node Problems in IEEE 802.15.7 Uplink Performance
## 5 Simulation Results Analysis
### Simulation Parameters
#### 5.1.3 Application Layer Simulation Parameters  
Application message sizes of 512 bytes, 1024 bytes and 1500 bytes were used for simulations (same size for all devices). The largest message size--1500 bytes--was selected according to the maximum transmission unit of the Ethernet standard. Simulation results did not vary significantly depending on application message size, since variation margin was below 5%. Hence, we decided to present here only the results obtained with the intermediate message size of 1024 bytes.  
The IEEE802.15.7 standard limits the frame size (PHY layer SDU) to 65,535 bytes when PHY II and PHY III is used. When PHY I is used, the maximum frame size is 1023 bytes.
# Implicit Subspace Iteration to Improve the Stability Analysis in Grinding Processes
## 3 Results
### Conclusions  
The results obtained from the comparison between subspace iteration and semi-discretization methods in cylindrical grinding process show that:  
* When obtaining stability maps, subspace iteration is between 6 and 138 times faster than semi-discretization in the analized cases, depending on the natural frequencies of the system.
* Both methods are dependent on the natural frequencies of the system, but subspace iteration is better when dealing with higher frequencies.
* When obtaining instability reduction ratio maps with continuous workpiece speed variation, subspace iteration is between 2.5 and 47 times faster than semi-discretization in the analyzed cases, depending on the natural frequencies of the system.
* The time necessary for the calculation of instability reduction ratio maps is higher than that required for the calculation of stability maps due to the necessity of calculating the step matrices over the speed variation period.  
Regarding the results obtained comparing subspace iteration and time simulation methods in centerless grinding process:  
* When obtaining instability reduction ratio maps with continuous workpiece speed variation, subspace iteration is about 27 times faster than time simulation in the analized case.
* Since time domain simulation does not depend on the workpiece speed variation frequency, the improvement of the subspace iteration calculation time is higher when analyzing higher variation frequencies.
# A Design Method of Code Correlation Reference Waveform in GNSS Based on Least-Squares Fitting
## 3 Results
### Boc(6,1)  
The settings for BOC(6,1) signals are listed in Table 4. The code correlation reference waveform type is the same as the W2 waveform. The least-squares process is performed only for the coherent discriminator case, because the realizable linear region of the given non-coherent discriminator is rather small due to the multi-peaked ACF of the BOC(6,1) signal.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scenario** & **Infinite Bandwidth** & **24.552 MHz** & **18.414 MHz** \\ \hline Fitting range (\(\pm\)chips) & 1 & 1 & 1 \\ Linear region (\(\pm\)chips) & 0.1 & 0.1 & 0.1 \\ Offset (chips) & 0.01 & 0.03 & 0.03 \\ Waveform range (chips) & \(-\)0.5\(\sim\)0.5 & \(-\)0.4\(\sim\)0.4 & \(-\)0.4\(\sim\)0.4 \\ Gate width (chips) & 0.01 & 0.05 & 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation parameters for the BOC(6,1) signal.  
Figure 27: Code correlation reference waveform diagram for the BOC(2,1) signal non-coherent discriminator (8.184-MHz bandwidth).  
Figure 26: Code correlation reference waveform diagram for the BOC(2,1) signal coherent discriminator (8.184-MHz bandwidth).  
The code correlation reference waveform diagram for the 24.552-MHz bandwidth case is shown in Figures 28 and 29. The strobe width is 0.05 chips, and the waveforms are distributed from \(-\)0.4-0.4 chips. The resulting S-curves can roughly follow the ideal S-curve, but the peak in the left side of the linear region is reduced to balance the linear regression error. There is also no false lock points in the fitted result.
# The Use of Augmented Reality for the Management of Equipment Ageing with a Virtual Sensor
## 3 Development of a User-Friendly Interface of the Virtual Sensor
### Human-Machine Interaction  
Figure 2 shows the flow diagram of the interaction between the inspector (user) and the App Desktop. The dotted line indicates a path that can be omitted, i.e., the case when the user does not elaborate future estimates but only visualises the corrosion surface and the parameters related to the current and past inspections. Figure 3 illustrates the interaction between the inspector and the App Mobile.  
