esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Example
### Setting Damage Scenarios and Building Database  
Owing to the joint effects of environmental factors and external loads, damage to the hangers of suspension bridges during operation is mainly manifested in rust or breakage of wires after aging and cracking of the protective sleeve. The corrosion and fracture of the hangers will cause the effective cross-sectional area to decrease. Ignoring the changes in the quality of the entire hunger after the steel wire is corroded and broken, this study adopts a method for reducing the elastic modulus of the hanger to simulate the damage of the hanger. The damage rate is defined to represent the damage severity of the hanger, and the value of the damage rate is the reduction rate of the elastic modulus of the hanger.  
For convenience, this article numbers all the hangers according to the following rules. From the west to the east side span, the hangers on the upstream side are sequentially numbered 1, 2, 3... 59, 60, 61, and those at the downstream side are sequentially numbered 62, 63, 64... 120, 121, 122, as shown in Figure 5.  
To test the performance of the proposed method, this section identifies the preset single-location damage, multi-location damage, and different damage severities of the hangers. The 1-31 hangers were chosen as the damage object, and the damage scenario was defined as a combination of damage type and degree of damage. There are three types of damages: damage to a single hanger, two hangers, and three hangers. The damage severity was set as 5, 15, 25, 35, and 45%. Therefore, there are 15 damage scenarios in this study, and each scenario corresponds to multiple damage combinations. The specific settings of the damage scenarios are shown in Table 1.  
According to the damage scenarios shown in Table 1, the finite element model was subjected to ambient excitation for dynamic analysis, and the vertical acceleration of hangers 1-31 was obtained. The ambient excitation was simulated by white noise excitation with a sampling frequency of 512 Hz, and the analysis time was 34 s. Each damage combination contained 31 acceleration data, and the sequence length of each data was 17,408. The Z-score normalization method was used to preprocess the acquired data. Figure 6 is part of the preprocessed acceleration data of hanger 15 under scenario 15.  
The 31 acceleration data collected under the same damage combination are composed of multi-channel data in the order of the hanger number, and a total of 510 acceleration data with a dimension of 17,408 \(\times\) 31 can be obtained. Then, each piece of acceleration data is divided into 17 samples with dimensions of 1024 \(\times\) 31, so a total of 8670 samples can be generated from 510 pieces of data. Each sample is marked with the damage location and degree, and all samples constitute the database. Finally, the database is divided into a training set, validation set, and test set, which approximately conforms to the ratio of 6:2:2. The number of samples is shown in Table 1.  
Figure 5: Layout of a long-span suspension bridge and numbering of hangers (unit: m).
## 4 Model Predictive Control for Power Optimization and DDPG-Based Optimization Scheduling
### Model Predictive Control for Power Optimization
#### 4.1.4 Optimal Control Inputs  
By solving the optimization problem, the optimal control input for the current time instant is determined and applied to the actual control system. In MPC-based rolling optimization, only the optimal control input for the present moment is implemented, while future control inputs are recalculated in subsequent iterations, ensuring adaptability to dynamic changes in the system state.
# An Improved Fuzzy Vector Signature with Reusability
## Preliminaries
### Universal Hash Function  
A collection \(\mathcal{H}\) of hash functions \(H:U\to V\) is \(1/|V|\)-universal if for any \(x_{1},x_{2}\in U\) such that \(x_{1}\
eq x_{2}\), it holds that \(\Pr_{H\leftarrow\mathcal{H}}[H(x_{1})=H(x_{2})]=\frac{1}{|V|}\).  
Lemma 1: (Reference [6]) _Let \(A,B,C\) be random variables. Then,_  
_(a)_ _For any_ \(\xi>0\)_, the conditional entropy_ \(\textbf{H}_{\infty}[A|B=b]\) _is at least_ \(\tilde{\textbf{H}}_{\infty}[A|B]-\log(1/\xi)\) _with a probability of at least_ \(1-\xi\) _over the choice of b._ _If_ \(B\) _has at most_ \(2^{\lambda}\) _possible values, then_ \(\textbf{H}_{\infty}[A|(B,C)]\geq\textbf{H}_{\infty}[(A,B)|C]-\lambda\geq \tilde{\textbf{H}}_{\infty}[A|C]-\lambda\)_. In particular,_ \(\textbf{H}_{\infty}[A|B]\geq\textbf{H}_{\infty}[(A,B)]-\lambda\geq\textbf{H}_ {\infty}[A]-\lambda\)_._  
Lemma 2: (Reference [27]) _Let \((X,Z)\) be any two jointly distributed random variables and \(Z\) has at most \(2^{\upsilon}\) possible values. Then, for any \(\epsilon>0\) it holds that \(\Pr[\textbf{H}_{\infty}[X|Z=z]\geq\textbf{H}_{\infty}[X]-v-\log(1/\epsilon)] \geq 1-\epsilon\)._  
**Lemma 3**.: _Let \(\mathcal{H}\) be a family of universal hash functions \(H:U\to V\), and let \((X_{1},\ldots,X_{q})\) be a joint distribution such that \(\mathbf{H}_{\infty}[X_{i}]\geq\alpha\) for \(i=1,...,q\) and \(\alpha\geq q\cdot\log|V|+3\log(1/\epsilon)+\Theta(1)\). Then, the distribution \((H_{1},H_{1}(X_{1}),\ldots,H_{q},H_{1}(X_{q}))\), where \((H_{1},...,H_{q})\leftarrow\mathcal{H}^{q}\), is \(2\epsilon q\)-close to the uniform distribution over \((\mathcal{H}\times V)^{q}\)._  
Proof.: Proof of Lemma 3 is in Appendix A.
