esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Dynamics Model of Vehicle-Curved Switch Rail
### Vehicle Dynamic Model  
A non-contact infrared wheel track profile measuring instrument is used to track the wheel profiles. According to the side wear depth, the worn wheels are divided into four wear stages and defined as wear type I profile, wear type II profile, wear type III profile, and wear type IV profile. The standard xp55 profile and worn profiles are shown in Figure 3.  
Figure 1: Key sections of curved switch rail in high-speed turnout.  
Table 1 shows the side wear values of wheel flanges in different wear stages. More serious wear is present on the wheel flange and in the middle of the tread. With the wear aggravation of the wheel, the side wear of wheel flange gradually deepened. For the standard xp55, wear type I, wear type II, wear type III, and wear type IV profiles, the wheel flange side wear values for the five types of wheel profiles are 0, 1.743, 3.432, 4.719, and 6.208 mm, respectively. The wear type IV profile wheels have the greatest depth of side wear at 6.208 mm. Compared with the standard xp55 wheel profile, the flange side wear depth of the wear type I wheel profile increases by 1.743 mm, the increase is the largest among the four wear stages.  
Side wear will lead to the thinning of the train wheel flange, which is one of the typical forms of wheel damage. In addition, it affects the safety of train operations. The limit value of wheel flange thickness in China is 22 mm. When the wheel flange thickness is less than 22 mm, it is necessary to repair it.  
The UM software is applied to build the vehicle model of the CRH5 EMU (electric multiple unit), as shown in Figure 4. The vehicle model is made up of one car body and two bogies. The main parts, such as the car body, bogie, wheelset, and axle box, are rigid solid bodies without simplification. The suspension components include the primary spring, primary vertical damper, secondary air spring, secondary longitudinal damper  
\begin{table}
\begin{tabular}{l l} \hline
**Wear Stages** & **Side Wear Values/mm** \\ \hline standard xp55 profile & 0 \\ wear type I profile & 1.743 \\ wear type II profile & 3.432 \\ wear type III profile & 4.719 \\ wear type IV profile & 6.208 \\ \hline \end{tabular}
\end{table}
Table 1: Side wear values of wheel flange with different wear stages.  
Figure 3: Wheel profiles of the standard and different wear stages.  
Figure 2: Principle of cross-sectional interpolation.  
(anti-serpentine shock absorber), and secondary lateral damper. Part parameters of CRH5 EMU are shown in Table 2.  
The evaluation indices for the safety and stability of high-speed EMU operation mainly include the wheel-rail vertical and lateral force, derailment coefficient, and vertical and lateral acceleration. In order to ensure the safe and stable operation of vehicles, the above-mentioned indicators are limited to a \"Specification for Dynamic Performance Assessment and Test Verification of Rolling Stock\" (GB/T 5599-2019), which is shown in Table 3.
# Heat Transfer in Thermoelectric Generators for Waste Energy Recovery in Piston Engines
## 2 Materials and Methods
### CFD Simulations
#### 2.1.4 Computational Mesh  
A hybrid tet/hex mesh was used to define the computational domain. More complex features, such as the inlet and outlet cones of the gas heat exchanger, were meshed with tetrahedral elements, and hexahedral elements were used for the core of the gas heat exchanger, the thermoelectric modules, and the water coolant circuit. A grid independence study with five different meshes (40,782, 85,221, 163,702, 252,711, and 396,086 elements) was conducted, monitoring all the relevant quantities (pressure drop, temperatures, and heat transfer), and mesh independence was found to be achieved with the grid of 252,711 elements, but to enhance the spatial resolution of the results, the mesh with 396,086 elements and a mean y+ value of 35 was used.  
Thermoelectric Generator, Gas Generator and Experimental Tests  
The body of the heat exchanger is made of stainless steel. The thermoelectric modules considered are made of Bi\({}_{2}\)Te\({}_{3}\) and manufactured by TEGTEC. The modules dimensions are 0.04 \(\times\) 0.04 \(\times\) 0.004 m, and their thermal conductivity is 1.4 W/mK. The maximum hot side temperature of the modules is 300 \({}^{\circ}\)C. The material of the water circuit is aluminum.  
