esa-sceva/satcom-chunk-collection huggingface dataset:
# Exhaustive Variant Interaction Analysis Using Multifactor Dimensionality Reduction
## 3 Results
### Reducing the Computational Cost by Leveraging High-Performance Computing Technologies
#### 3.2.2 Sample Scalability  
To study the scalability of the problem, we processed batches of data with different sizes (\(N=1\) M, 2 M, 3 M, 4 M, 5 M, and 6 M, where M equals million). Figure 3 shows that the behavior is completely linear (i.e., the execution time increases when the number of combinations increases at the same ratio). These results were expected since each pair is processed independently.  
Testing the proposed methodology in our HPC environment, we were able to process \(1\) million combinations in \(15\) min using three regular machines. Doing a simple extrapolation, using a supercomputer such as MareNostrum IV with 3456 nodes and 48 cores each, we will have a computational time of 100 days. With these experiments, we conclude that processing the whole dataset nowadays is unfeasible. For this reason, we performed a reduction of the data based on the degree of association with the disease (\(p\)-value \(<1\times 10^{-6}\)) using the chi-square test.
# Digital Twin-Based Automated Guided Vehicle Scheduling: A Solution for Its Charging Problems
## 3 Principle of the Proposed DTDAS
### Knowledge Support
#### 3.1.2 AGV Availability Prediction  
During the operation of the workshop transportation system, AGV failures, AGV low battery warnings, etc., are the main reasons for delaying the production plan. The DT-based dynamic scheduling system should avoid such events from origin through prediction and status monitoring. Since the AGV failure period usually only accounts for a small part of the working cycle, this article mainly discusses the impact of the charging process on availability. The previous section describes the establishment of the AGV energy  
Figure 4: Real-time data based AGV energy consumption model.  
consumption model in the digital twin system. As shown in Figure 5, the model can be based on the current SOC and schedule plan to predict the electricity characteristics of AGV operation for a while in the future. Generally, AGV battery SOC dropping to 20% is considered unavailable. Then we can obtain the AGV's availability result based on the predicted value of the AGV battery SOC, which will be used as the basis for the scheduling system to perform the charging schedule. The predicted value of the AGV battery SOC can be expressed as:  
\[SOC_{p}(t)=SOC_{0}-\hat{\mu}_{1}T_{1}-\hat{\mu}_{2}T_{2}-\mathrm{L}-\hat{\mu} _{n}(t-T_{n}) \tag{4}\]  
where \(SOC_{p}(t)\) is the predicted value of AGV battery SOC at time \(t\), \(SOC_{0}\) is the initial value of AGV battery SOC. \(T_{i}\) is the time required to perform transport task \(i\).
## 5 Conclusions  
In this paper, a greenhouse monitoring and control system based on a differential mid-infrared CO\({}_{2}\) concentration sensor and a dynamic Fuzzy-PID controller, respectively, was designed and implemented. With the moisture-proof structure, the fabricated sensor, based on NDIR technology, demonstrated a satisfying measurement performance, in the special range from 0 ppm to 4000 ppm corresponding to the common CO\({}_{2}\) concentration in a greenhouse, and showed feasible application under high humidity conditions. The stability and reliability of communication via WSN was established, based on the RSSI measurement, to supply an adopted automatic adjustable transmission power at different stages of plant growth. The CO\({}_{2}\) concentration in the greenhouse was controlled at a desired value, with a near 40-ppm fluctuation, according to the control parameters calculated by the dynamic Fuzzy-PID algorithm. Remote data monitoring and concentration control was also  
Figure 16: (**a**) Fabricated hardware circuit of the GSM communication; (**b**) Data synchronization through a Dashboard on an iPad.  
Figure 15: Data synchronization through a browser.  
synchronized to the internet network through a browser and mobile terminals like iPad. The proposed system shows desired performance through field tests.  
The authors wish to thank the National Key Technology R&D Program of China (Nos. 2014 BAD08B03 and 2013 BAK06B04), the National Key Research and Development Program of China (2016 YFC0303900), the National Natural Science Foundation of China (Nos. 61307124 and 11404129), the Science and Technology Department of Jilin Province of China (Nos. 20120707 and 20140307014 SF), the Changchun Municipal Science and Technology Bureau (Nos. 11 GH01 and 14 KG022), the State Key Laboratory of Integrated Optoelectronics, Jilin University (No. IOSKL2012 ZZ12).  
