esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Link Budget and Dual-Slope Path Loss Measurement Results
### Link Budget at 5.9 GHz and at 700 MHz  
Figure 6 shows the schematic diagram of the 5.9 GHz V2V measurement system and the components used to perform the link budget.  
According to (1) and taking the received power level \(P_{Rx}(d)\) as that related to the VNA input with \(P_{Tx}=-10\) dBm and subtracting the cable losses \(L_{CT1}=0.35\) dB, \(L_{CT2}=4.68\) dB, and \(L_{CR}=4.68\) dB and then adding the gains of the amplifier \(G_{AT}=33.38\) dB in Tx and the two cascaded amplifiers \(G_{AR}=2\times 34.06\) dB in Rx and the Tx and Rx antenna gains \(G_{Tx}=G_{Rx}=2.56\) dB, the value of \(P_{Rx}(d)\) is given by the following:  
\[P_{Rx}(d)=P_{Tx}-L_{CT1}+G_{AT}-L_{CT2}+G_{Tx}+G_{Rx}-PL(d)-L_{CR}+G_{AR}. \tag{2}\]  
Therefore, the path loss propagation at 5.9 GHz, expressed in (dB) and denoted by \(PL(d)\), is given by the following:  
\[PL(d)=76.67-P_{Rx}(d). \tag{3}\]  
Using the parameter \(b_{2}(f)\) measured by the VNA as the received power value, records were obtained to analyze the behavior of the path loss as a function of \(d\).  
The link budget for the 700 MHz test system was performed in the same manner as for the 5.9 GHz test system, as shown in Figure 7.  
Taking the received power level \(P_{Rx}(d)\) with respect to the SA input and also, taking into account a \(P_{Tx}=-20\) dBm and subtracting the cable losses \(L_{CT1}=0.45\) dB, \(L_{CT2}=2.14\) dB, and \(L_{CR}=2.14\) dB and then adding the gains of the amplifier \(G_{AT}=43.29\) dB in Tx and the amplifier \(G_{AR}=32.75\) dB in Rx and the Tx and Rx antenna gains \(G_{Tx}=G_{Rx}=-5.43\) dB  
Figure 6: V2V schematic diagram at 5.9 GHz.  
Figure 7: V2V schematic diagram at 700 MHz.  
gives the same expression as in Equation (2). In this case, the \(PL(d)\) at 700 MHz is expressed (in dB) by the following:  
\[PL(d)=40.45-P_{Rx}(d). \tag{4}\]  
Table 1 summarizes the main parameters used to configure the measurement systems.
## 3 Results and Discussion
### Variation of Air Speed and Ammonia Concentration in the AOR  
The simulation results at different wind incidence angles show that the ammonia concentration in the AOR with respect to the \(z\) axis is not homogeneous (Figure 4). It can be observed that with the wind direction at 0\({}^{\circ}\), air speed vectors of magnitude range between 0.3 to 0.5 m s\({}^{-1}\), and ammonia shows an increase from 8.5 to 14 ppm throughout the farm, presenting gas accumulation in the rear area of the farm due to the direction of the air flow. When the wind direction is at 45\({}^{\circ}\), the air speed is less than 0.4 m s\({}^{-1}\), and the ammonia concentration ranges between 9.3 and 10.5 ppm; however, in the rear area of the farm a decrease of 5.5 ppm is seen. With the wind direction at 90\({}^{\circ}\), the speed is less than 0.3 m s\({}^{-1}\), because the upper limit of the AOR is 10 cm below the height at which the farm windows are located, causing the air flow to pass through the installation without being hindered by the AOR. The ammonia concentration has a symmetrical behavior with respect to the center of the farm: in the frontal zone from 0 m to 5 m the ammonia increases from 5 to 11 ppm, in the central zone from 5 m to 20 m, the variation is from 11 to 11.6 ppm, and in the rear zone the ammonia decreases from 11 to 7 ppm.  
Figure 3: Area occupied by rabbits (AOR) and cross-sectional planes created for thermal gradient and ammonia concentration analysis.  
