esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Graph Models for Traffic Prediction
## 6 Experimental Results  
### Predictive Performance  
Table 1 shows the performance comparison of the proposed method and compared methods on the METR-LA and PeMS datasets. On the PeMS-BAY and METR-LA datasets, the proposed method achieves the best prediction results at different time steps. In order to mine the potential correlation between nodes, the HGM uses a dynamic adjacency matrix to represent the spatial information of the traffic network and uses a convolutional neural network to extract nonlinear temporal dependencies, which is beneficial for achieving better predictive performance. For example, on the METR-LA dataset, the error of the proposed method drops to 6.60%, 8.19%, and 9.65% at 3, 6, and 12 future time steps on MAPE, respectively, which shows that the proposed method achieves the best predictive  
performance for both short-range and long-range. From the experimental results in Table 1, the following results can be drawn. First, since traditional time series prediction methods such as HA and ARIMA focus on historical records and ignore the spatial characteristics of the traffic network, these methods cannot compete with the proposed method. Compared with HA and ARIMA, SVR further considers the spatial correlation and achieves better performance. Due to the complex spatial-temporal correlation of road conditions and high-dimensional feature information, the performance of traditional time series prediction methods is limited. The proposed method adopts a deep learning-based approach to build models, which is beneficial for extracting complex spatial-temporal information. Second, deep learning-based methods (such as FC-LSTM, STGCN, DCRNN, MTGCN, ASTGCN, etc.) alleviate the shortcomings of traditional methods. These methods utilize deep neural networks to automatically mine the complex spatial-temporal features of the traffic network for better predictive performance. However, LSTM considers temporal features while ignoring spatial features, which leads to poor predictive performance. Other methods represent spatial information through a static adjacency matrix, which cannot effectively model the global correlation. The proposed method devises a dynamic adjacency matrix to represent the topology of the transportation network. The HGM dynamically updates the correlation between nodes during the model training process, which is conducive to mining potential correlations, and has achieved the best performance in MAE, RMSE, and MAPE. For example, on the PeMS-BAY dataset, the proposed method achieves a reduction of 0.61% and 1.71% on MAPE over 12 future time steps compared to DCRNN and ASTGCN, respectively. Finally, the proposed method constructs the topology information from different visions, and the STA module and DGCN are designed to perform information extraction
Existing studies have typically been limited to either text-based sentiment analyses or image recognition, lacking a comprehensive multimodal approach [6]. As a result, they have failed to integrate multi-source data, uncover deep semantic relationships, and effectively capture authentic emotional feedback from the public on cultural heritage. Additionally, in commercialized historical districts, the presentation of cultural information is often overshadowed by commercial elements, leading to a weak cultural perception. The current heritage display methods lack emotion-driven optimization, making it difficult to enhance visitors' cultural understanding and emotional engagement. This challenge is particularly evident in Qianmen Street, Beijing, a historically significant commercial district located along Beijing's cultural axis [7]. This area features a rich blend of historical architecture, commercial culture, and artistic elements, serving as a representative case of cultural heritage conservation and commercial development [8]. To address these challenges, this study leverages affective computing and large language models to develop an innovative approach to multimodal cultural information mining and personalized presentation. By utilizing AI-driven methods, this study aims to precisely identify users' interests in the cultural aspects of commercial historical districts like Qianmen Street, ultimately enhancing the visitor experience. The primary contributions of this study are as follows:  
1. Proposing an LLM-based cultural information mining method that integrates affective computing. This method utilizes the QwenLM and RoBERTa pre-trained models to conduct a sentiment analysis on UGC, quantifying the emotional tendencies and multilayered cultural perceptions across different users. Additionally, it explores how distinct emotional features influence cultural experiences. 2. Developing a multimodal affective model driven by emotion-based feedback, which establishes semantic associations between text and image data in UGC. This model computes the sentiment tendencies across various cultural dimensions and maps these results into an interactive VR environment, enabling personalized and immersive cultural heritage presentations that enhance user cognition and emotional engagement. 3. Validating the effectiveness of the optimization of information presentation through eye-tracking experiments, analyzing how different presentation styles impact the distribution of user attention, emotional engagement, and cognitive load. This study further reveals how emotion-driven personalization enhances the dissemination and impact of cultural heritage information. This approach provides a systematic and AI-driven solution for improving the cultural experience in commercial historical districts, addressing key challenges in cultural heritage interpretation, sentiment analysis, and multimodal data integration.