All the authors engaged in the design and realization of the sensor network; [PERSON] and [PERSON] prepared and performed the experiments; [PERSON] analyzed the experimental data; [PERSON], [PERSON] and [PERSON] contributed to writing and revising the manuscript.  
The authors declare no conflict of interest.
# Acoustic Parametric Signal Generation for Underwater Communication
## 5 Conclusions  
The generation and analysis of parametric signals for a plane emitter transducer has been discussed in order to apply it to underwater acoustic communications. The formulations presented to optimize the design of an array according to the model of [PERSON] and [PERSON] lay the foundations for developing the design of the experiment. The dependence of the parametric signal with respect to the primary beam intensity, attenuation, and directivity, as well as the corresponding impact in communications in terms of bit reconstruction has been studied and evaluated. With respect to this, we can conclude that the parametric generation allows a better use of the communication channel, which allows transmitting in a more defined region, thus allowing a more private communication, or not adding acoustic contamination to protected areas. Moreover, for some cases, this method helps to improving the resistance against possible background noise and interference.  
On the other hand, the rapid absorption of high frequencies in the medium allows the low frequencies (secondary beam) to propagate at greater distances with a rather narrow directivity angle of the order of 10\({}^{\circ}\) for a frequency bandwidth between the 4 kHz and 40 kHz, as presented in this study. This is a very remarkable difference with respect to the use of conventional transducers, which usually present a directivity angle of \(\sim\)60\({}^{\circ}\).  
As future work, we are considering to extent the of study of the modulation signals by varying frequency ranges and time duration, so as to increase the communication speed and minimize the bit-error rates. Moreover, other kinds of modulations used in acoustic communication could also be explored for the parametric case. We are also considering the study of the application of the technique in shallow waters since the parametric beam is quite narrow to verify that the multipath effect is minimized with respect to the case of linear communications, much less directive. And finally, the application in situations where some specific region should not be exposed to the acoustic waves and messages due to either privacy or environmental reasons.  
Conceptualization, Methodology, Project Administration, Supervision, M.A. and J.A.M.-M.; Software, LF, M.C.-V., D.D.T.; Investigation, M.C.V.; Writing Original, M.C.-V.; Writing Review & Editing, M.A., J.A.M-M., I.F., D.D.T.; Resources and technical support, C.D.L. and P.C. This research was funded by Minesterio de Economia y Competitividad grant number FPA2015-65150-C32-P (MINECO/FEDER), and of the Generalitat Valenciana, grant number PrometeoII/2014/079. We acknowledge the Laboratorio de Hidroacustica (LHA). Centro Tecnologico Naval y del Mar (CTN). Ctra. El Estrecho-Lobosillo, Km. 2, 30320 Fuente Alamo, Murcia, Spain. The authors declare no conflict of interest.
# \(\alpha\)-Fluctuating Nakagami-\(m\) Fading Model for Wireless Communications
## 4 Performance Analysis of the \(\alpha\)-Fluctuating Nakagami Model  
It is commonly accepted to assess the performance of a WSN communication that operates in the presence of a fading channel in terms of the outage probability (OP), average bit error rate (ABER), and ergodic capacity (EC), which are defined as follows. For the \(\alpha\)-Fluctuating Nakagami model, these metrics derived in closed form as follows.
## 3 Results and Discussions
### CNN Implementation  
Figure 12 presents the accuracy of the CNN training process in one of the one hundred averaged iterations to determine the system's accuracy.  
As can be seen, the validation reaches stable accuracy values from the third iteration of around 87%. As mentioned by [PERSON] et al. [29], this is possible because the CNN (when they exclusively use the silhouette of objects, preventing textures) tends to be more error-prone.
