esa-sceva/satcom-chunk-collection huggingface dataset:
# Mechanical Properties Analysis of WAAM Produced Wall Made from 6063 Alloy Using AC MIG Process
## 3 Results and Discussion
### Hardness  
Each sample was produced in 50 layers, with hardness measurements taken approximately every two layers. The hardness distribution of the samples under different processing parameters using AC welding current is shown in Figure 6. Testing was performed on the sample along the path represented by the dotted line, as indicated in the chart. The arrow above the sample represents the direction of testing. Along the mid-width line of the samples, the average hardness values are 48.9 HV5 for sample 11, 51.7 HV5 for sample 12 and 50.3 HV5 for sample 13.  
When comparing the extreme average hardness values, it is evident that the maximum hardness is achieved when using an AC with the balance set to 0 (equal duration of the positive and negative periods in one cycle), while the minimum hardness is observed when using an AC with the balance set to 50. The difference in these extreme values is 5.3%, suggesting that variations in the balance of polarity distribution during the cycle do not significantly influence the overall hardness of the WAM-AC MIG-produced 6063 aluminum alloy walls. Due to the relatively high standard deviations (\(\pm\) 5.03 for sample 12, \(\pm\)4.75 for sample 13 and \(\pm\)3.83 for sample 11), the overlap in variability also means that the differences between the samples are not statistically significant. Therefore, although it can be assumed that sample 12 has the highest average hardness, this conclusion should be interpreted with caution. It can also be concluded that the maximum hardness values are achieved when using AC with a balance set to 0.  
Along the mid-height line of the samples, the average hardness values are 47.8 HV5 at the middle and 50.6 HV5 at the top of sample 11, 50.7 HV5 at the middle and 58.5 HV5 at the top of sample 12 and 49.9 HV5 at the middle and 54.8 HV5 at the top of sample 13. As observed from all obtained values, a linear increase in hardness is evident from the bottom towards the top of all samples produced using an AC, with no significant influence of the polarity balance.  
Figure 5: Comparison of microstructures of four WAM samples at higher magnification.  
Figure 6: Hardness distribution of samples produced by AC welding current (samples 11, 12 and 13).
## 3 Sensor Fault Conversion  
The faults of WECS include system faults, actuator faults, sensor faults and so on. Actuator faults usually occur due to wear and destruction of gears in gear boxes, and wear and deformation of bearing surfaces. Sensors usually fail due to their aging damage and uncorrected long-term use. Considering the faults of the WECS, the system (5) can be described as  
\[\left\{\begin{array}{l}\dot{x}_{p}(t)=A_{p}x_{p}(t)+B_{p}u(t)+F_{p}f_{a}(t) \\ y_{p}(t)=\mathbb{C}_{p}x_{p}(t)+G_{p}f_{s}(t)\end{array}\right. \tag{6}\]  
where \(A_{p}  
\[\left\{\begin{array}{l}\left[\begin{array}{c}\dot{x}_{p}(t)\\ \dot{z}_{p}(t)\end{array}\right]=\left[\begin{array}{cc}A_{p}&0\\ A_{f}C_{p_{2}}&-A_{f}\end{array}\right]\left[\begin{array}{c}x_{p}(t)\\ z_{p}(t)\end{array}\right]+\left[\begin{array}{cc}B_{p}\\ 0\end{array}\right]u(t)+\left[\begin{array}{cc}F_{p}&0\\ 0&A_{f}G_{1}\end{array}\right]\left[\begin{array}{c}f_{a}(t)\\ f_{s}(t)\end{array}\right]\\ \left[\begin{array}{c}y_{1}(t)\\ z_{p}(t)\end{array}\right]=\left[\begin{array}{cc}C_{p_{1}}&0\\ 0&I_{h}\end{array}\right]\left[\begin{array}{c}x_{p}(t)\\ z_{p}(t)\end{array}\right]\end{array}\right. \tag{9}\]  
Equation (9) is simplified and the new state variables and the system matrices are defined as follows:  
\(\dot{x}(t)=\left[\begin{array}{c}\dot{x}_{p}(t)\\ \dot{z}_{p}(t)\end{array}\right]\), \(x(t)=\left[\begin{array}{c}x_{p}(t)\\ z_{p}(t)\end{array}\right]\), \(A=\left[\begin{array}{cc}A_{p}&0\\ A_{f}C_{p_{2}}&-A_{f}\end{array}\right]\), \(B=\left[\begin{array}{cc}B_{p}\\ 0\end{array}\right]\), \(F=\left[\begin{array}{cc}F_{p}&0\\ 0&A_{f}G_{1}\end{array}\right]\), \(f_{a\delta}(t)=\left[\begin{array}{c}f_{a}(t)\\ f_{s}(t)\end{array}\right]\), \(C=\left[\begin{array}{cc}C_{p_{1}}&0\\ 0&I_{h}\end{array}\right]\).  
