esa-sceva/satcom-chunk-collection huggingface dataset:
# Machinery Fault Signal Detection with Deep One-Class Classification
## 2 Related Works
### One-Class Classification Methods  
Up to now, a number of OCC methods have been proposed, and they can be categorized according to the way to utilize the information on target signals, including density-based, ensemble-based, and boundary-based methods [19; 20]. Among them, we focus on the boundary-based methods because they can generate more flexible decision boundaries through a nonlinear feature mapping function, and an objective function to build the optimal decision boundary can be explicitly formulated.  
The SVDD is one of the most well-known boundary-based OCC methods. The SVDD attempts to find the most compact hypersphere to enclose as many target signal data as possible [30]. Then, if a signal falls outside of the hypersphere, it is rejected as a fault signal, and vice versa for the signal located inside of the hypersphere. However, if the hypersphere is constructed to involve all training target signals, the radius of the hypersphere might be too large, and too many fault signals are accepted as normal ones. To avoid the hypersphere being excessively large, the SVDD adopts slack variables, which allow a few training target  
IsoForest assumes that the fault signals require a small number of splits to be isolated because they have a different pattern than the target signal. By employing this approach, the anomaly score of IsoForest can be defined as the sum of the number of splits until it is isolated, and if the new signal has a small average number of splits, it is determined as a fault signal.  
Although these traditional OCC methods have been well performed in the structured tabular data, for the unstructured data (e.g., time-series signal, image and video, and text-formatted data), appropriate features should be derived to apply them. However, these features obtained from independent feature extraction procedures to the OCC methods may not be specialized to detect anomalous patterns [40]. Hence, conventional OCC methods might not produce satisfactory fault signal detection performance in that the signal-typed data are the most representative unstructured data and require a careful feature extraction procedure.
## 1 Introduction
Most published calibration studies are performed on arable soils [1; 4; 17], which are generally more homogenous due to cultivation compared to forest soils characterized by greater and more variable organic matter contents, bulk densities and stoniness. For forestry, soil water is of primary concern as an essential medium enabling movement of nutrients towards and within trees, strongly impacting their growth, yield and vitality over decades. Therefore, research sites in forests are often equipped with TDR or FDR soil moisture sensors for continuous water budget monitoring [18], although few calibration studies evaluated the trueness and precision of these sensors. Findings in forest soils were reported by [PERSON] et al. [9] using a CS615 sensor, a former version of CS616, and [PERSON] et al. [15] tested the water dynamics with the CS616 for an agroforestry practice. To our knowledge, this study is the first attempt to systematically perform in situ recalibration and validation of FDR sensors in European forest soils, and at various depths. Direct measurement of reference gravimetric \(\theta_{\mathrm{V}}\), with good practice, can be accurate to less than 0.01 m\({}^{3}\) m\({}^{-3}\)[11]. For most agricultural applications, [PERSON] et al. [11] recommended the sensor measurement accuracy for measuring \(\theta_{\mathrm{V}}\) to be less than 0.02 m\({}^{3}\) m\({}^{-3}\), while [PERSON] and [PERSON] [4] applied an acceptable mean bias less than 0.02 m\({}^{3}\) m\({}^{-3}\) and RMSE \(<\) 0.035 m\({}^{3}\) m\({}^{-3}\). The latter performance criteria were copied by [PERSON] et al. [1] to evaluate their CS616 FDR sensors in agricultural fields. For forest soils, the Expert Panel on Meteorology operating under the International Cooperative Program (ICP) Forests (Working Group on Effects, UNECE AIR convention) is recommending in its current manual [19] a minimum acceptable accuracy for \(\theta_{\mathrm{V}}\) measurements of 0.03 m\({}^{3}\) m\({}^{-3}\). In this study, we evaluated whether this accuracy level is achievable at single FDR sensor level using the standard calibration functions proposed by the manufacturer [20] or by recalibration functions using the direct field approach. Hence, the research questions of this study are: (1) Does in situ recalibration substantially improve the accuracy of soil moisture measurements compared to standard calibration? (2) Is calibration required for each sampling location and depth to achieve the given data quality objectives? (3) Which soil characteristics have the largest impact on the bias and precision error? (4) How many reference measurements over which period are needed for adequate calibration of the FDR sensors? The practical outcome of this study is a calibration dataset for each plot and adequate layer specific calibration functions for our long-term monitoring plots.