## 2 Literature Review and Related Works
### State-Of-The-Art  
EDM aims to predict student performance either using demographic data or socio-economic data or online activity on the learning management system at different educational levels. A problem arises when the data are imbalanced or where researchers have used other techniques alongside a simple classification method such as data balancing, cost-sensitive learning and genetic programing [8; 46]. Early dropout studies have been conducted by researchers to determine the factors that can influence student retention. Here, classical classification methods were also used to predict early dropout. Feature selection plays a vital role in the accuracy of the classification algorithms. The genetic algorithm was used and obtained 10 attributes out of 27, and the efficiency was improved [47; 48]. For our study into handling imbalanced data, we use the K-fold cross-validation technique; to reduce features, we use ranking and scoring, the genetic search algorithm, principle component analysis and the multiview learning approach using multivariate projection along with the CN2 Rule induction for easier interpretation, as the multiview learning approach uses sparse data where a high percentage of the variable's cells do not contain actual data.  
HEIs use different learning systems and the amount of data accumulated from these systems is enormous. The LA provides the analysis of data from these systems and finds meaningful patterns which can be helpful for educators and learners. From the literature review and related works, it is evident that, when analyzing student academic performance prediction, LA is a widely used method in EDM. The classification technique is also used to classify the parameters used for the prediction and success of the model. Due to innovative teaching and learning pedagogies and the implementation of flipped classrooms, the use of VBL is on the increase, where students can study prior to the class at their own leisure and come prepared to the class. Little research has been carried out on VLA and on determining students' attitude and behavior towards VBL. Educators apply different settings within or outside the classroom to create an effective learning experience for learners. For effective knowledge transfer, an educator needs a predictive model to understand the future outcomes for each student. This will help the educator to identify the methods which should be applied, to identify poorly performing students and provide better support to the students to obtain good grades at an early stage.
# Testing the Galileo High Accuracy Service User Terminal (HAUT) in Static Scenarios +
## 5 The Galileo HAS User Terminal  
The Galileo HAS User Terminal provides a precise positioning, velocity and time (PVT) solution based on the Galileo HAS User Algorithm for SL1, guaranteeing access  
Figure 1: Main blocks of user algorithm.  
to corrections from either the SIS or the Internet and offering a flexible approach to the configuration of the algorithm, communication means and logging capabilities.  
The User Terminal (Figure 2) is a small (8 cm, 18 cm, 25 cm), light (2.1 kg), configurable, portable and autonomous device that includes a professional multi-constellation, multi-frequency receiver and calculates a single- or multi-constellation (Galileo + GPS) Galileo HAS and Open Service (OS) PVT solution with an update rate ranging from 1 Hz to 100 Hz. The User Terminal can be configured to retrieve Galileo HAS corrections either from Galileo E6-B or RTCM 3 corrections over NTRIP and works in any of the User Algorithm dual- and triple-frequency modes.  
The User Terminal has RF interference detection capabilities in the E1, E5 and E6 bands. Its ingress protection is IP64 and its operating temperature range is \(-40^{\circ}\)-\(65^{\circ}\)C. Its ruggedized aluminum housing was designed and tested against vibration, shock, temperature, humidity and corrosion to make the unit a resilient piece of equipment for future testing campaigns. It can be interfaced with an HMI application running on a PC for configuration and real-time monitoring purposes.  
The UT has a wide logging capability: real time HAS/OS solution and monitoring streams, GNSS observations in RINEX and binary files (up to 100 Hz) and HAS corrections in ASCII and binary format. It has 1 TB internal memory and USB, WiFi and Bluetooth interfacing capabilities.  