Equation (9) can be transformed into:  
\[\left\{\begin{array}{l}\dot{x}(t)=Ax(t)+Bu(t)+Ff_{a,\delta}(t)\\ y(t)=\mathrm{C}x(t)\end{array}\right. \tag{10}\]  
Equation (10) is a system with an actuator fault \(f_{a}(t)\) and sensor fault \(f_{s}(t)\), which is equivalent to actuator faults in Equation (6).  
There is a nonsingular transformation matrix \(T_{p}\in R^{(n+h)\times(n+h)}\)[31], \(T_{p}x=\left[\begin{array}{c}x_{1}\\ x_{2}\end{array}\right]\), which places the original system Equation (10) under the action of \(T_{p}\), to obtain Equation (11):  
\[\left\{\begin{array}{l}\dot{x}_{1}(t)=A_{1}x_{1}(t)+A_{2}x_{2}(t)+B_{1}u(t) \\ \dot{x}_{2}(t)=A_{3}x_{1}(t)+A_{4}x_{2}(t)+B_{2}u(t)+\ F_{2}f_{a,\delta}(t)\\ y(t)=x_{2}(t)\end{array}\right. \tag{11}\]  
where \(x_{1}\in R^{n+h-p}\), \(x_{2}\in R^{p}\), \(T_{p}AT_{p}^{-1}=\left[\begin{array}{cc}A_{1}&A_{2}\\ A_{3}&A_{4}\end{array}\right]\), \(A_{1}\in R^{(n+h-p)\times(n+h-p)}\), \(A_{2}\in R^{(n+h-p)\times p}\), \(A_{3}\in R^{p\times(n+h-p)}\), \(A_{4}\in R^{p\times p}\); \(T_{p}B=\left[\begin{array}{c}B_{1}\\ B_{2}\end{array}\right]\), \(B_{1}\in R^{(n+h-p)\times(n+h-p)}\), \(B_{2}\in R^{p\times(n+h-p)}\); \(T_{p}F=\left[\begin{array}{c}0\\ F_{2}\end{array}\right]\), \(F_{2}\in R^{p\times(q+h)}\); \(CT=\left[\begin{array}{cc}0&I_{p}\end{array}\right]\). For the proof, refer to [16].
# Similarity Measurement and Classification of Temporal Data Based on Double Mean Representation
## 1 Introduction  
Time series data refer to a sequence of data points arranged in chronological order. The widespread use of smartphones, various sensors, RFID, and other devices in recent years has laid a solid foundation for the generation of massive amounts of time series data. For efficient data mining and querying of time series data, it is crucial to employ a rational and effective representation method. Time series data representation methods can be broadly classified into four categories: data-adaptive representation methods, non-data-adaptive representation methods, model-based representation methods, and data-indicative representation methods, as described in Table 1. The content of this overview is derived from the literature [1]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Representation** & **Publication Time** & **Type** & \
\begin{tabular}{c} **Algorithm** \\ **Complexity** \\ \end{tabular} &
\begin{tabular}{c} **Method** \\ **Source** \\ \end{tabular} \\ \hline Discrete Fourier Transform (DFT) & 1993 & T1 & O(n(log(n))) & [2] \\ Discrete Wavelet Transform (DWT) & 1999 & T1 & O(n) & [3] \\ Discrete Cosine Transform (DCT) & 1997 & T1 & N & [4] \\ Partitioned Aggregation & 200 & T1 & O(n) & [5] \\ Perceived Importance Points (PIPs) & 2001 & T1 & N & [6] \\ Chebyshev Polynomials (CHEBs) & 2004 & T1 & N & [7] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Main representation methods of time series data. (1) Data-adaptive representation methods express the original time series data as a combination of arbitrary-length segments, aiming to minimize the error in global representation. For example, the commonly used Singular Value Decomposition (SVD) method [4] is a typical data-adaptive representation method. It seeks c representative orthogonal vectors of dimensionality k (c \(\leq\) k) and maps the original time series data into a smaller space, achieving dimensionality reduction. Another approach, the Piecewise Linear Approximation (PLA) method proposed by [PERSON] et al. in 1998 [11], fits the original time series data using line segments. Furthermore, the improved Piecewise Constant Approximation (PCA) method [12] approximates each time series data segment using a constant. Similarly, the Adaptive Piecewise Constant Approximation (APCA) method [13] divides the original time series data into variable-length segments and represents each segment using mean and time-scale values. Symbolic Aggregate Approximation (SAX) [14,15] partitions the original data and uses means to represent each segment. Then, based on the normal distribution of the values in the time series data, the means are mapped to symbols, transforming the complete time series data into a string. Data-adaptive representation methods effectively capture the characteristics of the original time series data. However, due to the unequal lengths of the segments, similarity measurement based on this representation method becomes challenging.
