esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
MANET is a powerful self-composed mobile network with the absence of foundation and focal help. A mobile ad hoc network allows nodes to speak directly with one another, while nodes not in direct correspondence extend and utilize halfway nodes as switches to communicate. Nodes rely on different nodes to send data because of dynamic topologies, open systems, and energy constraints that make the MANET vulnerable to many attacks. Many secure routing protocols have been planned so far, such as SAODV, SEAD, ARIAN, and so forth, which practice numerous verification plots, including hash-chains and computerized mark.  
This safe routing protocol manages assaults made by malicious nodes yet does not manage the proximity of these pernicious nodes. To keep up the security and honesty of information while sending messages, we need to start to finish reliable correspondence. Due to the open medium and characteristic trust between nodes, it is exceptionally difficult to differentiate between ordinary and malicious nodes. As of late, many trust-based arrangements have been recommended that dodge the noxious nodes from correspondence by allotting some measurement to the node that chooses the trust level of those specific nodes. These protocols, nevertheless, face issues, such as face ID, distorted trust, and age-old distrust esteems. Likewise, either the source or goal node profoundly takes part in verifying trust levels of middle-of-the-road nodes in a similarly [1; 2; 3; 4].  
It is an assortment of autonomous nodes associated with methods for short-run remote connections, shaping a dynamic multi-jump arrangement in a decentralized manner [5]. With the system topology evolving progressively and the absence of a pre-built foundation, such systems require appropriate self-sorting of calculations to oversee themselves successfully. Task-based self-organizing critical significance right now depends on which few nodes are consecutively chosen as pioneers, relying upon their presentation-related qualities [6]. Such pioneer-based calculations have a wide assortment of utilizations, among which are routing coordination [7] and suitable interruption location [8]. For instance, on account of helpful interruption recognition, one node is chosen as a pioneer so as to fill in as the interruption location framework (IDS) for all system individuals, rather than every node running its own IDS. In addition to the benefits of pioneer-based self-sorting calculations, such calculations ought to consider the nearness of getting out-of-hand nodes in the system. Further, such systems can be powerless because of the nonappearance of an accreditation authority and a unified administration unit [9; 10; 11].
## 5 Conclusions  
Based on the results monitored from petroleum-contaminated sites, we estimated MNA by statistical methods, the Mann-Kendall test, and mass flux, and thus we came to the following conclusions:  
The contamination in the site was severe and needed remediation.  
The TPH concentrations were decreasing in the monitoring period but still seriously exceeded the standard (Quality Standards for Groundwater (GB/T 14848-2017) [49] of China).  
The attenuation rate in the site was from 0.00876 mg/L to 0.10095 mg/L, and the relationship between attenuation rate and distance met the exponential distribution. Remediation time was from 1.3 years to 10.6 years. Analysis of the TPH trend indicated that 62% of wells had decreasing or probably decreasing trends: these support the conclusion that the TPH plume is shrinking. Mass fluxes of the three CPs and changes in the plume also indicated that the TPH plume had reached a quasi-steady state and that mass flux was declining. The attenuation trend in the plume was more evident than the edge. The most essential process of MNA is biodegradation, and the second important is sorption. During the monitoring period, 393 g TPH attenuated, including 355 g TPH degraded by microbe. Biodegradation in the upstream is stronger. Iron(III) and manganese were the main electron acceptors utilized by microbes during the monitored period.  
Based on the result of monitoring and estimation, we were able to come to the same result: MNA is in progress, and MNA can be an effective method to remediate the petroleum-contaminated site.  
Conceptualization, Y.X. and Y.Y.; methodology, Y.X.; validation, B.W.; formal analysis, M.Y.; investigation, Y.X. and M.Y.; resources, Y.Y. and X.D.; data curation, Y.X.; writing--original draft preparation, Y.X.; writing--review and editing, B.W.; supervision, X.D.; project administration, Y.Y.; funding acquisition, Y.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Inner Mongolia Autonomous Region Department of Natural Resources, grant number 2022-TZH08; the China Geological Survey, grant number DD20230431; the National High-tech Research Development Program of China \"863\" project, grant number 2007 AA06Z343; and the Guizhou Provincial Science and Technology projects, grant number QKHJC-ZK(2022)-General 186.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.