The mean ammonia concentrations in the AOR are 12.14 ppm, 9.65 ppm, and 11.04 ppm with wind directions at 0\({}^{\circ}\), 45\({}^{\circ}\), and 90\({}^{\circ}\), respectively. This means that ammonia concentrations represent decreases with respect to the emission source of 21.2% when the wind direction is at 0\({}^{\circ}\), 37.3% at 45\({}^{\circ}\), and 28.3% at 90\({}^{\circ}\). It is observed that, of the three different directions studied, the most favorable case is when the wind hits with a direction of 45\({}^{\circ}\), as the ammonia concentration in the AOR is reduced by a greater percentage.  
Figure 4: Air speed and ammonia concentration: (**a**) wind direction at 0\({}^{\circ}\), (**b**) wind direction at 45\({}^{\circ}\), and (**c**) wind direction at 90\({}^{\circ}\).
###### Abstract
In the absence of any PU on a typical channel, SUs transmit within such a channel following the overlay spectrum access scheme and switch to the underlay spectrum access scheme (with limited transmission interruption experience) when at least one PU arrives on such a channel. The hybrid scheme is capable of improving SUs' throughput, while also ensuring that the QoS of PUs is satisfied [4]. Under this spectrum access scheme, SUs transmit with a higher data rate in the absence of PUs and do not vacate the channel whenever a PU arrives unlike in the conventional overlay channel access model, but adopt power control techniques by switching to underlay channel access scheme using the mode switching rate parameter. Hence hybrid spectrum access model can improve SUs' channel usage experience [5].
# COVID-19 Detection Model with Acoustic Features from Cough Sound and Its Application
## 4 Experiment
### Evaluation Index  
Accuracy, sensitivity, specificity, and precision, which are frequently used to evaluate the performance of classification models, were used to evaluate the training results. The focus was on sensitivity and specificity, which are primarily considered when measuring the reliability of the actual COVID-19 test diagnosis method. Figure 3 is a confusion matrix used to calculate the above indicators.  
Sensitivity is the ratio of data predicted as positive (TP) to the actual positive class (TP + FN), and specificity is the ratio of data predicted as negative (TN) to the actual negative class (FP + TN). A high sensitivity means that there is a low probability of a false negative, i.e., a low probability of a positive being falsely classified as negative.
# Predicting the Spread of a Pandemic Using Machine Learning: A Case Study of COVID-19 in the UAE
## 3 Methods and Datasets
### The UAE COVID-19 Dataset  
The UAE is home to approximately 9.28 million people and is divided into 7 Eminates, namely, Abu Dhabi, Ajman, Dubai, Fujairah, Ras Al Khaimah, Sharjah and Umm Al Quwain. Abu Dhabi is the largest emirate while Dubai is the most populated. The dataset used in this paper consists of data in time series format. The dataset is updated every day by the FCSC and provided through the official UAE COVID-19 updates website [7]. The data reported represent all values across the UAE and are not divided by Emirate. The dataset was taken over a duration of round 27 months, between 1 of February 2020 and 29 April 2022, resulting in 791 records (1 record per day). Each record consists of 10 attributes as follows:  
* Day: an index of number of days since the first record of the virus.
* Date: the date of when the data were recorded and added to the dataset.
* Confirmed Cases: the number of positive cases recorded on the specified date.
* Recovered Cases: the number of recovered cases recorded on the specified date.
* Confirmed Deaths: the number of deaths caused by the virus, recorded on the specified date.
* Tests: the number of PCR tests conducted on the specified date.
* Active Cases: a case is considered active if there is a possibility of the virus being active in the person's body. For instance, an active case can happen when a person shows symptoms but gets a negative PCR test, or when a person comes into contact with another person who has been diagnosed with COVID-19.
* Vaccine Doses: the number of vaccine doses administered on the specific date. This number does not differentiate between people who are having their first, second, or third dose.
* One Dose: the percentage of the population of the UAE that has had at least one single dose of any of the available vaccines. This record is cumulative, as each day adds to the previous day.
* Fully Vaccinated: the percentage of the population of the UAE that has had both doses of the vaccine. This record is cumulative, as each day adds to the previous day.
# Seismic Risk Analysis of Existing Link Slab Bridges Using Novel Fragility Functions
## 2 Seismic Reliability Framework
### Fragility Functions
#### 2.2.3 Damage Extent Fragility Functions  
Additional information we might be interested in retrieving is related to the extent of a specific failure mode; indeed, while performing seismic losses analysis, it would be useful to know the effective number/percentage of components showing a given degree of damage or exceeding a specific performance level, because this will guide and make us more aware of the reconstruction/repair cost assessment.  
