esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
For satisfactory traffic management of an intelligent transport system, it is vital that traffic microwave radar detectors (TMRDs) can provide real-time traffic information with high accuracy. In this study, we develop several information-aided smart schemes for traffic detection improvements of TMRDs in multiple-lane environments. Specifically, we select appropriate thresholds not only for removing noise from fast Fourier transforms (FFTs) of regional lane contexts but also for reducing FFT side lobes within each lane. The resulting FFTs of reflected vehicle signals and those of clutter are distinguishable. We exploit FFT and lane-/or time stamp-related information for developing smart schemes, which mitigate adverse effects of lane-crossing FFT side lobes of a vehicle signal. As such, the proposed schemes can enhance the detection accuracy of both lane vehicle flow and directional traffic volume. On-site experimental results demonstrate the advantages and feasibility of the proposed methods, and suggest the best smart scheme.  
t +
Footnote †: journal: Applied  
Article  
Information-Aided Smart Schemes for Vehicle Flow Detection Enhancements of Traffic Microwave Radar Detectors
# Performance-Based Evaluation of Courtyard Design in China's Cold-Winter Hot-Summer Climate Regions
## 2 Method for Courtyard Design Optimization
### Courtyard Design Optimization  
Courtyard layout influences building heat gain from solar radiation, and a natural ventilation system incorporating the courtyard area influences the convective heat transfer in residential areas [43]. However, most designers put more emphasis on the visual effect of peripheral landscapes and the layout of spaces with different functions, with little attention to ventilation in courtyard buildings and how to improve the thermal environment of courtyards. To fill this research gap, this study used the Expert Building area of Jiangnan Furniture Museum as a case study--a typical model of the \"two halls and one courtyard\" design type, to demonstrate, through numerical simulations, the performance-based evaluation of courtyard design integrating the ecological effects. In the southern Jiangsu Province, the northern wind is piercingly cold in winter while it is hot and humid in summer. In addition, it is extremely hot and humid in the Plum Rain Season. Natural ventilation can remove the residual heat and moisture from buildings in hot seasons and improve the comfort of dwelling spaces. The active wind field can bring in the fresh air and help to discharge indoor air, thus promoting human physical and mental health.  
Therefore, the courtyards should consider such natural conditions and make full use of passive ventilation and natural lighting to meet the daily use requirements of buildings as well as reduce the heating, ventilation, and air conditioning systems' energy consumption. In this study, research on the traditional Chinese quadrangle dwellings prototype is applied. During modeling, the central yard of a quadrangle dwelling was placed on one side, its backyard removed into a reverse setting with a three-section compound. Next, a small-scale courtyard space was added, to enrich the form of courtyard space. Third, rich spatial layers were built in connection with the landscape view and with the use of the grey and white walls.  
Scheme generation and evaluation were conducted using the simple ecological principle of the traditional courtyards to regulate the micro-climate. Impact analyses were also conducted to determine the effects of spatial layout and courtyard aspect ratio on the wind environment and the impacts of the ecological buffer space on the thermal environment of courtyards.
# Automatic Definition of Traffic Analysis Zones Based on Big Data
## 5 Process Flow
### Euclidean Distance Measurement  
In this module, the distance measurement between individual time series is performed. The distance does not denote physical distance, but only examines the behavioral differences between the respective sectors, evaluating how similar the two time series are to each other. The calculation method of the distance measurement algorithm is based on the following formula [19]:  
\[d_{EUK}=\sqrt{\sum_{i=0}^{23}(a_{i}-b_{i})^{2}} \tag{2}\]  
where:  
* a: first time series;
* b: second time series;
* i: hours of the day [0..23].  
The algorithm traverses the time series of individual sectors, and then, compares each curve pairwise. During the comparison, it juxtaposes the corresponding hour values and measures their distance, summing them up throughout the program. As a result of sector comparisons, a distance matrix is obtained which serves as the basis for clustering in subsequent steps.
# Research on Digital Meter Reading Method of Inspection Robot Based on Deep Learning
## 5 Conclusions  
For the issue of blurred images and difficult LED digital meter reading recognition caused by the moving inspection process of the inspection robot, this paper proposes a deep-learning-based method for blurred image repair and LED digital meter reading recognition. First, the image was processed via FFT and the average value of the image amplitude was counted to determine whether the image was blurred or not. Then, the blurred image was restored using an improved adversarial neural network. Finally, the LED digital meter in the image was represented by the detection strategy \"coarse-to-fine\" proposed in this paper. The experiments proved that the method in this paper cannot only effectively overcome the image blur caused by the motion of the detection robot, but can also achieve accurate reading recognition of the LED digital meter in complex environments. The method in this paper provides an efficient and robust detection idea with practical value in the field of detection robots. In future work, we will continue to study the application field of inspection robots based on machine vision, so that the inspection work will develop in the direction of unmanned intelligence.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Accuracy of Reading** \\ \hline Without preprocessing & 86.4\% \\ Preprocessing & 98.8\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: The reading accuracy of the CRNN model before and after preprocessing.  