# A Review on Distribution System State Estimation Algorithms
## 3 DSSE Fundamentals and Main Algorithms
### DSSE Algorithms
#### 3.2.2 Forecasting-Aided Algorithms  
The aforementioned conventional DSSE algorithms rely on a single set of measurements, taken on a certain moment. In this way, the evolution of the states over successive measurements is disregarded. The solution to this issue is the use of Forecasting-AidedState Estimation (FASE) [44,45]. The basic concept of FASE is to provide recursive updates of the estimated states. By these means, changes occurring during normal operation can be tracked. Moreover, since FASE is based on forecasts by nature, the usual problem of missing measurements can be addressed with the use of the forecasted states.  
In these sort of algorithms, Kalman-based filters are adopted to capture the dynamics of the distribution system. A typical dynamic model is presented in (17), where k is the time instant, \(\mathbf{F}(\mathrm{k})\) is the is (n \(\times\) n) state transition matrix, \(\mathbf{g}(\mathrm{k})\) denotes the trend behavior of the state trajectory, with size equal to (n \(\times\) 1), and \(\mathbf{w}(\mathrm{k})\), the size of which is also equal to (n \(\times\) 1), models the noise, which is usually assumed to follow a Gaussian distribution with zero mean [44].  
\[\mathbf{x}(\mathrm{k}+1)=\mathbf{F}(\mathrm{k})\mathbf{x}(\mathrm{k})+\mathbf{ g}(\mathrm{k})+\mathbf{w}(\mathrm{k}) \tag{17}\]  
There is a variety of DSSE algorithms based on variations of the Kalman filter, as reported in [46, 47, 48]. In nonlinear systems, two commonly used variations are the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF). More specifically, the EKF is based on the linearization of nonlinear systems via the Taylor series. This approach has been adopted by the authors of [49, 50, 51, 52] for a variety of purposes, from simple, passive distribution systems up to systems including Photovoltaics (PVs), Electric Vehicles (EVs), etc. On the other hand, the UKF is a nonlinear filtration algorithm that uses Unscented Transformation (UT) as an alternative to the linearization of nonlinear equations with the use of the Taylor series. Therefore, unlike EKF, no explicit calculation of Jacobian or Hessian matrixes are necessary in order to implement this algorithm [53]. The UFK has been adopted in [53, 52].
# LIransformer: A Transformer-Based Framework for Task Offloading in Vehicular Edge Computing
## 3 Method
### LTransformer  
In the task offloading optimization process, this paper proposes the LTransformer framework for trajectory prediction. The input datum of this framework is the set \(T=\{s_{1},s_{2},\ldots,s_{n}\}\), where \(s_{i}\in\ \mathbb{R}^{d_{in}},d_{in}=2\). \(s_{i}\) denotes the edge server nodes and \(d_{in}\) denotes the input dimension. Each edge server node \(s_{i}\) includes (1) the timestamp of the vehicle trajectory when it passes through this node; (2) the serial number of the edge server node. The output datum of this framework is the prediction result, \(p\), where \(p\in\ \mathbb{R}^{d_{out}},\ d_{out}=2\). The prediction result \(p\) represents the position of the predicted edge server node and \(d_{out}\) denotes the output dimension. \(p\) also contains two dimensions, namely the longitude and latitude of the edge server node. Therefore, this prediction process is defined as a function on any vehicle trajectory set, \(T\).  
\[f(T)=p \tag{3}\]  
The optimization objective of the LTransformer framework is  
\[\begin{array}{l}\min\sum_{i=1}^{m}\ \Pr\left(p=\hat{p_{1}}\right)\ \log(\Pr(p=p_{i}))\\ s.t.\ p_{i}\in\{p_{1},\ p_{2},\ldots,p_{m}\}\end{array} \tag{4}\]  
\(\Pr(p=p_{i})\) denotes the probabilities that the prediction result \(p\) is the edge server node with the serial number \(i\), and \(p_{i}\) belongs to a finite set of edge server nodes.  