If we analyze a single failure mode (e.g., piers slight damage attainment) identifying it as event \(E\), partitions of \(E\), e.g., subsets \(S_{i}\), \(i=1\ldots N_{\text{S}}\), can be defined to represent failures occurring in 1, 2, \(\ldots\) all \(N_{\text{S}}\) elements belonging to the same family.  
Sometimes, rather than creating partitions for individual elements, it might be convenient to work with percentages of the elements of the family (e.g., less than 30%, from 30% to 60%, and so on), thus creating rougher partitions \(S_{i}\), \(i=1\ldots N_{\text{R}}\), with \(N_{\text{R}}\ \leq\ N_{\text{S}}\).  
Therefore, subsets of disjoint events \(D_{i}\) can be considered as follows.  
\[\begin{cases}D_{i}=S_{i}&i=1\\ D_{i}=S_{i}/S_{i-1}&i=2,\ \ldots,\ N_{R}\end{cases} \tag{4}\]For example, if we assume the following partitions of the event \(E\), \(S_{1}=35\%\), \(S_{2}=70\%\), \(S_{3}=100\%\), the resulting disjoint events are \(D_{1}=[0\%,\ 35\%)\), \(D_{2}=[35\%,\ 70\%)\), \(D_{3}=[70\%,\ 100\%]\).  
Obviously, the probability of \(E\) can be retrieved by summing up the probabilities \(P[D_{i}]\) of all the identified \(N_{\text{R}}\) sub-events.  
\[P[E]=\sum_{i=1}^{N_{\text{R}}}P[D_{i}] \tag{5}\]  
Starting from the above assumptions, the following two types of fragility functions will be adopted in this work:  
\[F_{D_{i}}(im)=P[D_{i}|\text{IM}=im] \tag{6}\]  
provides the conditional (to the seismic intensity) probability of having a specific sub-set (e.g., between 70% and 100% according to \(D_{3}\) of the previous example) of elements (e.g., piers) failed;  
\[F_{S_{i}}(im)=P[S_{i}|\text{IM}=im] \tag{7}\]  
provides the conditional probability of a specific damage condition (e.g., 70% damaged elements).
## 2 Materials and Methods
### Selection of Important Wavelengths and Model Evaluation  
The wavelengths associated with peaks and valleys were selected as the important wavelengths (IW). A new simplified model was developed based on those IWs. This approach allowed for the elimination of noise and redundant information, potentially improving the model's accuracy. The predictive abilities of both the simplified model and full model were compared using the mean square error of calibration (RMSEC), validation (RMSEV), and cross-validation (RMSECV), as well as the determination coefficients of validation (\(\text{R}_{\text{v}}\)\({}^{2}\)), calibration (\(\text{R}_{\text{c}}\)\({}^{2}\)), and cross-validation (\(\text{R}_{\text{cv}}\)\({}^{2}\)).
# Topological Map-Based Autonomous Exploration in Large-Scale Scenes for Unmanned Vehicles
###### Abstract  
Robot autonomous exploration is a challenging and valuable research field that has attracted widespread research interest in recent years. However, existing methods often encounter problems such as incomplete exploration, repeated exploration paths, and low exploration efficiency when facing large-scale scenes. Considering that many indoor and outdoor scenes usually have a prior topological map, such as road navigation maps, satellite road network maps, indoor computer-aided design (CAD) maps, etc., this paper incorporated this information into the autonomous exploration framework and proposed an innovative topological map-based autonomous exploration method for large-scale scenes. The key idea of the proposed method is to plan exploration paths with long-term benefits by tightly merging the information between robot-collected and prior topological maps. The exploration path follows a global exploration strategy but prioritizes exploring scenes outside the prior information, thereby preventing the robot from revisiting explored areas and avoiding the duplication of any effort. Furthermore, to improve the stability of exploration efficiency, the exploration path is further refined by assessing the cost and reward of each candidate viewpoint through a fast method. Simulation experimental results demonstrated that the proposed method outperforms state-of-the-art autonomous exploration methods in efficiency and stability and is more suitable for exploration in large-scale scenes. Real-world experimentation has also proven the effectiveness of our proposed method.  
