esa-sceva/satcom-chunk-collection huggingface dataset:
# Parameter-Matching Multi-Objective Optimization for Diesel Engine Torsional Dampers
## 2 Research Methodology
### Free Vibration Calculation
The case study employs an eight-cylinder diesel engine with a cylinder bore of 170 mm, crank radius of 100 mm, and connecting rod length of 360 mm. Free vibration characteristics were computed using three methodologies: the conventional Jacobi iterative method, the modified Jacobi screening method proposed in this study, and the Lanczos method via ANSYS commercial software. The stability and fatigue life of the crankshaft system are primarily influenced by the natural frequencies of its lower-order modes, whereas higher-order modes predominantly induce localized vibrations with a negligible impact on the global dynamic behavior [21]. Consequently, this study validates the accuracy of the numerical model by comparing the first five natural frequencies of the crankshaft system against simulation data. As summarized in Table 1, the modified Jacobi sweep demonstrates exceptional agreement with Lanczos-derived results, exhibiting frequency deviations below 1% across all calculated modes. This precision validates the numerical stability of the threshold-enhanced Jacobi algorithm despite the system's inherent stiffness-inertia disparities. These findings confirm the method's robustness in handling ill-conditioned matrices typical of multi-cylinder crankshaft models, effectively bridging the gap between simplified numerical method and high-fidelity finite element analyses. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Harmonic Order**} & **Jacobi Iterative Method/(Hz)** & **Jacobi Screening Method/(Hz)** & **Lanczos Method/(Hz)** \\ \hline
1 & 305.04 & 108.66 & 108.64 \\
2 & 308.01 & 279.69 & 279.62 \\
3 & 410.68 & 461.42 & 461.33 \\
4 & 638.32 & 644.85 & 644.77 \\
5 & 817.73 & 825.15 & 825.07 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Crankshaft natural frequency table. Figure 2: Schematic diagram of free vibration calculation.
## 2 Materials and Methods
### Evaluation Procedures
#### 2.4.2 Categorical Evaluation Statistics  
The categorical statistics used in this study are the false alarm ratio (\(FAR\)), the probability of detection (\(POD\)), the critical success index (\(CSI\)) and the accuracy (\(ACC\)) shown in Equations (5)-(8). These measures have also been widely used [3; 5; 20] and are described in detail in [PERSON] [21].  
The \(FAR\) lies between 0 and 1, where 0 is the desired result. The \(POD\) and \(CSI\) also range from 0 to 1. For these two statistics 1 is the perfect score. Both metrics tell the accuracy with which the satellite correctly estimates the rainfall events but the \(CSI\) also considers the false alarms. Finally, the \(ACC\) measures the fraction of correct satellite estimates and its perfect score is 1.  
\[FAR=\frac{b}{a+b} \tag{5}\]  
\[POD=\frac{a}{a+c} \tag{6}\]  
\[CSI=\frac{a}{a+b+c} \tag{7}\]  
\[ACC=\frac{a+d}{a+b+c+d} \tag{8}\]  
where \(a\), \(b\), \(c\), \(d\) are the possible events shown in Table 1.  
Finally, a threshold of 0.1 mm has been used to differentiate precipitation and no precipitation events since precipitation values below this threshold may be the result of noise. Although the selection of this threshold is subjective, [PERSON] et al. [22] found that their results remained robust when they repeated the analysis for higher thresholds.
