esa-sceva/satcom-chunk-collection huggingface dataset:
# A Vibration Based Automatic Fault Detection Scheme for Drilling Process Using Type-2 Fuzzy Logic
## 4 Validation and Results
When the same number of MFs and the same type-reduction and defuzzification approach are utilised, Gaussian type-2 fuzzy logic systems are faster than trapezoidal type-2 fuzzy logic systems. Because small rule bases are commonly employed in practise, Gaussian type-2 fuzzy logic systems appear to be more cost-effective. The type-2 fuzzy system is defuzzified by implementing the technique proposed in [36]. The fuzzy laws are chosen on the basis of Theorem 1 concentrating on the condition \(\lim_{k\rightarrow\infty}\parallel e_{i}(k)\parallel^{2}=\frac{2\gamma(k) \hat{g}}{\pi_{1}(k)+\pi_{2}(k)}\). The learning rate used for the fuzzy laws is \(\Psi_{1}=\Psi_{2}=0.85\). The conditions extracted from Theorem 2 for the fault  
\[\ddot{x}=\frac{F_{x}}{mx}+\frac{u_{x}}{m_{xx}}-\frac{c_{xx}}{m_{xx}} \dot{x} \tag{78}\]9 \(y\)-axis representation of cutting force. detector estimator are \(R_{\theta}=0.99\) and \(\gamma=0.01\). In Figures 6 and 7, the drill tool's vibration is plotted on the \(x\) and \(y\) axes. For better clarity of the plot representation, a 20-s interval is shown. These vibration graphs show the pattern of vibration in the absence of induced flaws. At 45 s following the commencement of the drilling process, an artificial fault is induced along the \(x\) and \(y\) axes to the drilling operation simulation. The vibration of the drill tool with the induced flaws is represented by Figures 8 and 9. These plots show how plots with chatter change after 45 s, proving the fluctuation of vibration caused by induced faults. The period between 40 s and 90 s is illustrated for the clarity of the charts.The induced fault is a self generated sinusoidal signal. The defect detection in the drill tool is shown in Figures 10 and 11 along \(x\) and \(y\) components. The charts show that there is no change in the signal until 45 s, when it displays zero. At the instant of the detection of fault, the change in vibration signal is detected instantly along \(x\) and \(y\) components. From the vibration plots with induced faults (Figures 8 and 9), it is clear that the intensity of the faults increases after a period of 65 s. However, from the fault detection estimator, the variation of the plots is noticed instantly with the fault starting up, thus raising an alarm and preventing the damage of drill tool. Hence, it is validated that the interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy based observer fault detection algorithm is successful as a fault detector mechanism by the sounding alarm after a period of 45 s. The future work is intended towards the investigation of faults along \(z\) and \(\theta\) directions and comparing the results with \(x\) and \(y\) directions, thus predicting remaining useful life (RUL) of the drilling process system.
## 4 Discussion
### MACD_dS, MACD_dT, and MACD_dV  
The determined MACD_dS, MACD_dT, and MACD_dV values (Figure 6A-C) make it possible to further extend the analyses performed based on the trend change detection using the methods of stock exchange analysis [21]. The obtained values of the MACD_dV strongly correlate with the values of the COP mean velocity, which seems obvious. However, the knowledge of the time between trend changes and the distance covered by the COP at that time enables the collection of additional information on COP movement to be obtained [18]. Information on the changes in the MACD_dT and MACD_dS values may indicate if the resulting changes in the velocity values, or their lack thereof, result from the change in the length of individual leaps of the COP, the time in which such a leap takes place, or from both of these values simultaneously.  
Statistically significant differences between the OE and CE measurements and all other measurements occurred only in the case of the MACD_dS, where an increase in the median value was observed (only measurements between OE and 1.4_2 lacked significance). At the same time, most cases displayed a lack of statistically significant differences at the MACD_dT time. This showed that, in these cases, the observed increase in the velocity values occurred because of an increase in the mean distance between the subsequent COP locations in which the trend change took place, whereas the mean time between those leaps remained constant. An exception was the measurement performed for 0.7_after, where statistically significant differences were noted in the OE and CE measurements. A simultaneous increase in the MACD_dS and MACD_dT values caused a lack of change in the velocity between 0.7_after and CE, which could be seen both in the case of MACD_dV and the COP mean velocity.  
