esa-sceva/satcom-chunk-collection huggingface dataset:
# Optical OFDM for SiPM-Based Underwater Optical Wireless Communication Links
###### Abstract  
Underwater optical wireless systems have dual requirements of high data rates and long ranges in harsh scattering and attenuation conditions. In this paper, we investigate the advantages and limitations of optical orthogonal frequency-division multiplexing (O-OFDM) signaling when a silicon photo-multiplier (SiPM) is used at the receiver in order to ensure high sensitivity. Considering a light-emitting diode (LED) transmitter and taking into account the limited dynamic range imposed by the transmitter and the SiPM receiver, we study the performance of three popular O-OFDM schemes, i.e., DC-biased, asymmetrically-clipped, and layered asymmetrically-clipped O-OFDM (DCO-, ACO-, and LACO-OFDM, respectively). We consider a constraint on transmit electrical power \(P_{\text{Txe}}\) and take into account the required DC bias for the three considered schemes in practice, showing the undeniable advantage of ACO- and LACO-OFDM in terms of energy efficiency. For instance, for the considered SiPM and LED components, a spectral efficiency of \(\sim\)1 bps / Hz with a data rate of 20 Mbps, a link range of 70 m, and a target bit-error-rate (BER) of \(10^{-3}\), ACO and LACO allow a reduction of about 10 and 6 mW, respectively, in the required \(P_{\text{Txe}}\), compared to DCO-OFDM. Meanwhile, we show that when relaxing the \(P_{\text{Txe}}\) constraint, DCO-OFDM offers the largest operational link range within which a target BER can be achieved. For instance, for a target BER of \(10^{-3}\) and a data rate of 20 Mbps, and considering \(P_{\text{Txe}}\) of 185, 80, and 50 mW for DCO-, LACO-, and ACO-OFDM, respectively, the corresponding intervals of operational link range are about 81, 74.3, and 73.8 m. Lastly, we show that LACO-OFDM makes a good compromise between energy efficiency and operational range flexibility, although requiring a higher computational complexity and imposing a longer latency at the receiver.  
underwater wireless optical communications; optical OFDM; silicon photo-multipliers; dynamic range; clipping noise +
Footnote †: journal: Sensors
# Reconstruction Optimization Algorithm of 3D Temperature Distribution Based on Tucker Decomposition
## 6 Conclusions  
A stable and accurate optimization algorithm for empirical datasets with missing data based on Tucker decomposition has been developed, which realizes the reconstruction of three-dimensional temperature distribution under different working conditions and flame deflection. In the case of missing or distorted empirical data, using the proposed distribution reconstruction algorithm based on Tucker decomposition can repair the missing dataset and complete the reconstruction of temperature distribution. In order to verify the feasibility of the algorithm, different proportions of missing data are repaired and the temperature distribution of five groups of test conditions is reconstructed. The reconstruction error is less than 1%, and a good reconstruction effect is obtained. In the research process, the missing position of the original dataset and the position of the measuring point in the  
Figure 12: Optimized Effects of Reconstruction Algorithm with Noise: (**a**) SNR = 20; (**b**) SNR = 30.  
reconstruction process have a great influence on the reconstruction error, indicating that the information carried by different measuring point positions is different in the temperature distribution reconstruction algorithm. Therefore, optimizing the arrangement of measuring points has a significant effect on improving the accuracy of reconstruction results. Using the minimum condition number, the greedy algorithm and the hill-climbing algorithm can improve the reconstruction accuracy by about 4%. At the same time, different degrees of noise are added in the process of temperature reconstruction with errors less than 6.0%, which shows that the algorithm has strong robustness and anti-noise ability. The results show that the Tucker decomposition method is not only suitable for the reconstruction of the temperature distribution of complete datasets, but also for the reconstruction of the temperature distribution of historical missing datasets. This research work on the reconstruction of temperature distribution under the condition of missing empirical data broadens the application range of the reconstruction algorithm and provides a new solution to the problem of data loss and data distortion in industrial measurement.  