The LTransformer consists of four modules: the pre-training module, the input module, the encoding-decoding module, and the output module. The LTransformer uses the Transformer framework in general, and improves on the original Transformer by improving the input module and adding the pre-training and the output modules. In the pre-training module, stationary latitude and longitude data are embedded into the multidimensional space. There are three aspects considered in the input module, which are location, position, and time. In the location aspect, we use trainable embedding and local linear embedding (LLE). In the position aspect, we combine the trajectories with their serial numbers. In the time aspect, we add temporal information using the method in Informer [23]. In the output module, this paper proposes an error correction mechanism to remove the faulty results. The overall framework of the LTransformer is shown in Figure 3.  
The performance of the prediction is increased. In pre-training, the parameters for training are reduced to save time. In the input module, multiple aspects are considered to improve the accuracy. In the output module, faulty results are removed to correct the output.  
Figure 3: Overall framework of the LTransformer.
# An Interoperable Component-Based Architecture for Data-Driven IoT System
## 1 Introduction
According to Cisco [29], five quintillion bytes of data are produced every day and it would take a lifetime to manually analyze the data process by a single sensor on a manufacturing assembly line. _Harvard Business Review_ stated that only less than half of the structured data are actively used in decision-making and less than 1% of unstructured data is analyzed or used at all [29]. Hence, the process of converting data into useful information and knowledge still greatly relies on human effort. The data-driven approach is proven to be a solution to this issue. However, the currently available data-driven approaches are lack of flexibility and programmability to support interoperation for multiple application domains. In short, the two highlighted issues are as follows:  
* System interoperability: We define system interoperability for IoT systems as the ability of the system to accommodate and support the interoperation of hardware devices with different operating systems (OS) and protocols seamlessly. Since existing IoT devices operates under diverse OS and protocols, the creation of a IoT platform architecture that is flexible to integrate with these devices is a challenge. * Lack of flexibility in data-driven approach: The increase in data volume that is beyond possible for human effort to extract information reduces the usability of IoT systems. The data-driven approach is proven to be a solution to this issue. However, the currently available data-driven approaches lack flexibility and programmability to support the interoperation of application domains. To solve the issues mentioned above, we propose a loosely-coupled, component-based architecture for IoT that enables data-driven automation. These are our key contributions:  
* A component-based IoT architecture that supports system interoperation between IoT devices with different OS: The proposed architecture treats each component as an individual service provider that operates independent of other services components. This enables the accommodation of model-based IoT systems under one architecture with only the least required interface between each system. * The realization of flexible data-driven feedback control for IoT architecture: A proof-of-concept IoT system with the mechanism to perform data-driven automation is showcased. Furthermore, the easiness of data-driven optimization model interchange and update is demonstrated. The rest of the article is organized as follows: Section 2 briefly discusses some related work on IoT architectural design principals. In Section 3, we describe and explain our proposed IoT architecture in detail. The description and implementation of the composite real-world use case scenarios that we selected to apply and test the prototype IoT architecture are discussed in Section 4. In Section 5, the experiment results and findings are presented. Finally, we conclude the paper in Section 6.
## 1 Introduction
### Disc Cutter Wear  
Dynamic contact between the disc cutter ring and the excavated rock leads to wear of the cutter ring material. The most common type of wear is normal wear, in which the steel of the cutter ring is worn away evenly. This type of wear is considered optimal as it enables high utilization of the tool [34]. In addition, wear can be predicted during use so that the replacement of worn disc cutters with new ones can be planned.  
Five basic macroscopic types of wear can be distinguished in a visual wear analysis. Abrasive wear, tapering/grooving, and mushrooming are considered normal wear. The other two types, bearing blockage and brittle failure, are considered special types of wear. Although special wear types occur less frequently than normal wear types, their effects on TBM tunneling are often much more serious [12; 34]. The basic types of disc cutter wear are shown schematically in Figure 1.  