What is worth mentioning is the fact that no case showed a statistically significant decrease in the MACD_dT value. A MACD_dT value that does not significantly decrease is essential from the perspective of the ability to maintain balance by the tested individuals. A simultaneous increase in MACD_dS and a decrease in MACD_dT might indicate much longer leaps of the COP performed at a shorter time. Such a case might potentially lead to destabilization and a subsequent fall of the tested person [25; 26].
## 2 Theoretical Background
### TPACK Framework
#### 2.2.1 TCK-Reasoning  
Our examples of TCK-x reasoning focus on mathematics education while offering limited examples related to other subjects. In mathematics education, dLMs can enable different representations [16] that are otherwise not possible using analog material (TCK-1), or offer real-world representations of content (TCK-2), and allow for dynamic representations (TCK-3) [16; 46; 47; 48; 49]. Using dLMs, multiple representations can be dynamically linked in new ways [50]. All these are \"representation\" reasons for selecting dLMs.  
Another reason for integrating dLMs into mathematics education is their potential to decrease extraneous cognitive load (TCK-4) or to modify extraneous cognitive load (TCK-5) [9; 16; 51] by outsourcing repetitive tasks. This can help save lesson time and allow students to focus more on higher levels of mathematical thinking, leading to improved learning outcomes [6; 37; 41; 48; 51]. However, some dLMs can also increase the extraneous cognitive load if learners are unfamiliar with them or dLMs are inadequate for the specific learning content (TCK-6).  
Other teaching subjects can entail additional TCK-x reasoning specific to them, but not applicable to mathematics. Examples include the ability to simulate phenomena invisible to the eye in biology or chemistry [20; 52], connecting physical phenomena with their representations in physics [53], or understanding content through audio cues in language education [27].
# Remote Sensing Road Extraction by Road Segmentation Network
## 2 Materials and Methods
### The Proposed Method
#### 2.2.2 Segmentation Step  
The spatial context information in different convolutions is complementary: the global context in deep layers and the local context in shallow layers. Skip connections from shallow to high layer can introduce location information into semantic information. However, most of the information in images will cause disturbance to the road. Therefore, a self-attention mechanism is proposed to emphasize the target information in adjacent layers.  
The attention mechanism (AM) is shown in Figure 2. The feature of the low layer is denoted as \(F_{i}\), while the feature of the high layer is up-sampled with bilinear interpolation to the same size of \(F_{i}\), and then denoted as \(F_{j}\). The concatenation of \(F_{i}\) and \(F_{j}\) is sent to a series of convolution layers which contains 1 \(\times\) 1 and 3 \(\times\) 3. After the sigmoid layer, the AM obtains the weighted map, \(w\), which has two effects. The first is used to select the useful information and refine the concatenated feature with residual learning. The refined feature is the '\(F_{i}\)' for the next adjacent layer of network in the AM. The second introduces the skip connections to enhance the detailed information of road. Thus, there are two outputs of the AM: weighted map and refined feature. Mathematically, the AM computes as  
\[\hat{F}=R\big{(}Cat\big{(}F_{i},F_{j}\big{)}\big{)}\bigg{[}1+\frac{1}{1+e^{-H( Cat(F_{i},F_{j}))}}\bigg{]}\]  
where \(\hat{F}\) is the refined feature by AM, and the weight map \(w=1/\Big{(}1+e^{-H(cat(F_{i},F_{j}))}\Big{)}\). \(Cat(\cdot)\) denotes the concatenation on adjacent layer features \(F_{i},F_{j}\), \(R(\cdot)\) is three consecutive 3 \(\times\) 3 convolution operations, and \(H(\cdot)\) means the operation of alternating 3 \(\times\) 3 convolution and 1 \(\times\) 1 convolution.
