esa-sceva/satcom-chunk-collection huggingface dataset:
# Federated Learning for Secure In-Vehicle Communication
## 5 Results Analysis
### Time Evaluation  
Figure 19 illustrates the distribution of training time over 40 rounds for the two approaches under consideration using 1 and 5 epochs: FL-IVN-IDS-5E, LSTM-5E, FL-IVN-IDS-1E, and LSTM-1E.  
Figure 17: Loss Training History of Centralized Learning.  
Figure 18: Traffic Network exchanges during the training period.  
* Our FL-IVN-IDS model is computationally more effective by consuming significantly less training time per round compared to the LSTM-based model, hence being more deployable in near real-time applications.
* In comparison with the 1E (1 epoch per round) configurations, FL-IVN-IDS-1E trains for 3326.08 seconds, whereas LSTM-1E takes 4687.35 seconds, an improvement of 29% in training efficiency. The lower computational expense in FL-IVN-IDS-1E further validates that it can do well even in resource-constrained systems where saving processing time and energy consumption is paramount.
* For the 5E (5 epochs per round) configurations, our FL-IVN-IDS-5E model is trained in 10,202.43 seconds, as opposed to LSTM-5E in 17,309.56 seconds, reflecting a considerable 40.8% reduction in training time. This enhanced efficiency stems from the improved structure of FL-IVN-IDS that is able to effectively compromise between model complexity and training efficiency in a federated setup. The shortened training time per round indicates that FL-IVN-IDS-5E converges and updates faster than LSTM-5E, hence reducing computational expense and improving scalability.  
The experimentally observed differences between 1E and 5E environments show that boosting the epochs per round significantly influences training time, with LSTM-5E being the most impacted by unduly long training times due to its higher computational demands. Our FL-IVN-IDS model, both in 1E and 5E variant, achieves a better balance between model performance and training efficiency and thus becomes a more scalable choice for federated learning deployments in intelligent vehicular networks. By reducing training time without compromising high performance, FL-IVN-IDS is a perfect choice for real-time and large-scale federated learning, as it facilitates faster model updates, less device energy consumption, and improved adaptability to real-world limitations.
# FPGA-Based Frequency Tracking Strategy with High-Accuracy for Wireless Power Transmission Systems
###### Abstract  
Aiming at the problem of the unavoidable phase-tracking error of the wireless power transfer (WPT) system caused by dead time, MOSFET drive and other factors, this paper proposes a frequency-tracking method with high accuracy based on a Field-Programmable Gate Array (FPGA) to track the current and voltage phase differences on the transmitting side. Compared with fixed-phase systems, the proposed method accurately controls the phase difference between voltage and current. It detects the phase difference in real time and adjusts the phase compensation angle dynamically to ensure that the system always operates under the optimal zero-voltage switching (ZVS) state, which reduces system loss. Experiments under operating conditions of varying transmission distances and load resistance values are carried out on a prototype. The experimental results show that the proposed method achieves a desired phase difference of \(11.5^{\circ}\) under the conditions of varying transmission distances and load resistance values, which meets the expectation of a phase difference between \(10.5^{\circ}\) and \(13^{\circ}\) to achieve ZVS. Within the range of over-coupling conditions, the output power and transmission efficiency of the WPT system are more significantly improved than those of the fixed-frequency system, which verifies the feasibility of the proposed method.  
