esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### PM\({}_{2.5}\) Prediction Model for Nationwide
Therefore, with the overfitting problem reduced, the model with the highest average R-squared (month factor: included, hidden layer layout: 24 \(\times\) 24, threshold: 0.05) was determined as the optimal model. Assuming that the weather prediction is accurate, if the meteorological factor on the forecasting day is used instead of data from the previous day, the R-squared increased from 0.65 to 0.81 for in-sample and from 0.64 to 0.73 for out-of-sample as shown in Table 9. The absolute error n \(\sigma\) also increased, indicating that the accuracy of the model could be further improved if accurate weather predictions were used. The predicted and actual values of the optimal prediction model are presented in Figure 7. The distribution of most of the results proximate to the centerline indicates that the model adequately predicted PM\({}_{2.5}\). Figure 7b has a slightly smaller difference between the predicted and the measured values than Figure 7a. Figure 6: R-squared for nationwide models: (**a**) excluding month factor and (**b**) including month factor. Figure 7: PM\({}_{2.5}\) prediction vs. actual PM\({}_{2.5}\) for nationwide model: (**a**) not considering accurate weather prediction and (**b**) considering accurate weather prediction. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Without Accurate Weather Forecasting** & \multicolumn{2}{c}{**With Accurate Weather Forecasting**} \\ \hline  & **In-Sample** & **Out-of-Sample** & **In-Sample** & **Out-of-Sample** \\ \hline R-squared & 0.65 & 0.64 & 0.81 & 0.73 \\ Absolute Error in 1 \(\sigma\) & 0.91 & 0.91 & 0.96 & 0.92 \\ Absolute Error in 0.5 \(\sigma\) & 0.66 & 0.64 & 0.81 & 0.75 \\ Absolute Error in 0.25 \(\sigma\) & 0.40 & 0.37 & 0.50 & 0.46 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: R-squared and absolute errors in n \(\sigma\) of nationwide model with/without accurate weather forecasting (Month factor: included, hidden layer layout: 24 \(\times\) 24, threshold: 0.05).
# Prediction of Thermal Cracking During Construction of Massive Monolithic Structures
## 3 Results and Discussion
### Analysis of the Possibility of Reducing the Risk of Early Cracking  
To analyze the possibility of reducing the risk of early cracking, calculations were performed with a reduced block length. The dependence of the concrete elastic modulus on time was set according to option 2, as the worst. Figure 11 shows graphs of the change in time of maximum tensile stresses \(\sigma_{z}\) for different lengths of the conc  
Figure 10: Comparison of stresses obtained using a simplified method [31] and without simplifications.  
the block length by 2 times leads to a decrease in maximum stresses by 45%. When the block length is reduced by 4 times compared to the original, the maximum stresses are reduced by 1.8 times.  
Another well-known method of reducing the risk of cracking at the construction stage is to reduce the temperature gradient between the center and the periphery of the structure. This can be performed, for example, by installing cooling systems inside the structure [32]. However, thermal insulation of surfaces is used more frequently [33], which is a less labor-intensive and economical method. Figure 12 shows graphs of the change in time of maximum tensile stresses \(\sigma_{z}\) at different values of the heat transfer coefficient on the side surfaces of the wall \(h_{side}\) and on the upper surface \(h_{up}\). Compared with the original version (\(h_{side}=10\) and \(h_{up}=25\)), options \(h_{side}=h_{up}=10\) and \(h_{side}=h_{up}=25\) do not lead to a noticeable change in the maximum stresses. The option of insulating all surfaces \(h_{side}=h_{up}=4\) leads to an increase in the maximum stresses. Thus, the method of insulating surfaces, effective for foundation slabs, turns out to be ineffective for the considered example of the dock wall. This can be explained by the fact that in this problem the maximum stress depends not only on the temperature gradient between center and periphery of the structure but also on the absolute value of the maximum temperature, since the wall is rigidly connected to the foundation slab made of old concrete. The foundation slab acts as an external restraint for the wall, and cracks are usually initiated at the junction of the foundation and the wall [34; 35].  
The possibility of reducing the risk of early cracking by reducing the heat release of concrete was also investigated. Figure 13 shows a graph of the change in time of maximum stresses for the initial version, as well as in the case of a 1.5 times reduction in concrete heat release. With a 1.5 times reduction in \(Q_{28}\), the maximum stress decreases by 45%.  
Figure 11: Change in time of maximum tensile stresses \(\sigma_{z}\) for different lengths of the concrete block.