# Modeling a New AQM Model for Internet Chaotic Behavior Using Petri Nets
## 1 Introduction  
The Internet is a network that is designed to handle the load of streams of diverse traffic. Since its invention more than five decades ago [1, 2], the Internet has become a key instrument for billions to stay in contact with everyone, and its expansion has been uninterrupted. At the time this writing, with much of the world staying at home due to the worldwide pandemic situation, the Internet is experiencing an unprecedented surge in usage. In consequence, an evident increase in Internet traffic has been caused by manifold online activities, such as remote working, distance learning, and video calls, among others. In Ref. [3], the impact on the Internet performance (latency, packet loss, etc.) of the COVID-19 lockdown is evaluated. Due to this extraordinary situation for data networks, the topic of congestion control has arisen and needs active queue management (AQM) algorithms in routers supporting Internet traffic. AQM algorithms should be able to actively control the average queue length (AQL) and thus to be able to prevent congestion as much as possible. A large increase in traffic and poor congestion control can cause network performance to partially or fully degrade and, consequently, impact applications. In addition, the design process of AQM schemes should make use of formal description techniques in the various steps of implementation. This article is an extended version of the outcomes appearing in [4]; it focuses on modeling with Petri nets (PNs) a new discrete dynamical model of AQM for congestion control, due to the chaotic behavior of Internet traffic. The PN formalism is a powerful method for modeling, and the precise nature of the graphical representation facilitates the understanding of the behavior of our AQM. For validation, we used PN mathematical methods that satisfy key properties, such as reachability, boundedness, reversibility, deadlock, and liveness.  
The remainder of this article is organized as follows. Section 2 presents the related work and motivation--chaotic behavior of Internet traffic, congestion control, and formal description techniques. Section 3 introduces our dynamical model of AQM. Section 4 presents our solution based on Petri nets. Section 5 discusses the validation and correctness. Section 6 shows the numerical simulations and performance study based on the outcomes appearing in [4]. Finally, Section 7 presents the conclusions and ongoing work.
# Cooperative Roundabout Control Strategy for Connected and Autonomous Vehicles
## 2 Methodology
### Vehicle Merge-in Decision Control  
Given the optimal merge-in flows from the last subsection, the intersection controller needs to coordinate vehicles coming from four approaches on when they should merge to achieve the desired merge-in flows. Therefore, a probabilistic merge-in decision control model is developed to determine when the vehicles at the head of queues can start to merge in. In this way, the flow-level control inputs are converted into an optimal vehicle passing sequence, which can be connected to vehicle-level operations.  
Let the spare capacity \(s_{k}\) of each segment \(k\) denote the difference between the capacity \(Q_{S,max}\) and the flow on \(S_{k}\), that is \(s_{k}(t)=Q_{S,max}-\Pi_{S_{k}}(t)\). Define \(S_{1},\ S_{2},\ S_{3}\), and \(S_{4}\) as the upstream segment of approaches \(2,\ 3,\ 4\), and \(1\), respectively. Apart from the spare capacity from approach \(i\)'s upstream segment, there is additional \(p_{i}\) capacity available for vehicles on approach \(i\), which can be described as follows:  
\[p_{i}(t)=\sum_{j=1}^{4}\eta_{ji}q_{j}(t),\ \forall i=1,\ 2,\ 3,\ 4, \tag{7}\]  
Note that \(p_{i}\) can also be interpreted as the flow that diverges out at approach \(i\) (diverge-out flow). Therefore, the total available capacity for the merge-in flow of approach \(i\) (suppose its upstream segment is \(S_{k}\)) is \(s_{k}+p_{i}\). Let \(P_{i}\) denote the probability of the leading vehicle on approach \(i\) choosing to merge into the circular flow within the roundabout whenever there is available capacity. Then, the probability \(P_{i}\) should satisfy the following condition to achieve the desired merge-in flows:  
\[P_{i}=\frac{q_{i}}{Q_{S,max}-\Pi_{S_{k}}+p_{i}} \tag{8}\]  
where \(S_{k}\) is the upstream segment of approach \(i\). With this condition met, the actual merge-in flow from approach \(i\) would be \(P_{i}(s_{k}+p_{i})=q_{i}\). Following (3) and (7), it is proved that \(0<P_{i}\leq 1\), which guarantees that our decision control is applicable at all times.  
Since the merge-in decision control is probabilistic, the actual merge-in flows given by this model are different from the desired merge-in flows. Therefore, the optimal system performance given by the flow control model is an upper bound to the actual system performance, which is illustrated in our numerical studies as well.
