esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Experiment with the Multimodal User Emotion Detection System  
As explained in Section 6.1, once the confusion matrices of both components (GEVA and GEFA) have been experimentally obtained, the decision rule can be applied to determine the main user emotion. In order to understand this decision rule, let us apply it to the following example: the user greets the robot, and it perceives the voice and face of the user. Let us assume that the real user emotion is sadness.  
In our approach, the states and classifiers are the following:  
\(\mathbf{S}=\{happiness,sadness,neutral,surprise\}\)  
\(\mathbf{C}=J48,JRIP,CERT,SHORE\)  
Let us assume that the robot, according to the values of the confusion matrices, receives the outputs of each classifier:  
1. J48 (GEVA) \(\rightarrow\) Neutral (estimated emotion)
2. JRIP (GEVA) \(\rightarrow\) Sad (estimated emotion)
3. CERT (GEFA) \(\rightarrow\) Neutral (estimated emotion)
4. SHORE (GEFA) \(\rightarrow\) Neutral (estimated emotion)  
Applying the decision rule defined in Equation (7), the following results are obtained (in this experiment, it is assumed that all emotions are equally probable. This is the case, since the users are asked to simulate each of them):  
\[p(Neutral_{real}|Neutral_{J48})=\frac{80.28}{50+80.28+33.33+42.85}=\textbf{0.38} \tag{8}\]  
\[p(Sad_{real}|Sad_{JRIP})=\frac{77.77}{0+12.67+77.77+0}=\textbf{0.82} \tag{9}\]  
\[p(Neutral_{real}|Neutral_{CERT})=\frac{85.71}{0+85.71+71.42+54.54}=\textbf{0.40} \tag{10}\]  
\[p(Neutral_{real}|Neutral_{SHORE})=\frac{66.66}{0+66.66+22.22+10}=\textbf{0.67} \tag{11}\]  
According to these results (the results shown are the highest values given by each classifier), the classifier with the biggest confidence in its estimation is the JRIP used by GEVA. This may seem to be contradictory, since the other three classifiers estimate that the emotion expressed by the user is neutral instead of sad. Nevertheless, the decision rule decides that the output given by the classifier with the biggest confidence is the one that must be considered. In the case that the winner classifier does not have a sufficiently high confidence (under a certain threshold), then it can be determined that the estimation is not reliable. Therefore, in this case, the information about the user emotion cannot be obtained.  
If this decision limit is low, there is a risk of obtaining false positives (accepting emotions that have not been expressed by the user). On the contrary, if the limit is very high, then the dialog system will frequently lack information about the user emotion.
## 3 Results
### Effects on Cloud Microphysical Processes
#### 3.2.2 Cloud Microphysical Processes
The collections of supercooled cloud droplets and raindrops by graupel are two of the biggest source items in the growth process of graupel. The followed important source items are the deposition of graupel, ice-rain collection to graupel, collection of cloud droplets by snow to form \begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Process**} & \multirow{2}{*}{**Description**} & \multicolumn{2}{c}{**15 h**} & \multicolumn{2}{c}{**21 h**} \\ \cline{3-6}  & & **C-Case** & **P-Case** & **C-Case** & **P-Case** \\ \hline PRA & Accretion of droplets by rain & 0.7 & 0.4 & 1 & 1.8 \\ PRC & Auto-conversion of droplets to form rain & 0.0016 & \(6\times 10^{-5}\) & 0.0012 & \(5\times 10^{-5}\) \\ PSMLT & Melting of snow to form rain & 0.027 & 0.033 & 2.7 & 3.5 \\ PCMLT & Melting of graupel to form rain & 0.05 & 0.09 & 4.5 & 8 \\ PRACS & Collection of rain-snow to form rain & 0.0057 & 0.0087 & 0.18 & 0.25 \\ PRACG & Collection of rain-graupel to form rain & 0.0045 & 0.0017 & 0.5 & 0.8 \\ PRE & Evaporation of rain & \(-0.05\) & \(-0.03\) & \(-0.33\) & \(-0.6\) \\ \hline \end{tabular}
\end{table} 
Table 1: Source-sink terms of rain (after Morrison and Pinto [76]) and maximum conversion rates in its microphysical processes (\(10^{-3}\) g kg\({}^{-1}\) s\({}^{-1}\)). groupel, and collection of snow by super-cooled raindrop to form graupel. By contrast, during the early stage of precipitation, P-case has more super-cooled cloud water than C-case (see Figure 4), which contributes to the growth of snow and graupel through riming. In addition, Graupel also grows through the Wagner-Bergeron-Findeisen (WBF) process. These processes cause larger content of graupel in P-case than C-case (see Figure 4). During the maximum precipitation period in P-case, super-cooled cloud droplets are the main participants in forming graupel through accretion growth.