wireless power transfer; frequency tracking; phase difference control; zero voltage switching +
Footnote †: journal: Applied Physics Letters
## 1 Introduction
It was found that the considered ejector is more consuming for the plant energy, as compared to the liquid ring vacuum pump. The condensation of steam with the existence of non-condensing gas was studied in [12,13]. They explored that the existence of the non-condensing gases severely reduced the heat transfer coefficient in the steam condenser. A hybrid system for gas extraction in the plant condenser was studied by [PERSON] et al. [4]. In this hybrid system, a steam ejector integrated with a liquid ring vacuum pump was utilized. This proposed hybrid system was able to generate more vacuum in the steam condenser, while an observed increase in energy consumption was recorded. Significant attention has been directed toward the complicated air and water flow patterns and their application in the steam condenser. In this regard, [PERSON] et al. [14] studied numerically the two-phase flow of air/water in a small horizontal pipe. The obtained CFD results had an acceptable agreement with the considered experimental data and the CFD simulation accurately predicted the flow structures. The noticed small discrepancies between the experimental data and the CFD results may be attributed to the requirement for the full three-dimensional CFD simulations. In the same way, [PERSON] et al. [15] numerically presented the various two-phase flow profiles created in an inclined tube. They considered the effect of both surface tension and gravitational forces on the generated flow patterns. The air/water flow in a horizontal pipe was investigated numerically by [PERSON] et al. [16]. Different multiphase flow models were used for obtaining the flow patterns and the proposed CFD model was validated. The validation showed a reasonable agreement with slight deviations. Seven experimental facilities for the air and water co-current flow in sloping pipes were presented by [PERSON] and [PERSON] [17]. A CFD geometrical and operational analysis for preventing the air accumulation in the tube was proposed. [PERSON] [18] numerically and experimentally predicted the air and water flow in pipes. In this study, a framework for mixing air and water in the tube is fabricated. A circulating pump was utilized for controlling the water mass flow rate. This study revealed good validation in a wide range of data. Air and water two-phase flow in a tube with gas separator was presented by [PERSON] and [PERSON] [19]. This study ensured the ability of CFD models to accurately predict the behavior of the two-phase flow. Besides, the computational results had a good validation with the experiments. The objective of the present work is to numerically capture the two-phase flow patterns in the inverted U-tube when mixing water with a side mixture of air and water vapor. Furthermore, the required flow conditions and operational parameters for using the inverted U-tube for the side entrainment of the air-water vapor mixture will be determined. Additionally, a reduced scale experimental test rig is fabricated to validate the present CFD results.
## 4 Applications
### Differential Lines and Balanced Filters with Common-Mode Suppression
However, the rejection level of the common mode in the region of interest is very limited since it depends on the distance between resonators, and such inter-resonator distance is dictated by filter specifications. However, by cascading DS-CSRRs, the common-mode suppression is severely enhanced, as Figure 20 demonstrates [27]. The proposed differential filter is a second-order Chebyshev bandpass filter with a central frequency of 1.37 GHz, a fractional bandwidth of 10%, and 0.1-dB ripple. The substrate is _Rogers RO3010_ with \(h\) = 1.27 mm, and \(\varepsilon_{r}\) = 10.2. With these specifications and substrate, the layout of the filter is that depicted in Figure 19a (the design of the filter was done following the procedure described in [44]). The frequency response of the filter (differential mode, \(S_{dd21}\) and \(S_{dd11}\)) is shown in Figure 20. The common-mode insertion loss (\(S_{cc21}\)), also depicted in the figure, exhibits a rejection level of about 20 dB in the pass band region. However, by cascading two identical DS-CSRRs as shown in Figure 19b, the common-mode rejection is roughly increased up to 50 dB in the region of interest (see Figure 20). These results point out that DS-CSRRs provide an efficient path to enhance the common-mode noise rejection in balanced filters. Figure 19: Layout (**a**) and photograph (**b**) of the designed differential bandpass filter with improved common-mode rejection. Dimensions are: for the DS-CSRRs, \(c=d=0.2\) mm, longitudinal side length = 17.6 mm, and transverse side length = 10.8 mm; for the differential line, \(W\) = 1 mm, and \(S\) = 2.5 mm; for the differential filter, \(S_{M}\) = 0.5 mm, \(s\) = 2 mm, \(w_{0}\) = 1.2 mm, \(w_{I}\) = 0.7 mm, \(w_{2}\) = 3.7 mm, \(l_{I}\) = 12.8 mm, \(l_{2}\) = 7.5 mm, \(l_{3}\) = 5.4 mm, and \(t\) = 5.5 mm. The substrate is _Rogers RO3010_ with \(h\) = 1.27 mm, \(\varepsilon_{r}\) = 10.2, and tan\(\delta\) = 0.0023.