## 2 System Design  
An MZM schematic diagram of the DSB-OCS system is illustrated in Figure 1. The DSB-OCS technique is established using a Dual-Electrode Mach-Zehnder Modulator (DE-MZM) by receiving the optical signal with a wavelength of \(\Omega\) from the continuous wave (CW) laser diode (LD). Then, the optical signal is divided into two arms, the upper and lower DE-MZM electrodes. The arms contain lithium niobate (Nio3) materials, which cause disturbance to the optical signal to perform the optical modulation. Next, both arms are driven by the RF signal from the local oscillator (LO) with a frequency of \(\omega_{rf}\), except that only one of the arms is driven with a 180\({}^{\circ}\) phase-shifter. As a result, the system achieves a Dual-Sideband Optical Carrier Suppression as the output, where \(\Omega+\omega_{rf}\) and \(\Omega\)\(-\omega_{pf}\) emerge as the sideband from the original signal \(\Omega\), as shown in the block diagram in Figure 1 by manipulating the modulator's DC bias voltage. The DSB-OCS system was theoretically clarified in [27]. A baseband signal of 1.25 Gbps was injected or directly modulated via the CW LD and simulated the system performance via Optisystem software version 17. The simulation was performed using the setup shown in Figure 2 to generate the mm-wave with DSB-OCS technique. A direct modulation laser diode (DMLD) of 1550 nm was used to produce the CW optical signal with an amplitude signal around \(-\)10 dBm. The relative intensity noise (RIN) as set at \(-\)140 dB/Hz. In order to ensure a full DE-MZM coupling and to minimize the polarization-dependent loss, a polarization controller (PC) was used in the circuit. Then, an erbium-doped fiber amplifier 1 (EDFA1) was utilized to amplify the signal and to overdrive it before sending to a 20 GHz RF signal. A 3 dB splitter divided the RF signal into two equal parts with a 180\({}^{\circ}\) phase-shifter in one of the electrodes. Then, both parts drove the upper and lower DE-MZM electrodes. The power of RF signal was 20 dBm at frequency of 20 GHz. The half-wave voltage of the main \(V_{\pi}\) was deemed to be 5 \(V\), \(V_{DC1}\) was biased at 5 V, while \(V_{DC2}\) is biased at 0 V.  
The DE-MZM output was amplified by EDFA2. After that, the signal was linked to an optical fiber of 0.21 dB/km loss, and 16 ps/(nm.km) chromatic dispersion. At RAU, the photodetector (PD) with 0.6 A/W was used to detect the signals which were then filtered by a lowpass filter (LPF) with cutoff frequency of 0.75x BitRate. Finally, the output signal was connected to an analyzer to collect the data and to perform analysis of the signal.
# Smart Platform for Monitoring and Control of Discrete Event System in Industry 4.0 Concept
## 2 Preliminaries and Problem Formulation
### Cloud Server Development
#### 3.3.2 App Service  
The next development step was to ensure access to the data using the backend application. Uploading the application was made possible by MS Azure [33] through the App Service. Here, it was necessary to configure the server environment. We chose the operating system under which the server should work, the Java version of the platform, its location, and which project should be uploaded to it. We chose the location in the data center. Linux, which is compatible with Java applications, was chosen as the operating system, and the Java version of the Java 17 SE platform. As the source of the project, we chose a repository on the Github platform [34], where we later uploaded the project. In this case, the project is uploaded automatically after the new version of the project is uploaded to the repository.  
The backend application had to be created and tested in the initial phase on a local environment. We used the Intelij tool for development, used the same version of the Java platform as on the server, and created a new project in Springboot framework version 3.0.4 [35]. First, we connected the application to the MySQL cloud database by implementing the mssql-jdbc library and entering the login credentials. We used the springboot-hibernate library to work with data in the database. With this, we created the basis for our project, and we could further work on creating endpoints for working with data.  
The project was based on the REST API architecture in the Springboot framework. For each of the tables, it was necessary to create a model with all attributes. Using the lombok library, we could easily add better and setter methods for all models using annotations.  
Next, we created a repository that directly queried the database. Just like the models, the repositories had to be created separately for each model. Since the repository in the Springboot framework serves as an extension class of the JpaRepository class, there was no need to add new features. All necessary functions were already included in the JpaRepository class. Furthermore, we created services for individual production lines, which provided us with data obtained from repositories for controller classes. In general, the architecture is designed so that the controller configures the path and type that the application queries. Data processing will take place in the service and the repository will provide the data of the given model. In the controller, as already mentioned, we set the endpoint mapping and the http method it responds to. Since data were provided from the backend application, it was a GET-type http method.  
We then tested the backend application using the Postman tool, where, after calling the endpoint, the backend application returned data from the database.  
Figure 7: UML diagram of a cloud database.
## 3 Simulation Results
### Joint Design System for Downlink Communication  
#### 3.2.1 Rayleigh with AWGN Channel  
In this scenario, we consider a joint design communication and sensing system at the base station equipped with four transmit antennas (\(N_{t}=4\)), while the number of receive antennas (\(N_{r}=1,2,4\)) varies from one to two and four. These antennas are uniformly spaced linear arrays with half-wavelength spacing. To ensure that noise does  
Figure 6: S21, CFR, CIR, and averaged PDP of the channel in the meeting room.  
not significantly affect the analysis of sensing and communication interference, we set the noise power to a small value.  