autonomous exploration; path planning; PCATSP; prior information; topological map
# A Novel Low-Complexity Fault Diagnosis Algorithm for Energy Internet in Smart Cities
## 5 Conclusions  
The optimized power consumption for a smart energy system of a smart city is crucial. Although a variety of fault diagnosis algorithms have been developed, how to efficiently detect faults with low complexity poses a great challenge. To this end, we propose the \(SFBT\) that works on a clustered network. It first applies a strong tracking \(UKF\) (\(STUKF\)) and a modified Bayes' classification algorithm (\(MB\)) to detect fault-free cluster-heads in a centralized manner. Then, a decentralized modified three Sigma test (\(MTS\)) is developed to identify faulty cluster-members to overcome the \"masking\" problem within the cluster with a faulty cluster-head. Besides, a faulty-free cluster-head can detect any faulty cluster-member by simply comparing the data of its own and the one of the cluster-member. The theoretical analysis and experiment results indicate that the \(SFBT\) achieves higher diagnosis accuracy over some contemporary strategies with a complexity of \(O(n)\).  
In fact, each faulty free cluster-head detected by the \(SFBT\) is only one hop away from corresponding cluster-members. That suggests data collected by the cluster-head do not differ much from that of cluster-members, which makes the three Sigma test work. However, by doing so, more cluster-heads are required. Thus, our future work includes how to detect faulty cluster-members at least two hops away with a complexity no more than \(O(n)\).  
Conceptualization, J.W.; methodology, H.Z. ([PERSON]); software, D.L.; validation, H.F.; formal analysis, T.W.; investigation, H.Z. ([PERSON]); writing--review and editing, X.W. All authors have read and agreed to the published version of the manuscript. This research was funded by National Nature Science Foundation of China under Nos.61702103, 61871204, 61702103, and 61703195, Industrial Robot Application of Fujian University Engineering Research Center (Minjiang University) under No. MJUKF-IRA1906, Guiding Project of Fujian Science and Technology Department under No.2019I/0026, Fujian Science and Technology Department Project under Nos.2017J01768 and 2017J01506, Fujian Natural Science Foundation Grant No. 2019J01756, and Fuzhou Technology Planning Program Grant No.2018-G-96. The authors declare no conflict of interest.
## 1 Introduction
This structure includes multiple input variables, input neurons, a certain number of hidden layers, and an appropriate number of output neurons responsible for predicting multiple desired variables. The disadvantage of this structure is that it is not flexible enough to evaluate all quality factors, because the output neurons must use the same features (the output of the last hidden layer) for all variables. If the input variables fundamentally affect each of the output variables in a different way, this structure may not produce acceptable results [7]. For example, when constructing a model using a MIMO structure, changing the weights and biases associated with the input variables to improve the prediction accuracy of one quality factor may result in a decrease in the prediction accuracy of other output values. This study applied the data-based intelligent neural networks algorithm developed for the injection molding of a light guide module with a fine pattern on a large area mold core. This paper proposes a model for the correlation between improved injection molding process conditions and the multiple qualities of molded products using multi-task learning-based ANNs. This ANN takes the form of establishing multiple task structures with separate branches for different sets of output parameters, all sharing common input parameters. Injection molding experiments were conducted for bowl products and a dataset was established. Six process conditions including melt temperature, mold temperature, injection speed, packing pressure, packing time, and cooling time were set as input parameters. Output parameters included mass, diameter, and height. Based on this, two multi-task learning-based ANN architectures were constructed. One architecture groups the length parameters represented by diameter and height into a single category, while the remaining parameter, mass, is placed in a separate group. The other architecture constructsa multi-task learning-based ANN where mass, diameter, and height are all distinguished into separate groups. Then, comparisons are made between the predictive performance of two multi-task learning ANNs and that of a single-task MISO structure. Based on this, structural guidelines for ANNs to predict multiple qualities of injection molded products are presented.
