esa-sceva/satcom-chunk-collection huggingface dataset:
# Sphero-Conical Modeling for the Estimation of Very Long Baseline Interferometry Invariant Point
## 2 General Description
### Estimation Procedure  
Figure 2 shows the overall plan for estimating the VLBI IVP based on a local ground survey. All survey measurements were obtained from the permanently equipped pillars surrounding the VLBI antenna. Four different types of observations were considered in this study: slope distance and horizontal/vertical angles using a total station, along with the height difference from traditional spirit leveling. Once ground surveying is completed, we can apply the (partial) minimum norm least-squares solution (PMINOLESS/MINOLESS) to minimize the distortion of the (local) reference frame.  
The VLBI IVP, coordinates of the pillars, and targets were estimated based on the mathematical model developed in this study. The estimation process is based on the TLS within the GHM, in which the coordinates of the pillars/targets are estimated simultaneously.  
In the final step, the VLBI IVP should be represented in the global reference frame (e.g., ITRF2014), which can be performed using the Helmert transformation typically performed in geodetic reference frames [19; 20; 21; 22; 23]. The transformation parameters were estimated using  
Figure 1: The schematic drawing of VLBI IVP estimation process. The IVP is the geometric center of the VLBI antenna, and the local tie vector with respect to the GNSS is the primary concern. The IVP is estimated based on the ground surveying measurements (slant distances, horizontal/vertical angles, etc.) from total station mounted on the permanent pillars.  
pillars. The GNSS campaign should be conducted to obtain coordinates aligned to the ITRF. Finally, the local tie vectors should be represented with respect to each other (in this study, we used the GNSS antenna as a reference point), along with the full variance-covariance matrix between the VLBI IVP and GNSS reference point.
## 3 Development of Energy Measuring Devices
### Aluminum Honeycomb Type of Measuring Device  
Figure 6 shows a cross-sectional view of the aluminum honeycomb device. It is mainly composed of a base, rib plates, end caps, brackets, a cylinder, a partition plate, a compressing plate, a compression adjustment lever, and a pin puller. Aluminum honeycombs can be installed between the partition plate and the compressing plate and between the partition plate and the right end cap to ensure the energy absorption capacity of the aluminum honeycombs under big impact. The compression adjustment lever can finely adjust the compressing plate in the horizontal direction to ensure that the compressing plate is in close contact with the aluminum honeycomb. Vent holes are set on the partition plate, the cylinder, and the left end cover, which can ensure that the gas in the aluminum honeycomb can be discharged in time during the test. When the ignitor is fired, the generated high-temperature and high-pressure gas pushes the piston and the compressing plate to squeeze the aluminum honeycomb, which causes the aluminum honeycomb to deform. The transferred energy can be calculated by multiplying the deformation length by the strength of the aluminum honeycomb. The 5052 aluminum honeycomb has a wall thickness of 0.05 mm, a side length of 4 mm, and a height of 50 mm. The physical objects of the aluminum honeycomb and aluminum honeycomb device are shown in Figures 7 and 8, respectively.  
Figure 5: Drop-weight test tooling.  
Figure 6: Cross-sectional view of the aluminum honeycomb type of measuring device. 1—base, 2—rib plates, 3—vent port, 4—right end cap, 5—bracket, 6—cylinder, 7—partition plate, 8—compressing plate, 9—left end cap, 10—compression adjustment lever, 11—shell body of pin puller, 12—pin-rod, 13—cartridge port, 14—pressure transducer port.
# High Performance Receiver Design for RX Carrier Aggregation
## 2 Receiver Architecture
#### Single-\(g_{m}\) Receiver Architecture
Then, CA-supporting LNTA architectures discussed in Section 3 are considered the extension of the inductor-degenerated LNTA with multi-output support.
# Update class center using momentum-based smoothing
## 3 Experiments and Results
### Experimental Results and Analysis
#### 3.3.3 Ablation Experiment
To better understand the contribution of each structural component in LightExNet, we conducted an ablation study based on the baseline MobileNet V2 architecture. As shown in Table 7, three key modules were gradually integrated into the backbone. The base model (MobileNet V2) achieves 67.02%, 93.26%, and 81.41% accuracy on FER2013, CK+, and RAF-DB, respectively. When only the feature fusion module (1) is added (Scheme 1), the performance on FER2013 unexpectedly drops, suggesting that shallow features alone may be insufficient without additional attention. The introduction of the attention mechanism (2, Scheme 2) significantly improves performance across all datasets, with +3.60% accuracy gain on RAF-DB. Adding the center loss module (3, Scheme 3) also enhances generalization, especially on CK+. The full LightExNet model (Scheme 4) integrates all three components and achieves consistent improvement on all datasets, including a +3.21% gain on FER2013, +4.41% on CK+, and +5.60% on RAF-DB, while reducing parameter count by 6.57% and FLOPs by 4.66% compared to the baseline. This confirms that the proposed components work synergistically to improve both efficiency and effectiveness.