# Spatial Distribution of Forest Fire Emissions: A Case Study in Three Mexican Exoregions
## 2 Materials and Methods
### Variables for Estimating Emissions
#### 2.2.3 Burned Areas  
The burned areas were defined as the areas affected by forest or agriculture fires and are characterized by the presence of coal and ash, by the removal of vegetation, and by the alteration of the structure of the vegetation [6; 37; 38]. MCD45 and MCD64 satellite products, which are generated from the MODIS data, were evaluated and compared with the burned areas that were identified in high resolution images and polygons from fieldwork (CONAFOR). MCD64 was selected because it identifies burned areas better than MCD45 does; it displays burned and unburned pixels with a 500 m spatial resolution by showing persistent changes in the surface identified with the normalized difference between the MODIS bands 5 and 7, as well as by applying a dynamic threshold using the spatial and temporal distribution of active fires [39]. In October 2016, MCD64 Collection 6 was published with improvements. We validated this product with Landsat images from 2014 and 2017 by following the method described in [40].
# Simplified Derivative-Based Carrierless PPM Using VCO and Monostable Multivibrator
## 6 Discussion and Analysis of Results
### Noise Resistance Analysis  
The quantitative noise analysis reveals important characteristics of the proposed system. Under noise-free conditions, the Pearson correlation coefficient of \(-0.8908\) indicates strong signal recovery fidelity. The negative correlation values reflect the inherent phase relationship in derivative-based demodulation, where the recovered signal corresponds to the differentiated input rather than the original signal directly.  
Performance degradation under noise conditions follows predictable patterns, with correlation coefficients ranging from \(-0.1505\) at SNR = \(-8.44\) dB to \(-0.8802\) at SNR = \(6.18\) dB. The system maintains reasonable performance even under challenging noise conditions, demonstrating practical viability for real-world implementations.
## 4 Discussion
While this study did not include cases of ASO in the maxilla and lower full arch distalization, if the treatment plan requires retracting the maxillary incisor roots by approximately 4.6 mm (average in this study) and retroclining the mandibular incisors by approximately \(\Delta-\)10.40\({}^{\circ}\) (average in this study), a suitable approach could entail ASO in the maxilla and total arch distalization using TADs in the mandible. Several limitations must be acknowledged when interpreting the results of this study. One of the notable limitations of this study is that the pretreatment conditions of patients undergoing ASO and NASO did not completely align in terms of SNA, ANB, and IMPA. The ASO group exhibited a more pronounced skeletal Class II tendency, per the pretreatment measurements. For example, while both the ASO and NASO groups demonstrated skeletal Class II features primarily due to mandibular retrognathism, as evidenced by relatively normal SNA and decreased SNB values compared to Korean norms (SNA = 81.6 \(\pm\) 3.10\({}^{\circ}\), SNB = 79.1 \(\pm\) 2.4\({}^{\circ}\)), it is noteworthy that the ASO group showed a significantly more retrusive B point as measured by B to N-perpendicular (\(-\)9.35 mm vs. \(-\)6.67 mm, \(p\) = 0.04). Therefore, we posit that with an increase in sample size and the inclusion of more borderline cases, a more comprehensive analysis of hard tissue changes in patients undergoing ASO compared to those undergoing NASO can be achieved. Moreover, this study investigated only hard tissue changes; therefore, further research is necessary  
Figure 3: (**A**) Superimposition of an ASO patient—initial (black line) and final (red line). (**B**) Superimposition of a NASO patient—initial (black line) and final (red line). to evaluate the corresponding soft tissue responses. The absence of long-term follow-up further restricted the ability to assess the durability and stability of the treatment outcomes over time. Relapse or delayed adverse effects may not have been captured within the observation period, potentially leading to an overestimation of treatment efficacy. Furthermore, the potential for clinician bias in treatment assignment cannot be overlooked. As treatment modalities were not randomly allocated, the decision to pursue ASO or NASO may have been influenced by subjective clinical judgment or patient-specific factors not fully accounted for in the analysis. Variability in patient compliance--such as the inconsistent use of elastics, poor oral hygiene, or missed appointments--may also have influenced treatment outcomes and contributed to inter-individual differences in responses, which could have introduced confounding variables and compromised the internal validity of the study. Lastly, the analyses were based on 2D cephalometric radiographs. Despite efforts to standardize the measurements, the 2D analysis may not have fully captured the complexity of skeletal and dental changes, especially in the transverse dimension.
