esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Engine Fault Diagnosis Method Based on Multi-Sensor Data Fusion
## 3 The Proposed Method for Fault Diagnosis
### BPA Combination and Decision Making  
After \(BPA_{1},BPA_{2},...,BPA_{N}\) are obtained through the method in Section 3.3, these \(N\) BPAs can be combined using Equations (8)-(12) to obtain final BPA. Decision making rules based on BPA are required because it is a set of BPA ultimately obtained through the fault diagnosis method based on multi-sensor data fusion. Hence, the decision making rules based on the obtained final BPA in this paper are as follows. If \(F_{k}\) is the final diagnostic fault class of the equipment, the following decision making rules should be simultaneously met:  
1. \(F_{k}\) should have the maximum belief in final BPA that is \(m(\{F_{k}\})=\max_{i}(m(\{F_{i}\}))\) (\(i=1\), \(2\),..., \(M\)) and \(m(\{F_{k}\})\) should exceed a threshold \(k_{1}\). \(k_{1}\) is the minimum belief of \(F_{k}\). \(k_{1}\) should be a slightly larger value to ensure the high credibility of diagnostic result \(F_{k}\) so as to make sure the correctness of diagnostic result. Here, \(k_{1}\) is designated as 0.5. The purpose of this rule is to make the reliability of the diagnostic result \(F_{k}\) sufficiently large.
2. The BPA of dual set proposition and multi-subset proposition, such as \(m(\{F_{1},F_{2}\})\), \(m(\{F_{1},F_{3},F_{6}\})\), \(m(\{\Theta\})\), should be less than a certain threshold \(k_{2}\). \(k_{2}\) is the maximum belief of the uncertain components. If larger than \(k_{2}\), it is considered that the uncertainty of diagnostic result is too large, which may lead to the wrong diagnostic result. \(k_{2}\) should be a smaller value so that the uncertainty of the diagnostic result is small. Here, \(k_{2}\) is designated as 0.3. The purpose of this rule is to make the uncertainty of the diagnostic result smaller.
3. The difference between \(m(\{F_{k}\})\) and \(m(\{F_{l}\})\) should be larger than a certain threshold \(k_{3}\) that is \(m(\{F_{k}\})-m(\{F_{l}\})>k_{3}\), where \(m(\{F_{l}\})=\max_{i}(m(\{F_{i}\}))\) (\(i=1\), \(2\),..., \(M,i\
eq k\)). \(k_{3}\) is minimum difference between the maximum and the second largest in \(m(\{F_{i}\})\) (\(i=1\), \(2\),..., \(M\)). If larger than \(k_{3}\), it is considered that the diagnostic result can be clearly distinguished from the credibility of the other fault types, which can avoid false diagnostic results. Hence, to avoid false diagnostic results, \(k_{3}\) can not be too small. Here, \(k_{3}\) is designated as 0.2 because \(k_{3}\) that is too large may cause a missing alarm. The purpose of this rule is to make the difference between the maximum and the second largest in \(m(\{F_{i}\})\) larger, which means that \(m(\{F_{k}\})\) is relatively larger in \(m(\{F_{i}\}))\) (\(i=1\), \(2\),..., \(M\)).  
If such \(F_{k}\) can not be found, it is considered that the decision making can not be made by the obtained final BPA. The above decision making rules are based on the same fundamental principle with traditional decision making methods based on BPA [74].
# Smart Factory Web--A Blueprint Architecture for Open Marketplaces for Industrial Production
## 5 Smart Factory Web Approach  
### Motivation  
There are significant technical challenges to realizing the SFW vision as sketched above. The Smart Factory Web provides an open software architecture that enables the design and set-up of sustainable and resilient industrial ecosystems [14]. As such, it claims to define a blueprint architecture to be instantiated depending on the intended purpose. For instance, the SFW may be instantiated as an open marketplace for industrial production, including their supply chains, but also as a research platform for AI-based negotiation protocols in marketplaces, or as a testbed technology to validate the interoperability of IIoT technologies according to the specifications of the Platform Industrie 4.0, the Industrial Internet Consortium (IIC) and the IDS [15].  
In its form as an enabler for marketplaces it appears as the Smart Factory Web portal, i.e., as a service platform with a human, Web-based interface where factories can offer production capabilities and share resources. This enables them to improve order fulfillment with greater flexibility than is currently possible with available technology. The SFW seeks to provide the technical basis for new business models, especially for small lot sizes, by flexibly assigning production resources across factory locations.  