Figure 14: Digital image effects after preprocessing.  
Figure 13: Digital image and its BGR images under different lighting conditions. **(a)** Digital image and its BGR image under normal exposure; **(b)** digital image and its BGR images under overexposure; **(c)** digital image and its BGR images under weak exposure.  
**Author Contributions:** Conceptualization, C.Z. and W.L.; methodology, C.L. and W.L.; software, J.T.; validation, C.Z.; formal analysis, Z.Z., W.L. and C.Z.; investigation, Z.Z.; resources, C.L. and C.Z.; data curation, J.T.; writing--original draft preparation, W.L., C.Z. and Z.Z.; writing--review and editing, C.Z. and W.L.; visualization, C.L.; supervision, J.T. and C.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Scientific and Technological Project of Quanzhou City under grant no. 2022C022R and Scientific and Technological Project of Fengze District, Quanzhou City, under grant no. 2022 FZ01.  
Not applicable.  
**Data Availability Statement:** The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.
## 3 Results
### Evaluation of the Coagulation Bath
#### 3.1.2 Area Index of Functional Group  
After the fibres were produced in the three different systems, the DMF peak was evaluated using the ATR-FTIR test (Figure 4a,b). This test allowed the behaviour of the area of the characteristic DMF peak corresponding to the carbonyl functional group (C=O) to be observed [29]. It provided the respective data necessary to calculate the index.  
Figure 4: Comparison of the characteristic peaks of DMF in the ATR-FTIR spectra for the three coagulation bath lengths (0.50, 1.00, and 1.25) m. (**a**) F\({}_{\mathrm{uni}}\)\(\_\)50, F\({}_{\mathrm{uni}}\)\(\_\)100 and F\({}_{\mathrm{uni}}\)\(\_\)125; (**b**) F\({}_{\mathrm{hol}}\)\(\_\)50, F\({}_{\mathrm{hol}}\)\(\_\)100 and F\({}_{\mathrm{hol}}\)\(\_\)125.  
The bar graph in Figure 5 represents the index calculated for the fibres (F\({}_{uni\_50}\), F\({}_{uni\_100}\) and F\({}_{uni\_125}\)) and (F\({}_{hol\_50}\), F\({}_{hol\_100}\) and F\({}_{hol\_125}\)) produced in the different coagulation baths.  
The study of the carbonyl functional group indices highlighted the influence of the coagulation size on the removal of the toxic solvent DMF and, consequently, on the efficient coagulation of CA in the fibres.  
In the index calculated for the F\({}_{uni}\), there was a considerable decrease in the amount of DMF as the length of the coagulation bath increased, presenting a higher index value for the initial bath of 0.50 m and a lower value for the bath of 1.25 m. This progressive decrease may be related to the fact that a uniaxial fibre, without a core, has a homogeneous structure, which allows more efficient evaporation of DMF in more extended coagulation baths since the entire surface of the F\({}_{uni}\) is in direct contact with dH\({}_{2}\)O for longer [30; 31].  
On the other hand, the indices calculated for F\({}_{hol}\) showed a significant decrease in the amount of DMF when comparing the 0.50-metre and 1.00-metre coagulation baths. However, for the 1.25-metre bath, the DMF index was slightly higher than for the 1.00 metre bath, indicating a possible change in the extraction equilibrium. This increase in the DMF index is related to the difficulty in handling these F\({}_{hol}\) to the collector, where they broke and were not uniform. This proves the fact discussed earlier that those co-axial fibres, with the presence of a core in the liquid state, break in larger coagulation baths due to a lack of structural cohesion [32]. This collapse may be related to problems of osmosis or diffusion of the DMF, since inside the fibres there must be a higher concentration of solvent molecules, and resistance to their escape can cause internal pressure. This dynamic can generate internal tensions, which compromise the uniformity and cohesion of the structure along the fibre produced [18; 30].  
Therefore, regarding the initial objective of identifying the appropriate coagulation size to produce PCF, it was possible to conclude that a medium coagulation bath, that is, 1.00 metre, was the most adequate among those tested, as it showed a significant reduction in DMF, uniform fibres, and ideal morphology.