Figure 2: Flow diagram interaction between the inspector and the App Desktop.  
To use the virtual sensor, the inspector starts the App Desktop and enters the equipment ageing data. This requires that he/she uploads:  
* the Excel files of the inspection carried out by means the ageing fishbone model [21] (ageing index method, whose application is suggested by the Italian Ministry of the Environment);
* the text files containing the thickness measurements sampled during the inspections with the relative spatial coordinates.  
Finally, the inspector can choose whether to elaborate information relating only to the current state of the equipment, to the past, or to the future, i.e., by referring to the dates of the previous inspections or selecting from 1 to 5 different future years. He/she can choose to also produce the estimates of the ageing-related metrics (critical pit probability, RUL, corrosion rate, etc.) and the corrosion surfaces at the same time.  
Then, the application reads and codes these data, processes them according to the implemented models (see Section 2.1), and creates graphs and tables of the ageing parameters for a selected year. Once the processing is complete, the application automatically stores the documents in the defined path. Next, the inspector can visualise the information produced to migrate it to the App Mobile, or reset the interface fields to make a new entry and perform a new process. A further operation that the inspector can perform by using the App Desktop, after entering the name of the equipment, is the generation of the QR code to be placed close to the equipment to be examined if it has not been coded yet. The final step is to exit the App Desktop and then start the App Mobile.
# A Survey of Visible-Light-Communication-Based Indoor Positioning Systems
## 7 Conclusions  
This paper presents a comprehensive overview of visible light communication (VLC)-based indoor positioning systems (IPSs), comparing and analyzing different positioning technologies. VLC-based systems differ from traditional solutions by offering unique features and novel advantages. This study discusses their system designs, including channel modeling, localization algorithms, and multiple access schemes, and provides practical implementation and usage guidelines.  
Key findings and comparisons are succinctly summarized in Table 4, facilitating a deeper understanding of the strengths and weaknesses inherent in different VLC-based IPSs. In addition, this paper identifies future research directions and challenges that need to be addressed to further improve the performance and applicability of VLC-based positioning systems. Although VLC-based IPSs perform well in many ways, factors such as multipath effects, signal occlusion, and ambient light interference can affect positioning accuracy. In addition, there are some practical issues that need to be addressed when deploying and maintaining the system, such as the optimal layout of the LED and the sensitivity adjustment of the receiver. Overall, this paper contributes to the advancement of indoor positioning technologies by highlighting the potential of VLC-based systems and providing valuable insights for researchers and practitioners in the field. With continued research and development, VLC-based IPSs are poised to revolutionize location-based services by significantly improving the accuracy and reducing the cost of indoor positioning.  
**Author Contributions:** Conceptualization, G.N., C.S.C. and S.-W.H.; Methodology, R.W., G.N., Q.C., C.S.C. and S.-W.H.; Software, R.W., G.N. and Q.C.; Validation, R.W., G.N., Q.C., C.S.C. and S.-W.H.; Formal Analysis, R.W., G.N., Q.C., C.S.C. and S.-W.H.; Investigation, R.W., G.N., Q.C., C.S.C. and S.-W.H.; Resources, R.W., G.N., Q.C., C.S.C. and S.-W.H.; Data Curation, R.W., G.N., Q.C., C.S.C. and S.-W.H.; Writing--Original Draft Preparation, R.W., G.N., Q.C., C.S.C. and S.-W.H.; Writing--Review & Editing, R.W., G.N., Q.C., C.S.C. and S.-W.H.; Visualization, R.W., G.N. and Q.C.; Supervision, G.N., C.S.C. and S.-W.H.; Project Administration, G.N., C.S.C. and S.-W.H.; Funding Acquisition, G.N. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Fundamental Research Funds for the Central Universities NO. 20103237888.  
**Acknowledgments:** A part of the work has been carried out at the Laboratory for Information, Networking and Communication Sciences (www.lincs.fr). In addition, we would like to thank [PERSON] for her help to this work. She was with Bell Labs during her internship at Nokia Paris-Saclay.  