This testbed is chiefly designed as a step towards establishing a manufacturing marketplace where one can look for factories with specific capabilities and assets to meet production requirements. Factories with those capabilities can then register to join the marketplace. This requires up-to-date information about the capabilities and status of assets in the factory. The specifications of the products--availability, quality and price as well as information about the supply chain--may be provided at the discretion of the factories in order to be able to participate fully in the negotiation process in case of competing offers. Another reason to provide such information is to fulfil compliance requirements on production and supply chains. The usage of this information may be subject to restrictions and in general needs data sovereign concepts.  
Figure 2: Smart Factory Web workflow in three steps.
## 4 Discussion
### Limitations and Challenges with the Capstan System  
This study also revealed areas where the capstan clamping system needs to be improved. Notably, 20% of the specimens failed outside of the gage section and within the clamping region, suggesting that while the capstan design reduces stress concentrations at the clamping region, it does not entirely eliminate them with the given setup. This finding indicates that further optimization of the clamp's geometry is necessary, particularly to accommodate the diverse range of tendon sizes and shapes. The fixed nature of the current design limits its ability to adjust for variations in specimen dimensions, which could impact the reproducibility of test results. Future designs should incorporate adjustable components to allow for more consistent sample positioning and preload conditions, which are crucial for obtaining reliable biomechanical data [5,6]. Achieving a more uniform stress distribution across varying specimen dimensions could reduce the occurrence of failures in the clamping region [6].  
Freezing of samples has been identified as an influential factor with an impact on the material properties of tendons. Though controversial results exist on this topic [36, 37, 38, 39], and material data acquisition has not been the primary objective of this study, this area remains a field of future study. The material selection for the 3D-printed clamps also proved to be a critical factor. While PETG was chosen for its cost-effectiveness and ease of modification, it exhibited significant wear and deformation after only one test cycle, particularly in the guiding sections and pressure plates, only allowing for single use. These findings suggest that PETG may be unsuitable for repeated use under this testing setup with capstan clamps when compared to other experimental setups [5]. Using more robust engineering plastics, such as Nylon or ABS, could enhance the durability and reusability of the clamping system without sacrificing the benefits of 3D printing.  
Another issue is moisture that occurs at the tendon surface during the testing, making the application of an effective speckling pattern for digital image correlation impossible. The onset of moisture squeezed out from tissues disrupts the speckle pattern, leading to issues with light reflection and tracking accuracy. Such issues are particularly pronounced in tendons such as the FDP. Future studies should explore the use of moisture-resistant coatings or improved environmental controls during testing to mitigate these issues. Refining the application of speckle patterns or utilizing alternative surface tracking techniques may also be necessary to enhance digital image correlation accuracy.  
Further optimization of the clamping technology presented herein may be achieved using numerical modeling. Such an approach, which is in line with previous research on the topic, may be the focus of future studies, in an effort to identify and (further) minimize stress concentrations related to tissue clamping [4,40].
## 2 Materials and Methods
### Linear Extractions  
#### 2.2.1 Traditional Remote Sensing Image Lineament Extraction  
The common lineament extractions are based on digital filter edge filtering technology to extract the lineaments of remote sensing images, such as Hough transform and edge detection. For the traditional lineament extraction, this paper chooses the Canny operator to extract the lineaments of a Landsat 8 Oli image. Three steps are identified as follows:  
(1) The preprocessed Landsat 8 Oli image was conducted by Gaussian filtering. The standard deviation of the Gaussian low-pass filter was set to 0.4 to enhance its inner edge width.  
(2) In image edge detection, a threshold within a specified range is used to judge the edge of the image to avoid the sitution where the image edge specified by a certain threshold leads to a blurred or lost edge of the image. The maximum upper threshold and the minimum lower threshold are determined in a specified range, and the image edges are set within the upper and lower threshold ranges so that the image edges can be clearly determined. The low threshold was set to 0.03 and the high 0.11, which were used to determine the pixel edge in the image by the Canny algorithm.  
(3) The length screening was performed to eliminate the pixel information that the computer fails to identify, and the lineaments interpretation map was obtained. The image results and lineaments interpretation results after low-pass filtering are shown in Figures 7 and 8.