## 4 Experiments
### Local Feature Explainability  
Local feature explainability methods provide insights into individual predictions by focusing on specific instances rather than the overall model. This subsection explores two prominent techniques: the Local Interpretable Model-agnostic Explanations (LIME) and Shapley Additive Explanations (SHAPs).  
Datasets optimized through RFE were used to train RF models, ensuring explanations were based on the most relevant features.  
The optimal number of trees for each target variable was determined by evaluating out-of-bag (OOB) error rates. Here are the findings:  
* Diagnosis model: with 450 trees, the model achieved an impressive OOB error rate of 3.45%, indicating high accuracy in distinguishing between appendicitiss and non-appactitis cases.
* Management model: using 750 trees, this model had an OOB error rate of 7.93%. While effective at classifying common management strategies, it struggled with less frequent categories such as secondary surgical management, leading to higher error rates.
* Severity model: employing 650 trees, this model attained a 6.27% OOB error rate, showing strong performance in identifying uncomplicated cases but facing challenges with complex cases.  
LIME and SHAP were integrated with RF models using a custom model wrapper that supports prediction and model type identification, facilitating interpretable explanations:  
* LIME: provides local explanations by perturbing the input data and fitting a simple, interpretable model to these variations. This approach is useful for understanding individual predictions, such as those for specific patient cases.
* SHAP: offers a theoretically grounded method for evaluating feature importance through Shapley values. It attributes contributions by considering all possible feature combinations, providing both local and global explanations.  
While LIME and SHAP enhance interpretability, interpreting their results can be challenging, especially when explanations deviate from clinical expectations. Despite high performance metrics, individual case explanations might not always align with broader patterns, highlighting the need for generalizability and caution against data artifacts.
# Genomic Approaches for Monogenic Kidney Diseases:
## 2 Genetic Testing: The Current Framework
### Whole-Genome Sequencing
\begin{table}
\begin{tabular}{p{128.0 pt} p{128.0 pt} p{128.0 pt}} \hline \hline  & **Advantages** & **Disadvantages** \\ \hline Whole-Exome Sequencing & Detection of the coding-region variants. & Low coverage, mainly in GC rich and homology sequences. \\ \cline{2-3}  & Applicability in unclear CKD case. & Need for gap filling with Sanger. \\ \cline{2-3}  & Cost effective options compared to WGS. & Burden of incidental findings. \\ \cline{2-3}  & Possibility to create virtual panel of analysis to reduce the burden of VUS variants detected. & \\ \hline Whole-Genome Sequencing & Detection of the coding-region and non-coding variants. & Higher costs compared to WES. \\ \cline{2-3}  & Detection of structural variants. & Need for dedicated infrastructure and trained specialists. \\ \hline Applicability in unclear CKD case. & Relevant burden for VUS variants and incidental findings. \\ \hline Ability to detect CNVs and variants in high-homology regions (PKD1): one strategy for all the detections. & The superiority in diagnostic yield is debated (from 2\% to 30\%). \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Advantages and disadvantages of whole-exome sequencing and whole-genome sequencing. In other studies, the incremental rate between WES and WGS varies from 2 to 9% [69; 80]. [PERSON] et al. [69] reported an additional 9% yield of WGS with 13 new diagnoses in cases previously unresolved by WES analyses. These additional detected diagnoses were related to unknown gene-disease associations, insufficient sequencing coverage and copy number variations (CNVs). WGS offers advantages, including high coverage and the ability to detect CNVs. In the context of genetic kidney disease, where genes such as PKD1 and MUC1 (_Mucin I_) pose challenges due to high-homology regions and variable-number tandem repeats (VNTR) [81], WGS allows for comprehensive screening of all genes and associated variants. However, the routine implementation is hindered by challenges such as the need for expertise, infrastructure, variant interpretation and cost, making it a significant burden.