Conceptualization, Z.L. and S.L.; methodology, Z.L.; software, P.Y.; validation, Z.L., Y.Z. and P.Y.; formal analysis, Z.L.; data curation, Z.L. and P.Y.; writing--original draft preparation, Z.L.; writing--review and editing, Y.Z.; supervision, S.L.; project administration, S.L.; funding acquisition, S.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by NSFC (No.61871181).  
The authors declare no conflict of interest.
# Extreme Learning Machines to Combat Phase Noise in RoF-OFDM Schemes
## 4 Proposed Phase Noise Suppression Methods through Extreme Learning Machines
Calculate the Moore-Penrose generalized inverse of the hidden layer output matrix (\(H^{\ddagger}\)) from the received pilot subcarriers (\(\overline{p_{i}}\)). Determine the output weight \(\beta\) by the product of the matrix \(H^{\ddagger}\) and the transmitted pilot subcarriers (\(p_{i}\)), Equation (8). The learning procedure is finished. Obtain the hidden layer output matrix (\(H\)) from the received data symbols (\(\overline{d_{i}}\)). Find the corrected data symbols (\(\overline{d_{i}^{c}}\)) through the relationship between \(H\) and \(\beta\), Formula (9). The testing step is done. ```  
**Algorithm 1**The complex ELM  
```
Input: The training test (the pilots subcarriers) \(\Pi=\{(\overline{p_{i}},p_{i})\mid\overline{p_{i}}\in\mathbb{C},p_{i}\in \mathbb{C},i=1,...,N_{p}\}\). The real activation function \(g(\cdot)\). The number of hidden nodes \(\widetilde{L}\). Output: The corrected data symbols \(\Delta=\{\overline{d_{i}^{c}}\in\mathbb{C},i=1,...,N_{d}\}\). Obtain the in-phase and quadrature numbers of both the training samples (\(\Re\{\overline{p_{i}}\}\), \(\Re\{p_{i}\}\), \(\Im\{\overline{p_{i}}\}\), \(\Im\{\overline{p_{i}}\}\), and \(\Im\{p_{i}\}\)) and the received data subcarriers (\(\Re\{\overline{d_{i}}\}\) and \(\Im\{\overline{d_{i}}\}\)). for Each component (the real or imaginary part)do Randomly choose the real input weights \(w_{i}\) and the real biases \(b_{i}\). Calculate the Moore-Penrose generalized inverse of the hidden layer output matrix (\(H^{\ddagger}\)) from the received pilot subcarriers in a single component (\(\Re\{\overline{p_{i}}\}\)\(\bigvee\Im\{\overline{p_{i}}\}\)). Determine the output weight \(\beta\) by using the matrix \(H^{\ddagger}\) together with the transmitted pilot subcarriers in one dimension (\(\Re\{p_{i}\}\)\(\bigvee\Im\{p_{i}\}\)), Equation (8), for the real domain. Obtain the hidden layer output matrix (\(H\)) from the received data symbols in a single part (\(\Re\{\overline{d_{i}}\}\)\(\bigvee\Im\{\overline{d_{i}}\}\)). Find the corrected data symbols for the studied component (\(\Re\{\overline{d_{i}^{c}}\}\)\(\bigvee\Im\{\overline{d_{i}^{c}}\}\)), Formula (9), for the real domain. Combine the in-phase and quadrature numbers of the equalized data symbols, namely \(d_{i}^{c}=\Re\{\overline{d_{i}^{c}}\}+\hat{\jmath}\Im\{\overline{d_{i}^{c}}\}\). ```  
**Algorithm 2**The real ELM  
Equation (9) and Algorithms 1 and 2 demonstrate that in a simple and efficient way, the introduced ELM approaches may reduce phase noise and AWGN effects in a typical pilot-based RoF-OFDM signal.