# A Survey on Penetration Path Planning in Automated Penetration Testing
## 2 Overview
### Penetration Path Planning  
#### 2.2.1 Background and Definition  
In cybersecurity, an attack path refers to the logical sequence of actions taken by an attacker, from the initial point of entry to achieving a specific objective, using various attack techniques. In the context of PT, this is often termed a penetration path. [PERSON] et al. [9] define a penetration path as the complete route from the entry point, utilizing security vulnerabilities in combination to achieve an attack on the target asset. In the Internet of Things environment, [PERSON] et al. [17] define a penetration path as a series of steps in which attackers exploit IoT devices as intermediaries to launch attacks on critical infrastructure and services. [PERSON] [18] employs the MITRE ATT&CK framework to define the penetration path as a series of actions formed by the effective combination of tactics, techniques, and procedures. Although the precise definitions of a penetration path may vary across different studies, the core concept remains consistent, focusing on the strategies, methods, and sequences of actions used by attackers.  
At present, approaches to attack paths are primarily focused on three areas: penetration (or attack) path planning [9; 17; 18], attack path prediction [19], and attack path detection [20; 21]. Attack path detection places a strong emphasis on post-attack analysis and response. Its objective is to trace the origins of attacks and identify the specific steps and methods employed by attackers; attack path prediction is concerned with the early  
Figure 1: Model-based automated PT framework.  
detection of potential security threats, through the correlation of alerts and the subsequent steps of attackers. This enables the prompt identification of any such threats and the implementation of appropriate defensive measures; conversely, penetration path planning is concerned with the analysis and planning of potential attack paths, effectively mapping out routes from the starting point to the target. Penetration path planning provides clear guidance for automated PT.
## 1 Introduction
WUE can reflect water use strategy of the different species or same species in different habitats and can also be used to estimate the impact of water resources on the carbon sink/source function of terrestrial ecosystems [31; 32; 36]. Environmental factors that affect WUE include the vapor pressure deficit (VPD), air temperature (TA), precipitation, soil water content (SWC), solar radiation, and others [8; 35]. Additionally, [PERSON] et al. (2014) introduced the underlying water use efficiency (uWUE = GPP \(\times\) VPD\({}^{0.5}\)/ET) and verified it with eddy-related data. Since the uWUE fully considers the nonlinear effect of VPD on the coupling of carbon and water fluxes, it can better describe the relationship between GPP and ET, which makes it widely used and greatly improves our understanding of the carbon-water coupling process [31; 37; 38; 39]. In addition, the light use efficiency (LUE) of the ecosystem can reflect the response of plant carbon sequestration ability to light, which is of great significance for revealing the water-carbon coupling mechanism of plants [22; 40]. The LUE value is the ratio of GPP to photosynthetically active radiation (PAR) or the absorbed fraction of PAR (APAR) [24]. Therefore, exploring the WUE and LUE of forest ecosystems and their influencing factors will help us to better understand the water-carbon coupling mechanism of forest ecosystems in drylands. The eddy covariance (EC) system achieves the direct measurement of ecosystem carbon and water fluxes dynamics between land surface and atmosphere and is a useful tool for accurately measuring the carbon and water fluxes of forest ecosystems [16; 21]. The system includes a 3D sonic anemometer to measure three-dimensional wind speed and temperature fluctuations, and an open path infrared gas analyzer to measure carbon and water vapor densities [4; 41]. It enables the detection of NEE changes at a half-hourly time resolution [42]. NEE can be divided into GPP and RECO, and WUE and LUE of ecosystem can be analyzed using GPP, ET, and meteorological factors [31; 43; 44]. This study was carried out in the dryland of Northwest China, and the carbon and water fluxes of two coniferous forests (_Larix principis-rupprechtii_ and _Pinus tabualeformis_) were measured by the eddy correlation method [16; 45]. Our objectives were to (1) compare the daily, monthly, and annual dynamics of NEE, RECO, GPP, and ET in the two forest ecosystems; (2) investigate the main environmental drivers of changes in NEE, RECO, GPP, and ET; and (3) illuminate the response of WUE, uWUE, and LUE to environmental factors and the relationship between the water and carbon in two forest ecosystems. This study will help to identify the environmental controlling mechanisms of water and carbon fluxes, WUE and LUE of two forest ecosystems in the dryland of Northwest China, and provide a reference for selecting afforestation trees in future, especially under climate warming conditions.