Fractures on the disc cutter ring often appear as cutter chipping. [PERSON] et al. [12] investigated this abnormal type of wear and categorized it into four types, granule chipping, patch chipping, primary collapse, and secondary collapse, depending on the size of the damage. Material chipping is shown schematically in Figure 2.  
When tunneling with TBMs, it is important to pay attention to the wear of the disc cutters and to carry out appropriate tool maintenance. Failure to replace a disc cutter in a timely manner will accelerate the wear of the surrounding cutters which, in turn, can lead to seal and bearing failures or even damage to the cutter head. About 75-90% of the total consumption of disc cutters is due to normal wear [35; 36].  
Figure 1: Five types of basic disc cutter wear. A and B indicate new (light grey color) and worn disc cutter rings (dark grey color), respectively. C indicates a broken part of the cutter ring due to a brittle fracture. In the lower part of the illustration, only half of the new light-grey-colored cutter ring tip is shown to make the worn cutter ring more visible (adapted from [34]).  
The wear of disc cutters leads to reduced tunneling efficiency. The cost and time required to replace worn disc cutters can account for up to 30% of the total cost and time when excavating in hard rock or rock with high levels of abrasion [5,37]. Therefore, investigating the wear of disc cutters enables better knowledge of the cutting processes and plays an important role in the optimization of TBM tunneling.  
The wear of the cutter ring profile is usually measured physically using a special measuring tool: a wear ruler. This allows the amount of profile wear to be measured directly and in millimeters [18,26,36,38]. Wear rulers differ depending on the geometry of the cutter ring profile. This measurement method is suitable for quick assessments of normal wear on the construction site and in the laboratory if the cutter ring is worn evenly around its circumference. In addition, a visual macroscopic inspection is still desirable, especially to detect special types of wear.
# Statistical Power Analysis in Reliability Demonstration Testing: The Probability of Test Success
## 3 Calculation of the Probability of Test Success  
In order to calculate the \(P_{\text{bs}}\) for the specific reliability test configuration at hand, the distributions of the test statistic, \(\tau\), under \(H_{0}\) and \(H_{1}\) need to be calculated, as do the two integrals of (6) and (7). Four different methods are presented here:  
1. A general calculation method
2. An analytic and exact calculation method for SR tests
3. An analytic and approximate calculation method for failure-based tests
4. A calculation method using test simulation.  
The general calculation method uses a bootstrap approach and enables the calculation of \(P_{\text{bs}}\) for all possible test scenarios in the desired accuracy. The analytic and exact calculation for SR tests makes use of the binomial approach of the SR test. The approximate method for failure-based tests allows the \(P_{\text{bs}}\) for EoL tests to be analytically calculated using the asymptotic properties of the sample quantile and the MLE. The calculation method, which simulates the reliability test, abstains from the hypothesis testing framework and solely relies on the law of large numbers.
# Topology and Response Surface Optimization of a Bicycle Crank Arm with Multiple Load Cases
## 3 Results and Discussion  
### Topology Optimization Result  
The topology optimization history can be seen clearly in Figure 7. It shows the compliance minimization of the sample with fine mesh (967,769 nodes and 677,089 elements) showing the results from the 5 th to the last 22 th iteration. It costs around 150 min of computing time. The computer used in this research has the AMD Ryzen Threadripper 1950X 16-Core Processor 3.40 GHz with 32.0 GB installed RAM. The iteration shows that the optimization procedure attempts to reduce mass in the low-stress distribution are which is, in this case, located mainly in the middle of the arm and around the pedal hole. Indeed, this involves the stress result from the initial crank arm geometry that has also been already conducted in the process. This phenomenon is typical with the previous study for thin-walled structures [21]. Moreover, the center location of the crank arm also has the most homogeneous material so that the density also has similar properties. Meanwhile, both the shaft and pedal holes are retained from reduction since both holes are set to be the nondesign region. They are also the place where force is applied and fixed boundaries are implemented as mentioned before.  