We assume that the downlink communication channel follows a Rayleigh distribution and is i.i.d. Gaussian distributed with zero mean and unit variance. Both the communication signal (\(s(n)\)) and the radar signal (\(x(n)\)) are modulated as QPSK.  
The channel is assumed to be Rayleigh fading because it represents a worst-case scenario where there is no dominant line-of-sight (LoS) path between the base station (BS) and the user equipment (UE). In Rayleigh fading, the signal undergoes multiple reflections and diffractions caused by the surroundings, leading to a complex multipath environment. By assuming Rayleigh fading, we aim to capture the unpredictability and variability in real-world wireless communication channels more accurately.  
Although there may indeed be an LoS channel between the antennas, the presence of multipath components due to reflections and diffractions justifies modeling the channel as Rayleigh fading. This approach ensures that we account for the most challenging propagation conditions, which is crucial for designing robust communication systems.  
Figure 7 depicts the communication performance in terms of BER versus the sensing performance in terms of mean square error (MSE) with fixed sensing power and varying communication power.  
These figures illustrate how communication and sensing performance vary with fixed sensing power and varying communication power. With an increase in the number of receive antennas, both communication and sensing performance show improvement. Examining the BER curves, we observe a consistent trend shifting towards the left side as the number of receive antennas increases. This shift can be attributed to the influence of communication power: higher communication power enhances detection performance. However, increased interference power leads to a reduction in sensing performance. Overall, outfitting the joint design system receiver with more antennas enhances detection performance.
# A Novel Multi-Agent-Based Approach for Train Rescheduling in Large-Scale Railway Networks
## 4 Multi-Agent System for Train Rescheduling Problems  
This section presents a systematic MAS designed to address train rescheduling challenges in large-scale railway networks. The section is organized as follows: First, we introduce the approach used to decompose the railway network and the allocation of the optimization process across different local areas. Next, we detail the structure of the proposed MAS, defining the roles and functions of each agent within the system. Following this, we explain the searching algorithm employed by the MAS. Finally, we describe the Condorcet-voting-based collaborative approach, which ensures effective coordination among agents.
# A Survey of Adversarial Attacks: An Open Issue for Deep Learning Sentiment Analysis Models
## 2 Adversarial Examples and Deep Learning Models
### Adversarial Examples for Text Applications  
In recent years, the design of adversarial examples has attracted research interest with the aim of identifying the vulnerabilities of DNN models against modified inputs and thereby proposing defense mechanisms that guarantee the safety of the results. The original idea of adversarial examples was explained by [PERSON] et al. [7] when evaluating the robustness of image classification DNN models by making minor pixel-level modifications. Nevertheless, when evaluating a textual DNN model, it is impossible to apply the same methods from evaluating image DNN due to three main differences:  
1. **Input space.** Image inputs are continuous, while text data are symbolic, i.e., discrete. Thus, it is hard to define perturbations in texts that maintain the semantic and syntactical properties of the inputs.
2. **Perceptibility.** While small changes in image pixels are usually difficult to perceive, small changes in text, e.g., changing characters or words, can be much more easily perceived.
3. **Semantics.** In the case of images, small changes usually do not change the semantics of the image, while perturbation of text can easily change the semantics of words or sentences.  
Considering these principal differences, works involving adversarial examples for textual DNNs have proposed novel methods using different techniques to carefully preserve the semantic inputs and the imperceptibility of modifications. [PERSON] and [PERSON] [9] were the first to consider designing adversarial examples for text applications with DNN models. In their work, they experimented by inserting small text fragments into the input data. These fragments were created synthetically based on observed terms in the training set (refer to Figure 2). Following [PERSON] and [PERSON] [9], different adversarial works oriented to text-based applications have shown that performing modifications at the character, term, or sentence level by inserting, deleting, substituting, or exchanging characters or terms can cause models to produce incorrect results [16; 17; 18; 19]. When generating adversarial examples, the modifications should be as small as possible while remaining capable of fooling the model. For text-based tasks, the modifications should not make drastic changes to the text's semantics or syntax, and should maintain the readability of the input message.