# Factorial Numbers and Their Practical Applications
## 3 Theory of Factorial Numbers
### Interval Search of Factorial Numbers  
Using arithmetic operations of addition and subtraction over factorial numbers allows for solving the problem of enumerating factorial numbers in ascending and descending order for any integer value of the enumeration interval, including one.  
The operation of addition with one is that each factorial number can be transformed by adding one into a neighboring number that is one greater. So, for example, after the number \(F=3\)\(4\)\(0\)\(0\)\(1\)\(0=457_{<10>}\), as a result of the operation of its addition with \(1\), the number \(3\)\(4\)\(0\)\(1\)\(0\)\(0=458_{<10>}\) will appear. Then, after it comes the number \(3\)\(4\)\(0\)\(1\)\(0\)\(0\)\(+\)\(1=3\)\(4\)\(0\)\(1\)\(1\)\(0=459_{<10>}\) and so on until the factorial number \(5\)\(4\)\(3\)\(2\)\(1\)\(0\)\(=719_{<10>}\).  
Sequential enumeration with an integer greater than one is carried out similarly to addition with one. For example, the first result of adding the factorial number \(2\)\(1\)\(1\)\(0=15_{<10>}\) with the number \(1\)\(1\)\(0\)\(0=8_{<10>}\) will give the number \(2\)\(1\)\(1\)\(0\)\(+\)\(1\)\(1\)\(0\)\(0=3\)\(2\)\(1\)\(0=3\)\(3!+2\)\(2\)\(1\)\(+\)\(1\)\(\times\)\(1!+0\)\(\times\)\(0!=23_{<10>}\). Then, the second addition occurs with the number \(8\): \(3\)\(2\)\(1\)\(0\)\(+\)\(1\)\(1\)\(0\)\(0=11_{<10>}\).  
Enumerating factorial numbers in descending order consists of subtracting the same integer at each step. So, at the first step, \(2\)\(2\)\(0\)\(0\)\(-\)\(1\)\(1\)\(0\)\(0\)\(0\)\(=1\)\(1\)\(0\)\(0\)\(0=8_{<10>}\). At the second step, \(1\)\(0\)\(0\)\(-\)\(1\)\(1\  
**Example 2**.: _Convert the factorial number \(F=2\) 3 2 2 0 0 to the number \(X\) in the decimal number system._  
**Solution 2**.: \(F=2\cdot 5!+3\cdot 4!+2\cdot 3!+2\cdot 2!+0\cdot 1!+0\cdot 0!=240+72+12+4=328\).  
The transformation of the number \(X\geq 1\) from the natural number system into the factorial number \(F\) occurs in steps, at each of which the quotient obtained in the previous step is divided by one of the divisors \(1,2,\ldots,n-1\) until the quotient becomes less than the divisor. In this case, the first quotient is the number to be translated, which is divided by one. The last quotient and the sequence of remainders from the divisions are written out from right to left. The result will be the required factorial number.  
**Example 3**.: _Convert decimal number \(X=69\) to factorial number \(F\)._  
**Solution 3**.: \(\frac{69}{1}=69q+0r\); \(\frac{69}{2}=34q+1r\); \(\frac{34}{3}=11q+1r\); \(\frac{11}{4}=2q+3r\).  
**Result 3**.: \(F=2\) 3 1 1 0.  
**Verification 3**.: \(2\times 4!+3\times 3!+1\times 2!+1\times 1!+0\times 0!=48+18+2+1=69\).
## 5 Conclusions  
In this study, we proposed a hybrid resource allocation scheme (i.e., ILPSO) to optimise the resource allocation and achieve a trade-off between throughput maximisation and system fairness in a downlink NOMA system-based 5G network. Resource allocation complexity can be reduced by separating the user paring and power allocation schemes. The PSO algorithm was applied to two variables of power allocation with the assistance of a penalty function to prevent the power constraint from reaching the upper and lower power bounds. Therefore, PSO achieved acceptable solutions with simple control parameters and a minimum number of iterations. The design of SMOO problems that used WSM was applied to the optimisation problem to convert it into a SO function. Three ILPSO scenarios were investigated to evaluate system fairness for the cost of total throughput maximisation. The proposed method outperformed other approaches. In Scenario 3, it outperformed GOS by 17%, MWF by 21.9%, and OFDMA by 33%. Moreover, the system exhibited greater fairness than existing schemes, with Scenario 3 outperforming Scenarios 1 and 2. Finally, the proposed scheme can satisfy throughput maximisation and fairness for future NOMA system-based 5G networks.  