# Optimal Steady-State Range Prediction Filter for Tracking with LFM Waveforms
## 2 Problem Definition and Conventional Filter Design  
This section defines the tracking problem taking into regard the LFM waveforms assumed and summarizes briefly the conventional filter design methods and their problems.
## Effect of Biochar and Hydrochar on Forms of Aluminium in an Acidic Soil
###### Abstract  
Biochars and hydrochars have a significant effect on soil properties linked to fertility or to carbon cycling and have been proposed as an amendment to increase soil productivity, particularly in acidic soils. Phytotoxic forms of aluminium (Al) are relatively abundant in acidic soils and, for a long period of time, limiting has been used to correct this. Ca(OH)\({}_{2}\), a pig manure (PM) and two biochars and two hydroxyls prepared from pig manure were studied for their effects on Al fractions. Biochars were prepared at 450 \({}^{\circ}\)C (BPC450) and 600 \({}^{\circ}\)C (BPC600) and hydrochars were obtained using a pig manure solution (ratio 30:70) that was heated at 200 \({}^{\circ}\)C (HPC200) and 240 \({}^{\circ}\)C (HPC240). A treatment with an amount of Ca(OH)\({}_{2}\) necessary to increase soil pH to the same pH value as the average in the treatments BPC450, BPC600, HPC200, HPC240 and PM was used for comparison. The fractionation of Al was studied, with the liming treatment allowing the differentiation between changes in fractionation driven by pH changes from other mechanisms. In relation to the control, all soil amendments presented high capacity of controlling toxic Al, similar to a traditional liming product (Ca(OH)\({}_{2}\)) and decreased the exchangeable Al extracted by NH\({}_{4}\)Cl. Both types of materials (biochars and hydrochars) lead to the formation of an increased number of organo-aluminium complexes (OAC). Biochars lead to the formation of OAC of low to medium stability, while hydrochars promoted the formation of OAC of high stability.  
biochar; hydrochar; aluminium; manure
# A Set of Active Disturbance Rejection Controllers Based on Integrator Plus Dead-Time Models +
### More Realistic and Consistent Plant Modeling  
The main difference of this work compared to the original paper [20] is related to the use of more realistic and at the same time more consistent plant models. They are based on the Integral Plus Dead Time (IPDT) model with the estimated gain \(K_{m}\) and dead time \(T_{m}\)  
\[S(s)=\frac{Y(s)}{U(s)}=\frac{K_{m}}{s}e^{-T_{m}s} \tag{1}\]This model represents the most commonly used plant approximation in PID control design. In order to use the state-space approach, the dead time was initially avoided [34; 35]. For this reason, the first approximations were dead-time free plants. To increase the accuracy of an \(n\)th order model, only a single parameter-an \(n-1\) tuple time constant was added for the delay approximation. In this way, a set of \(n\)th order transfer functions \(S_{n}(s),n=1,2,...\)  
\[\begin{split} S_{1}(s)&=\frac{Y(s)}{U(s)}=\frac{K_{ s}}{s}\\ S_{2}(s)&=\frac{Y(s)}{U(s)}=\frac{K_{s}}{s(T_{1}s+1 )}\\ S_{3}(s)&=\frac{Y(s)}{U(s)}=\frac{K_{s}}{s(T_{2}s+1 )^{2}}\\ &\vdots\end{split} \tag{2}\]  
were established, characterized by the corresponding unit step responses (Figure 1)  
\[\begin{split} y_{1}(t)&=K_{s}t\\ y_{2}(t)&=K_{s}\Big{[}t-T_{1}\Big{(}1-e^{-t/T_{1}} \Big{)}\Big{]}\\ y_{3}(t)&=K_{s}\Big{[}t\Big{(}1+e^{-t/T_{2}}\Big{)}-2 T_{2}\Big{(}1-e^{-t/T_{2}}\Big{)}\Big{]}\\ &\vdots\end{split} \tag{3}\]  
By choosing \(T_{i}=T_{m}/i,i=n-1,n=2,3,...\), the introduction of \(S_{2}(s)\), or \(S_{3}(s)\) for \(y_{2}(t)\) and \(y_{3}(t)\) allows to keep the same asymptote of the IPDT system. In this chain of approximations, \(y_{1}(t)\) was an exception that was inconsistent with the measured IPDT step response and its higher-order dead-time-free approximations. In order to remove this inconsistency and also to avoid an increase in sensitivity to the measurement noise that occurs due to the increased state-space controller orders \(n\), in this work the plant models are considered in the following form  
\[\begin{split} S_{n}(s)&=\frac{Y(s)}{U(s)}=\frac{K_{ s}e^{-T_{d}s}}{s(T_{i}s+1)^{i}}\\ \text{for }n&=1;\ \overline{T}_{d}=T_{m};\ \text{ i.e., }\tau=1;\ T_{i}=0;\\ \text{for }n&=2,3,...;\ i=n-1;\ \overline{T}_{d}= \tau T_{m};\ T_{i}=T_{m}\frac{1-\tau}{i};\ \tau\in(0,1)\end{split} \tag{4}\]  
Here, \(\tau\) denotes the part of the identified dead-time \(T_{m}\) that is modeled by the transport delay \(\overline{T}_{d}\), and the rest is considered as the contribution of the \(i\) time constants \(T_{i}\). It should be remember, however, that since such approximations allow the reconstruction of higher-order output derivatives, they can also be useful for the control of loops with pure dead time  
Figure 1: Approximations of IPDT step responses considered in [20].  