## 2 Materials and Methods
### Detection of Apnea Events based on Relative Changes in Respiratory Baseline
#### 2.3.2 Classification with Gaussian Mixture Models (GMM)  
A Gaussian mixture model (GMM) is a probabilistic modeling framework. In this model, the probability density function (PDF) of \(\mathbf{x}\in R^{d}\) is defined as a finite weighted sum of \(k\) Gaussian distributions:  
\[p(\mathbf{x}|\mathbf{\Theta})=\sum_{i=1}^{k}\gamma_{i}p(\mathbf{x}|\theta_{m}) \tag{13}\]  
such that \(\mathbf{x}\) is the 2-dimensional feature vector \([I_{c},A_{c}]^{T}\) computed every time \(W_{b}\) and \(W_{m}\) are constructed, \(\sum_{i=1}^{k}\gamma_{i}=1\), \(\mathbf{\Theta}\) is the mixture model, \(\gamma_{i}\) corresponds to the weight of component \(i\), and the density of each component is given by the normal probability distribution:  
\[p(\mathbf{x}|\theta_{m})=\frac{|\Sigma_{m}|^{-\frac{1}{2}}}{(2\pi)^{d/2}}\exp \bigg{\{}-\frac{1}{2}(\mathbf{x}-\mu_{m})^{T}\Sigma_{m}{}^{-1}(\mathbf{x}- \mu_{m})\bigg{\}} \tag{14}\]  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Labels/ Segments** & **Training Set** & **Testing Set** & **Total** \\ \hline
**Normal** & 208,456 & 20,789 & 229,245 \\
**Apnea** & 81,055 & 8048 & 89,103 \\
**Total** & 289,511 & 28,837 & 318,348 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Segments for training and testing classification models.  
The parameters \(\gamma\), the mean \(\mu\), and the covariance \(\Sigma\) are optimized during the training process using the expectation maximization algorithm [50] such that the log-likelihood of the model is maximized. During testing, a likelihood estimate is obtained for the apnea class, defined by the model \(\Theta_{A}\), and for the non-apneic (normal respiration) class, defined by the model \(\Theta_{N}\). Using the Bayesian classification formula, the likelihood estimates are combined to compute the posterior probability of apnea for the sample \(\mathbf{x}\) :  
\[P(A|\mathbf{x})=\frac{p(\mathbf{x}|\Theta_{A})P(A)}{p(\mathbf{x}|\Theta_{A})P( A)+p(\mathbf{x}|\Theta_{N})P(N)} \tag{15}\]  
where \(P(A)\) and \(P(N)\) are the prior probabilities of the apnea and non-apnea (normal) classes respectively. These probabilities were set by symmetry to be equal \(P(A)=P(N)=0.5\) assuming we have no prior knowledge about them. The combination of the two GMMs and the Bayesian classification formula in Equation (15) form the GMM classifier [51].
# Provisioning of Fog Computing over Named-Data Networking in Dynamic Wireless Mesh Systems
## 4 Performance Evaluation
### Numerical Results  
#### 4.3.1 Static Mesh Environment  
We start investigating the system performance by reporting the fraction of satisfied Interest requests and mean full delay in Figure 9 for static mesh network conditions as a function of the frequency of Interest per second, \(5\times 5\) nodes configuration, enabled cache, multiple consumers, and single producer. Please note that from now on, the latter three parameters are encoded as a triple \((C/N,K,M)\), where \(C/N\) defines whether cache enabled (\(C\)) or not (\(N\)), \(K\) denotes the number of consumers, \(M\) refers to the number of producers.  
Figure 9: Loss and delay performance measures in static mesh.  
Analyzing the reported results, one may observe that for all the reported Interest frequencies, the mesh is capable of successfully satisfying the compute requests with the fraction reaching 1 for all considered numbers of consumers. The major differences between the considered cases lie in the mean full delay behavior reported in Figure 9b. Here, one may observe that the delay increases with the increased number intensity of Interests as well as with the increased number of consumers. However, even for the most loaded case corresponding to 3 consumers and 30 Interests per second, the mean delay stays well below 50 ms.  
Consider now the share of received compute results in time jointly characterizing reliability and full delay in Figure 10 for the same input parameters of the system. As one may observe, in all the considered cases, all the requests are satisfied as the metric of Interest eventually approaches one. The most pronounced effect is caused by increasing the number of consumers from one to three. The rationale is that this increases the overall network load, thus leading to higher latency. Still, even in the most loaded considered case with three consumers and intensities of 30 Interests per second, all the requests are served in just 70 ms.