## 5 Experimental Study
### Comparative Analysis
#### 5.2.1 Impact of Motion Prediction Error  
The motion prediction error is the measure of rate of error acquired during the prediction of motion of the vehicle. Figure 7 depicts a comparison of the motion prediction error of our proposed MODAL-IoCV approach and existing approaches, with respect to the number of vehicles. The motion prediction error increases with the increase in number of vehicles. The proposed approach possesses low motion prediction error due to the effective  
\begin{table}
\begin{tabular}{c c} \hline \hline
**PARAMETERS** & **DESCRIPTION** \\ \hline \multicolumn{3}{c}{**Network Parameters**} \\ \hline Area of simulation & 500 \(\times\) 500 m \\ Simulation time & 300 s \\ Number of RSUs & 4 \\ Number of Edge nodes & 6 \\ Number of cloud node & 1 \\ Number of vehicles & 100 \\ Type of traffic & Traffic control interface model \\ Rate of transmission & 200 Mbps \\ Range of transmission & 200–250 m \\ Transport protocol & TCP \\ Size of packet & 512 bytes \\ Total number of packets & 10,000 (approx.) \\ Mobility model & Random way point \\ \hline \hline \end{tabular}
\end{table}
Table 6: Simulation parameters.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Software Requirements** & \
\begin{tabular}{c} Network simulator \\ Traffic simulator \\ OS \\ RAM \\ CPU \\ Hard disk \\ Processor \\ \end{tabular} &
\begin{tabular}{c} OMNET++ \\ SUMO \\ Ubuntu \\ 8 GB \\ 2.90 GHZ \\ 1 TB \\ Intel core \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 5: System requirements.  
prediction of vehicle's motion using HMM by considering both the past mean velocity and report by the nearby vehicles. By doing so, even if the speed of the vehicle increases the prediction of motion is not interrupted. The increase in speed affects the motion prediction accuracy of the existing approaches due to a lack of consideration of important parameters, thereby resulting in an increased prediction error.  
The motion prediction error of our proposed approach is compared with the existing approaches with respect to the number of edge nodes, as illustrated in Figure 8. The motion prediction error decreases with an increase in the number of edges due to the fast computations provided by the edge nodes. The motion prediction error of our proposed approach is low than the existing approaches due to the increased efficiency of monitoring the specific regions by the respective edge nodes. In existing approaches, the error decreases with an increase in edge nodes, but the inefficient prediction of motion still results in an increased error.
###### Abstract  
The need to increase the energy efficiency of buildings, as well as the use of local renewable heat sources has caused heat meters to be used not only to calculate the consumed energy, but also for the active management of central heating systems. Increasing the reading frequency and the use of measurement data to control the heating system expands the requirements for the reliability of heat meters. The aim of the research is to analyze a large set of meters in the real network and predict their faults to avoid inaccurate readings, incorrect billing, heating system disruption, and unnecessary maintenance. The reliability analysis of heat meters, based on historical data collected over several years, shows some regularities, which cannot be easily described by physics-based models. The failure rate is almost constant and does depend on the past, but is a non-linear combination of state variables. To predict meters' failures in the next billing period, three independent machine learning models are implemented and compared with selected metrics, because even the high performance of a single model (87% true positive for neural network) may be insufficient to make a maintenance decision. Additionally, performing hyperparameter optimization boosts the models' performance by a few percent. Finally, three improved models are used to build an ensemble classifier, which outperforms the individual models. The proposed procedure ensures the high efficiency of fault detection (>95%), while maintaining overfitting at the minimum level. The methodology is universal and can be utilized to study the reliability and predict faults of other types of meters and different objects with the constant failure rate.  
heat meter; district heating;fault detection; predictive maintenance; Machine Learning (ML); Artificial Neural Network (ANN); Bagging Decision Tree (BDT); Support Vector Machines (SVM); hyperparameter optimization; ensemble model +
Footnote †: journal: Applied Physics Letters
# Research on a Service Load Prediction Method Based on VMD-GLRT
### Related Work  
Service load data can typically be viewed as a time series analysis problem, which is typically approached from three perspectives: statistical methods, machine learning methods, and deep learning methods. To reduce the high maintenance costs associated with over-provisioning or under-allocating resources in the cloud environment, many recent studies have proposed solutions based on machine learning and deep learning to predict cloud host resource utilization.
# Fault Detection in Power Transformers Using Frequency Response Analysis and Machine Learning Models
## 3 Methodology
### Machine Learning Algorithms for Fault Detection  
Various machine learning models were employed to classify transformer faults based on FRA data. The process involved data preparation, feature extraction, classifier training, performance evaluation, and validation.