## 2 Numerical Simulation of LCO
### CFD Simulation  
It is well known that the leading-edge configuration has significant effects on the critical flutter wind speed and post-flutter LCO amplitudes. The validated CFD method was utilized to simulate the flutter and post-flutter responses of the bridge decks with various leading-edge angles \(a_{d}\) (as defined in Figure 3), including \(\alpha_{d}=120^{\circ}\), \(\alpha_{d}=126^{\circ}\), \(\alpha_{d}=130^{\circ}\), \(\alpha_{d}=134^{\circ}\), \(\alpha_{d}=140^{\circ}\), \(\alpha_{d}=150^{\circ}\) and \(\alpha_{d}=165^{\circ}\). The structural parameters of the bridge deck system with various leading-edge angles are listed in Table 3. The initial wind angle of attack \(\alpha_{0}\) was set to be \(+5^{\circ}\) to ensure that the post-flutter LCO phenomenon can be observed. A wide range of wind speeds were employed in the CFD simulations to clearly present the evolvement of the bridge deck LCO amplitudes.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **CFD Result** & **Experimental Result** & **Relative Error** \\ \hline Critical wind speed & & & \\ (m/s) & 19.3 & 19.4 & 0.5\% \\ \hline \end{tabular}
\end{table}
Table 1: Critical flutter wind speed comparison.  
Figure 3: Bridge deck section geometry (unit: m).  
Figure 2: Mesh details of validation bridge system.  
The steady-state LCO amplitudes as a function of wind speeds for various leading-edge angles are shown in Figure 4. The critical flutter wind speed decreased with the leading-edge angle. Under each wind speed, the LCO amplitude increased with the leading-edge angle. Both the LCO amplitude and its growth rate increased with the wind speed in the post-flutter region. To closely examine the aeroelastic forces on the bridge deck, the time histories of the CFD simulation results with a deck leading-edge angle of 165\({}^{\circ}\) and wind speed of 11.5 m/s are shown in Figure 5. It was noted that higher-order harmonics were observed in both the aeroelastic lift force and torsional moment time histories.
# A Distributed Multicast QoS Routing Construction Approach in Information-Centric Networking
## 4 Simulation
This seed was used to generate data sources, determine the behavior of receivers, and establish the timing for receivers to initiate join requests. Therefore, in comparative experiments with the same parameters, different simulation experiments using different topologies and routing algorithms would use the same source nodes, receiver nodes, and the same join request time sequence, even though these parameters were randomly generated. Source nodes and receiver nodes are selected using a uniform random distribution. The timing of join requests sent by receivers follows an exponential random distribution. Variations in link latency values are determined by a Gaussian random distribution, whereas the time intervals for changes in link latency follow an exponential random distribution. We conducted the simulation on a Ubuntu 22.04 server equipped with 192 GB of RAM and dual 16-core 32-thread processors. A comprehensive and systematic assessment was performed on 15 distinct topologies for each set of comparative experiments. The outcome was derived by averaging the results across these 15 topologies. The evaluation setting is shown in Table 3. \begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline CPUs & 64 Intel(R) Xeon(R) Silver 4216 CPU @ 2.10 GHz \\ Memory & 192 GB \\ Operating system & Ubuntu 22.04 \\ Topology parameters & Waxman, \(\alpha=0.063,~{}\beta=2.2,~{}degree=4\) \\ Number of nodes & 200 \\ Number of groups & 200 \\ Members per group & 5, 10, 15, 20, 25, 30, 35, 40 \\ QoS requirement (ms) & 50, 60, 70, 80, 90, 100, 110, 120 \\ \hline \end{tabular}
\end{table} 
Table 3: Summary of experimental parameters.
# GAN-Based Approaches for Generating Structured Data in the Medical Domain
## 2 Methods
### Selected GAN Variants
#### 2.1.2 Specific GANs for Tabular Data  
Another extension of GANs, developed specifically for tabular data, is the Tabular Generative Adversarial Networks (TGANs) model [51]. Tabular data are the most common form of data, widely used in a range of applications, including the medical field. They usually contain various features with continuous numerical and (discrete) categorical values. While continuous features may be distributed over some range, categorical features usually consist of highly imbalanced discrete values, which complicates the modeling. Therefore, Conditional Tabular Generative Adversarial Networks (CTGANs) were designed to account for categorical data in particular [45]. CTGANs use a conditional generator and the training-by-sampling method to overcome the complications of imbalanced data. They also introduce the mode-specific normalization technique to deal with more complicated non-Gaussian and multimodal distributions, for example [45]. Yet another interesting variation of GANs based upon the CopulaGAN [52]. CopulaGANs consider a probabilistic model to generate samples with high fidelity. The model uses normalizing flows to learn nonlinear correlations between features, and models synthetic data with the distribution that resembles the original real data [52].