## 3 Results and Discussion  
### Effect of the Radius of the Apex of the Nanotip  
To look into the effect of the radius of the apex on the local electric field around the apex, for the sake of simplicity, the simulation model is a typical model shown in Figure 1 without the substrate. Considering the electrochemical corrosion and focused ion beam etching processes in experiment which can fabricate a tip with a minimum radius of \(\sim\)10 nm, the radius of the apex in simulation is set from 10 to 50 nm to figure out the influence of the geometry of the nanotip on the detection sensitivity and the electric field distribution character around the apex. A Drude model with \(\varepsilon_{\mathrm{lattice}}=9.1\), plasma frequency \(\omega_{\mathrm{p}}=1.2\times 10^{16}\) rad/s, scattering rate \(\gamma=1.2\times 10^{14}\) rad/s is used to represent the frequency-dependent dielectric constant of the Au nanotip in terahertz range [20]. First, the distribution of the electric field near the apex of the Au nanotip with a radius of 10 nm is shown in Figure 2a. About 25-fold local electric field enhancement around the apex is observed from Figure 2a. When the radius of the apex increases from 10 to 50 nm, the corresponding maximum electric field amplitude \(E\) decreases from 24.9 to 14.3 V/m as seen in Figure 2b. This is consistent with the lightning rod effect that was reported elsewhere [16,21]. As shown in Figure 2c, with the increase of the distance away from the nanotip apex, the electric field amplitude along the \(x\)-axis decays rapidly and a good reciprocal square law relation can well fitted the trend. The rapid decrease indicates that the enhanced region of the effective electric field is small and is confined to a region under the apex of the nanotip, only within a nm-scale distance from the tip can the local electric field be effectively enhanced. The electric field distribution along the \(y\)-axis  
Figure 1: Model of a terahertz (THz) near-field microscopy composed of a nanotip and a substrate under the THz radiation.  
(Figure 2d) shows that the full width at half maximum (FWHM) of the nano-scale enhanced THz field focused by the Au nanotip in air increases from 18 to 72 nm when the radius of nanotip increases from 10 to 50 nm. Therefore, the FWHM of nano-scale enhanced terahertz electric field generated by Au nanotip follows closely with the radius of the apex, in marked departure from conventional wisdom based on the optical diffraction limit, where the FWHM is dictated by the wavelength of the radiation.
# Spectral Coexistence of QoS-Constrained and IoT Traffic in Satellite Systems
## 1 Introduction
### Notation  
We denote random variables (vectors) by uppercase (bold) letters, while we refer to their realizations in lowercase, e.g., \(X\) and \(x\); \(\mathbf{X}\) and \(\mathbf{x}\). The probability mass function (PMF) of a discrete r.v. is denoted as \(p_{X}(x):=\mathbb{P}\{X=x\}\) and that of a discrete random vector as \(p_{X}(x):=\mathbb{P}\{X=\mathbf{x}\}\). Moreover, we indicate the conditional PMFs \(\mathbb{P}\{X=x\,|\,Y=y\}\) as \(p_{X}(x|y)\) and \(\mathbb{P}\{\mathbf{X=x\,|\,Y=y\}\) as \(p_{X}(\mathbf{x}|y)\). Finally, the expectation operator is denoted as \(\mathbb{E}[\cdot]\), while \(\mathbf{l}_{k}\) is the identity matrix of size \(k\).
## 3 Dataset Construction  
The field of aerospace research has strict requirements for confidentiality because it involves national security, high technology and major economic interests. The information generated during the design, manufacture and operation of spacecraft, including technical details, performance parameters, material science, propulsion systems, etc., is often highly sensitive.  
Due to these special characteristics of the spaceflight domain, it is almost impossible to find a complete and detailed set of spacecraft-related SysML models in the open channel. Most detailed system models and associated documentation are protected by strict access controls and confidentiality agreements. These measures ensure that critical information is available only to authorized personnel to maintain the security and integrity of the project. Therefore, when conducting research related to spacecraft systems engineering, this paper choses to work on the basis of constructing a dataset. There are two sources of datasets: one is a manually constructed set of spaceflight-related SysML models based on corporate internship experience, and the other is publicly available domain textual information.  