###### Abstract  
In the C-V2X sidelink Mode 4 communication, the sensing-based semi-persistent scheduling (SPS) implements a message collision avoidance algorithm to cope with the undesirable effects of wireless channel congestion. Still, the current standard mechanism produces a high number of packet collisions, which may hinder the high-reliability communications required in future C-V2X applications such as autonomous driving. In this paper, we show that by drastically reducing the uncertainties in the choice of the resource to use for SPS, we can significantly reduce the message collisions in the C-V2X sidelink Mode 4. Specifically, we propose the use of the \"lookahead\", which contains the next starting resource location in the time-frequency plane. By exchanging the lookahead information piggybacked on the periodic safety message, vehicular user equipment (UEs) can eliminate most message collisions arising from the ignorance of other UEs' internal decisions. Although the proposed scheme would require the inclusion of the lookahead in the control part of the packet, the benefit may outweigh the bandwidth cost, considering the stringent reliability requirement in future C-V2X applications.  
cellular V2X (C-V2X); sensing-based; semi-persistent scheduling (SPS); message collision resolution +
Footnote †: journal: Sensors
## 2 Materials and Methods
### UV/Vis Measurements  
An aliquot of 5 mg-C/L from each of the four SHS solutions was measured with a UV/Vis spectrophotometer (Hitachi, U-2900, Tokyo, Japan) for absorbance, at a scanning wavelength of 800-200 nm. Before the measurement, the SHS solutions were adjusted to pH 7 with 0.3 N H\({}_{2}\)SO\({}_{4}\). The background was corrected per the [PERSON] et al. (2018) method. The UV/Vis indicator SUVA\({}_{254}\) (L/mg-C/m) = (UV\({}_{254}\)/(HS)) \(\times\) 100, where UV\({}_{254}\) was the UV/Vis absorbance at 254 nm (cm\({}^{-1}\)) of the sample, and (HS) was the DOC concentration (mg-C/L) of the SHS solution (Hitachi, U-2900, 24). The other UV/Vis indicators, S\({}_{275-295}\), S\({}_{\text{R}}\) (S\({}_{275-295}\)/S\({}_{350-375}\)), and A\({}_{250-400}\) were also analyzed. However, these indicators were not significantly different between the AD and FD samples; they are not discussed in the present study and the S\({}_{275-295}\), S\({}_{\text{R}}\), and A\({}_{250-400}\) data are listed in the Supplementary Information (Table S1).
###### Abstract  
Particle damping (PD) is a passive mean of vibration control in which small metallic or ceramic particles are placed inside a cavity that attached to the primary structure at the place of high vibration amplitudes. The kinetic energy of the primary structure is dissipated by non-elastic impact and friction between particles and walls. This paper represents a series of experimental investigations of the effects of multi-unit particle dampers (MUPD) attached to a bracket structure under harmonic excitation and random excitation. As a platform to investigate the particle damping characteristics under extreme acceleration environments, the bracket structure was featured by an extremely high response on the top, and its maximum acceleration exceeds 50 times gravity acceleration when the bracket structure was subjected to resonance. This broad range of acceleration conditions was far beyond the scope concerned in most previous work. The experimental results show that for a small weight penalty (no more than 8.8%), multi-unit particle damper can reduce the resonance of the primary structure by more than 50%, whether under sinusoidal excitation or random excitation. And the response of the primary structure depends on the type of cavities and filled coefficient. Layering the cavity in the direction of the main vibration can improve the damping capacity of the multi-unit particle damper. And the damper with small particle size and large number of features is suitable for vibration reduction under high acceleration conditions.  
multi-unit particle dampers (MUPD); sinusoidal excitation; random excitation; bracket structure
## 3 Bridge Element Maintenance Decision Optimization Model  
### Sequential Decision Making Theory  
In addition to multi-objective decision-making methods, decision tree methods and a range of self-learning methods such as semi-Markov decision processes [20] (or Markov decision processes [21], artificial neural networks [22], probabilistic neural networks [23], analytic hierarchy process [24], and reliability methods [25]) have also been employed for project-level bridge maintenance decision making. The sequential decision making process (SDP) can also be used for bridge project-level maintenance decisions. SDP refers to the process of making a series of decisions in successive time steps to achieve a certain goal, and it is a mathematical model used to describe the problem of progressively optimal decision making in a dynamic environment. Sequential decision making processes can be applied to a variety of decision problems, including Markov decision processes (MDPs) and semi-Markov processes (SMDPs). MDPs consist of a tuple <\(S\),\(A\),\(P\),\(R\),\(\gamma\)>, and the conditional distribution of future states only depends on the current state, which is independent of past states, where \(S\) represents the set of states; \(A\) represents the set of actions; \(P(s^{\prime}|s,a)\), represents the state transition function, which denotes the probability of transitioning from state s to state \(s^{\prime}\) when action a is taken; \(R\) represents the reward or values function, which depends on state s and action a; and \(\gamma\) represents the discount factor.  