###### Abstract  
This paper explores the application of boron oxide (B\({}_{2}\)O\({}_{3}\)) as a sintering additive in silicon-based (SiO\({}_{2}\)) ceramics, focusing on its impact on the sintering process and resulting ceramic properties. Composite silicon-based ceramic shells with boron oxide content ranging from 0 to 4 W\(\%\) were prepared using digital light processing (DLP) technology. This study examines the effects of boron oxide on sintering temperature, apparent porosity, shrinkage rates, and mechanical properties during high-temperature sintering. Experimental results indicate that, as boron oxide content increases, the shrinkage rate of the ceramic samples rises progressively, with shrinkage in the XY direction increasing from 4.57\(\%\) to 16.40\(\%\) and in the Z direction from 6.25\(\%\) to 17.03\(\%\). Concurrently, the apparent porosity decreases with higher boron oxide content, ranging from a maximum of 36.17\(\%\) to a minimum of 23.27\(\%\). Bulk density also improves from 1.49 g/cm\({}^{3}\) to 1.78 g/cm\({}^{3}\). The bending strength trend first rises and then declines, peaking at 15.39 MPa at a boron oxide content of 3 W\(\%\). X-ray Diffraction and Energy Dispersive Spectroscopy analyses confirm that the addition of boron oxide promotes the formation of beta-quartz and that its liquid-phase behavior at high temperatures aids in enhancing ceramic densification. This study demonstrates that an appropriate amount of boron oxide can effectively lower the sintering temperature of silicon-based ceramics while improving their mechanical properties, providing a theoretical foundation and practical basis for broader industrial applications.  
silicon dioxide; boron oxide; light-curing printing technology; high-temperature sintering +
Footnote †: journal: Applied sciences
## 2 Laser Acceleration Simulations
### Particle Dynamics  
The time-dependent dynamics of an ensemble of interacting ions is considered. An ion indexed by \(j\), of mass \(m\) and charge \(q\) is accelerated to relativistic energy and momentum, respectively, of \(\mathcal{E}_{j}=\gamma_{j}mc^{2}\) and \(\mathbf{p}_{j}=\gamma_{j}mc\mathbf{\beta}_{j}\), where \(\mathbf{\beta}_{j}\) is the velocity of the particle divided by \(c\), the velocity of light in vacuum, and \(\gamma=(1-\beta^{2})^{-1/2}\) is the Lorentz factor, when interacting with the time-dependent fields \(\mathbf{E}\) and \(\mathbf{B}\) of a laser pulse. Thus, the dynamics is described by the coupled Newton-Lorentz equations, given in SI (International System of Units):  
\[\frac{d\mathbf{p}_{j}}{dt} = q\left(\mathbf{E}(\mathbf{r}_{j})+\mathbf{E}_{j}^{\text{int}}+c\mathbf{\beta}_{ j}\times(\mathbf{B}(\mathbf{r}_{j}))\right) \tag{4}\] \[\frac{d\mathcal{E}_{j}}{dt} = qc\mathbf{\beta}_{j}\cdot\left(\mathbf{E}(\mathbf{r}_{j})+\mathbf{E}_{j}^{\text {int}}\right)\]  
The electric field of the inter-ionic interaction is approximated by \(\mathbf{E}_{j}^{\text{int}}=-\sum_{k\
eq j}\
abla\phi_{jk}\), with the Coulomb interaction scalar potential  
\[\phi_{jk} = \frac{q}{4\pi\epsilon_{0}}\frac{1}{|\mathbf{r}_{j}-\mathbf{r}_{k}|} \tag{5}\]  
Here, \(\epsilon_{0}\) is the vacuum permittivity. The presence of plasma electrons can be neglected when simulating the ions' acceleration dynamics, as they are blown off first by the pulse, as it was shown by particle-in-cell simulations [29].  
Since the electromagnetic fields have a complex mathematical structure, and the inclusion of the ions' interaction leads to coupled motion, one has to solve the above differential equations numerically. A numerical integration of Equation (4) yields the particles' velocities \(\mathbf{\beta}_{j}\) and thus also their energy gain at a final time equal to many laser cycles.