## 2 Literature Review
### Industry 4.0 Technologies
\begin{table}
\begin{tabular}{l l l l l l l l l l l l l} \hline \hline
**No.** & **Source** & **A** & **B** & **C** & **D** & **E** & **F** & **G** & **H** & **I** & **J** & **K** \\ \hline
1 & [PERSON] et al. [9] & ✓ & & ✓ & ✓ & & & ✓ & & & & & \\
2 & [PERSON] et al. [34] & ✓ & ✓ & ✓ & & & & & & & & \\
3 & [PERSON] and [PERSON] [35] & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & & ✓ & & & \\
4 & [PERSON] et al. [36] & ✓ & ✓ & ✓ & & ✓ & & ✓ & ✓ & ✓ & & \\
5 & [PERSON] et al. [37] & ✓ & ✓ & ✓ & & ✓ & & ✓ & ✓ & & & ✓ \\
6 & [PERSON] and [PERSON] [38] & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & & ✓ & ✓ & ✓ \\
7 & [PERSON] et al. [39] & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & & & & ✓ & ✓ \\
8 & [PERSON] et al. [40] & ✓ & & ✓ & ✓ & & & ✓ & & ✓ & & & ✓ \\
9 & [PERSON] et al. [41] & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & & & \\
10 & [PERSON] et al. [30] & ✓ & ✓ & ✓ & & & & & & ✓ & & & ✓ \\
11 & [PERSON] [29] & ✓ & ✓ & ✓ & ✓ & ✓ & & & & ✓ & & ✓ \\
12 & [PERSON] et al. [42] & ✓ & ✓ & ✓ & ✓ & ✓ & & ✓ & & ✓ & & ✓ \\
13 & [PERSON] et al. [33] & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & & ✓ & \\
14 & [PERSON] [43] & ✓ & ✓ & ✓ & ✓ & & ✓ & ✓ & & & ✓ & \\
15 & [PERSON] et al. [3] & ✓ & ✓ & ✓ & ✓ & & ✓ & ✓ & & ✓ & & \\
16 & [PERSON] et al. [44] & ✓ & ✓ & ✓ & & & & & & & ✓ & \\
17 & [PERSON] et al. [23] & ✓ & ✓ & ✓ & & ✓ & ✓ & ✓ & ✓ & & \\
18 & [PERSON] and [PERSON] [5] & ✓ & ✓ & ✓ & & ✓ & ✓ & ✓ & & & \\
19 & [PERSON] et al. [18] & ✓ & ✓ & ✓ & & ✓ & ✓ & ✓ & & & \\
20 & [PERSON] et al. [45] & ✓ & ✓ & ✓ & ✓ & ✓ & & ✓ & & ✓ & \\
21 & [PERSON] et al. [46] & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & & & & \\
22 & [PERSON] [47] & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & & \\
23 & [PERSON] et al. [48] & ✓ & ✓ & ✓ & & & & & ✓ & & \\
24 & [PERSON] and [PERSON] [49] & & ✓ & ✓ & ✓ & & & & & & ✓ & \\
25 & [PERSON] and [PERSON] [31] & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & & ✓ & \\
26 & [PERSON] et al. [50] & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & \\
27 & [PERSON] et al. [4] & & & ✓ & ✓ & ✓ & ✓ & & & & & ✓ & ✓ \\  & Frequency & & 25 & 24 & 17 & 16 & 14 & 13 & 13 & 9 & 9 & 7 \\ \hline \hline \end{tabular} Notes: A: Cloud computing; B: IoT; C: Big data and analytics; D: Additive manufacturing; E: Autonomous robots; F: AR; G: Simulation; H: CPS; I: Cybersecurity; J: AI; K: Mobile technology; ✓: Source in the literature.  
\end{table} 
Table 1: Industry 4.0 technologies in the literature. are cloud computing, IoT, big data and analytics, additive manufacturing (3D printing), autonomous robots, augmented reality (AR), simulation, cyber-physical system (CPS), cybersecurity, artificial intelligence (AI), mobile technology, VR, blockchain, and MES. According to [PERSON] et al. [53], Industry 4.0 technologies can be categorized into base and front-end technologies. Front-end technologies further consist of smart supply chain technologies, smart manufacturing technologies, smart working technologies, and smart product technologies. As depicted in Figure 1, base technologies (e.g., AI, big data, and cybersecurity) furnish front-end technologies (e.g., autonomous robots) with connectivity, intelligence, and security.