This paper introduces an improved Markov model that incorporates Weibull distribution and forms a semi-Markov model, where the duration time in each state follows the Weibull distribution, thus providing necessary support for the decision model.
## 2 Model Establishment and Analysis
### Multi-Objective Optimization
The MAPE comparison of the three models is shown in Figure 14, where the blue bar represents the BP neural network prediction model, the red bar represents the random forest algorithm prediction model, and the green bar represents the hybrid model prediction model. It can be observed that the hybrid model has the smallest MAPE among the three models, and its MAPE is significantly smaller than that of the other two models. In this work, which predicts the fatigue life under condition 3 based on the known thicknesses of the ten components of the fifth wheel, the hybrid model's accuracy is 47.62% higher than that of the BP neural network. This approach enables more accurate prediction of the fatigue life based on the thickness of individual components, eliminating the need for complex parameter settings and extensive model calculations.
## 2 Overview of Compressive Sensing Algorithm
### CS Performance Metrics  
There are certain performance evaluation metrics that can be used to assess the reconstructed signal quality and amount of compression achieved. Some of the notable metrics that are extensively used in CS literature are the percentage root-mean-squared difference (PRD), Quality Score, Root mean square error, signal to noise ratio (SNR) and the compression ratio (CR) [89; 90; 91]. The mathematical definition of them is as follows  
CR ratio of (N) original signals to (M) compressed signals  
\[\text{CR}=\frac{\text{N}}{\text{M}} \tag{6}\]  
PRD ensures the quality of the reconstruction; thus, it is the measure of error between the reconstructed signal and the original signal,  
\[\text{PRD}(\%)=\frac{\text{x}\ -\ \bar{\text{x}}}{\text{x}}*10 \tag{7}\]  
where \(\bar{\text{x}}\) is the reconstructed signal and x being the original signal.  
Quality Score (QS): it is used to measure the overall performance of data compression, it considers both the CR and reconstructed signal quality, for better compression performance the QS should be higher.  
\[\text{QS}=\frac{\text{CR}}{\text{PRD}} \tag{8}\]  
If the CR value is high, to recover the original signal less data is sufficient. The recovery signal quality and confidence are accessed through SNR and PRD values. If the PRD value is low the reconstructed signal has a high degree of confidence similarly the high value of SNR indicates that the reconstructed signal is of high quality. The other common measures widely used are reconstructed signal to noise ratio (RSNR), which represents differences between signals energy before and after the compression and root mean square error (RMSE).  
Apart from CS performance metrics there are some other metrics to be considered in ECG signal classifications [92; 93; 94]  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{9}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{10}\]  
\[\text{Sensitivity}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{11}\]  
\[\text{Specificity}=\frac{\text{TN}}{\text{TN}+\text{FP}} \tag{12}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{13}\]  
\[\text{PPV}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{14}\]  
\[\text{NPV}=\frac{\text{TN}}{\text{TN}+\text{FN}} \tag{15}\]  
TP, TN, FP, FN, PPV and NPV are the numbers of True Positive, True Negative, False Positive, Positive Predictive Value and Negative Predictive Value respectively.
## Materials and Methods
### System's Description
#### 2.1.2 Classification  
The chosen algorithm for classification is the MLP neural network. These structures have already been used as a classifier in ECG biometrics, achieving good performances [29; 30]. Despite of the simplicity of this artificial neural network, it is considered a promising algorithm in the context of ECG biometrics [31]. MLP networks are applied in supervised learning and they have three main parts: input, output, and hidden layers, as represented in Figure 3. The input layer is formed by nodes or neurons that represent the different input features \(\{x_{i}|x_{1},x_{2},\ldots,x_{n}\}\). Every feature is labelled with its correspondent class, \(\{y_{i}|y_{1},y_{2},\ldots,y_{n}\}\). In the case of only having one hidden layer, the output layer gives the function in Equation (1) [32]:  
\[f(x)=W_{2}g(W_{1}^{T}x+b_{1})+b_{2}. \tag{1}\]Where \(W_{1}\) represents the sets of weights applied to every feature in the input layer. These weights vary between them, in the way that every feature \(x_{i}\) has \(m\) different weights: one per node in the following hidden layer. On the same way, \(W_{2}\) represents the weights applied in the hidden layer,  
Figure 3: Multilayer Perceptron (MLP) with one hidden layer.  