As a gas generator, a turbocharged, 2.2 L diesel engine coupled to an eddy-current dynamometer was used in this work. A front view of the experimental engine test bench can be seen in Figure 1. The engine operating condition was representative of highway driving, where it was found that thermoelectric recovery was beneficial for cars. Nevertheless, the gas exhaust conditions, design process, and findings could also be applicable to any means of transport that works around steady operating conditions, such as piston aircraft in cruise or ground cargo transport.  
The numerical schemes and models employed have been thoroughly validated for similar cases in terms of heat transfer, pressure drop, and module-by-module thermoelectric production. For the engine mode selected, the errors in the relevant quantities are shown in  
Figure 1: Experimental engine test bench.  
More information about the recommended numerical schemes for these types of simulations and their limitations can be found in [PERSON] et al. [1; 9].
## 5 Flexible Body Dynamics Based on Rayleigh-Ritz Shape Matrix
\right] \tag{92}\]  
Upon manipulating Equation (92), the dynamics can be written as follows:  
\[\overline{M}\ddot{q}_{i}=\overline{F} \tag{93}\]  
\[\overline{M}=M_{ii}-M_{id}B_{d}{}^{-1}B_{i}+B_{i}{}^{T}\!\left(B_{d}{}^{T}\! \right)^{-1}\!M_{dd}B_{d}{}^{-1}B_{i}-B_{i}{}^{T}\!\left(B_{d}{}^{T}\!\right)^ {-1}\!M_{di} \tag{94}\]  
\[\overline{F}=\overline{Q}_{v}+\overline{Q}_{\dot{q}}+\overline{Q}_{g}+ \overline{Q}_{e}+\overline{F}_{s} \tag{95}\]  
\[\overline{Q}_{v}=(Q_{v})_{i}-B_{i}{}^{T}\!\left(B_{d}{}^{T}\!\right)^{-1}\!( Q_{v})_{d} \tag{96}\]  
\[\overline{Q}_{\dot{q}}=M_{id}B_{d}{}^{-1}\dot{B}\dot{q}-B_{i}{}^{T}\!\left(B _{d}{}^{T}\!\right)^{-1}\!M_{dd}B_{d}{}^{-1}\dot{B}\dot{q} \tag{97}\]  
\[\overline{Q}_{g}=\left(Q_{g}\right)_{i}-B_{i}{}^{T}\!\left(B_{d}{}^{T}\! \right)^{-1}\!\left(Q_{g}\right)_{d} \tag{98}\]  
\[\overline{Q}_{e}=(Q_{e})_{i}-B_{i}{}^{T}\!\left(B_{d}{}^{T}\!\right)^{-1}\!( Q_{e})_{d} \tag{99}\]  
\[\overline{F}_{s}=-K_{i}q+B_{i}{}^{T}\!\left(B_{d}{}^{T}\!\right)^{-1}\!K_{d}q \tag{100}\]  
The forward dynamics is given by:  
\[\ddot{q}_{i}=\overline{M}^{-1}\overline{F} \tag{101}\]  
For the purpose of numerical integration, Equation (101) can be presented in the following state space representation:  
\[\left[ \begin{array}{c}\dot{Y}_{1}\\ \dot{Y}_{2}\\ \vdots\\ \dot{Y}_{15}\end{array} \right]=\left[ \begin{array}{c}Y_{16}\\ Y_{17}\\ \vdots\\ Y_{30}\end{array} \right] \tag{102}\]  
\[\ddot{q}_{i}=\left[ \begin{array}{c}\dot{Y}_{16}\\ \dot{Y}_{17}\\ \vdots\\ \dot{Y}_{30}\end{array} \right]=\left[ \begin{array}{c}\ddot{x}\\ \ddot{y}\\ \ddot{\theta}\\ \ddot{q}_{f}\end{array} \right] \tag{103}\]  
\[\left[ \begin{array}{c}\dot{Y}_{1}\\ \dot{Y}_{2}\\ \vdots\\ \dot{Y}_{30}\end{array} \right]=\left[ \begin{array}{cc}I_{15x15}&0_{15x15}\\ 0_{15x15}&\overline{M}_{15x15}\end{array} \right]^{-1}\!\left[ \begin{array}{c} Y_{16}\\ \vdots\\ Y_{30}\\ \overline{F}_{15x1}\end{array} \right] \tag{104}\]  
Numerically integrating Equation (104) gives the pose of the moving platform as well as the elastic displacements \(q_{f}\) and their corresponding velocities. The acceleration of the moving platform can be computed by imposing the obtained pose and velocities of the moving platform to Equation (101). The velocities and accelerations of the sliders can be obtained based on the coordinate partitioning method as the following:  