# Semantic Segmentation of Aerial Imagery Using U-Net with Self-Attention and Separable Convolutions
## 2 Literature Review
### Ensemble CNN-Based Semantic Segmentation in High-Resolution Aerial Imagery  
In 2016, ([PERSON] et al., 2016) elucidated a semantic segmentation model that leveraged high-resolution aerial imagery. The approach incorporated an ensemble of convolutional neural networks (CNNs), specifically the FCN, alongside adapted CNNs, highlighting their heightened efficiency when applied to standard datasets. This detailed exploration undersocored the model's effectiveness in semantic segmentation and demonstrated the superior performance achieved through the integration of an ensemble of CNN architectures [16].
# Research Progress of Visual Inspection Technology of Steel Products--A Review
## 4 Detection Methods
### Method Based on Machine Learning  
#### 4.4.1 Artificial Neural Networks  
Since 1980, artificial neural networks (ANN) have been a hotspot in the field of artificial intelligence. ANN abstracts the human brain neural network from the perspective of information processing to establish a simple model, and can then be used to form different networks according to different connection modes to achieve various functions (Figure 7). As early as 2000, [PERSON] [28] proposed an adaptive learning classification for surface defects of hot-rolled steel, and the average percentage classification accuracy was 84% for training data and 64% for test data. Later, an improved BP (Back propagation) algorithm based on error function was mentioned in [PERSON] [39] to conduct the surface quality inspection of cold-rolled strip. [PERSON] [81] came up with an improved BP algorithm based on singular value decomposition and a generalized inverse matrix for five common defects (cracks, oxide, skin, holes, and scratches) of steel plate, overcoming the slow training of the traditional BP algorithm, with results showing that it could meet real-time requirements.
## 2 Circular Cavity Metasurface Antenna with a Diameter of 0.25 \(\lambda_{0}\)
### Experimental Results  
A prototype of a metasurface antenna in a cavity of diameter \(0.25\,\lambda_{0}\) has been manufactured. The cavity inner diameter is \(a=32\) mm (\(0.25\,\lambda_{0}\)) and the outer one equals \(b=42\) mm. The cavity height is chosen from the above simulation results, and corresponds to the maximum or nearly maximum BW: \(h_{\mathrm{c}}=15.0\) mm. All dimensions have been optimized using CST Microwave Studio [14] in order to minimize the reflection coefficient at 2300 MHz. As per design guidelines, the parameter \(g\) is first optimized to tune the frequency (increasing \(g\) increases the frequency). The value of \(f_{\mathrm{x}}\) is chosen to ensure that the feeding point lies on the left strip (and not on its edge). Secondly, the parameters \(L_{t}\) and \(W_{t}\) are optimized to minimize the reflection coefficient and to finely tune the frequency. The final parameters are provided in Table 1. A photograph of the prototype is given in Figure 8: The feeding point is visible on the bottom strip, whereas a second hole can be seen on the top strip. These two centering holes are used to align the three printed layers during the assembly process. After this step, the coaxial cable is soldered to the top metasurface and to the tapered line at the bottom of the stack up. The proposed antenna presents a total thickness of 1.92 mm, and is glued into the cavity. The metallization forming the metasurface has a gap \(g=2.3\) mm. The other dimensions can be found in Table 1. The cavity below the dielectric layers is kept free of material, i.e., with a relative permittivity of \(\varepsilon_{\mathrm{r}}=1\).  
Simulation results (including losses) are given in Figure 9. The antenna BW equals 82 MHz. The measurement results are in close agreement with the simulations, having a slightly larger BW of 90 MHz. This BW enlargement might come from a possible undervaluation of the insertion due to the glue. Moreover, a second resonance can be observed in measurement, which also contributes to the BW enlargement. The measured gain is of 3.9 dBi (simulated gain is 4.5 dBi). The measured  
Figure 7: Cross-section view of the circular patch antenna on dual-layer substrate.