# Concrete Silos: Failures, Design Issues and Repair/Strengthening Methods
###### Abstract  
The review article investigated failure, design issues, repair and strengthening of reinforced concrete (RC) silos, primarily in agricultural set-ups. The durability of RC structures was influenced by the nature of the bulk solids, materials used in the reinforcement of the structures. Traditionally, high-grade steel has been used in silo wall reinforcement because it is affordable and readily available. However, it is susceptible to corrosion. In contrast, fiber-reinforced polymers (FRP) have better mechanical properties (tensile strength, elastic modulus, and Poisson's ratio) and are not corroded. Additionally, there are limited scalable and facile methods for commercial production. The low ductility elevates the risk of brittle fracture in external pre-stressing concrete repair/strengthening. Beyond the material factors, the existing silo design codes such as BS EN 1991-4:2006, Australian Standard AS 3774-1996, and American Society of Agricultural Engineers ANSI/ASAE EP433 DEC1988 (R2011), and American Concrete Institute ACI 313-97 are limited by simplified characterization of loading/unloading scenarios and exclusion of specific hopper geometries and configurations. The funnel and mass flow scenarios and accumulation of bulk materials contribute to silo failure. In brief, the present repair/strengthening strategies (external pre-stressing, insertion/removal of inserts, shear columns, and FRPs alternatives to steel) do not adequately address the diverse variables that elevate the risk of material failure.  
s 17 May 2020; Accepted: 2 June 2020; Published: 5 June 2020  
**Keywords:** silos; design issues; failure; repair; strengthening methods
## 4 Calibration and Application Experiments
### Response Time Test  
According to the International Standard Organization (ISO) technical report [11], the response time of a precision level is an important performance factor that determines whether the precision level  
Figure 8: Measurement repeatability test: (**a**) photo of the experiment set-up; (**b**) measured positions.  
Figure 7: Comparison experiment: (**a**) experiment set up; (**b**) results.  
is practical or not. To verify the response time of the proposed dual-axis precision level, the precision level was fixed on a linear stage of a CNC (computer numerical control) lathe. When the linear stage moved 100 mm at a speed of 60 mm/s, the raw data of the proposed dual-axis precision level from the start to the end of the movement were saved by the software with the sampling rate of 1000 samples per second. When the jitter of the reading of the precision of the dual-axis level was less than 0.1 arc-sec, the reading of the level was considered to be stable. The response data are shown in Figure 10. It can be seen that the settling time to steady-state condition was less than 1 s. This performance is much better than our previous system that needs a 3 s settling time [21].  
It should be noted that the response time of the precision level designed according to the measurement principle proposed in Section 2 is affected by the viscosity of the liquid in the container. The filtering algorithm used in the signal processing system would also affect the response time. The liquid used in the prototype of the precision dual-axis level is a synthetic silicone oil with a viscosity of about 200 centistokes. The filtering algorithm used in the signal processing system was a moving average filter, and the length of the sampling window of the moving average filter is 10.
# Upscaling Customer Access Network Using Spectrum Conversion-Slicing-Duplication Technique
## 3 Results and Discussion  
### Eye Diagram, Q Factor and Bit Error Rate (BER)  
The eye diagram is an intuitive graphical representation of the signals of electrical and optical communication. From the appearance of the eyes, the quality of this signal can be observed. From the eye diagram, the bigger the area of the eye opening, the better the result. If the area of the eye opening is small, then it shows that the signal that reaches the users cannot be interpreted.  