\[\dot{q}_{d}=B_{d}{}^{-1}\!\left(-B_{i}\dot{q}_{i}\right) \tag{105}\]  
\[\ddot{q}_{d}=B_{d}{}^{-1}\!\left(\dot{B}\dot{q}-B_{i}\ddot{q}_{i}\right) \tag{106}\]
## 3 System Model and Problem Formulation
### Problem Formulation
#### 3.3.1 Network Partition Subproblem  
The purpose of the network-partitioning stage is to determine the number of controllers reasonably. For this purpose, we establish a network partition model to optimize network performance while minimizing network deployment costs. Based on the network analysis, delay, switch security, and load are the main factors that affect the number of controllers. Therefore, the formula for network partitioning is as follows:  
\[minimize\ C_{place}=\sum_{i}{(\sum_{j}{d_{ij}\times\lambda_{i}\times\varphi_{i }})}, \tag{21}\]  
where \(C_{place}\) represents the network deployment cost, \(d_{ij}\) represents the length of the propagation path between switch \(i\) and switch \(j\), \(\lambda_{i}\) represents the average flow request volume of switch \(i\), and \(\varphi_{i}\) represents the failure cost of switch \(i\).
## 5 Conclusions  
In this paper, based on the kinematic characteristics of a cone spiral milling hole, the material removal characteristics of forward and reverse cone spiral milling holes were analyzed, and the ultrasonic milling mechanism was introduced to reveal the material removal law of the ultrasonic enhanced progressive cone spiral milling holes, which made it possible to achieve both machining efficiency and quality. The following conclusions were obtained through experimental verification.  
The main contributions of this study can be summarized as follows:  
1. Based on the research of the material removal mechanism, it is verified that taper progressive spiral milling can realize integral processing of drilling and reaming shaving. For the bottom edge drilling and edge reaming hole to carry out rough machining, the application of the top edge to complete semi-finishing, and of the reverse cutting edge to complete finishing, the material removal of the end edge is about 0.44 that of a conventional cylindrical spiral milling cutter, which greatly reduces the axial force and improves the cutting efficiency and quality of the parts.
2. The ultrasonic vibration-assisted progressive cone-spiral milling strategy changes the material removal mechanism, making the bottom edge beat the impact material at high frequency, the peripheral edge gradually impact the material, and the reverse cutting edge shear and rolling remove the material, making the cutting more brisk with a lower cutting force.
3. Based on the plate and shell theory and linear elastic fracture mechanics, ultrasonic vibration-assisted progressive cone spiral milling can effectively increase the rigidity of the parts, improve the critical force of delamination, and reduce the axial force. With the increase in the number of milling holes, after 90 holes were machined, the axial force was only 23.3 N, which was still at a relatively low level, which improves the surface quality of the holes, and no obvious delamination defects were found in the parts.  