# Application of Enhanced YOLOX for Debris Flow Detection in Remote Sensing Images
## 3 Data and Methodology
### Methodology
#### 3.2.3 Loss Function  
YOLOX employs bounding box regression technology for precise target localization. Bounding box regression is extensively utilized across object detection networks, with numerous mainstream models adopting this strategy for localization tasks. The principle behind bounding box regression involves using the overlap area between the predicted and actual boxes as the loss function, continuously iterating to refine the predicted box. This approach is commonly referred to as the intersection over union (\(IoU\)) loss function. The IoU metric serves as a crucial component in optimizing the accuracy of target localization, reinforcing the model's effectiveness in detecting and delineating objects within an image.  
The formula for calculating \(IoU\) is as follows:  
\[IoU=\frac{\left|B^{pr}\cap B^{gt}\right|}{\left|B^{pr}\cup B^{gt}\right|} \tag{1}\]  
In this context, \(B^{pr}\) denotes the predicted bounding box, while \(B^{gt}\) signifies the ground-truth box.  
The formula for calculating loss function of \(IoU\) loss function is calculated as follows:  
\[L_{IoU}=1-\frac{\left|B^{pr}\cap B^{gt}\right|}{\left|B^{pr}\cup B^{gt}\right| }=1-IoU \tag{2}\]  
The formula for calculating \(EIoU\) is as follows:  
\[L_{EIoU}=L_{IoU}+L_{dis}+L_{asp} \tag{3}\]  
\[L_{IoU}=1-IoU \tag{4}\]  
\[L_{dis}=\frac{\rho^{2}\big{(}b,b^{gt}\big{)}}{c^{2}} \tag{5}\]  
\[L_{asp}=\frac{\rho^{2}\big{(}w,w^{gt}\big{)}}{C_{w}^{2}}+\frac{\rho^{2}\big{(} h,hs^{gt}\big{)}}{C_{h}^{2}} \tag{6}\]  
The formula for calculating loss function of \(EIoU\) loss function is calculated as follows:  
\[L_{EIoU}=1-IoU+\frac{\rho^{2}\big{(}b,bs^{gt}\big{)}}{c^{2}}+\frac{\rho^{2} \big{(}w,w^{gt}\big{)}}{C_{w}^{2}}+\frac{\rho^{2}\big{(}h,hs^{gt}\big{)}}{C_{ h}^{2}} \tag{7}\]  
In this context, \(b\) represents the predicted bounding box, \(b^{gt}\) represents the ground-truth bounding box; \(\rho^{2}\big{(}b,bs^{gt}\big{)}\) represents the Euclidean distance between the center points of the predicted and ground-truth boxes, and \(c\) is the length of the diagonal of the smallest  
Figure 8: The PAFPN module after it is improved.  
enclosing box that contains both the predicted and ground-truth boxes. Thus, \(\frac{\rho^{2}\left(b,bit^{st}\right)}{c^{2}}\) represents the ratio of the Euclidean distance between the centers of the predicted and ground-truth boxes to the diagonal distance of the smallest enclosing box. Similarly, \(\frac{\rho^{2}\left(w,bit^{st}\right)}{C_{w}^{2}}\) and \(\frac{\rho^{2}\left(h,bit^{st}\right)}{C_{w}^{2}}\) represent the ratios of the Euclidean distances of width and height to the width and height of the smallest enclosing box, respectively.
## 4 Experiments and Results
### Datasets and Evaluation Metrics
\begin{table}
\begin{tabular}{l l l l l l l} \hline
**Dataset** & **\#Seqs** & **\# Frames** & **Resolution** & **Annotations** & **Scenarios** & **Challenges** \\ \hline MOT15 & 22 (11/11) & \(\sim\)11,200 & 640 \(\times\) 480 to 1920 \(\times\) 1080 & Blues & Urban Streets, Indoor Scenes & Occlusions, Low Resolution, Camera Motion \\ \hline MOT16/17 & 14 (7/7) & \(\sim\)14,200 & 108 vp & Blues & Crowded Urban Areas & Dense Crowds, Varying Illumination, Occlusions \\ \hline MOT20 & 12 (8/4) & \(\sim\)13,400 & 1173 \(\times\) 880 to 3384 \(\times\) 2710 & Blues & Urban Crowds & Heavy Occlusions, High Density \\ \hline DanceTrack & 100 (40/25/35) & \(\sim\)106,000 & 1280 \(\times\) 720 to 1920 \(\times\) 1080 & Blues & Indoor Dance & Rapid Motions, Frequent Sequences & Occlusions, Pose Changes \\ \hline WildTrack & 14 (7/7) & \(\sim\)2800 & 108 vp & Blues, 3D Localization & Campus Scenes, Cross-Camera Occlusions, Varying Lighting \\ \hline \end{tabular}
\end{table} 
Table 1: Overview of datasets used in ReTrackVLM experiments. * **ML (Mostly Lost):** ML refers to the number of ground truth trajectories that are tracked for less than 20% of their length. A lower ML value is desirable as it indicates fewer instances of lost tracks.