# Analysis of Pre-Driver and Last-Stage Power--Ground-Induced Jitter at Different PVT Corners
## 4 PSIJ Sensitivity Study of Two-Stage Driver
### Two-Tone Jitter Superposition
#### 4.4.2 Numerical Results  
The obtained results of the pre-driver and last stage are illustrated in Figures 15 and 16, respectively. They compare the measured output jitter, \(J_{m}\), from transient data against the simulated modelled output jitter, \(J_{s}\), of the pre-driver-stage case and last stage using Equations (9) and (10), respectively.  
The predicted and measured jitter from simulation data show that a good correlation is observed in Figures 15a-c and 16a-c when the amplitude of the PSV noise is below 25% of the nominal \(V_{DD}\) and \(V_{DDQ}\) supply voltage. The inaccuracy of the superposition of the PSIJ TF of Equations (9) and (10) is below 4.8% in this case.  
As the amplitudes of the PSV variation exceed some specified number, the linear superposition of the applied two-tone voltage contribution of the pre-driver and the last-stage circuit is not valid anymore, as shown in Figures 15d and 16d, respectively. This is mainly due to the fact that the PU and PD transistor current is highly nonlinear, depending on the applied voltage difference between its terminals. The inaccuracy of the superposition of the PSIJ TF of Equations (9) and (10) is below 33.3% in this case.  
Figure 15: Pre-driver stage case: correlation between \(J_{m}\) and \(J_{s}\) based on the superposition of (9): (**a–c**): small-signal two tones. (**d**): large-signal case.  
\begin{table}
\begin{tabular}{c c c} \hline
**Cases** & \(f_{L_{1}}\)**,\(f_{P_{1}}\) (MHz)** & \(f_{L_{2}}\)**,\(f_{P_{2}}\) (MHz)** \\ \hline
1 & 57 & 123 \\ \hline
2 & 57 & 507 \\ \hline
3 & 57 & 905 \\ \hline
4 & 123 & 57 \\ \hline
5 & 123 & 905 \\ \hline
6 & 123 & 2033 \\ \hline \end{tabular}
\end{table}
Table 8: Last stage and pre-driver: frequency settings of Figures 14 and 15.  
Cases 1 and 4, where the frequency of the tow tones are a mixture of 75 MHz and 123 MHz, show the highest PSIJ for the studied pre-driver and last-stage cases, as shown in Figures 15 and 16, respectively. This observation confirms the results of the previous analysis which were performed to study the the frequency-dependent jitter sensitivity.
## 1 Introduction  
It is important to produce high-quality software, yet it is difficult to achieve such quality because of time and budget constraints. Identifying and correcting software defects using the most efficient quality assurance techniques is a challenging and expensive process [1]. Software defect prediction may be utilized to classify faulty fragments of software systems. Therefore, quality assurance methods can be implemented on the selected parts instead of a complete system inspection. The process of finding and classifying software components that are vulnerable to defects through machine learning techniques is known as software defect prediction. There are several benefits to software defect prediction--for instance, decreasing the expensiveness of testing and producing software products in a timely manner, thereby enhancing the overall quality of software products.  
Various machine learning classifiers, such as decision trees and ensemble learning, have recently been employed to improve the detection of software defects [2]. A variety of software metrics derived from the source code are typically used to construct the predictive models. McCabe metrics, Halstead metrics, and static code metrics are the most often used for defect prediction [2]. Ensemble learning, such as stacking and boosting, was shown to be successful in predicting software defects, and it exhibited excellent prediction outcomes in comparison with individual classifiers [3]. However, most of the previous work has utilized ensembles models in the context of software defect prediction with the default hyperparameter values, which are considered suboptimal [4]. Hyperparameter optimization has a large impact on the performances of classifiers, and the few studies that have utilized an optimization technique reported improved prediction performance [5,6].  
In this study, we investigated the effects of stacked ensembles and hyperparameter optimization on tree-based ensembles. We predicted software defects using tuned and untuned tree-based ensembles based on a set of software metrics. The included ensembles were: extra trees, XGBoost, CatBoost, gradient boosting and histogram gradient boosting. The selection of those ensembles was based on the fact that very few studies have explored these ensembles. Furthermore, we explored the prediction performance of a stacking ensemble built using fine-tuned tree-based ensembles. We conducted a comprehensive empirical study to examine the efficacy of various ensemble models at predicting software defects.  
The rest of the paper is structured in the following manner. The background on tree-based and stacking ensembles used to predict software defects is addressed in Section 2. Section 3 examines previous work in ensemble learning and hyperparameter optimization for software defect prediction. Section 4 explains the process of the empirical study that was performed. The findings and discussion are presented in Section 5. Finally, Section 6 summarizes the conclusions and possible future work.