Conceptualization, C.W.; methodology, C.W.; validation, C.W. and W.X.; writing--original draft preparation, G.W.; writing--review and editing, G.W. and C.W.; project administration, T.C.; funding acquisition, T.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Projects of the National Natural Science Foundation of China, grant number 51975168.  
Figure 12: Schematic diagram of cutting force change as the number of holes increases.
## 4 Results and Discussion
### F-Measure Values of Various Word-Level n-Grams Across Different ML and DL Models  
The bar chart of F-measure values for different word-level n-grams (uni-, bi-, and trigram alone or their combined uni + bi + tri) based on machine learning (ML) and deep learning (DL) models employed for the offensive language detection on the dataset of Urdu language is shown in Figure 2. F-measure (or F1-score) is a performance metric that combines precision and recall in a way that preserves the model's performance concerning correctly identifying offensive language, as well as false positives and false negatives.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Features** & **Model** & **Precision** & **Recall** & **FI-Score** & **Accuracy** \\ \hline \multirow{4}{*}{Uni} & Logistic Regression & 93.62 & 93.56 & 93.56 & 93.56 \\ \cline{2-6}  & Naive Bayes & 72.16 & 70.55 & 69.86 & 70.55 \\ \cline{2-6}  & SVM & 94.5 & 94.48 & 94.48 & 94.48 \\ \cline{2-6}  & Random Forest & 93.6 & 93.56 & 93.56 & 93.56 \\ \hline \multirow{4}{*}{Bi} & Logistic Regression & 85.19 & 85.12 & 85.12 & 85.12 \\ \cline{2-6}  & Naive Bayes & 80.34 & 80.21 & 80.21 & 80.21 \\ \cline{2-6}  & SVM & 84.85 & 84.82 & 84.82 & 84.82 \\ \cline{2-6}  & Random Forest & 77.06 & 75.77 & 75.39 & 75.77 \\ \hline \multirow{4}{*}{Tri} & Logistic Regression & 72.48 & 68.25 & 66.42 & 68.25 \\ \cline{2-6}  & Naive Bayes & 77.24 & 74.39 & 73.82 & 74.39 \\ \cline{2-6}  & SVM & 76.68 & 74.23 & 73.75 & 74.23 \\ \cline{2-6}  & Random Forest & 69.4 & 59.2 & 52.31 & 59.2 \\ \hline \multirow{4}{*}{Uni + Bi + Tri} & Logistic Regression & 91.26 & 91.26 & 91.26 & 91.26 \\ \cline{2-6}  & Naive Bayes & 83.17 & 82.98 & 82.93 & 82.98 \\ \cline{1-1} \cline{2-6}  & SVM & 89.45 & 89.28 & 89.36 & 89.28 \\ \cline{1-1} \cline{2-6}  & Random Forest & 80.32 & 79.98 & 80.15 & 79.98 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of ML models with different Count Vectorizer n-grams on the proposed dataset.  
**ML Models.** For general n-gram-based configurations, it can be seen from the overall F-measure scores that F-measure values are highest on settings used for the combined uni-+ bi-+ tri-gram dataset, with Logistic Regression, SVM, and Random Forest consistently beating other models. From the ML models, [PERSON] places poorly, especially for the tri-grams.  
**DL Models.** CNN and Bi-LSTM models achieve high F-measure values and are relatively stable across various n-grams, which suggests these two models learned to generalize to the complexity of language (pay attention to the difference between the max-micro and min-micro for each model, which is quite small). It is similar for DL models where the combined uni- + bi- + tri-gram configuration achieves higher accuracy. The contrast indicates that although the n-gram combined setting enhances the performance of all models to some degree, deep learning (CNN and Bi-LSTM models) shows superior performance against all traditional ML models with robust performances against specific combined settings.