by higher-order controllers. Therefore, similar to fractional-order control, we introduced \"two additional tuning knobs\", \(n\) and \(\tau\), which can be used to optimize the loop dynamics. In this way we extend the range of ADRC modifications [12] that are suitable for time-delayed systems.
## 1 Introduction  
Crude oil, exploited industrially since 1867, has become the world's most important energy resource. Called 'black gold', it is one of the world's most precious commodities, which means that the oil industry plays a vital role in many national economies [1]. In addition, many products and by-products derived from crude oil are widely used: for example, gasoline and diesel oil for transportation; paraffin wax for packaging materials and some cosmetic applications; lubricants, which have many uses including as rust and corrosion inhibitors and engine oils [2].  
However, despite the countless advantages of crude oil use, it also is a source of environmental pollution. Total petroleum hydrocarbons (TPHs) are common environmental contaminants originating from crude oil [3]. They include a complex mixture of compounds with carbon numbers ranging from C\({}_{6}\) to C\({}_{35}\), some of which are carcinogenic to humans. TPHs are highly stable, resistant to decomposition, and are highly volatile [4]. As a result, they pose a serious health threat to humans living near the pollution source.  
During the last decade, research efforts have focused on the biodegradation of crude oil pollution. Degradation efficiency has been reported to be dependent on the substrate type in addition to many geological, climatological, and ecological factors [5]. Compared with natural attenuation, microbial degradation technology has attracted the most interest, owing to its unique adaptability and convenience. For example, bacterial strains such as _Rhodococcus rhodochrous_, _Pseudomonas alcaligenes_, _Rhodococcus erythropolis_, and some _Acinetobacter_ species have been found to degrade petroleum, depending on the source of carbon and temperature mostly [6; 7; 8; 9; 10]. In addition, genera including _Aeromonas_, _Alcaligenes_, _Bacillus_, _Corynebacteria_, _Flavobacterium_, _Microococcus_, _Mycobacterium_, _Nocardia_, _Pseudomonas_, and _Rhodococcus_ have been revealed to be capable of degrading aromatic hydrocarbons with a biodegradation efficiency around 60 \(\sim\) 70% and biodegradation ability was indicated as an adaptation mechanism. Once the bacteria adapted to the local environment, the metabolism was accelerated and nutrient stimulation was necessary, which could help the microorganism overcome the innate characteristics of nutrient addition to the site, ensuring the sustainability of the long-term process of bioremediation. [11; 12; 13; 14; 15; 16].  
To build upon this previous work, the aim of this investigation was to discover native bacteria that could be utilized to reduce TPH pollution. In this study, we isolated seven strains from contaminated soil and determined their optimum growth conditions. Furthermore, we identified each of the isolated strains using 16s rRNA sequencing and evaluated their degradation efficiency in different growth media.