Before model training, dataset construction and data processing are required to ensure the performance and accuracy of the model. The research of dataset construction mainly includes the following parts: construction of the SysML model, SysML model format transformation as well as parsing, construction of training dataset and construction of candidate word set. Through the construction of datasets, the data quality can be effectively improved, the model learning process can be optimized and the generalization ability of the model can be ultimately enhanced. The construction of the dataset allows for further validation of its effectiveness, thereby supporting subsequent research and analysis in an effective manner.  
It should be noted that the purpose of this section is not to construct the complete aerospace dataset, but to prepare for and exemplify the training of the newly proposed method, and is not limited to a specific domain.
## 5 Data Analysis and Method Implementation
### Third Stage: Model Selection and Visualization
For Haiti, the confusion matrix in Figure 12a shows that class 2 has the maximum correct predictions for the unseen test data, and class 3 has the least correct predictions. The ROCAUC plot in Figure 12b also depicts the highest value for class 2. The micro and average macro value for the classes are nearly the same. The same pattern can be seen in the AUC values, with the minimum value for class 3 (0.73) and maximum for class 2 (0.94). Nepal and Pohang have a 4 \(\times\) 4 matrix, based on their number of damage scale. Confusion matrix for Nepal in Figure 13a has class 3 with the correct predictions and maximum for class 1. The AUC value for class 3 in Figure 13b is the maximum. The micro and macro-average values of AUC for all the classes are 0.97 and 0.96, respectively. Figure 11: Performance of ET on Ecuador. Figure 12: Cont. In the confusion matrix result for Pohang, Figure 13(a) illustrates that class 1 has the maximum correctly predicted test examples, and a similar pattern is visible for the AUC  
Figure 12: Performance of ET on Haiti. Figure 13: Performance of ET on Nepal. value in Figure 13(b), with class \(1\) having a maximum AUC value. The micro and macro value for all the classes are 0.74 and 0.76, respectively. Insufficient data for Pohang resulted in several zero values in the confusion matrix. Overall, while predicting the test set, Nepal has attained the highest accuracy of 72% out of all the datasets.
# GAN-Based Inversion of Crosshole GPR Data to Characterize Subsurface Structures
## 5 Inversion Results  
### Synthetic Data Inversion Results  
As shown in Figure 7, we created seven test numerical models with dimensions of 2.00 m \(\times\) 4.00 m \(\times\) 0.40 m to simulate diaphragm walls with relative permittivity of 9 and electric conductivity of 0.001 S/m. In cases 1 to 4, a cuboid defect with relative permittivity of 20 and electric conductivity of 0.01 S/m was embedded inside each model to simulate the target. In order to evaluate the inversion resolution of the network, we set the sizes of the targets to one time, half, and one-quarter the wavelength, respectively, and considered a target of one-quarter the wavelength in the corner of the model with sparse data coverage. We also considered multiple-target inversion. In the last three cases, two defects were arranged horizontally, vertically, and diagonally in the numerical models, respectively. We simulated the crosshole GPR measurements using a 500 MHz source wavelet, and data were collected in the same way as in network training.  
Then, we utilized these cases to compare our inversion network with two classic crosshole GPR data inversion methods, including ray-based tomography and FWI [10, 42]. From the second to the last rows in Figure 7 display the ground truth and the inverted defect reconstruction images of different methods, respectively. FWI demonstrated the best inversion capability, as it achieved the highest precision in all cases in reconstructing the targets. On the other hand, ray-based tomography failed to resolve targets smaller than half the wavelength and was plagued by considerable noise. Our method used fully connected layers and horizontal connections to extract global features and correctly pinpointed almost all target locations.  
Table 3 summarizes MSE, SSIM, and time cost of the inversion results to quantitatively evaluate the inversion performance of different methods. It is evident that FWI had the best MSE and SSIM performance in all the cases compared with the other methods, yet it suffered from considerable longer inversion time. Although the MSE values of the proposed method were apparently greater than those of FWI, the SSIM values were slightly lower than those of FWI (\(\sim\)7%), indicating that the reconstructing capability of the proposed method was close to that of FWI. Notably, the inversion time of the proposed method was significantly shorter than that of FWI, which is crucial in large-scale inversion tasks. Compared with ray-based tomography, both the MSE and SSIM performance results of the proposed method were much better, with similar time costs.  
Figure 6: Training losses of the proposed framework: (**a**) loss curves of generators (G) and (**b**) loss curves of discriminators (D).