# Impact Analysis of Satellite Geometry Variation on ARAIM Integrity Risk over Exposure Interval
## 3 Integrity Risk Evaluation over the Exposure Interval
### Evaluation of \(P_{\text{HMI}}\)
where \(\beta\) is the reciprocal time constant of the first-order Gaussian Markov process. \(\Delta t\) is a time interval between adjacent two \(x\)'s. The sample rate of the receiver \(t_{\text{s}}\) is typically 0.2-0.5 s based on 2-5 Hz sampling frequencies. For the purposes of this article, a lower sampling interval of 2 s is employed to ease computation times for the MC simulation described below. This is a conservative parameter setting from the perspective of integrity risk evaluation, which was proved by [21]. Furthermore, the numerical examples in Section 4 are calculated for several values of the reciprocal time constant \(\beta\), ranging from 10 to 1000, to represent different levels of correlation strength. Here, the integrity risk over the exposure interval can be determined by substituting (7) into (2). In the current ARAIM algorithm, the evaluation of integrity risk often disregards the influence of allowable \(t_{\text{TTA}}\), which results in a conservative evaluation result. In contrast, we chose to accurately calculate the integrity risk within the allowable \(t_{\text{TTA}}\). The impact of geometry variation on the ARAIM's integrity performance can be evaluated. The integrity risk evaluation method has been described in the above related sections, and the detailed integrity risk evaluation process can be described below:  
Step 1. Run geometry simulation to obtain the satellite geometry information within the exposure interval in specified time. Step 2. Collect all the geometry matrix \(\mathbf{H}_{i}\) in the exposure interval and calculate \(F_{k}^{(i)}\) based on (4). The \(F_{k}^{(i)}\) will be employed later to reflect satellite geometry variation. Step 3. Describe the time-correlation effect of the HMI events in the allowable \(t_{\text{TTA}}\) and determine the normalized positioning errors covariance matrix \(\Sigma\) of adjacent epochs based on (10). Step 4. Given the \(F_{k}^{(i)}\) and \(\Sigma\) determined by step 2 and step 3, calculate the integrity risk \(P\Big{\{}\text{HMI}_{\text{TTA},i}^{k}\Big{\}}\) based on (7) and (8) across all fault modes and epochs. Step 5. Substitute (7) on (2), and calculate the actual integrity risk \(\overline{P}_{\text{HMI,actual}}\) in the entire exposure interval. In order to rigorously examine the integrity performance of the ARAIM, an integrity risk evaluation method is proposed to account for the satellite geometry variation in the exposure interval. The integrity performance of the ARAIM under different exposure interval times will be tested later.
## 1 Introduction
The Residual Spectral-Spatial Attention Network (RSSSAN) [10] constructs deeper feature representations by learning residual mappings, which aids in addressing the gradient vanishing issue in deep networks, enabling the network to more effectively learn intricate features. The Attention Bidirectional Long Short-Term Memory (AB-LSTM) model [11] supplies a richer context when processing sequential data, enhancing performance across a range of sequential processing tasks. It adeptly handles long-range dependency issues and excels in the processing of complex textual data. The Deep Feature Fusion Network (DFFN) [12] boosts model performance by merging features from diverse levels, which aids the model in capturing more holistic information. The Spectral-Spatial Feature Tokenization Transformer (SSFTT) [13] was introduced to more effectively encapsulate complex features within hyperspectral images. Through the process of feature tokenization, the model gains a deeper understanding and representation of the data. Speformer [14] leverages the sparsity of data to decrease storage demands, thereby accelerating processing speed. This model optimizes data representation, rendering it more efficient for hyperspectral data processing. To surmount the challenges outlined, this study proposes a novel Group-Sensitive Selective Attention Transformer (GSAT) for HSI classification. The key contributions of this research are as follows:  
1. Advanced Pixel Group Mapping (PGM) Module: Our study introduces an innovative PGM module, which effectively divides the full spectral range into distinct, nonoverlapping subchannels. This module is engineered to extract comprehensive spatial-spectral features from each subchannel, thereby enhancing the model's proficiency in capturing both local and global characteristics within hyperspectral images (HSI). The integration of such features significantly boosts the model's classification accuracy, ensuring a more nuanced understanding of the data. 2. Sensitivity Selection Framework (SSF) Module: We have developed the SSF module, a critical component that selectively focuses on the most pertinent tokens. This strategic selection reduces redundancy and efficiently captures multi-scale feature information, which in turn enriches the model's local representation capabilities. The SSF module plays a vital role in refining the classification process, leading to a marked improvement in accuracy. 3. Exceptional Performance Across Benchmark Datasets: Our research showcases the superior performance of the proposed network compared to existing deep learning techniques. Across six different HSI datasets, our model consistently achieves higher classification accuracy, demonstrating its robustness and effectiveness in the field of hyperspectral image analysis. This empirical evidence underscores the network's potential as a leading method for HSI classification.