It can also be interestingly discussed that starting from the 16 th iteration, the topological pattern does not considerably change. There are only slight differences in small particular sections where the effect of the changes can be considered neglected. This means that at the 16 th iteration, the optimization process is nearly in steady-state condition until the 22 nd iteration with neglectable changes. After that, no more pattern occurs from the optimization process. Eventually, the iteration stops at the 22 nd iteration whereas the absolute steady-state optimized ge  
Figure 7: The topology optimization history: (**a**) 5 th iteration, (**b**) 10 th iteration, (**c**) 16 th iteration, (**d**) 22 nd iteration.
## 4 Circuit Implementation  
Based on the above-mentioned circuit analysis and mathematical derivations, a 40 W, 110 V\({}_{\text{ac}}\)/95 V\({}_{\text{dc}}\) half-bridge, self-excited, single-stage LED driver was implemented and realized. Table 1 outlines the circuit specification. The design criteria are briefly described as follows.  
1. The PFC current \(i_{LPFC}\) is designed to operate in boundary conduction mode (BCM) when the input voltage gets near the peak value. By this way, the PFC converter would operate at DCM over the whole input line cycle.
2. By using this self-excited control mechanism, the duty ratio \(D\) of both MOSFETs is 0.5. The dc-bus voltage \(V_{clus}\) should be sufficiently high to ensure the PFC converter operate at DCM. Theoretically, \(V_{clus}\) should be higher than two times of the amplitude of the input line voltage at 0.5 duty ratio. In this illustrative example, \(V_{Chus}\) is designed to be 320 V.
3. Due to the self-excited mechanism, the driving voltages are fetched from the center-tapped transformer by means of the resonant current. The switching frequency is in synchronism with the resonant frequency. The resonant frequency is controlled by the selection of the resonant inductor and the resonant capacitor as is designed with 36 k Hz resonant frequency. The higher resonant frequency will detrimental to self-excited signal response. It will increase the rise and fall times of switching current, thus leading to extra switching loss.
4. Following determination of the resonant frequency \(f_{r}\), only one single resonant capacitor is used with capacitance 10 nF. Accordingly, the resonant inductance can be achieved by Equation (1).  
Substituting input voltage \(V_{in}\), dc-bus voltage \(V_{Chus}\) and switching frequency \(f_{s}\) into Equations (13), (16) and (17), the PFC inductor \(L_{PFC}\) can be obtained by the aid of numerical computation.
6. Since all the transferred power goes through the resonant capacitor, it is suggested to use a capacitor with low equivalent series resistance (ESR).
## 2 Methods
### Indicators to Evaluate Node Importance  
The RSCN characterizes the station's topological structure by depicting the sequential relationships of TSs along the train's movement path. On this basis, topological analysis can be used to evaluate the importance of each TS.  
However, existing complex network analysis metrics, such as degree centrality, betweenness centrality, and eigenvector centrality, among others [51; 52; 53; 54], do not fully account for the multi-layered, directed characteristics of RSCN. Specifically, these metrics overlook the sequential relationships among TSs as trains move through the station yard, as well as the coupling of different train route types under the coordinated conditions of arrival-departure tracks and throat areas. Therefore, it is necessary to extend relevant topological metrics to better suit the node importance evaluation in railway station networks.  
In this study, the concept of CS is proposed, inspired by degree centrality, to characterize the topological significance of nodes. Additionally, DNC and SNC are introduced to assess the influence-based importance of nodes, while NE is proposed to evaluate a node's impact on overall network efficiency under specific conditions. Furthermore, an EPR is proposed to measure node importance from the perspective of connection tightness. Each of these metrics is positively correlated with node importance, meaning that a higher calculated value indicates a more critical node.  
It is important to emphasize that the proposed node importance evaluation framework assumes a fully operational railway network, where all track sections and interlocking systems remain functional. The five evaluation indicators used in this study are designed to capture the structural layout and operational characteristics of railway stations. However, this model does not explicitly account for track closures, signal failures, or infrastructure maintenance, which are critical factors affecting real-world throughput capacity and functional safety. Future work should incorporate dynamic failure modeling and probabilistic disruptions, allowing for a more comprehensive risk-aware assessment of railway station efficiency.