# Hardware Design for Cascade-Structure, Dual-Stage, Current-Limiting, Solid-State DC Circuit Breaker
## 3 Fault Handling Method and Theoretical Discussion
### Time (\(t_{1}<t<t_{3}\))  
The current detection module in the steady-state branch continuously sends the current signal to the microcontroller via AD analog-to-digital conversion. When the current in the steady-state branch rises to the threshold value, the microcontroller controls the turn-off of T\({}_{0}\) and VT\({}_{1}\), while simultaneously sending a signal to conduct the thyristor and MOSFET in the power electronic branch. The current in the steady-state branch quickly drops to zero, and the thyristor automatically turns off due to the lack of holding current. The fault current is forced to flow into the power electronic branch and continues to rise, as shown in Figure 3c. If the current transfer time is ignored, i.e., assuming that the fault current is completely transferred to the power electronic branch at time \(t_{2}\), then substituting \(t_{2}\) into Equation (1) yields \(i_{2}(t_{2})=i_{dc}(t_{2})=I_{2}\). According to [PERSON]'s current and voltage laws, we know that:  
\[\left\{\begin{array}{l}i_{dc}=i_{L1}+i_{R1}=i_{L1}+\frac{L_{1}}{R_{1}}\frac {di_{L1}}{dt}\\ U_{L1}=L_{1}\frac{di_{L1}}{dt}\\ U_{dc}=(L_{s1}+L_{s2}+L_{line})\frac{di_{K}}{dt}+U_{L1}+i_{dc}(R_{line}+R_{F} )\end{array}\right. \tag{2}\]  
Obtain \(i_{L1}\) and \(U_{L1}\):  
\[i_{L1}=i_{dc}(1-e^{-(R_{1}/L_{1})t})\]  
\[U_{L1}=L_{1}\frac{di_{dc}}{dt}(1-e^{-(R_{1}/L_{1})t})+R_{1}i_{dc}e^{-(R_{1}/L_ {1})t}\]  
If \(R_{1}/L_{1}\) is larger, then \(e^{-(R_{1}/L_{1})t}\) during this phase will approach 0. Therefore,  
\[\left\{\begin{array}{l}U_{L1}=L_{1}\frac{di_{K}}{dt}\\ U_{dc}=(L_{s1}+L_{s2}+L_{line}+L_{1})\frac{di_{K}}{dt}+i_{dc}(R_{line}+R_{F}) \end{array}\right. \tag{3}\]  
Then, the short-circuit fault current is solved as:  
\[i_{z}=i_{dc}=I_{2}e^{-(t-t_{2})/\tau_{2}}+\frac{U_{dc}}{R_{line}+R_{F}}(1-e^{ -(t-t_{2})/\tau_{2}}) \tag{4}\]In the equation: \(\tau_{2}=\left(L_{line}+L_{s1}+L_{s2}+L_{1}\right)/\left(R_{line}+R_{F}\right)\). The current rise rate during this phase is defined as the second rise rate of the fault current. The introduction of the main current-limiting circuit ensures that \(\tau_{2}>\tau_{1}\), reducing the second rise rate of the fault current and achieving the optimal current-limiting effect.
## 4 Privacy-Preserving Smart Metering with Authentication Capability
### Discussion  
In the proposed scheme, smart meters forward their own encrypted measurements, as well as those received from their child meters in the aggregation tree. Upon receipt of all the encrypted measurements for the aggregation path, the concentrator decrypts the aggregated data with the corresponding path key. In this approach, smart meters that are closer to the concentrator send and receive up to several orders of magnitude more bits than those on the periphery of the spanning tree. Thus, there is an imbalance between the smart meters in terms of the amount of data communicated.  
One solution to this is to construct the spanning tree using a breadth-first traversal of the graph, starting at the concentrator. In this way, the height of the tree is the same as the shortest distance from the furthest meter to the concentrator. It reduces the maximum number of hops for the longest aggregation path, thereby reducing the end-to-end aggregation time and partially relieving (but not perfectly solving) the imbalance problem in terms of communication overhead.  