# Tempcore Process Simulator to Analyze Microstructural Evolution of Quenched and Tempered Rebar
## 1 Introduction
The reliability of sub-scale thermomechanical testing decreases significantly due to reduced similarities, and the rapid cooling necessary for the Tempcore process is hard to achieve by conventional water quenching. Figure 2 compared hardness profiles of rebars after shop floor Tempcore treatment and 5 s water quenching  
Figure 1: Schematic of temperature profile and microstructural evolution in Tempcore treated rebars. from 1000 \({}^{\circ}\)C. As shown in the figure, the tempered martensite rim shown in the Tempocored rebar was not obtained from conventional water quenching. The mechanical properties of Tempcore rebar have been the subject of several studies. The impact toughness [26], fatigue behavior [27], weldability for seismic application [28], and behavior at elevated temperature [29; 30; 31] have been studied. Nevertheless, data on practical experimental simulation is very scarce even though this is a very important factor to save time and cost. Hence, this paper focuses on the development of a TPS capable of precise simulation of shop floor operations and of producing high-fidelity Tempcore data. TPS mainly consists of a vacuum induction furnace, pilot rolling mill, box furnace, and cooling unit to simulate shop floor operations. The operational parameters that affect the Tempcore process are the chemical composition of the bar, the temperature at the end of rolling, and the cooling rate. The aim of this work is to develop TPS and show how the mechanical properties of the material are related to the different operational parameters. Therefore, a series of experimental tests were successfully carried out in TPS with various parameters, such as reheating temperature, water flow, water pressure, and cooling time to study their effects on microstructural evolution and the mechanical properties of quenched and tempered rebar.
## 3 EEG Essentials: Unveiling Standard Protocols for Advanced Clinical Practice
### Technical Setup and Recording Protocols  
The 10-20 system is anchored on specific anatomical landmarks of the skull, using the nasion, located at the bridge of the nose, and the inion, a prominent bone at the back of the skull, as primary reference points. Electrodes are systematically positioned at intervals of 10% or 20% of the total distance between these landmarks. This strategic placement encompasses diverse regions of the scalp, including the frontal, central, and parietal lobes providing comprehensive coverage of the occipital and temporal areas and a view of the cerebral cortex.  
In the studies reviewed, electrodes typically consist of silver/silver chloride (Ag/AgCl) that are chosen for their stability and low impedance. These electrodes are positioned according to the 10-20 system map, which includes standard locations such as Fp1 and Fp2 (frontal polar); F3 and F4 (frontal); C3 and C4 (central); P3 and P4 (parietal); O1 and O2 (occipital); F7 and F8 (frontal lateral); T3 and T4 (temporal); T5 and T6 (temporal lateral); and Fz, Cz, and Pz (midline electrodes). To facilitate accurate and efficient electrode placement, an elastic cap embedded with electrodes aligned according to the 10-20 system is often employed [79; 80]. This cap ensures consistent positioning and adjusts to different head sizes, significantly enhancing patient comfort during the procedure.  
To optimize electrical conductivity between the scalp and the electrodes, a conductive gel or paste is applied. This practice reduces impedance and enhances signal quality, ensuring that each electrode maintains optimal contact with the scalp. The weak electrical signals generated by neuronal activity are then amplified by a sophisticated amplifier, designed with high input impedance and a broad bandwidth to capture a wide range of EEG frequencies.  