# The Forecast of the Wind Turbine Generated Power Using Hybrid (LTC + XGBoost) Model
## 4 Methodology
### Feature Engineering  
To create a robust and stable model, the following features were added to the dataset:  
* _Time-based features_: attributes such as month were extracted to capture seasonal variations in wind power generation. Each month has its own wind speed trend, with distinct patterns that reflect the influence of seasonal changes, such as higher wind speeds in winter months and lower wind speeds in summer. Including these monthly trends helps the model account for the natural fluctuations in wind power generation throughout the year [56].
* _Lag features_: wind power values from previous time intervals (lags of 1, 2, and 3 steps) were included. These lagged features provide a way to model the natural dependence of wind power on past values. Initially, these were populated using factual historical data [57].
# Improved DRUS 4H-SiC MESFET with High Power Added Efficiency
## Device Structure and Simulation Method and Fabrication Feasibility  
### Device Structure  
Schematic cross-section of the DRUS-MESFET and the LDUS-MESFET are shown in Figure 1a,b, respectively. They both have a semi-insulating substrate, a p-type buffer layer, an n-type channel layer, and two highly doped n-type cap layers. The semi-insulating substrate is modeled as a compensation-doped (vanadium) semiconductor with a high concentration of deep level impurities [13]. Among them, the role of the p buffer layer is to reduce the influence of substrate defects on the active layer and improve noise performance and device gain [14]. Both of these structures have upper and lower gates, which control a thinner and a thicker part of the channel, respectively. The difference between the two structures is that the LDUS-MESFET has a layered doping under the lower gate, wherein the upper layer region has a doping thickness of \(H_{1}\) = 0.05 \(\upmu\)m and a concentration of \(6\times 10^{17}\) cm\({}^{-3}\) (to have a meaningful comparison, the lower layer region doping concentration of the LDUS-MESFET based on the DRUS-MESFET is also set at \(3\times 10^{17}\) cm\({}^{-3}\)), and the thickness of the undoped space region (US) is optimized for high efficiency. The optimization result is \(H_{2}\) = 0.05 \(\upmu\)m. The other parameters of the two structures are shown in Table 1.  
Figure 1: Schematic cross sections of the (**a**) double-recessed 4H-SiC MESFET with partly undoped space region (DRUS-MESFET), (**b**) 4H-SiC metal semiconductor field effect transistor with layered doping and undoped space regions (LDUS-MESFET).
# Transparent Redirections in Content Delivery Networks
## 3 SDN Based Transparent Redirection
### System Architecture  
As introduced in the state of the art, to be able to achieve TCP session handoff the underlying network must be able to provide an action that is capable of incrementing the sequence and acknowledge numbers on the fly over the network. For this purpose, it is crucial to use a software-controlled network. In the domain of SDN, the data plane consists of a set of Forwarding Elements (further referred to as forwarders in this document). The choice for the SDN architecture is based on OpenFlow 1.3 [27], as the protocol since this version supports experimenter actions, which allows us custom action implementation.  
In our architecture we distinguish two types of forwarders:  
* Access Forwarders: conventional OpenFlow 1.3 enabled forwarder aligned with the specification;
* Core Forwarder: conventional OpenFlow 1.3 enabled forwarder with support for experimenter actions that can modify the TCP sequence and acknowledge numbers.  