### Contributions  
In this paper, by utilizing the tools of the queuing theory, we formulate and solve a general slicing RAN problem for \(K\) traffic aggregates utilizing both resource reservation and pre-emptive-priority service discipline. In the considered system, each slice is assigned a certain dedicated share of radio resources \(g_{k}\) such that \(\sum_{k}g_{k}<C\), where \(C\) is the overall amount of radio resources. The shared pool of resources, \(C-\sum_{k}g_{k}\) is regulated by the pre-emptive priority service procedure to improve the degree of statistical multiplexing. The system is then solved for user- and system-centric KPI, session drop/pre-emption probability as well as system resource utilization, by utilizing the queuing-theoretic formalism. The proposed system allows to satisfy inherently contradictory requirements of having a strong degree of isolation between traffic classes and high efficiency of resource usage. The performance of the proposed system is demonstrated by utilizing a three-class slicing scheme serving an ultra-reliable low-latency service, an enhanced mobile broadband service, and a massive machine-type communication (mMTC) service at the same radio interface.  
The main contributions of our study are the following:  
* Mathematical framework for mixed reservation- and priority-based network slicing at the radio interface along with parameterization by accounting for wireless channel specifics that include numerous special traffic isolation strategies such as full reservation and priority;
* Observation that the mixed reservation- and priority-based strategy allows extremely high resource utilization to be maintained, approaching 95% in overloaded system conditions, while still providing a strong degree of traffic isolation;
* Observations that short-lived lightweight low-priority traffic, such as mMTC, is best suited for the mixed reservation-priority strategy while elastic eMBB traffic benefits more from the full reservation strategy in terms of both drop and pre-emption probabilities.  
The rest of the paper is organized as follows. First, in Section 2 we introduce our system model. The mathematical model is formalized and solved for performance metrics of interest in Section 3. Numerical results of the coexistence of three services, URLLC, eMBB, and mMTC, are presented in Section 4. Conclusions are drawn in the last section.
# Time-Sensitive Networking Technologies for Industrial Automation in Wireless Communication Systems
## 4 IEEE 802.11 Technology for TSN
### TSN Time Synchronization  
This section introduces studies for time synchronization for TSN deployment in 802.11-based WLAN. TM or FTM has been used for time synchronization [5]. Figure 5 shows the FTM operation. The FTM request from the slave station initiates the FTM mechanism. The master station sends the FTM message within 10 ms of receiving the FTM request. The master station transmits the FTM(n) message to the slave station and measures the transmission time, \(t1(n)\). The records of \(t1(n-1)\) and \(t4(n-1)\) are piggybacked in the FTM(n) message to inform the slave station. When n is 1, \(t1\) and \(t4\) information is excluded. When the slave station receives the FTM(n) message, it measures \(t2(n)\). The slave station measures the transmission time \(t3(n)\) while transmitting the ACK message. Finally, \(t4(n)\), which is the time when the master station receives the ACK message, is recorded. \(D(n)\), which is the transmission delay time from the master station to the slave station, is calculated as \(t2(n)-t1(n)\). Moreover, the lowest among n delay times is used in the time synchronization calculation [30].