The amplified signals are digitized using a data acquisition system that includes an analog-to-digital converter (ADC). This system is complemented by software for detailed signal processing and analysis, with sampling rates, as specified above, typically set at 256 Hz or higher to ensure precise capture of the EEG signal and avoid aliasing. The review notes that EEG recordings are generally conducted in a quiet, dimly lit room to minimize external stimuli and reduce artifacts, with patients comfortably positioned to minimize movement-induced artifacts.  
The 10-20 system's standardization offers numerous benefits, providing a consistent methodology for electrode placement that facilitates data comparison across diverse studies and clinical contexts. It ensures comprehensive cortical coverage, thereby enhancing the diagnostic value of EEG. The system's design also prioritizes user-friendliness, enabling quick and accurate electrode placement, which is particularly advantageous in both clinical settings and large-scale research studies [81; 82; 83; 84].
## 3 Control System Design
### Curved Road Driver Model  
The curved road driver model is utilized in this study to simulate how a human driver controls the vehicle on a curved road. This model assumes a constant longitudinal vehicle speed and focuses only on the lateral control of the vehicle. The widely used pure pursuit control algorithm is used for lateral control. Figure 3 provides a simplified diagram of this model. A reference path is established along the lane centerline, starting from the vehicle's rear axle center position A. Along this path, a preview point C is chosen by projecting it forward along the rear axle centerline for a certain distance. The steering wheel angle is then determined based on the geometric relationship among the preview distance \(l\), the turn radius \(R\), and the azimuth angle \(\alpha\) of the preview point in the vehicle's coordinate system.  
To ensure that the vehicle's rear axle center reaches point C along the arc trajectory, \(\triangle\)OAB must satisfy Equation (1). The front wheel steering angle \(\beta\) can be calculated using Equation (2). By using Equations (1) and (2), the functions of \(\beta(t)\) and \(\alpha(t)\) varying with time \(t\) can be obtained, as shown in Equation (3). Additionally, defining the lateral error \(e_{y}\) as the difference between the vehicle's current orientation and the lateral position of the preview point, and assuming the angle in Equation (3) is small, the value of \(e_{y}\) can be determined as Equation (4). Finally, the required adjustment of the steering wheel angle can be calculated through the front wheel steering angle.  
\[\frac{l}{\sin 2\alpha}=\frac{R}{\sin\left(\frac{\pi}{2}-\alpha\right)} \tag{1}\]  
\[\tan\beta=\frac{L}{R} \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Control** & **System** & **AEB System** & **LKA System Activation Conditions** & **Conditions** \\ \hline System \(\copyright\) & AEB & Risk of rear-end & / \\ System \(\copyright\) & Independent control & Risk of rear-end & Lane departure tendency \\ System \(\copyright\) & of AEB and LKA & collision & Lane departure tendency \\ System \(\copyright\) & Integrated control of & Risk of rear-end & Lane departure tendency or \\  & AEB and LKA & collision & AEB system is triggered \\ \hline \hline \end{tabular}
\end{table}
Table 1: Different control systems.  
Figure 3: Simplified diagram of pure pursuit model.  
\[\beta(t)=\tan^{-1}\left(\frac{2L\sin(\alpha(t))}{l}\right) \tag{3}\]  
\[e_{y}=l\sin\alpha\approx\frac{l^{2}}{2L}\beta(t) \tag{4}\]  
To verify the effectiveness of the driver model, simulation tests are conducted when the vehicle runs on a curved road with a 90 m radius at a speed of 60 km/h. The simulation results are shown in Figure 4, where Figure 4 illustrates the variation in steering wheel angle and Figure 4 depicts the trajectory of the vehicle's rear axle center during the tracking process. It can be observed that the steering wheel angle remains relatively constant throughout the control process, and the vehicle's rear axle center consistently remains close to the lane centerline.