This design was chosen, to eliminate the need to put modified forwarders to the access layer. Request router and the surrogate servers of the CDN are connected directly to the core forwarders. The network topology must be connected to an extended SDN controller with the additional experimenter actions defined.  
This approach was chosen to eliminate the need to use modified SDN forwarders in the networks. Modified forwarders are only required where a surrogate server or the request router is attached. Other forwarders in the network can be any OpenFlow 1.3 compliant legacy forwarders.  
To make the CDN work, a request router must be placed, which is capable of establishing the initial TCP connections with the clients and which further establishes TCP connections to surrogate servers. A DNS server is placed in the network too, which handles domain translation for the domain name _dizp.bt_. The content is distributed over the CDN via the _cdn.dizp.bt_ domain. Surrogate servers are addressed by prepending se(ID), e.g., se1.cdn.dizp.bt. The domain name for the content origin is _www.dizp.bt_, which is a static web server hosting various files of various sizes. To be able to make redirection decisions the request router must be able to communicate with the SDN controller. The SDN controller provides a WebSocket based communication channel over the Northbound API [26]. Figure 4 depicts the proposed high-level architecture. The presented network represents a single L2 domain, where each component of the network resides on the same broadcast domain.  
Figure 4: High-level architecture overview.
## 5 Conclusions  
In this paper, we propose two novel VC-FFs (VC-DFF and VC-SRFF) based on a VC-SOT MTJ device. The VC-FFs consist of a limited number of transmission gates, CMOS inverters and two VC-SOT MTJs. Without additional write peripheral circuits and system power state detection circuits, the proposed VC-FFs show a significant reduction in both area overhead and operation complexity compared to the state-of-the-art NVFFs. In our work, a symmetrical structure is used to maximize the margin and the stability of the PCSA. The VC-FFs are designed and evaluated with a 40 nm CMOS design kit. The simulation results show that our proposed VC-FFs reduce the write power consumption by more than 7.2\(\times\) and 18.7\(\times\) compared to the STT-based NVFF and SOT-based NVFF, respectively. Additionally, the recovery power consumption of our work is 1.54 times less than that of the STT-based NVFF and twice as low as than that of the SOT-based NVFF.  
Conceptualization and methodology, X.L. and W.K.; software, L.J.; validation, Y.Z., D.L. and B.P.; formal analysis, Y.Z. and D.L.; investigation, X.L.; resources, W.K. and B.P.; data curation, E.D.; writing--original draft preparation, X.L. and W.K.; writing--review and editing, L.L., L.J. and E.D.; visualization, L.L.; supervision, B.P.; project administration, W.K.; funding acquisition, W.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grants No. 62274008, 62001019), Beijing Nova Program from Beijing Municipal Science and Technology Commission (No. Z201100006820042 and No. Z211100002121014), Beijing Natural Science Foundation (L223004), the Fundamental Research Funds for the Central Universities (YWF-23-L-1241), the Laboratory Open Fund of Beijing Smart-chip Microelectronics Technology Co., Ltd. (21-JS-223). (Corresponding authors: [PERSON]).  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **STT-Based** & **STT-Based** & **SOT-Based** & **SOT-Based** & **Proposed** \\  & **NVDFF**[12] & **NVDFF**[24] & **NVDFF**[19] & **NVDFF**[8] & **VC-DFF/VC-SRFF** \\ \hline Technological Node (nm) & 40 & 20 & 40 & 45 & 40 \\ Number of Devices & 21T + 2 MTJ & 28T + 2 MTJ & 21T + 2 MTJ & 16T + 1 MTJ & 20T + 2 MTJ/24T + 2 MTJ \\ T\({}_{\text{write}}\) (ns) & 35.7 & 4.1 & 1.6 & 4 & 1.7 \\ Write Power & 4780 & 54.5 & 131.4 & 218 & 6.64 \\ Consumption (f/bit) & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ T\({}_{\text{Recovery}}\) (ns) & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ Recovery Power & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ Consumption (f/bit) & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ \hline \end{tabular}
\end{table}
Table 2: Performance comparison.