## 4 Energy Consumption
### Average Energy Consumption during Normal Cycles
#### 4.6.2 C2 Sensor Nodes  
The average energy consumed by RN2 in a _normal_ cycle can be expressed as being composed of different terms, each of them associated with the occurrence of the possible outcomes of the RN2 contention process. RN2 switches to _sleep_ mode when any of the following events occur: (i) RN2 completes the transmission of a PF with success, \(E_{tx,s}^{nr,2}\) ; (ii) RN2 detects a collision when transmitting a PF, \(E_{tx,f}^{nr,2}\) ; (iii) an active RN2 detects that it has lost the contention with other C2 SNs, \(E_{oh}^{nr,2}\) ; (iv) in cycles in which all C2 are inactive, \(E_{id}^{nr,2}\). (v) along any cycles in which C1 SNs access the channel and, therefore, the medium is busy for C2 SNs, \(E_{bm}^{nr,2}\) ;\[\begin{split} E_{tx,s}^{nr,2}&=\sum_{i=1}^{Q_{2}}\sum_{k=0}^ {M_{2}}\pi_{2}(i,k)P_{s,k,2}\Big{[}T_{1}^{nr,2}-(BT_{s,k,2}+\alpha_{2}\,t_{DATA}) \Big{]}P_{sl}\,R_{1,0}\,,\\ T_{1}^{nr,2}&=T-T_{sc,2}-\left(t_{RTS}+t_{CTS}+t_{ ACK}+4D_{p}\right)\,,\\ \alpha_{2}&=min(i,F_{2})\,.\end{split} \tag{28}\]  
\[\begin{split} E_{tx,f}^{nr,2}&=\sum_{i=1}^{Q_{2}} \sum_{k=1}^{M_{2}}\pi_{2}(i,k)P_{f,k,2}\Big{[}T_{2}^{nr}-BT_{f,k,2}\Big{]}P_{sl }\,R_{1,0}\,,\\ T_{2}^{nr,2}&=T-T_{sc,2}-\left(t_{RTS}+2D_{p}\right) \,.\end{split} \tag{29}\]  
\[\begin{split} E_{dh}^{nr,2}&=\sum_{i=1}^{Q_{2}}\sum _{k=1}^{M_{2}}\pi_{2}(i,k)kP_{s,k,2}\Big{(}T_{3}^{nr,2}-BT_{s,k,2}\Big{)}P_{sl }\,R_{1,0}\\ &+\sum_{i=1}^{Q_{2}}\sum_{k=2}^{M_{2}}\pi_{2}(i,k)\widehat{P}_{ f,k,2}+\Big{(}T_{3}^{nr,2}-BT_{f,k,2}\Big{)}P_{sl}\,R_{1,0}\,,\\ T_{3}^{nr,2}&=T-T_{sc,2}-t_{RTS}-D_{p}\,.\end{split} \tag{30}\]  
\[\begin{split} E_{id}^{nr,2}&=\sum_{k=0}^{M_{2}}\pi_ {2}(0,k)(T-T_{sc,2})P_{sl}\,R_{1,0}\,.\end{split} \tag{31}\]  
\[\begin{split} E_{bm}^{nr,2}&=(T-T_{sc,2})P_{sl}\,(1 -R_{1,0})\,.\end{split} \tag{32}\]  
The average energy consumed by RN2 in _normal_ cycles can be expressed as,  
\[\begin{split} E_{nr,2}&=E_{tx,s}^{nr,2}+E_{tx,f}^{nr,2}+E_{oh}^{nr,2}+E_{id}^{nr,2}+E_{bm}^{nr,2}\,.\end{split} \tag{33}\]
## 4 Parametric Study
### Comparison after Normalization  
In order to compare the effects of each parameter (\(\varphi\), \(\delta\), \(c\), \(q\), \(B\), \(h\)) in Figures 12-14, this study standardized the parameters to \(i\), the ratio of the difference between the current value and the minimum value of the parameter to the range, that is (current value - minimum value)/(maximum value - minimum value). The standard values of the resultant force (\(E_{\text{wh}}\)/(\(y\)/\(h^{2}\)/2)) and its standard action point (\(h_{\text{w}}\)/\(h\)) calculated according to Equations (20) and (21) change with the parameter \(i\), as shown in Figure 15. (\(E_{\text{wh}}\)/(\(y\)/\(h^{2}\)/2)) increases as \(B\) and \(q\) increase but decreases as \(c\), \(\varphi\), \(\delta\), and \(h\) increase. The impacts of the parameter on (\(E_{\text{wh}}\)/(\(y\)/\(h^{2}\)/2)) from largest to smallest are as follows: \(\varphi\), \(\delta\), \(c\), \(B\), \(q\), and \(h\). (\(h_{\text{w}}\)/\(h\)) increases with increasing \(\varphi\), \(\delta\), and \(q\) but decreases with increasing \(B\), \(c\), and \(h\). The impacts of the parameters on (\(h_{\text{w}}\)/\(h\)) from largest to smallest are as follows: \(c\), \(\delta\), \(q\), \(\varphi\), \(B\), and \(h\). Controlling of the key influencing factors, i.e., increasing the soil-wall friction and decreasing the limited soil width (not less than the minimum boundary calculated using Equation (19)), will increase the safety and economic efficiency of the retaining structure in a dense underground environment significantly.