# A Review of Regional and Global Gridded Forest Biomass Datasets
## 3 Current Gridded Forest Biomass Maps
### Forest AGB Maps from Field Inventory Data and Optical and/or Radar Remotely Sensed Data
#### 3.2.4 Aboveground Biomass in Interior Alaska (Yukon River Basin), 30 m, 2009-2010  
[PERSON] et al. [64] combined a field biomass dataset with Landsat-derived spectral variables and land surface temperature (LST) using a regression model, and produced a biomass map at 30 m resolution for the Yukon Flats ecoregion of interior Alaska. The geographic feature metrics included land cover type sourced from the NLCD 2001, burned areas from the Monitoring Trends in Burn Severity (MTBS) data and the Alaska Historical Wildland Fire Perimeters data, and topographic futures extracted from National Elevation Dataset (NED) data. Additionally, before building the regression model, principal component analysis (PCA) was used to prevent variable multicollinearity. A high-spatial resolution biomass map was generated using the principal components and field data through a regression method. This map appears relatively accurate as evidenced by a threefold cross-validation that showed a mean absolute error of 21.8 Mg/ha and a mean bias error of only 5.2 Mg/ha.
# Automated System-Level Malware Detection Using Machine Learning: A Comprehensive Review
## 4 Classification Algorithms
### Decision Tree
This approach aims to enhance the detection capabilities of intrusion detection systems, allowing for improved identification of both known and unknown attack patterns. Ref. [19] introduced a novel approach for categorizing anomalous and normal behavior in computer network ARP data. They utilized the k-Means clustering and ID3 decision tree learning algorithms in a host-based combinatorial technique. The k-Means clustering algorithm divided the training samples into k groups based on their similarity, measured by Euclidean distance. An ID3 decision tree was then constructed for each cluster. To determine the anomaly scores and ID3 decision tree options, the researchers incorporated the k-Means clustering method. Integrating the results of both approaches and deriving final anomaly score values required a specialized procedure. The threshold rule was employed to evaluate the normality of test instances. The experiments were conducted using data collected from network ARP traffic. Various unexpected scenarios were created and applied to the acquired ARP data to simulate typical training scenarios. The performance of the proposed technique was compared empirically against individual k-Means clustering algorithms, ID3 decision tree classification algorithms, as well as other approaches based on Markovian chains and stochastic learning automata. The comparison was based on five specified criteria. The results showed that the proposed technique achieved a specificity of 96% and a positive predictive value of 98% when tested with the data.
# Research on High-Performance Fourier Transform Algorithms Based on the NPU
## 4 Methods
### Acceleration Effect and Discussion  
#### 5.2.1 Acceleration Effect of Experiment Results  
In this section, we validated the acceleration effect of NPUs on the Fourier Transform following the experimental settings mentioned in the former section. The execution times for performing one edge filter process of various algorithms are shown in Table 1, and their line graphs are shown in Figure 4. The data shown in Table 1 were obtained by executing the edge filter 100 times and calculating the average time consumption. We have bolded the notable outcomes for each set of inputs in Table 1.  
The experiment result showed that the execution time of MM-2 DFT, MV-2 FFT, FFT-CL, and FFT_CPU increased with the input scale but at different rates. In detail, the result demonstrated that MM-2 DFT has the shortest processing time for most input sizes (64 \(\times\) 64-1024 \(\times\) 1024), while FFT_CL has the longest processing time with an input size under 256 \(\times\) 256. As the input size increased, the gap between their runtimes progressively narrowed and eventually reversed. When the input size increased to 2048 \(\times\) 2048, FFT_CL achieved the highest running speed among the three algorithms, surpassing the second place MV-2 FFTv by 27%. Similarly, MV-2 FFTv ran 16% faster than MM-2 DFT for that input size. Additionally, FFT_CPU showed the second-best performance with the smallest input data size (64 \(\times\) 64). However, its time consumption increased substantially on larger input  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Input Matrix’s** & **FFT\_CL** & & & & **FFT\_CPU** \\
**Order** & **(Tegra X2)** & **MM-2 DFT** & **MV-2 FFTm** & **MV-2 FFTv** & **(with O2)** \\ \hline
64 & 8.41 & **1.92** & 6.93 & 6.22 & **2.26** \\
128 & 11.09 & **1.68** & 7.35 & 6.62 & 10.00 \\
256 & 22.32 &  
data sizes, surpassing other algorithms. This suggested that other computing platforms may have significant advantages over the CPU for this task.  
Apart from the growth rate of time consumption, we made another finding. Namely, the MV-2 FFTv algorithm consistently exhibited over 10% higher performance compared to MV-2 FFTm, and this advantage grew with increasing input sizes.