The signal-to-noise ratio (SNR) determines the quality of the received signal at a certain bit rate. The minimum Q factor is 6, where the signal must be equal or more than 6 to be said to have an SNR at the acceptable level. The Q factor for this network that reaches approximately 196,608 users is 6.43617, which is more than the minimum Q factor required. The other parameter that determines the performance of the optical network  
Figure 4: General design for network scaling using spectrum slicing and duplication techniques.  
Figure 5: Flowchart of network scaling design and simulation.  
is the bit error rate (BER). The BER is the number of bit errors per unit of time at the specific bit rate. The BER is the measurement of the number of bits that are errors during the transmission's completion, and the minimum requirement for the BER is 1 \(\times\) 10\({}^{-9}\). Based on our simulation, our proposed network that accommodates 96,608 users has a BER = 4.52 \(\times\) 10\({}^{-11}\), as shown in Figure 6.
## 4 Discussion
### Limitations and Management Implications  
Although the results of this study provide new information regarding wind-induced phenological variations that control carbon uptake, some limitations remain. Owing to the instrument malfunctions and a lack of maintenance, missing data inevitably impacted this study. Second, our research emphasizes the role of wind as a major environmental factor in regulating phenology, yet it neglected the influence of VPD on the photosynthesis leaf development rates in plants. Finally, we found that the RE, comprising autotrophic and heterotrophic respiration, decreased under wind stress during periods outside the absolute maturity phase (June and July). The reasons for this decrease are multifaceted. One possibility is the potential reduction in soil respiration due to soil moisture being carried away by high winds. Another factor could be slower leaf growth and more prolonged leaf senescence as an adaptive response to wind stress, which could hinder the decomposition of RE within the constraints of this study.  
Forest management is often approached from a phenological perspective. Considering the phenological and carbon sequestration patterns of both locations, wind performed a crucial role in linking their interrelationship. Strong winds seem to reduce soil moisture and inhibit leaf growth through physical or chemical actions, thereby extending the time for leaf expansion and shortening the maturation period of trees. However, these changes in phenological traits may represent plants maximizing their carbon sequestration potential. Therefore, in conditions of abundant rainfall, the effects of wind may be beneficial for carbon sequestration. Forest management strategies should consider long-term climate trends and employ adaptive measures, such as selecting resilient tree species and adjusting planting patterns. In this study, the poplars showed positive feedback in response to marine wind stress through phenological adjustments, with increased (rather than decreased) carbon sequestration even at a relatively low stage of soil water content, demonstrating that poplars are a superior choice in light of climate change.
## 4 Selection of Seismic _IMs_ for the PCA Algorithm
### Principal Component Analysis of IMs  
Following the removal of weakly correlated _IMs_ (Section 4.2) and the elimination of multicollinearity _IMs_ (Section 4.3), the final set of 11 _IMs_ selected for analysis in this study included PGV, PGD, vRMS, SED, CAV, CAD, DSI, Svmax, Sdmax, Sv(T\({}_{1}\)), and Sd(T\({}_{1}\)). The condition numbers of the final set of 11 _IMs_ are shown in Figure 9. Meanwhile, the Spearman correlation coefficient values between the final set of 11 _IMs_ were computed using Equation (1) and are presented in Figure 10.  
It can be seen from Figure 9 that the maximum condition number of the final set of 11 _IMs_ is 63.09, indicating that the final set of 11 _IMs_ did not have a severe multicollinearity problem. As shown in Figure 10, the minimum and maximum correlation coefficient values of the final set of 11 _IMs_ are 0.34 and 0.97, respectively, which shows that there are no weakly correlated _IMs_, and the perfectly correlated _IMs_ have also been removed. As a result, the final set of 11 _IMs_ selected for this study did not exhibit a weak correlation or severe multicollinearity issues. As such, these 11 _IMs_ represent an optimal combination of seismic _IMs_ that meet the requirements for dimensionality reduction analysis using the PCA method.  