## 4 Case Study
### Performance Evaluation of Information Extraction Compared to Other Models
#### 4.3.4 Performance Evaluation of Information Extraction by GPT-4 and Other Models
The models' robust contextual understanding enables them to capture subtle dependencies in the text, making them particularly effective in extracting complex semantic structures such as interface requirements and design constraints, outperforming BERT and RoBERTa in these tasks. Between GPT-4 and GPT-3.5-turbo, GPT-4 is an iterative upgrade of GPT-3.5-turbo. GPT-4 has a significant increase in the number of parameters, reaching trillions of parameters compared to the billions of parameters in GPT-3.5-turbo. This higher parameter count enables GPT-4 to better understand complex contextual information, thereby enhancing its reasoning ability and the accuracy of text generation. Current research shows that GPT-4 outperforms GPT-3.5 in various tasks. According to an official report from OPENAI, in \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Requirement Category** & **GPT-4** & **GPT-3.5-Turbo** & **BERT** & **RoBERTa** \\ \hline Functional requirement & 85.00\% & 77.50\% & 62.50\% & 60.00\% \\ Interface requirement & 90.00\% & 90.00\% & 50.00\% & 40.00\% \\ Performance requirement & 83.33\% & 83.33\% & 58.33\% & 75.00\% \\ Physical requirement & 86.36\% & 81.82\% & 68.18\% & 72.73\% \\ Design constraints & 80.95\% & 71.43\% & 47.62\% & 42.86\% \\ Overall accuracy & 84.76\% & 79.05\% & 59.05\% & 59.05\% \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Model performance comparison. complex information extraction tasks, GPT-4's error rate is 25% lower than that of GPT-3.5-turbo [55; 56; 57]. A. In SR information extraction tasks, GPT-4 demonstrates higher extraction accuracy compared to GPT-3.5-turbo. For requirement descriptions primarily composed of noun phrases, such as functional requirements, physical requirements, and performance requirements, the performance of all models is relatively strong. These requirement categories rely more on explicit object-action relationships rather than deep contextual interpretations, so even label-based models like BERT and RoBERTa can achieve high accuracy. \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline
**Requirement Category** & **Model** & **Extracted Structured Data** \\ \hline \multirow{8}{*}{Transparency} & \multirow{2}{*}{Description} & The Semi-automatic Ticket Verding Machine should have the following key functions: processing ticket refunds, ticket replacements, fare top-ups, ticket validation, and ticket exchanges for passengers.
# A Modified Bearing Capacity Model for Inclined Shallow Anchor Cable with Experimental Verification
## 2 Theoretical Model for Calculating Bearing Capacity of Anchor Cable
### Mechanical Model  
As shown in Figure 2a, the failure surface morphology of inclined anchor cables is represented as an inverted conical shape. Based on the symmetrical nature of the failure zone, the mechanical response of the overall failure zone can be easily understood by presenting the representative section through the center of the anchor cable, as shown in Figure 2b. The dimensions of the failure zone can be described by critical parameters, i.e., inclination angle, failure angles (\(\beta_{1}\) and \(\beta_{2}\)), and the maximum failure diameter (\(b\)). Note that the fixed anchor length (\(l_{\text{fixed}}\)) is the grouted portion of the anchor cable. The free anchor length (\(l_{\text{free}}\))is the ungrouted portion of the anchor cable. The burial depth (\(h_{\alpha}\)) is defined as the vertical distance from the ground surface to the center of the fixed anchor length.  
According to the limit equilibrium principles, the bearing capacity mainly depends on the normal and shear forces acting on the failure surface, as well as the self-weight of the failure zone. The dimensions of the failure areas can be calculated using the following equation:  
\[l_{OB}=\frac{1}{2}\Big{(}2{l_{AB}}^{2}+2{l_{\text{BC}}}^{2}-{l_{\text{AC}}}^{2 }\Big{)}^{\frac{1}{2}},\]  
Figure 1: Failure models of the test [32].  
Figure 2: Mechanical model: (**a**) three-dimensional failure model; (**b**) central cross-section.  
\[l_{\text{BC}}=\bigg{(}h_{\alpha}+\frac{1}{2}l_{\text{fixed}}sin\alpha\bigg{)} \frac{1}{sin\beta_{2}},\]  
\[l_{\text{AC}}=\bigg{(}h_{\alpha}+\frac{1}{2}l_{\text{fixed}}sin\alpha\bigg{)} \bigg{(}\frac{1}{tan\beta_{1}}+\frac{1}{tan\beta_{2}}\bigg{)}, \tag{1}\]  
where \(l_{AB}\), \(l_{\text{AC}}\) and \(l_{\text{BC}}\) are dimensions of the failure surface's central plane; \(\beta_{1}\) and \(\beta_{2}\) are the failure angle between the failure surface and the ground surface; \(\theta=2\beta_{0}\) is the apex angle of the inclined conical failure surface.  
Based on the sine theorem, the angle between the centerline of the cone and the ground surface can be calculated using Equation (5)  
\[sin(\beta+\alpha)=\frac{l_{AB}}{l_{OB}}sin\beta_{1} \tag{2}\]  
After introducing Equation (1), \(\beta\) can be calculated as follows:  
\[\beta=arcsin\bigg{(}2 sin\beta_{1}sin\beta_{2}\left(2 sin^{2}\beta_{2}+2 sin^{2} \beta_{1}-sin^{2}(\beta_{1}+\beta_{2})\right)^{-\frac{1}{2}}\bigg{)}-\alpha \tag{3}\]  
Based on the expression of \(\beta\) in Equation (3) and applying the limit equilibrium principle, the bearing capacity of anchor cables at different inclinations can be calculated.