# A Mathematical Model for Efficient and Fair Resource Assignment in Multipath Transport
## 2 Terminology  
When discussing fairness aspects of multipath transport, some terms and definitions have to be specified to avoid ambiguities. Inside a multipath network, there are _participants_ who share a common _resource_. An analogous example is a number of wireless stations, which, as participants, share the wireless medium which is the resource. Resources and participants are defined in Sections 2.1 and 2.2. Furthermore, clarification is needed as to what exactly the notion of _fairness_ means, which is covered in Section 2.3.  
The discussions in this paper at several occasions refer to (MP)TCP to identify different types of connections such as flows or subflows. The MPTCP-related wording should, however, be interpreted without loss of generality; it can be transferred to any multipath supporting transport protocol.
# Research on Clock Synchronization of Data Acquisition Based on NoC
## 2 The General Scenario
### The Principle of White Rabbit
Assuming that the original phase difference between \(clk_{a}\) and \(clk_{b}\) is \(\Delta\Phi_{clk}\), and the phase difference between the output signals Q1 and Q2 is \(\Delta\Phi_{Q}\), they satisfy the relation in Equation (5), while the magnification factor \(A\) is given by Equation (6). \[f_{vco}=f_{clk}\frac{N}{M}(M>N) \tag{3}\]  
\[f_{Q}=f_{clk}-f_{vco} \tag{4}\]  
\[\Delta\Phi_{Q}=A*\Delta\Phi_{clk} \tag{5}\]  
\[offset=\frac{(t_{2}-t_{1})-(t_{4}-t_{3})}{2} \tag{1}\]0  
The magnified phase difference can be measured by the system clock count or the time digital converter (TDC). However, the amplification factor \(A\) cannot be increased indefinitely, and it is limited by the performance of the D flip-flop. If the amplification factor \(A\) is too large, the D flip-flop will be in a metastable state. A larger \(A\) leads to a more serious metastable state. As a result, the low-frequency signals Q1 and Q2 output by the D flip-flop will be inaccurate. Therefore, the amplification factor \(A\) should be properly selected to ensure the desired phase difference resolution. DDMTD amplifies the phase difference between the synchronous clocks, and thus, it can overcome the shortcoming of PTP. The clock synchronization precision can be better than one clock cycle, up to sub-nanosecond level.
## 4 Calculation and Results  
### Calculation Model  
In the context of RTRL, drivers intending to make a left turn within the diversion area must effectively identify the right-side exit from the mainline in front of them to navigate the right diversion safely, as shown in Figure 13. Findings from driver simulation studies indicate that drivers encounter a more intricate scenario in path selection when facing the design of RTRL. Thus, ensuring an adequate decision sight distance within the right-turn ramp on the left diversion area holds particular significance.  
According to _A Policy on Geometric Design of Highways and Streets_ (_The Green Book_), decision sight distance refers to the distance necessary for a driver to detect unexpected or challenging-to-perceive elements or conditions in a visually dense roadway environment. Notably, these values significantly exceed the values of the stopping sight distance. However, the policy lacks specifications regarding the decision sight distance values required for the exit of interchanges at different design speeds, and also does not provide a defined scope. On the other hand, the _Intersections Guidelines_ of China outline the decision sight distance value, which needs to be met before the diversion nose end of the interchange. This guideline considers the diversion nose end as the endpoint of the decision sight distance. Nevertheless, the triangular area preceding the diversion nose typically comprises a solid line, prohibiting vehicles from crossing. Consequently, diverging vehicles are required to complete the decision and lane-change progress before reaching the triangle. Suppose the length of the triangle is part of the decision sight distance. In this case, it might inadvertently suggest allowing vehicles to breach the triangular marking to change lanes, thereby contravening traffic management regulations.  
[PERSON] [33] conducted an investigation and visual gaze point location analysis experiment and concluded that considering its supportive role in efficient vehicle diverging, the starting point of the taper of the deceleration lane is deemed the decision sight distance destination. Consequently, this study defines the decision sight distance range as starting from the driver's perspective of the exit position up to the initiation of the downstream taper of the deceleration lane.  
The decision sight distance model (Figure 14) was developed based on the most unfavorable driving conditions, where before the diversion area diverging vehicles were driving on the second outer lane. Therefore, the model proposes that the complete diversion  
Figure 13: The right-turn ramps to the left of the diversion area.  
behavior should include decision-making, queuing for gaps, and executing lane-changing stages, as shown in Equation (10).  
\[S=S_{1}+S_{2}+S_{3} \tag{10}\]  
where \(S\) denotes decision sight distance (m), \(S_{1}\) denotes decision distance (m), \(S_{2}\) represents queuing distance (m), and \(S_{3}\) signifies execution of lane-change distance (m).