Another solution to the problem is for the smart meters to perform the aggregation of their encrypted measurement with that received from the child meter. That is, smart meter \(s_{i}\) performs \(c_{j}+c_{i}\), where \(c_{j}\) is the ciphertext received from its child meter \(s_{j}\), and forwards it, rather than forwarding \(c_{j}\) and \(c_{i}\) individually. This approach completely solves the imbalance in the smart grid network; however, it does not allow the concentrator to verify the measurements due to the malleability property of homomorphic encryption. Therefore, there is a trade-off between communication overhead and data authentication.
# A Survey on Video Big Data Analytics: Architecture, Technologies, and Open Research Challenges
## 5 Open Research Challenges and Future Directions
### Security, Privacy, and Ethical AI  
With the expansion of VBDA into privacy-sensitive sectors such as healthcare and public safety, data protection and responsible AI usage have become critical. FL offers a promising approach by enabling local model training without sharing raw video data. However, FL remains vulnerable to threats like model poisoning, gradient leakage, and degraded performance under non-IID data distributions [154]. Addressing these risks requires privacy-preserving techniques such as asynchronous FL, HE, and ZKP. While effective, these methods often introduce high computational overhead. SMPC and DP offer additional safeguards but still face challenges in scaling, particularly in real-time or bandwidth-constrained deployments.  
To strengthen privacy and trust, ViMindXAI embeds asynchronous FL with FedProx regularization in the DaAL layer to reduce client drift under non-IID conditions. Privacy is further reinforced via secure aggregation protocols and optional DP, depending on the sensitivity of the application. For highly sensitive domains like clinical video analysis, we plan to extend support for HE and SMPC. Beyond security, ViMindXAI addresses fairness by integrating inherently interpretable models and fairness auditing pipelines, enabling explainability-by-design in tasks such as identity recognition and behavioral analysis.
## 2 Flexibility Services  
The present Section aims to highlight the reasons that demonstrate the validity of the flexible approach and the possible way that it can be used.  
The services associated with the flexibility products offered by DERs can be helpful for services as a reserve, black start for participation in the recovery of the electricity system, reactive power support for reducing losses and voltage regulation, active power support for voltage regulation, local congestion mitigation, demand response and load modulation, etc. [35; 36].  
Some of them can be considered global system services (e.g., frequency regulation, reserve, etc.), offered to the TSO in the AS market; others are local services provided to the DSO for managing issues that occurred in some networks at a given time or for allowing the participation of DERs in the AS market [36; 37].  
The global services can be aggregated in at least two use cases of the distribution flexibility available for the TSO [38]:  
1. Balancing--The TSO uses flexibility for system balancing purposes: frequency containment reserve, automatic frequency restoration reserve and manual frequency restoration reserve.
2. Reactive Power Management in Transmission Networks--Flexible DERs are used for reactive power management to minimise grid losses or support the voltage on the transmission network.  
From the DSO point of view, the flexible services offered by the DERs may be expended for more use cases:  
1. Local Congestion Management in Distribution Networks--This use case refers to the use of flexibility to relieve one or more congested lines.
2. Congestion Avoidance at TSO/DSO Interface--For countries where the transformers at the TSO/DSO interface are owned and operated by the DSOs, transformer congestions can be avoided using flexibility from the distribution grid.
3. Voltage Control in Distribution Network (active power modulation)--In this use case, the DSO uses flexibility to control the voltage via active power management.
4. Voltage Control in Distribution Network (reactive power support)--In this use case, the DSO activates one or several flexible units connected to its distribution network to perform voltage control in its network via reactive power flows support.  
Based on the use cases, an assessment concerning general technical requirements for the coordinated use of flexibility can be made. The paper focuses on the use cases T1 and the relevant interdependency between TSO and DSO. The expected block of the activation of flexible units because of local congestion issues in the distribution system or the reduction in expected balancing capacities caused by the activation of flexible units for voltage or congestion management is analysed in the paper on a large scale. The methodology can also be used for assessing possible blocks of flexibility provision concerning D1-D3.