# Lightweight Semantic Segmentation Network for Real-Time Weed Mapping Using Unmanned Aerial Vehicles
## 5 Conclusions  
UAV remote sensing and precision spraying are two important components of SSWM. However, the time gap between image collection and herbicide treatment significantly limits the application of SSWM technology. This study used real-time image processing onboard a UAV with the goal of eliminating the time gap between image collection and herbicide treatment. First, we established a hardware environment for real-time image processing that integrates map visualization, flight control, image collection, and real-time image processing onboard a UAV based on secondary development. Second, we exploited the proposed model design and presented a lightweight network architecture for weed mapping tasks. The proposed network architecture was evaluated and compared with mainstream semantic segmentation models. Experimental results demonstrated that the proposed network is almost two times faster than the mainstream segmentation models with competitive accuracy. Next, we conducted optimization of the inference process. Precision calibration was applied on both desktop and embedded devices and the precision was reduced from FP32 to FP16. Experimental results demonstrated that precision calibration further improves inference speed while maintaining reasonable prediction accuracy. Our modified network architecture achieved an accuracy of 80.9% on the testing samples and the inference speed of 4.5 fps on the Jetson TX2 module, which demonstrates its potential for practical agricultural monitoring and precise spraying applications.  
In the future, we plan to collect additional UAV images for model training and validation. Additionally, we plan to combine our system with variable spraying technology. The classification results of UAV images can provide decision-making information for sprayers, which can help maintain pesticide effects while reducing the use of chemicals.  
Conceptualization: [PERSON], [PERSON], and [PERSON]; funding acquisition: [PERSON] and [PERSON]; methodology: [PERSON] and [PERSON]; project administration: [PERSON] and [PERSON]; software: [PERSON] and [PERSON]; writing--original draft: [PERSON]; writing--review and editing: [PERSON], [PERSON], and [PERSON] All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key Area Research and Development Planning Project of Guangdong Province (grant no. 2019B002221001), Guangdong Provincial Innovation Team for General Key Technologies in Modern Agricultural Industry (grant no. 2019 KJ133), the Science and Technology Planning Project of Guangdong Province, China (grant no. 2018A050506073), the National Natural Science Foundation of Guangdong Province, China (Grant No. 2018B030306026), the National Key Research and Development Plan, China (grant no. 2016 YFD0200700), and the 111 Project, China (D18019).  
The authors declare no conflict of interest.
# Beyond Spectrograms: Rethinking Audio Classification from EnCodec's Latent Space
## 1 Introduction
Similarly, NVIDIA's NeMo Mel Codec [7] achieves the same objective by encoding disjoint mel-bands separately and quantizing them using Finite Scalar Vector Quantization (FSQ). All codebook embeddings are then concatenated and fed into a HiFi-GAN [8] decoder,  
Figure 1: EnCodec-based versus spectrogram-based audio data processing pipeline. which reconstructs the final waveform. Meta has also released AudioDec [9], an open-source, high-fidelity neural audio codec designed for streaming. It utilizes SoundStream as the codec along with HiFi-GAN neural vocoder. The authors argue that combining a robust vocoder with a well-trained encoder results in the highest audio quality. AudioDec is particularly optimized for real-time applications, and they also propose an efficient training paradigm. Finally, SNAC, a multi-scale neural audio codec [10], employs multi-scale RVQ instead of the traditional RVQ approach. In conventional RVQ, tokens are generated at a fixed temporal resolution, whereas SNAC utilizes a hierarchy of quantizers operating at multiple temporal resolutions. This design allows the codec to capture both coarse and fine audio details more effectively. This paper proposes a novel approach to audio classification using EnCodec, which simplifies and improves the classification process. We hypothesize that EnCodec's latent representation of audio signals can be used to classify audio signals more efficiently than spectrogram-based classification techniques. In addition, the computational load of the training process gets reduced, as the latent representation is more suited for this kind of task because of its reduced size and better data representation. In order to prove this hypothesis, we demonstrate that (1) the network converges on the same or fewer epochs using the EnCodec latent representation rather than the spectrogram, (2) the accuracy obtained using the latent EnCodec representation is better than that obtained using the spectrogram with the same epochs, and (3) after training, classifying with EnCodec is faster than with the spectrogram.