The UT can be powered through USB or Ethernet (PoE I/F). It has internal batteries allowing autonomy for more than 3 h off the grid. Portability is also considered (transport box, external batteries).  
The current HAS specifications are described in references [2; 3; 4], which include the service definition document and signal and ground specifications. Additional details on the development and verification of the User Terminal are provided in a previous paper [1]. Previous work on HAS user performance from other developments and using different algorithms can be found in [5; 6; 7].
## 7 Conclusions  
We have proposed an OTN/WDM network architecture to transport Ethernet and CPRI flows generated in a C-RAN. The network topology is composed of optical rings connecting Access Switches to an OTN/WDM Hub in which the baseband functionalities of a C-RAN are performed. The RRUs are equipped with active antennas and some MIMO configurations can be supported according to the traffic to be handled. An analytical model has been introduced to dimension the wavelengths of the rings when the Ethernet and CPRI circuits can be re-configured. We have shown how the introduced reconfiguration solution allows for a remarkable saving with respect to a static solution in which the optical rings are dimensioned to support the RBSs and RRUs when they work at full load. The wavelength saving can reach 90%. Finally we have shown how the proposed solution can be very effective when each AS handles the traffic generated by many sub-areas and the mix of business and residential user traffic, with peak average traffic in the PHI occurring in different stationary intervals.  
The research has been supported by the project \"Investigation of Trade-off between Power and Bandwidth Consumption in 5G Reconfigurable XHAUL Network Architecture\" funded by University of Roma Sapienza.  
[PERSON] has organized and written the paper. [PERSON] has implemented the analytical model in Matlab language. [PERSON] and [PERSON] have provided useful comments for the results section.  
The authors declare no conflict of interest.
# Method to Predict Performances of PCB Silicone Conformal Coating under Thermal Aging
## 3 Results
### Experimental Observation: Reliability Test  
PCBs coated with conformal coating A and B were prepared for a reliability performance study. Aging conditions, including thermal shock (TS) and thermal cycling (TC), are described in the 2.4 paragraph. Reliability results are summarized in Table 10. We observed that conformal coating A passed 517 thermal shocks without cracks, while cracks appeared after 350 cycles of thermal cycling (Figure 17). Conformal coating B pass 3 rounds of 517 thermal shocks followed by 517 cycles of thermal cycling (Figure 18). These results agree with the conclusions of the proposed evaluation method that conformal coating A shows lower performance in cases of extreme external stress exposure compared to conformal coating B. Coating B is, therefore, more appropriate and is recommended for applications requiring stability under extended thermal shock and thermal cycling PCB operating conditions. This result also aligns with the previous discussion about the importance of both CTE and mechanical properties' impact on material failure and criteria for coating selection. From the above study, it is concluded that conformal coating A is more suitable for applications requiring higher anti-scratch performance and less thermal shock/ cycling needs. Conformal coating B is more suitable for harsh thermal shock/ cycling resistance requirement applications, highlighting the benefit of using a highly deformable material under extreme operating conditions.  
Figure 16: FEA modeling of conformal coating A stress distribution at 125 \({}^{\circ}\)C; x, y units are in mm.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Property** & **Unit** & **Coating A** & **Coating B** \\ \hline Max. local stress (\(-\)40 \({}^{\circ}\)C, 2D FEA model) & MPa & 10.65 & 0.15 \\ Max. local stress (125 \({}^{\circ}\)C, 2D FEA model) & MPa & 0.16 & 0.44 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Conformal coating max. local stress by 2D FEA modeling of chip attached to PCB.
# Improved Control Strategy of MMC-HVDC to Improve Frequency Support of AC System
## 4 Improved Inertia Control of MMC-HVDC
### MMC-HVDC System  
In this study, we used the half-bridge MMC-HVDC to connect the offshore wind turbine to the AC system. The MMC converters use vector control to calculate the reference current of the d-q transformer, and the offshore-side MMC converter uses the P-Q control strategy, which consists of the inner control loop, outer loop control, and PI controller. The AC-side MMC converter uses the Q-Vdc control strategy. This control method includes a circulating current suppression controller, which controls the sub-modulator voltage ripple caused by second harmonics. The capacitor time constant of the MMC-HVDC takes into consideration the characteristics of the number and capacitance of submodules as obtained using (10) as follows [21; 23]:  
\[\tau=\frac{6N_{SM}C_{arm}\ V_{DC}^{2}}{S_{DC_{total}}} \tag{16}\]where \(V_{DC}\) is the rated voltage of the MMC-HVDC system, \(N_{SM}\) is the per-arm number, \(C_{arm}\) is the per-arm capacitance of the MMC-HVDC, and \(S_{DC_{total}}\) is the rated MVA of the MMC-HVDC system.  