# Study on Fire Smoke Distribution and Safety Evacuation of Subway Station Based on BIM
## 4 Fire Smoke Analysis of Subway Station
### Fire Simulation Analysis at the End of the Platform
#### 4.3.4 Visibility  
The visibility at the height of 1.65 m from the platform ground in the fire in the middle of the platform is shown in Figure 13. With the continuous burning of the fire source, the visibility gradually decreases. When t = 60 s, only the visibility around the fire source is less than 10 m; when t = 120 s, the visibility decreases, but does not drop below 10 m; when t = 180 s, the visibility of the effective evacuation area at the platform floor drops below 10 m, reaching the critical value of danger; until t = 360 s, with the spread of smoke, the visibility of the whole platform layer is continuously reduced. That is, when the fire reaches 180 s, the visibility of the four groups of stairs and escalators in the platform layer decreases to 10 m, which affects the safety evacuation of people at the platform layer.  
By sorting the data results of fire visibility in the middle of the platform, the time for each monitoring point to reach the critical value of danger is obtained, as shown in Figure 14. It can be seen that the visibility of the 00-07 monitoring points decreases to below 10 m at 181 s, 163 s, 95 s, 117 s, 95 s, 90 s, 119 s and 117 s, respectively (namely, the critical value is reached).  
In summary, when a fire occurs in the middle of the platform, the time when the smoke temperature, CO concentration and visibility at each stair and escalator exit reach the critical value of danger is shown in Table 5. Considering the influence of various factors on the available safe evacuation time, it is determined that the available safe evacuation time of the four groups is 163 s, 95 s, 90 s and 117 s, in sequence.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **No.1 Entrance** & **No.2 Entrance** & **No.3 Entrance** & **No.4 Entrance** \\ \hline Smoke temperature & 360 & 360 & 360 & 360 \\ CO concentration & 360 & 360 & 360 & 360 \\ Visibility & 163 & 95 & 90 & 117 \\ ASET & 163 & 95 & 90 & 117 \\ \hline \hline \end{tabular}
\end{table}
Table 5: ASET for the fire (unit: s).  
Figure 13: Visibility in the fire in the middle of the platform.
## 4 System Analysis for a Four User System and Machine Learning
### Optimization of Reflective IBFD with ML
#### 4.1.1 Machine Learning-Based Genetic Algorithm  
MLGA is based on the principles of natural collection and genetics. The problems with mixed continuous-discrete variables, discontinuous variables and non-convex spaces entail optimization for the system design. On-line linear programming methods of optimization can also be useful for the aforementioned problems; though, they will lead to computational expenses and inefficiency. Consequently, the usage of MLGA will be productive. Similar to natural genetic progressions, MLGA is also founded on reproduction, crossover and mutation. Figure 3 characterizes the whole technique that is required for the employment of an MLGA in the corresponding optimization problem [28]. The opted randomly generated parameters are used to obtain the fitness function which is the secrecy capacity in this article. If the fitness function does not achieve the required maximum possible value then the algorithm selects the randomly generated parameters, responsible for the current highest fitness function. The selected parameters are mixed together and exploited to obtain better parameters with crossover. The new parameters are being mutated with some of the old parameters to obtain the best possible fitness function. The process goes on until the best fitness function has been obtained.  
The MLGA is a suitable ML technique for an optimization problem. It uses the survival-of-the-fittest principle of nature to maximize the fitness function of the offered problem [38]. In this paper, we used the secrecy capacity as the fitness function of the problem.