**Conflicts of Interest:** Author [PERSON] was employed by Nokia Bell Labs. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 Materials and Methods
### Study Areas  
This work considers two areas of analysis. The first one corresponds to a sector in the northeast of Corrientes province, Argentina, near its border with the neighboring Misiones province, see Figure 2a. Corrientes is amongst the most important areas in terms of forest production in Argentina, with pine and eucalyptus as the main planted species. This is the main reason for choosing this region, only surpassed by Misiones [44]. In this sense, the retrieval of forest plantations' height, which is directly linked to their productivity, is very relevant both for local producers and government agencies [5]. As can be seen in the zoomed area of the same figure, three sub-regions were selected for the analysis performed on this site. Region C forms part of a forest field, from which in-field inventories were available. Region A corresponds to a forested area which, according to GEDI measurements, see Section 2.5, presented lower tree heights than region C. Region B, finally, covers a bare-soil area, used to compare the behavior of coherence between the land covers in Section 3.1.  
The main planted species of pine in the Corrientes site corresponds to _Pinus taeda_, spanning a surface of over 250,000 ha for the whole province [45]. According to field measurements provided by local producers, the analyzed forest stands of region C have an age of around 18 to 20 years, with a mean average canopy height of 24 m, obtained from ground campaigns. As indicated by [46], the _Pinus taeda_ trees in Corrientes reach heights of around 23 m at the age of 20, which is in agreement with the given figures.  
The second area of analysis is located near the Domuyo Volcano, in the Neuquen province, Argentina, see Figure 2c, which is much less important than Corrientes in terms of forest production. This area was included to assess the InSAR coherence in a different environment, where trees generally reach lower heights in contrast with the northeast of the country. This comparison based on forest height will be addressed in Section 3.4.  
The tree plantations in the second area correspond to pines of the _Pinus ponderos_ species, which is one of the most important in the Patagonian Andes of Argentina, with a total surface of 56,000 ha [47]. The analyzed forest stands have an age of around 30 years, with a mean average canopy height of 15 m, obtained from ground campaigns. In this case too, these numbers are in agreement with previous studies that developed growth models for _Pinus ponderosa_ in the region [48].
# A Novel Decomposed Optical Architecture for Satellite Terrestrial Network Edge Computing
## 5 Scalability and Discussion
### Physical Performance under Different Port Counts  
In the optical computing architecture with decomposed hardware, all the on-board interconnect buses are removed, and NOS based flat optical network is applied to interconnect hardware cube. Thus, the port count of NOS is significantly important for the network scale of optical computing architecture with decomposed hardware. Due to the broadcast & select structure, the input power of OSA based optical gates is dramatically reduced when port count of NOS increases. Therefore, the output Optical Signal Noise Ratio (OSNR) of NOS is evaluated with a range of NOS port counts (four-port to 64-port). The output OSNR of decomposed hardware cubes is 62.3 dB in the experiment. With four different SOA operation currents (60 mA, 90 mA, 120 mA, and 150 mA in the experiment), the received OSNR at the decomposed hardware side is measured by increasing the NOS port count from 4 to 64. It is shown in Figure 7a that, with SOA-based optical gates operated at 150 mA, the decomposed hardware cube receives optical signals of 43.3 dB OSNR utilizing a four-port NOS, and obtains optical signals of 30.5 dB OSNR utilizing a 64-port NOS. In addition, it is shown that, when deploying the NOS with small port counts in the  
Figure 6: (**a**) Network latency components when accessing memory cube; (**b**) Cumulative network packet loss over 80 h.  
experiment, the current values applied for SOA-based optical gates (from 60 mA to 150 mA) do not impact the received OSNR of decomposed hardware cubes too much. Meanwhile, when applying the NOS with larger port counts (>16) and a small SOA operation current (<100 mA), the received OSNR dramatically increases under a larger operation current.  