## 4 Discussion  
Combining all the obtained results with the general parameters in Figure 10, the following test methods were compared in order to determine the strength gain over time:  
* The compression testing of the cubes with an edge size of 10 cm by [14] represented by square markers;
* The non-destructive testing of the boxes using the IPS-MG 4.01 sclerometer [22] represented by triangle markers;
* The maturity-strength method by [13] represented by a dashed line;
* The proposed complex maturity method, based on [13] and modified, accounting for additional influencing parameters, such as the ambient temperature and relative humidity represented by the circle markers on the graph.  
Since all the standards used in this work, or rather, the results obtained by assessing the strength of concrete, are compared with the results from an assessment of the strength of cubic samples, Figure 11 shows the calibration dependence of the above methods in relation to the direct method of the compression testing of cubic samples along the \(x\)-axis. The corresponding trend lines were supported by the coefficients of determination for each method.  
The results for the coefficients of determination characterize the degree of convergence or reliability of the presented methods, in comparison with those from the compression testing of cubic samples. Thereby, the highest coefficient of determination R\({}^{2}\) = 0.976 was revealed for the proposed method of complex maturity. The lowest R\({}^{2}\) = 0.8929 was revealed for [22], representing the non-destructive measurement method of strength in  
Figure 9: Strength of concrete in boxes measured by IPS-MG 4.01 sclerometer.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Age, Day**} & \multicolumn{5}{c}{**Results of Strength Tests, MPa**} & \multicolumn{3}{c}{**Estimated Strength, MPa**} \\ \cline{2-9}  & **ASTM C192** & **GOST 10180** & **GOST 22690** & & **ASTM** & **Proposed** \\ \cline{2-9}  & **Cylinders** & **Cubes** & **Box 1** & **Box 2** & **Average in** & **C1074** & **Proposed** \\  & **(\(\mathbf{\uptheta 15\times 15\text{ cm}}\))** & **(\(\mathbf{\uptheta 10\times 10\times 10\text{ cm}}\))** & **(\(\mathbf{\uptheta 50\times 50\text{ cm}}\))** & **(\(\mathbf{\uptheta 50\times 50\text{ cm}}\))** & **(\(\mathbf{\uptheta 50\times 50\text{ cm}}\))** & **Boxes** & & **C1074** & **Proposed** \\ \hline
1 & 9.67 & 12.2 & 13.15 & 14.35 & 13.75 & 12.25 & 9.65 \\ \hline
3 & 16.06 & 20.45 & 24.25 & 21.65 & 22.95 & 17.92 & 16.96 \\ \hline
7 & 22.17 & 24.25 & 27.75 & 28.45 & 28.1 & 22.63 & 22.15 \\ \hline
14 & 23.99 & 26.75 & 30.3 & 29.95 & 30.125 & 26.48 & 26.11 \\ \hline
28 & 31.72 & 34.2 & 31.3 & 31.35 & 31.325 & 30.27 & 30.40 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of strength tests determined by various methods.  
boxes by sclerometer. The Nurse-Saul maturity method [13] for estimating the current strength of concrete, which considers only internal temperature, was found to be reliable enough, although it is worth noting that the ambient parameters are also important for the concrete curing process.