Figure 2: Scheme for signal pre-processing and data segmentation. Variable \(rng1\) represents the data points on the left of the R peak and \(rng2\) the data points after, including the R peak. \(Npeaks\) refers to the number of selected R peaks in the matrix.  
at nodes \(\{a_{j}|a_{1},a_{2},\ldots,a_{m}\}\). Value \(b_{1}\) is the bias in the hidden layer while \(b_{2}\) is the bias on the output layer. The activation function is represented by \(g(\cdot)\). The most common functions are identity (or no activation function), logistic, hyperbolic tangent (tanh) and rectified linear unit function (ReLU). All the corresponding functions are represented in Table 1.  
As this structure works for supervised learning, the weights need to change in every connection after the data is processed to decrease the processed error. In this case, it is done by backpropagation, which comes from the Least Mean Squares (LMS) algorithm. These weights can be updated differently, depending on the approach for their optimization. The most common solver is of Stochastic Gradient Descent (SGD). Its formula depends on a factor called learning rate, which ensures the weights converge quickly.
## 3 Ergodic Performance Analysis of the Proposed Scheme Using Rayleigh and Rician Fading Channels
### The First Scenario
#### 3.1.2 Upper Bound for the Achievable Rate Using Rayleigh Fading Channels for \(D_{2}\)  
**Proposition 2**.: _The upper bound for the \(D_{2}\) using the Rayleigh fading channels is given as_\[R_{\text{upper},\text{D}_{2}}^{(1)}=\text{log}_{2}\Bigg{(}1+\text{min} \Bigg{\{}\text{E}\bigg{[}\widetilde{\gamma_{x_{1},\text{D}_{2}}^{(1)}}\bigg{]}, \text{E}\bigg{[}\widetilde{\gamma_{x_{2},\text{D}_{2}}^{(1)}}\bigg{]}\Bigg{\}} \Bigg{)} \tag{20}\] \[=\text{log}_{2}\Bigg{(}1+\text{min}\Bigg{\{}\frac{\gamma\chi_{1} \big{(}\frac{1}{6}\psi_{1}+\frac{1}{8}\psi_{2}+\frac{1}{6}\psi_{3}\big{)}}{ \gamma\chi_{2}\big{(}\frac{1}{6}\psi_{1}+\frac{1}{8}\psi_{2}+\frac{1}{6}\psi_{ 3}\big{)}+\big{(}\text{V}_{2}^{3}+\text{V}_{2}^{2}\big{)}\gamma\big{(}\frac{1} {6}\psi_{1}+\frac{1}{8}\psi_{2}+\frac{1}{6}\psi_{3}\big{)}}{\big{(}\text{V}_{2 }^{3}+\text{V}_{2}^{2}\big{)}\gamma\big{(}\frac{1}{6}\psi_{1}+\frac{1}{8} \psi_{2}+\frac{1}{6}\psi_{3}\big{)}+1},\frac{\gamma\chi_{2}\big{(}\frac{1}{6} \psi_{1}+\frac{1}{8}\psi_{2}+\frac{1}{6}\psi_{3}\big{)}}{\big{(}\text{V}_{2}^{ 3}+\text{V}_{2}^{2}\big{)}\gamma\big{(}\frac{1}{6}\psi_{1}+\frac{1}{8}\psi_{2} +\frac{1}{6}\psi_{3}\big{)}}\Bigg{\}}\Bigg{)},\]  
where \(\psi_{1}=d_{S_{1}}^{-a}d_{I_{1}}^{-a}\eta^{2}N_{1}\big{(}16+(N_{1}-1)\pi^{2} \big{)}\), \(\psi_{2}=d_{S_{1}}^{-a}d_{I_{1}}^{-a}d_{D_{2}}^{-a}d_{S_{2}}^{-a}d_{I_{2}}^{-a }\eta\)  
\(\sqrt{N_{1}N_{2}\big{(}16+(N_{1}-1)\pi^{2}\big{)}\big{(}16+(N_{2}-1)\pi^{2} \big{)}}\), \(\psi_{3}=d_{S_{2}}^{-a}d_{I_{2}}^{-a}\eta^{2}N_{2}\big{(}16+(N_{2}-1)\pi^{2} \big{)}\).  
Proof.: See Appendix B.