Conceptualisation, O.A.; methodology, O.A.; software, O.A.; validation, O.A., M.R. and Z.B.Y.; investigation, O.A., M.R., Z.B.Y., L.L.C. and P.W.L.; writing--original draft preparation, O.A., M.R. and Z.B.Y.; writing--review and editing, O.A., M.R. and Z.B.Y.; visualisation, O.A., M.R., Z.B.Y., L.L.C. and P.W.L.; supervision, M.R. and Z.B.Y.; project administration, O.A. All authors have read and agreed to the published version of the manuscript.  
This work is supported and funded by the Fundamental Research Grant Scheme-FRGS/1/2021/ICT09/MMU/02/1, Ministry of Higher Education, Malaysia.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 5: Algorithm convergence vs. achieved throughput.
# An Improved Method for Ship Target Detection Based on YOLOv4
## 3 Methods
### YOLOv4 Model  
#### 3.1.1 Network Structure  
As shown in Figure 1, the YOLOv4 network is comprised of backbone CSPDarknet53, neck SPP and PANet [22] and a detection head. CSPDarknet53 is used to extract features, which consist of a convolutional block as well as a series of residual structures. When the size of an input image is 608 \(\times\) 608 \(\times\) 3, the sizes of three output feature layers are 76 \(\times\) 76 \(\times\) 256, 38 \(\times\) 38 \(\times\) 512 and 19 \(\times\) 19 \(\times\) 1024, respectively. SPP is used to increase the receptive field and separate out the contextual information, which consists of three max-pooling layers with kernel sizes of 5 \(\times\) 5, 9 \(\times\) 9 and 13 \(\times\) 13. The input feature map is pooled three times, and the results are stacked with the input feature map to obtain the output feature map. PANet is used for feature fusion which consists of a top-down pyramid and a bottom-up pyramid. The top-down pyramid passes down the strong semantic features from the upper layers to enhance the semantic information. The bottom-up pyramid passes up the strong localization features from the lower layers to enhance the localization information. The fused features contain both semantic and localization information. The detection head is used to generate object bounding boxes and predict object classes.
# Dental Microstructural Imaging: From Conventional Radiology to In Vivo Confocal Microscopy
## 4 In Vivo Dental Imaging
### In Vivo Raman Spectroscopy  
Raman spectroscopy has delivered promising results in dental fields, mainly focusing on the study of periodontal tissues [66; 67].  
A work by [PERSON] and colleagues explains the utility of in vivo RS to evaluate the soft tissues of the oral cavity. In detail, they performed a novel in vivo approach with Raman spectroscopy for the chairside evaluation of the lip barrier to explore the potential of Raman resolution parameters for quantifying water content, natural moisturizing factors, and lip ceramide content to evaluate and cytoarchitecturally characterize the stratum comeum in a transepidermal vision of the oral and periodal mucosa [68]. Furthermore, the investigators compared the outcomes obtained through Raman measurements with those calculated through two already validated in vivo techniques: aquaflux and corneometry. Althoughthe achieved data show certain variability, the authors prompted further improvements and encouraged using Raman spectroscopy for the in vivo evaluation of the oral cavity's structures. Further in vivo studies have investigated the quality of alveolar bone in subjects suffering periodontitis [69], but specific works on in vivo RS for caries and dental surface evaluation are still lacking.  