Taking the final set of 11 _IMs_, which included PGV, PGD, vRMS, SED, CAV, CAD, DSI, Svmax, Sdmax, Sv(T1), and Sd(T1), as the basic variables, a seismic _IM_ vector matrix \(\textbf{{IM}}=\left[\textbf{PGV}\quad\textbf{{PGD}}\;\cdots\quad\textbf{{Sd(T1) }}\right]\) was formed. The PCA analysis of the seismic _IMs_ was carried out according to Equations (3)-(6), resulting in 11 uncorrelated principal components \(F_{1}\), \(F_{2}\), \(\cdots\), and \(F_{11}\), composing the above seismic _IM_ vector. Meanwhile, the contribution rate and accumulated contribution rate of each principal component were calculated according to Equations (7)-(8); the accumulated contribution rate of each principal component is shown in Figure 11.  
Figure 10: Bubble chart of the correlation coefficients of the final 11 selected _IMs_.  
Figure 9: Condition numbers of the final 11 selected _IMs_.  
From Figure 11, it can be seen that the accumulated contribution rate of the first three principal components, \(F_{1}\), \(F_{2}\), and \(F_{3}\), reaches 92%, indicating that the first three principal components could effectively reflect most of the information contained within the final set of \(11\)_IMs_. Therefore, this study employed the first three principal components as clustering indices to conduct a clustering analysis, simplifying the high-dimensional variable system into a low-dimensional variable system. Moreover, the variables within the new variable system were all uncorrelated with each other, which not only preserved a significant amount of information related to the seismic _IMs_ but also mitigated the issue of overlapping information between each _IM_, thereby reducing the complexity of the clustering analysis.
# Coarse-Fine Combined Bridge Crack Detection Based on Deep Learning
## 2 Methodology
### Semantic Segmentation Network Models (SSNMs)
#### 2.2.3 DeepLabV3+  
As shown in Figure 8, DeepLabV3+, proposed by the Google research team in 2018, combines the advantages of hollow convolution and multiscale models. Based on deep CNNs, this model expands the receptive field through hollow convolution to improve the ability to perceive the context of images [52]. At the same time, multiscale information fusion and decoder modules are introduced to effectively integrate global context information and underlying features, thereby enhancing adaptability to objects of different scales [53]. Trained on a large-scale labelled semantic segmentation dataset, DeepLabV3+ learns rich semantic representations and demonstrates excellent segmentation results in real-world scenarios, especially in handling boundaries and details. Due to its excellent performance, the model has been widely used in medical image analysis, automated driving, remote sensing image interpretation and other fields.
## 3 Network Structure  
In this section, the UWSN model required for implementation of the proposed algorithm is presented along with some assumptions. The underwater nodes of the UWSNs are randomly distributed within a predetermined range, while the sink nodes (BN) are distributed at the center of the water surface and have no energy constraints to forward the packets from the UW-sensors to the base station.  
The UW-sensors are deployed with moorings that stop moving after a certain distance with the current, and their relative positions throughout the individual network structure are relatively stable. Each UW-sensor has acoustic-electrical communication equipment for collecting packets from the seafloor and transmitting the packets to the surface buoy node, and the BPSK [20] modulation technique modulates and demodulates the signal. Each underwater node periodically obtains the position of its neighboring nodes through a position algorithm [21] and maintains a node adjacency list locally; this communication record and status about the neighboring nodes help in choosing the NF. In addition, the UW-sensor maintains a local communication log table with neighboring nodes to continuously record the communication status and history with neighboring nodes.