The reference value of the DC voltage (\(V_{DC_{ref}}\)) can be calculated using the change in the system frequency caused by a system disturbance. As the DC voltage changes, the change in the active power will be the same in the capacitors of the MMC submodules. The short term is given as follows [24; 25]:  
\[\tau_{eff}=\frac{6N_{SM}C_{arm}\ V_{DC}^{2}}{P_{DC_{total}}} \tag{17}\]  
where \(P_{DC_{total}}\) is the rated active power of the MMC-HVDC system. In this case, the MMC-HVDC is composed of a DC cable between the offshore-side MMC converter and the onshore-AC-side MMC converter. We assume that the DC cable loss is negligible. The formula for the change in the active power obtained using the capacitor time constant (17) is given as follows:  
\[\frac{6N_{SM}C_{arm}\ V_{DC}}{P_{DC_{total}}}\frac{dV_{DC}}{dt}=P_{WT_{in}}-P_{ AC_{out}}=\Delta P_{2} \tag{18}\]  
where \(P_{WT_{in}}\) is the active power injected from the offshore-wind-farm side, and \(P_{AC_{out}}\) is the active power transmitted to the onshore AC side. We assume that the cable losses are 0 for the entire system. We combine the Equations (15) and (18) to obtain the new equation of the inertia constant of the MMC-HVDC in the next subsection.
# Application of Pitch Derived Parameters to Speech and Monophonic Singing Classification +
## 3 Results
### Analysis of Computation Times  
We measured the time needed by each speech/singing discrimination method to train and classify the Bertsolaritza database using 5x2 cv cross-validation. The processes have been run in an Intel Xeon CPU E5-2660 v2. The results obtained are shown in Table 7.  
We can see that all processing times are comparable, except for the one of the GMM built with MFCC. Considering that the proposed system has the best classification results overall, we can see that the better results achieved by the MFCC system are produced at the expense of bigger dimensionality and computation time.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Method** & \(p\) **(Singing)** & \(p\) **(Speech)** \\ \hline \(\Delta f_{0}\) & 8.099 \(\times\) 10\({}^{-5}\) & 2.858 \(\times\) 10\({}^{-5}\) \\ DFT-\(f_{0}\) & 0.010 & 0.001 \\ MFCC & 0.084 & 0.063 \\ Tony & 0.004 & 0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 6: \(p\)-value of the results of the proposed optimized algorithm compared with the rest of the systems.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Precision**} & \multicolumn{2}{c}{**Recall**} & \multicolumn{2}{c}{**F-Score**} \\ \cline{2-7}  & **Bertsolaritza** & **NUS** & **Bertsolaritza** & **NUS** & **Bertsolaritza** & **NUS** \\ \hline \(\Delta f_{0}\) & 0.95 \(\pm\) 0.00 & 0.79 & 0.85 \(\pm\) 0.03 & 0.73 & 0.90 \(\pm\) 0.01 & 0.76 \\ DFT-\(f_{0}\) & 0.93 \(\pm\) 0.00 & 0.76 & 0.83 \(\pm\) 0.01 & 0.77 & 0.88 \(\pm\) 0.01 & 0.76 \\ MFCC & **0.99 \(\pm\) 0.00** & 0.88 & **0.97 \(\pm\) 0.00** & **0.97** & **0.98 \(\pm\) 0.00** & **0.92** \\ Tony & 0.97 \(\pm\) 0.00 & 0.83 & 0.92 \(\pm\) 0.01 & 0.95 & 0.94 \(\pm\) 0.01 & 0.88 \\ Proposed & 0.98 \(\pm\) 0.00 & **0.92** & 0.96 \(\pm\) 0.00 & 0.92 & 0.97 \(\pm\) 0.00 & **0.92** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of singing classification.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Precision**} & \multicolumn{2}{c}{**Recall**} & \multicolumn{2}{c}{**F-Score**} \\ \cline{2-7}  & **Bertsolaritza** & **NUS** & **Bertsolaritza** & **NUS** & **Bertsolaritza** & **NUS** \\ \hline \(\Delta f_{0}\) & 0.58 \(\pm\) 0.04 & 0.72 & 0.81 \(\pm\) 0.01 & 0.78 & 0.68 \(\pm\) 0.03 & 0.75 \\ DFT-\(f_{0}\) & 0.53 \(\pm\) 0.01 & 0.73 & 0.76 \(\pm\) 0.01 & 0.72 & 0.63 \(\pm\) 0.01 & 0.73 \\ MFCC & **0.90 \(\pm\) 0.01** & **0.96** & **0.97 \(\pm\) 0.00** & 0.85 &
# Compact Design of Annular-Microstrip-Fed mmW Antenna Arrays
## 2 Theory and Analysis of Annular Microstrip Feeding Design
### Verification of the Basic Structure of Outer-Annular Microstrip-Fed Antenna Array  
#### 2.2.1 Comparison of Simulation Result and Experimental Result  
In the verification, we choose to make the first basic structure into manufactured object. The metal patch was printed on an aluminum oxide substrate with \(\varepsilon_{\mathrm{r}}=9.8\) and \(H=0.12\) mm, as depicted in Figure 1. The patch is made of silver with the thickness of \(q=0.006\) mm. The antenna adopts the coaxial feeding port, and the feeding point is located as Figure 1 shown. The antenna is connected to the vector network analyzer by using a 1 mm connector from Southwest Microwave, Inc. The manufactured object is  
Figure 4: Radiation pattern of the outer-annular microstrip-fed antenna array (**a**) \(x\)-\(z\) plane, (**b**) \(y\)-\(z\) plane, (**c**) \(x\)-\(y\) plane.  