The most in-depth study reporting on the evaluation of decayed teeth is the work of [PERSON] et al. [70]. Based on the recent evidence of dental tissues pH measurements for early caries detection [71], the authors combined Raman spectroscopy with pH status analysis of decayed teeth surfaces [70]. In detail, the authors developed a combined system of wireless pH sensors with a handheld Raman spectroscope to validate the use and applicability of the system as a chairside diagnostic tool for daily dental practice. Despite still being ex vivo on extracted decayed teeth, their results reported the sensibility of the technique and the friendliness of the system, thus endorsing the method's applicability in cariology. However, it must be considered that, under clinical conditions, environmental factors such as light, vibrations, and body fluids (saliva) could interfere with the spectra by influencing the focus and image acquisition of Raman spectroscopy [72].
# Predictive Power Management for Wind Powered Wireless Sensor Node
## 5 Experimental Results  
The entire wind-powered sensor node is a comprehensive system, which is composed of some unique sub-units. For deep learning of the whole system's working efficiency, it is important to analyze the capability of each subsystem, and perform energy flow comparison at each energy transfer stage. The proposed concept for this EH-node with predictive power manager technique has been implemented in hardware prototype for laboratory testing. Some detailed components are shown below in Table 1.  
The designed EH-WSN system is connected with a commercially wireless node supplied by Arduino with LoRa, as shown in Figure 4. The WTG tail could adjust the wind-front direction to face the incoming wind at the right angle. As such, the wind incident angle is always perpendicular to the turbine plane. The LoRa node uses the ESP8266 32-bit ultralow-power microcontroller, and it is paired with the SX1278 RF transceiver that is also designed for low-power application. A 2 F & 5.5 V supercapacitor bank of Panasonic is chosen as the energy storage. Since the microcontroller's strong computation capability, it is very convenient to make use of it to achieve a more accurate and faster MPTT scheme than a dedicated MPTT analog circuit.
## 5 Conclusions  
In this study, continuous and synchronous observations of canopy SIF and environmental factors were made in Chinese cork oak, in 2020. We found that canopy SIF had distinct seasonal and diurnal dynamics, which were mainly driven by PAR. The obvious variation in the f\({}_{\text{esc}}\) in the intra-day may be the main reason for the asymmetry of the diurnal variation of SIF on sunny days. PAR, Ta, VPD and Ws significantly affected the daily and half-hour scale SIF during the growing season. The correlation between SM and SIF was robust during the spring and autumn, when the precipitation was low, but not significant in the summer. After eliminating the covariant effect of PAR with Ta and the VPD, the relationship between Ta and SIF in the total growing season disappeared due to the different effects of Ta on SIF in the different seasons. The effect of the VPD on SIF was negative. This study showed that the influence of environmental factors related to water and heat on SIF varied with the seasons. In relatively dry seasons, the SM and VPD have more significant effects on SIF through the canopy structure and fluorescence yield. In the autumn, when the leaves were aging and the temperature was low, Ta became a photosynthetic-limiting factor and promoted SIF by affecting the fluorescence yield. This study further clarified the response mechanism of SIF to environmental factors, providing a basis for analyzing the differences between SIF and photosynthesis in the coping strategies necessary for climate change. It would be helpful for subsequent research to investigate or predict the relationship between SIF and the GPP under different conditions and to understand the possible causes of this.  
Conceptualization, J.Z. and H.H.; methodology, M.H. and X.C.; software, M.H. and Y.Z.; validation, X.W. and Q.P.; writing--original draft preparation, M.H.; writing--review and editing, J.Z. and C.G.; visualization, M.H.; supervision, J.Z. and H.H.; funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript.  
This study was funded by grant number CAFYBB2020 QD002-2.  
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.  
The authors thank the staff of the Henan Xiaolangdi Earth Critical Zone National Research Station on the Middle Yellow River for their support and help in the field experiment. The authors declare no conflict of interest.
## 3 Deep Learning-Based Crack Sizing Method
### Height Measurement Architecture  
It is observed in Equation (5) that the height measurement accuracy is related to the arrival time measurements from the diffracted waves. However, the diffracted waves are likely to overlap and contain noise, as shown in Figure 2a, which is very detrimental to the measurement. Current signal processing technologies for measuring the parameters of ultrasonic diffraction waves have shortcomings in one way or another, as described in Section 1. Therefore, these sophisticated methods need to be further developed to accurately estimate ultrasonic diffraction signals. In this study, the arrival time of diffraction waves is precisely identified by the intelligent denoising framework previously proposed [29; 30], combined with Hilbert transform.  