# Removal of Methylene Blue by Metal Oxides Supported by Oily Sludge Pyrolysis Residues
## 2 Materials and Methods
### Kinetic and Diffusion Models  
Intraparticle diffusion models, first-order kinetics (Lagergren equation), and second-order kinetics (McKay equation) were used to study the kinetics of methylene blue removal from composite materials. The nonlinear formulations for these kinetic models are as follows:  
Intraparticle diffusion equation:  
\[Q_{t}{=K_{d}t^{0.5}}{+}b \tag{3}\]  
Lagergren equation:  
\[Q_{t}{=Q_{e}\left(1-e^{-K_{1}t}\right)} \tag{4}\]  
McKay equation:  
\[Q_{t}=\frac{K_{2}Q_{e}^{2}t}{1+K_{2}Q_{e}t} \tag{5}\]  
where \(K_{d}\) (min\({}^{-1}\)), \(K_{1}\) (min\({}^{-1}\)), and \(K_{2}\) (min\({}^{-1}\)) are the rate constants of the intraparticle diffusion model and [PERSON] and McKay models, respectively; \(b\) is the boundary layer effect; \(Q_{e}\) (mg/g) is the saturated adsorption capacity; and \(Q_{t}\) (mg/g) is the adsorption amount at time \(t\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Factor**} & \multicolumn{3}{c}{**Levels**} \\ \cline{2-4}  & \(-\)**1** & **0** & **+1** \\ \hline A—dosage (g) & 0.5 & 0.7 & 0.9 \\ \hline B—time (min) & 30 & 60 & 90 \\ \hline C—concentration (mg/L) & 3 & 5 & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The experimental range and level of independent variables.
## 5 Conclusions  
To determine the influence of high peening coverage on Almen intensity and RCS, a quantitative description of the peening coverage has been developed in this paper firstly. Based on the quantitative description, the finite element simulation of the Almen test and Almen test are carried out. The following conclusions can be drawn:  
1. With the same peening coverage, arc height of FE simulation and Almen test agrees well, which indicates that the quantitative description of peening coverage and the FE simulation is effective.
2. With dent size of 0.125, 0.14, and 0.16 mm, the Almen intensity all increases firstly and then decreases with the peening coverage increasing, there is a maximum value of Almen intensity, which is very important for shot peening process.
3. With dent size of 0.125, 0.14, and 0.16 mm, there is a maximum absolute value of surface RCS and maximum RCS with the peening coverage increasing. With certain peening coverage, the surface RCS and maximum RCS increases as dent size increases.  
Before this study, it is difficult to predicate the influence of high peening coverage on the Almen intensity and RCS well. This study can supply an effective method to solve this problem.  
[PERSON] conceived the paper. [PERSON], [PERSON] and [PERSON] wrote the paper and clarified the key methods and results. [PERSON] helped in the F.E. simulation. [PERSON], [PERSON] and [PERSON] edited the manuscript. All authors have read and agreed to the published version of the manuscript.  
The authors are grateful to the support by National Key R&D Program of China [Grants No. 2016 YFE0125600] and the National Natural Science Foundation of China [Grants No. 51405452].  
The authors declare no conflict of interest.