## 2 Literature Review
### New Product Development (NPD), Technology Selection and Supplier Selection
In stage two, the dynamic information generated in different stages of product design was obtained by using a dynamic evaluation method based on the fuzzy theory, the importance weights of the indices were generated by the fuzzy Karnik-Mendel Algorithm (KMA), and the ranking of the design partner alternatives was calculated by using the Fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje (in Serbian) (Fuzzy VIKOR). [PERSON] et al. [44] constructed a framework for assessing product design alternatives by integrating AHP, Gray Correlation (GC), and TOPSIS. The weights of criteria were obtained by the AHP, and the alternatives were evaluated by using an integrated approach involving TOPSIS and GC. [PERSON] et al. [45] proposed a hybrid MADM model for evaluating smart home product improvement. The Dominance-based Rough Set Approach (DRSA) was first applied to determine core factors, and the DEMATEL technique was subsequently adopted to understand the interrelationships among core factors. A DEMATEL-based Analytic Network Process (DANP) was then used to calculate the influential weights of the factors, and fuzzy integration was applied to generate a final ranking of the smart home alternatives. The evaluation of a business process information system, such as Enterprise Resource Planning (ERP), not only involves the information system itself but also requires one to consider the cooperation of the system provider. [PERSON] et al. [46] proposed a hybrid multi-criteria decision-making model for evaluating business process information systems. The DEMATEL was first adopted to determine the interrelationships among the criteria to shorten the length of the FANP questionnaire. The FANP was subsequently applied to obtain the priorities of sub-criteria. Finally, fuzzy TOPSIS was used to generate a final ranking of the business process information systems. [PERSON] et al. [47] constructed an ERP system evaluation framework by integrating DEMATEL, ANP, VIKOR, and fuzzy set theory. The framework could be adopted to facilitate the selection of the most appropriate ERP system. [PERSON] et al. [48] proposed a decision-making model with intuitionistic fuzzy information for selecting ERP systems. An optimization model based on cross entropy was adopted to calculate criteria weights, and an integrated Intuitionistic Fuzzy Improved Measurement Alternatives and Ranking based on the Compromise Solution (IF-IMARCOS) approach was developed to aggregate the criteria values. A comparison of some selected research works in terms of dependency structuring, evaluation of weights, ranking of options, new product development, technology selection, and fuzzy logic is shown in Table 1, in which a checked mark indicates that the issue is covered in the work.
## 4 Discussion
Furthermore, the resultant acceleration at the thigh could detect interlimb asymmetry in the VGRF with high sensitivity, specificity, positive predictive value, and negative predictive value (>0.80). The between-limb difference in acceleration at the thigh was found to be significantly positively correlated with the between-limb difference in VGRF and knee power absorption during running after ACLR [18]. The results of this previous study are consistent with those of the present study. A recent scoping review showed that wearable IMUs consistently identify residual kinematic and kinetic deficits after ACLR and have the potential to bridge the gap as an alternative when laboratory measurements are difficult [21]. If wearable IMUs can evaluate the kinetics during movement with high accuracy, they can be used in more clinical applications. The results of this study show that the thigh IMU has the potential to detect patients with asymmetrical VGRF. However, the coefficient of determination was lower for the involved limbs than for the uninvolved limbs. In addition, the slope of the regression line in LSI was 0.4-0.8 for LSI and the intercept was 18.6-62.3%. Therefore, it should be noted that the LSI of IMUs is not the same as that of the GRF. Concerning clinical application, the results of this study provide an alternative method to assess the VGRF during landing in clinical settings. The resultant acceleration of the thigh IMU may be used to detect patients with interlimb asymmetry in the VGRF during single-leg drop landing after ACLR. However, it should be noted that the coefficient of determination was lower in the involved limb than in the uninvolved limb. In addition, future studies are needed to clarify the relationship between interlimb asymmetry in the resultant acceleration and clinical outcomes, such as return to sports level, secondary ACL injury, and subjective knee function. The present study had some limitations. First, the sample size is relatively small, and fewer participants in this study showed interlimb asymmetry in the VGRF and/or the resultant acceleration. The results of this study may differ for patients who have not yet returned to sports and demonstrate greater biomechanical asymmetry. Second, we did not assess kinematics during landing. To understand the relationship between acceleration and VGRF, it is necessary to measure the kinematics and check the landing strategy of the patient. Third, the present study did not compare patients who have undergone ACLR with healthy people who had not suffered an ACL injury. Patients who have undergone ACLR have been demonstrated to have a different landing strategy than healthy individuals [22]. Therefore, future studies need to compare the results with healthy people. Finally, we examined only single-leg drop landings. As double-leg landing and single-leg hopping have been reported as methods for assessing landing movements after ACLR [16; 43], further studies are needed to investigate other movement tasks.