The intelligent denoising framework can achieve signal reconstruction from noisy ultrasonic data by combining basic statistical analysis with a series of machine learning algorithms. The proposed framework analyzes abundant information from numerous raw signals to distinguish the desired signal from complicated noises, avoiding the limitation of information provided by only a single signal. Its complete workflow is shown in Figure 5. Firstly, the database of the ultrasonic signals taken from test specimens is set up. The database is later delivered to our self-developed machine learning algorithms. The algorithms start with similarity analysis in signals, especially for the database with various complicated noises, using a k-means algorithm. These highly similar signals or the same class of signals are subsequently passed on to the core part, the denoising autoencoder, for training it. With the trained autoencoder, we will move on to the denoising step where  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Number of** & **Number of** & **Kernel Size/** & **Features** & **Padding** \\
**Layer Type** & **Input** & **Output** & **Stride** & **Maps** & **Padding** \\  & **Channel** & **Channel** & & & \\ \hline Conv 1 & 1 & 16 & \(8\times 1/2\) & \(16\times 445\times 1\) & Same \\ Pooling 1 & - & - & \(2\times 1/2\) & \(16\times 223\times 1\) & Same \\ Conv 2 & 16 & 32 & \(4\times 1/2\) & \(32\times 111\times 1\) & Same \\ Pooling 2 & - & - & \(2\times 1/2\) & \(32\times 56\times 1\) & Same \\ FC dropout & - & - & 0.25 & - & - \\ FC & - & - & 200 & - & - \\ Output & - & - & 2 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the designed CNN framework.  
the noise can be automatically reduced according to the rule learned by the autoencoder. The detailed steps of removing noise were shown in the previous work [33; 34]. It has been successfully applied to various complicated noises in practice, e.g., grain noises and surface roughness noise, which are almost the same as the desired signal.  
So, the original ultrasonic diffraction signals acquired are denoised through the above framework. The envelopes of the clean signals are extracted by Hilbert transform. In this way, the arrival time of diffracted waves can successfully be determined in the envelope.
# Performance of the 5 th Generation Indoor Wireless Technologies-Empirical Study
## 2 Materials and Methods
### Evaluation
#### 2.2.3 Network Configuration  
The measurement campaign included throughput and delay measurements for the Aruba Wi-Fi 6 access point and the Nokia 5G small cell indoor base station. Table 2 lists the most important parameter values for both the base stations. The 5G base stations used 3540 MHz carrier frequency, 60 MHz bandwidth, QPSK/4 PSK modulation, 4 \(\times\) 4 closed-loop spatial multiplexers, and 21 dBm maximum transmit power. The Wi-Fi 6 base station used 5150-5850 GHz carrier frequency, 160 MHz bandwidth, 4 \(\times\) 4 MIMO, High Efficiency (HE) 20/40/80/160 modulation, and 24 dBm transmission power. The carrier aggregation (CA) technique, where multiple frequency blocks are assigned to the same user, was disabled in the 5G access point. By enabling the CA, the maximum possible data rate per user can be increased.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Ping** & \multicolumn{2}{c}{**iPerf3**} \\ \hline Repeats & 600 & Duration & 300 s \\ Packet Size & 32 B & UDP Datagram Size & 1440 B \\ Interval & 100 ms & UDP Target Bitrate & 1 Gbps \\  & & Buffer Length & 410 KB \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ping and iPerf3 parameters.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **5G** & **Wi-Fi 6** \\ \hline Carrier frequency & 3540 MHz & 5.150 to 5.250 GHz \\  & & 5.250 to 5.350 GHz, \\  & & 5.470 to 5.725 GHz, \\  & & 5.725 to 5.850 GHz \\ midrule Bandwidth & 60 MHz & 160 MHz \\ \hline Maximum transmit power & 21 dBm & 24 dBm \\ \hline MIMO mode &
\begin{tabular}{c} 4 \(\times\) 4 Closed Loop \\ Spatial Multiplexing \\ \end{tabular} & 4 \(\times\) 4 MIMO \\ \hline Modulation Scheme & Automatic & HE 20/40/80/160 \\ \hline Carrier Aggregation & Disabled & - \\ \hlineullaDeltaSintrMax & 15 & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: 5G and WLAN configurations.