In order to guarantee the stable communication among decomposed hardware cubes, the required power compensation for none-error network transmission is investigated ranging from four-port NOS to 64-port NOS. In the experiment, the required power compensation is measured with a BER of 1 \(\times\) 10\({}^{-9}\). As depicted in Figure 7b, the optical computing architecture with decomposed hardware requires a power compensation of 0.9 dB for the none-error transmission with an eight-port NOS. With a 64-port NOS, the required power compensation increases to 1.5 dB for none-error transmission. The reason is that, with the broadcast and select structure, there is much more power loss after optical signal splitters (18 dB extra loss under NOS port count of 64). When amplifying the optical signal at SOA-based optical gates, more noise is introduced under a lower input power. The experimental investigation shows that the proposed optical computing architecture with decomposed hardware is feasible and stable under a larger network scale.
## 7 Results
### Processing Time
#### 7.2.1 Ttr Calculations  
In our experimentation with the proposed MFHS using MATLAB, the total energy consumption for transferring one byte of data equates to 1.6 \(\times\) 3 mJ, resulting in 4.8 mJ or approximately 0.0048 joule, which can be approximated as 0.005 joule. In parallel, considering a maximum CPU power consumption of 28 watts, translating to 100.8 KJ per hour, and with a packet size of 1500 bytes, we deduce that the average energy required for one byte packet transfer is approximately 0.0049 joule or 0.0049/1000 KJ.  
Furthermore, it is evident that 100.8 KJ of energy is consumed within 1 h, corresponding to 3600 s. Therefore, 1 KJ of energy is consumed in 3600/100.8 s. Consequently, the energy consumption of 0.0049/1000 KJ amounts to (3600 \(\times\) 0.0049)/(100.8 \(\times\) 1000) seconds, multiplied by the packet size of 1500 bytes, which results in approximately 0.2625 s.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number of Packets** & **Etr** & **Ec + Ea** & **Etotal** \\ \hline
20 & 0.15 KJ & 0.38 KJ & 0.53 KJ \\
40 & 0.3 KJ & 0.76 KJ & 1.06 KJ \\
60 & 0.45 KJ & 1.14 KJ & 1.59 KJ \\
80 & 0.60 KJ & 1.52 KJ & 2.12 KJ \\
100 & 0.75 KJ & 1.9 KJ & 2.65 KJ \\ \hline \hline \end{tabular}
\end{table}
Table 7: Energy consumption of the MFHS for 20, 40, 60, 80, and 100 packets.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number of Packets** & **OLB** & **FOFSA** & **ATAT** \\ \hline
20 & 1.15 KJ & 0.56 KJ & 2.16 KJ \\
40 & 2.6 KJ & 1.44 KJ & 3.3 KJ \\
60 & 3.4 KJ & 2.5 KJ & 4.03 KJ \\
80 & 4.1 KJ & 2.8 KJ & 5 KJ \\
100 & 5 KJ & 3.3 KJ & 6 KJ \\ \hline \hline \end{tabular}
\end{table}
Table 8: Total energy consumption (Etotal) of FOFSA, ATAT, and OLB.
# Time-Sensitive Networking Mechanism Aided by Multilevel Cyclic Queues in LEO Satellite Networks
## 6 Conclusions  
This paper presented an LEO satellite network management architecture for time-sensitive services. On the basis of the architecture, we proposed a time-sensitive network mechanism based on multilevel cyclic queueing (TSN-MCQ) for LEO satellite networks. This mechanism could differentiate the forwarding of different levels of time-sensitive services to maximize the protection of high-level TSFs. By eliminating the gating mechanism of CQF, TSN-MCQ reduced the time-out ratio of time-sensitive traffic in all load cases. Meanwhile, the proposed mechanism demonstrated an excellent packet loss ratio and throughput performance. The simulation results show that the proposed mechanism reduced the time-out ratio by 14.5% compared to CQF in high-load cases. Future research work will focus on the number and size of queues, and overall network performance to achieve better overall network performance with fewer queue resources.  
Conceptualization and methodology, X.M.; validation, S.L. and Z.G.; investigation, J.L. and Y.W.; writing--review and editing, H.S. and H.G. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Key R&D Program of China (No. 2021 YFB2900604).  
Figure 7: Normalized throughput with an cyclic period of 500 μs and a buffer size of 153.6 KB.