## 6 Conclusions  
The anti-friction performance of the textured raceway is theoretically analyzed and experimentally studied, considering the impact of the grinding surface of the ball screws on the EHL interaction between the ball and the textured raceway. The main conclusions of the paper are as follows:  
* Theoretical simulation and experimental measurement of the ground surface topography of the screw raceways are conducted based on the formation mechanism of the grinding surface and the geometric model of the screw raceway. The actual measurement and theoretical simulation of the surface roughness of the screw raceway are 0.2128 \(\upmu\)m and 0.2072 \(\upmu\)m, respectively, and the relative error is only 2.63% to verify  
Figure 27: Comparison of experimentally measured and simulated friction torques; (**a**) Change texture diameter; (**b**) Change texture depth; (**c**) Change areal density of the texture. the correctness of the forming mechanism of the grinding surface topography of the screw raceway. * The influence of geometric features of the textures on the anti-friction effect under different rotation speeds and axial loads of the ball screws is investigated and discussed. The textured raceway of the ball screws provides a micro-hydrodynamic pressure perturbation effect that reduces the friction coefficient of the contact system of the ball screws by 15.2% at a normal contact force of 60.23 N, an entrainment speed of 167.5 m/s, a texture diameter of 40 \(\upmu\)m, a texture depth of 10 \(\upmu\)m, and a texture areal density of 10%. * The results of the experiments show that the variation trend of the friction-reducing property with the change in the geometric features of the manufactured textures is well in accordance with the theoretical simulation, and the maximum and minimum relative errors are 6.2% and 1.3%, respectively. The correctness of the EHL model of the textured raceway of the ball screws and the effectiveness of the friction reduction effect of the surface texture technology of the ball screws are verified. Software, X.G. ; Validation, P.G. and X.C. ; Data curation, X.Y. ; Writing--original draft, Y.Z. ; Project administration, M.W. All authors have read and agreed to the published version of the manuscript. This study was financially supported by the National Natural Science Foundation of China (Grant Nos. 51975020, 51575014), Beijing Municipal Natural Science Foundation (Grant No. 3202005), Guizhou Provincial Science and Technology Projects (ZK2024-ZD062). Not applicable. The raw data supporting the conclusions of this article will be made available by the authors on request. I am deeply indebted to Beijing University of Technology for providing an outstanding academic environment. The authors
###### Abstract  
Orthogonal Frequency Division Multiplexing (OFDM) is one of the key modulations for current and novel broadband communications standards. For example, Multi-band Orthogonal Frequency Division Multiplexing (MB-OFDM) is an excellent choice for the ECMA-368 Ultra Wideband (UWB) wireless communication standard. Nevertheless, the high Peak to Average Power Ratio (PAPR) of MB-OFDM UWB signals reduces the power efficiency of the key element in mobile devices, the High Power Amplifier (HPA), due to non-linear distortion, known as the non-linear saturation of the HPA. In order to deal with this limiting problem, a new and efficient pre-distorter scheme using a Neural Networks (NN) is proposed and also implemented on Field Programmable Gate Array (FPGA). This solution based on the pre-distortion concept of HPA non-linearities offers a good trade-off between complexity and performance. Some tests and validation have been conducted on the two types of HPA: Travelling Wave Tube Amplifiers (TWTA) and Solid State Power Amplifiers (SSPA). The results show that the proposed pre-distorter design presents low complexity and low error rate. Indeed, the implemented architecture uses 10% of DSP (Digital Signal Processing) blocks and 1% of LUTs (Look up Table) in case of SSPA, whereas it only uses 1% of LUTs in case of TWTA. In addition, it allows us to conclude that advanced machine learning techniques can be efficiently implemented in hardware with the adequate design.  
ECMA-368; MB-OFDM; HPA; PAPR; pre-distortion; neural networks; FPGA Article  
of the Art Neural Network-Based HPA  
1 SITC Team, Ibn Zohr University, Agadir 80000, Morocco; [EMAIL_ADDRESS]  
2 MSC Lab, Cadi Ayyad University, Marrakech 4000, Morocco; [EMAIL_ADDRESS]  
3 Department of Signal Theory and Communications, University Carlos III de Madrid, 28911 Leganes, Madrid, Spain  
4 IMT Atlantique, Lab-STICC, UMR CNRS 6285, F-29238 Brest, France; [EMAIL_ADDRESS]