# A Congestion Control Strategy for Power Scale-Free Communication Network
## 4 Simulation Results
### Simulation Results for Integrated Routing
Despite the number of nodes increasing, the network is always prone to congestion at the hub nodes, and network throughput is small. The critical packet generation rate of global dynamic routing and integrated routing increases with the increasing of the network size. With the same average degree and network size, the network's capacity under the integrated routing strategy is the largest. Under the integrated routing, the critical packet generation rate of the Beijing power communication network vs. the I-level packet generation proportion \(\lambda\) is as shown in Figure 17.  
Figure 16: The critical packet generation rate Rc vs. network size N with the same average degree <k> = 3.2 (Beijing power communication network average degree) under the three routing strategies. Figure 15: The maximum transmission time <Tmax> and average transmission time <Tavg> vs. packet generation rate R under different routing strategies. It can be seen from Figure 17 that the critical packet generation rate of the integrated routing increases first and then decreases with the increase of \(\lambda\). \(\lambda\) is the proportion of the I-level packets at each time step. When \(\lambda=0\), only II-level packets are in the network, and the global dynamic routing is selected for transmission; when \(\lambda=1\), only I-level packets are in the network, and the shortest path routing is selected for transmission. When \(\lambda=0.4\), the critical packet generation rate is the largest, but when \(\lambda>0.6\), the critical packet generation rate of the integrated routing will be smaller than the global dynamic routing. Because the number of I-level packets is increasing, this results in congestion occurring easily at the hub nodes. In the realistic power communication network, the number of the key operational services of the I-level is small, and the number of affairs management services of the II-level is large, which accounts for the majority of the whole power communication services. Therefore, \(\lambda<0.5\) is more realistic and reasonable, that is the integrated routing has a larger critical packet generation rate and can effectively reduce congestion.
## 2 Literature Review  
### Data Envelopment Analysis  
In previous studies, [PERSON] [5] offered a model with multiple inputs and a single output for performance assessment. [PERSON] et al. [6] then proposed DEA (Data Envelopment Analysis), which is a non-parametric, data-oriented approach for measuring the performances of decision-making units (DMUs). The method analyzes and calculates the relative efficiency of the DMUs from multiple inputs and output factors. [PERSON] [7] proposes an efficiency calculation on the basis of the slacks-based measure (SBM) of efficiency from data envelope analysis (DEA). The SBM model is based on the non-radial estimation method, taking into account the slacks with the introduced items at the same time, with the estimated efficiency value being between 0-1. In another paper, [PERSON] [8] put forward the non-radial slacks-based measure of super-efficiency (Super-SBM), which is able to solve the problem when the SBM efficiency value of multiple decision-making units is the same as 1. [PERSON] [9] has also proposed the resampling model. This model is based on the variations for gauging the confidence intervals of DEA scores. The research applied the past-present and past-present-future models to a dataset consisting of Japanese municipal hospitals. The past-present model uses past-present data to estimate the change in data applied to chronological weights, which is provided by the Lucas series (a type of Fibonacci sequence). The past-present-future model processes the future. The model aims to forecast the efficiency score in the future and the confidence interval of each DMU.  
[PERSON] and [PERSON] [10] propose an integrating gauge and forecast research procedure for solving the macroeconomic performance problem. The researchers applied two variants of the DEA resampling model, past-present and past-present-future to measure and predict the macroeconomic performance of developed countries and developing countries in Asia. [PERSON] [11] suggests a two-stage process in DEA to solve the problem of evaluating and ranking advanced manufacturing technologies (AMTs). The proposed method is based on the several implemented DEA models computing cross-efficiency measurements for AMT facilities and the generation of a unique ranking of them by constructing a DEA-like indicator. The method offers the company management robust indications about the manufacturing technology to select. [PERSON] [12] conducted a performance evaluation of the global semiconductor industry by the DEA method in 1998 through a series of comparative analyses based on company-level variables. Semiconductor companies in the United States, Japan, South Korea, and Taiwan have different comparative advantages in adopting multiple evaluation criteria employed. Moreover, these companies have become major competitors in this industry.