Figure 3: Moduli and the detailed vector plot of the instantaneous current distribution of the outer-annular microstrip-fed antenna array at around 60 GHz.  
shown in Figure 5. While hermetically packaging, the coaxial connector is attached to the threaded mounting holes where a glass bead is positioned mid-way between them. The 1 mm connector is shown in Figure 6 and the manufactured object after soldering is shown in Figure 7. The connector is soldered on the bottom side of the ground plane by flange as shown in Figure 7b.  
Figure 5: Photo of the manufactured object of the first structure, (**a**) the top view, (**b**) the bottom view, (**c**) the side view.  
Figure 6: Photo of the 1 mm connector, (**a**) 3D view from pin to thread, (**b**) 3D view from thread to pin.  
The experimental work was carried out in Rohde & Schwarz (r) open laboratory in Shanghai with the ZVA110 vector network analyzer (10 MHz--110 GHz). The results are shown in Figure 8. The resonant frequency of the antenna is 60.65 GHz and the minimum reflection coefficient of it is \(-14.55\) dB at 60.65 GHz with the bandwidth of 0.54 GHz. Because the antenna structure in this paper is easily affected by the 1 mm connector structure, the antenna's resonant frequency experimental result has shifted to a certain extent with the similar entire trend compared with the simulation result.
## 4 Discussion
### Real-Time Time Environment Challenges  
Real-time data pose unique challenges in AI applications because of the need to process and analyze data in near real-time or with minimal delays. Some key challenges associated with real-time data in AI applications are as follows:  
1. Real-Time Data Volume and Velocity: Real-time data often come in high volumes and at high velocities, requiring efficient processing and analysis techniques. AI systems must handle incoming data streams and make timely decisions or predictions based on these data.
2. Latency and Response Times: Real-time applications demand low latency and fast response times. AI models must be designed and optimized to provide quick insights and actions in real-time scenarios. High computational requirements and complex algorithms can hinder real-time performance.
3. Real-Time Data Quality and Noise: Real-time data can be noisy, incomplete, or contain outliers. Ensuring data quality becomes challenging as there is limited time for data validation and cleaning. AI models must be robust in order to handle such noisy data and make accurate predictions or decisions.
4. Scalability and Resource Constraints: Real-time AI applications often require scalability to handle large volumes of incoming data. Scaling AI models and infrastructure to handle the increased workload can be challenging considering resource constraints such as computing power, memory, and network bandwidth.
5. Data Synchronization: Real-time data may come from multiple sources and must be synchronized for accurate analysis and decision-making. Aligning and integrating data streams from various sources in real time can be complex, particularly when dealing with data in different formats or time zones.
6. Real-Time Model Training and Adaptation: Updating or retraining AI models in real-time can be challenging. Continuous learning and model adaptation may be required to incorporate new data and adjust model parameters to changing conditions. Balancing the model stability with the need for real-time updates is crucial.
7. Real-Time Analytics and Visualization: Effectively analyzing and visualizing real-time data to extract meaningful insights and support decision making is a challenge. Real-time analytics techniques and interactive visualization tools are required to process and present data in a timely and actionable manner.  
Addressing these challenges requires the careful design, optimization, and integration of AI algorithms, infrastructure, and data-processing pipelines. It often involves a combination of efficient data streaming techniques, distributed computing, real-time analytics frameworks, and scalable AI architectures to enable real-time decision making and insights.