###### Abstract  
The adjustment of delay time in open-pit bench blasting is a research hotspot in vibration control. Its core lies in utilizing the periodic characteristics of vibration waves to achieve the superposition and cancellation of wave peaks and troughs. However, due to the spatiotemporal variability in the propagation of blast-induced vibration waves, the optimal delay time determined for vibration control requirements at a specific protected area (monitoring point) makes it difficult to achieve the misalignment superposition effect simultaneously at multiple monitoring points. To address the challenge of multi-area vibration control in open-pit bench blasting, this paper proposes an adjustment method based on local delay adjustment. First, a spatiotemporal relationship model between blast holes with monitoring points is established to calculate vibration wave arrival times. This enables rapid hole identification during dense wave arrivals at monitoring points, with waveform separation achieved through initiation delay adjustments. Following the Anderson principle, reconstructed single-hole vibrations are superimposed according to the wave arrival sequence to validate control efficacy. Statistical analysis of concurrent wave arrivals across all-direction monitoring points identifies high-probability vibration hazard locations. Targeted delay adjustments for blast holes within clustering arrival periods at these locations enable comprehensive vibration reduction. Field data confirm that single-point control reduces peak vibration by >10.55% through simultaneously reducing the amount of waves in clustering arrival periods. Multi-point control resolves seven hazard locations across two directions, attaining 88.57% hazard elimination efficiency and 14.05% peak velocity attenuation. This method achieves vibration control through local delay adjustments while maintaining the fragmentation effect of the original scheme, providing a new approach to solving the challenge of vibration control in large-scale blasting areas.  
p 2025 15 4641 +
Footnote †: journal: Applied sciences  
Article
# An Adaptive Buffering Scheme for P2P Live and Time-Shifted Streaming
## 3 Adaptive Buffering Scheme
### Peer Group Management  
As shown in Figure 2, in our proposed P2P live and time-shifted streaming system, the tracker server assigns each period to a group consisting of peers whose playback positions are within the period after dividing the entire playback time at regular intervals. We select the peer with maximum upload capacity as a super peer from each group. The super peer manages the playback position information of all peers belonging to its group by periodically gathering the information.  
Each peer asks the tracker server for the address of the super peer of the group to which it will belong when it joins the system or when it needs to join another group when its playback position changes due to time-shifted viewing. The peer then connects to the super peer to make a request to select multiple peers as its neighbors. The super peer first tries to select peers belonging to its own group. However, if the number of neighbors to satisfy the minimum playback quality is not sufficient within the group, it requests super peers in adjacent groups to select some peers belonging to those groups. Nevertheless, if the peer cannot eventually obtain a sufficient number of neighbors in the entire system, it should receive data directly from a source server.
## 5 Integration Options
### Interconnection of MEC Systems  
The last scenario of integration is the pure interconnection of MEC systems. Here it is considered that such interconnection is achieved at the MEO level, as presented in Figure 6, by the definition of a new external interface, named MD-Mx3, in consistency with the Mx1 and Mx2 interfaces as already defined in the MEC reference architecture.  
Figure 5: Integration of MEC providers at service level.  
Figure 6: Interconnection of MEC systems from different providers.  
The business rationale for this option is the alliance of full MEC providers, which federate to offer a more complete commercial offer to their respective MEC customers. Each of the providers in the federation has its own portfolio and customer base, but they can leverage in the federation in order to constitute a more compelling commercial offer in terms of coverage, services, etc. In the more extreme case of interconnection, it could be even possible for an MEC provider to implement only the MEO--that is, without their own resources nor platform. In this situation, such a provider would play a role of broker of MEC systems from some other MEC providers that could participate in a kind of exchange or federation of MEC systems.
## 2 Challenges and Routing in 6G Networks
### Challenges of Routing
#### 2.1.4 Heterogeneous Structure of 6G  
It is aimed that 6G and next-generation networks will meet the various service needs of many communication environments with different dynamics. Virtual reality, the integration of space, air, ground, and even underwater spaces, IoT, holographic technologies, smart cities, unmanned aerial vehicles, and unmanned ground vehicles need better and new approaches and heterogeneous systems to meet and optimize the increasing demands of users. The independence of communication from place and time is among the greatest expectations of users and systems. It requires a coalition of cellular and ad-hoc networks to serve this expectation in heterogeneous networks (HWNs). Ad-hoc networks are network connections that do not need a wireless access point or router for communication. In these networks, the devices act as a router and wireless access points [59]. HWNs provide communication through stations by using the structure of the cellular network or by directly using the structure of ad hoc Networks. HWNs, thus, represent an increased version of the capability of both cellular networks and ad-hoc networks. This ensures high-speed transmission of data [60; 61].