esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Fuel Characterization of Treatment Samples
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Sample** & **R.P** & **T.P** & **A.P** & **T.A.P** & **R.K** & **T.K** & **A.K** & **T.A.K** \\ \hline Moisture (wt.\%, ar) & 8.31 & 1.62 & 1.95 & 1.07 & 9.18 & 1.96 & 2.18 & 1.08 \\ \hline Ultimate analysis (wt.\%, db) & & & & & & & & \\ C & 45.79 & 51.34 & 46.24 & 52.13 & 41.70 & 49.37 & 45.56 & 51.29 \\ H & 5.78 & 5.72 & 5.91 & 5.83 & 5.47 & 4.98 & 5.96 & 5.38 \\ N & 0.08 & 0.11 & 0.06 & 0.09 & 0.63 & 0.82 & 0.56 & 0.78 \\ O a  & 46.73 & 41.50 & 47.53 & 41.65 & 48.29 & 39.19 & 46.27 & 40.25 \\ S & 0.49 & 0.19 & 0.01 & 0 & 0.08 & 0.10 & 0.02 & 0.02 \\ Ash & 1.13 & 1.14 & 0.25 & 0.30 & 3.83 & 5.54 & 1.63 & 2.28 \\ \hline Proximate analysis (wt.\%, db) & & & & & & & & \\ VM & 79.41 & 75.26 & 82.18 & 78.87 & 76.44 & 63.36 & 80.37 & 70.85 \\ FC & 19.46 & 23.60 & 17.57 & 20.82 & 19.72 & 31.09 & 17.99 & 26.87 \\ Ash & 1.13 & 1.14 & 0.25 & 0.30 & 3.83 & 5.54 & 1.63 & 2.28 \\ \hline HHV (kcal/kg, ar) & 4282 & 4845 & 4594 & 4807 & 4162 & 4980 & 4385 & 5053 \\ \hline Atomic ratio (-) & & & & & & & & \\ H/C & 1.26 & 1.09 & 1.27 & 1.12 & 1.31 & 1.01 & 1.31 & 1.05 \\ O/C & 1.02 & 0.81 & 1.03 & 0.79 & 1.16 & 0.79 & 1.02 & 0.78 \\ \hline \hline \end{tabular}  
* Calculated by difference. ar: as received, db: dry basis, FC: fixed carbon; VM: volatile matter; HHV: higher heating value; R.P. Raw PSD, T.P: Torrefied PSD, A.P. Ashless PSD, T.A.P: Torrefied Ashless PSD; R.K: Raw Kenaf, T.K: Torrefied Kenaf, A.K: Ashless Kenaf, T.A.K: Torrefied Ashless Kenaf.  
\end{table} 
Table 1: Fuel properties of raw and pretreated biomasses (pine sawdust (PSD) and Kenaf). case of A.P, although the moisture and ash contents reduced in the manufacturing process, the increase in carbon was not large because there was no decomposition of oxygen. However, in the case of T.A.P, the highest carbon content was confirmed with the application of ash removal and torrefaction. In addition, Kenaf samples showed similar results to PSD. One consequence of the changing carbon, hydrogen, and oxygen contents is an increase in high heating values. After the ashless process, the higher heating value (HHV) of R. PSD and R. Kenaf increased by 7% and that of A. [PERSON] increased by 5%. After torrefaction, these values for T. PSD and T. A PSD increased by 13% compared to R. PSD, while [PERSON]. [PERSON] and [PERSON] increased by 20% compared to R. Kenaf. This indicates that the HHV of torrefied kenaf after the ashless process improved compared to other samples.
# Life-Cycle Oriented Risk Assessment Using a Monte Carlo Simulation
###### Abstract  
State of the art mechatronic systems are complex assemblies of various parts and subsystems. In such an interconnected system, even relatively cheap parts can have a major impact on the overall performance due to unexpected failure. Hence, lifecycle management has major implications on the successful modification of existing products. Potential savings due to changes in production and procurement must be compared to the implied risk of products failing in the field due to these changes. This work documents a generic approach for risk assessment based on the distribution of the expected savings and incident costs over the whole lifecycle. To do so, a stochastic model is introduced to quantify the expected savings and costs given a non-risk-free product modification. Using a Monte Carlo simulation, the effects of uncertainty are incorporated into the risk management. The model and simulation are deployed within an industrial use case. The application demonstrates both the appropriateness of the tool and its useability.  
Lifecycle-management; risk management; Monte Carlo simulation +
Footnote †: journal: Applied Sciences  
1  
quantifying the risks, capturing the contextual issues, and allowing a more rational decision to be made with a risk mitigation process. It can also support the root cause analysis and allow allocation of risk [6; 7].  
For complex and nonlinear systems, the use of Monte Carlo simulations in the context of risk management is recommended in literature [8; 9; 10; 11; 12; 13]. The concept can further be used to estimate the expected lifecycle costs distribution [14]. The purpose of this paper is to describe a case where the existing concept of Monte Carlo simulations for lifecycle cost estimations is used to support risk management associated with the change management process over the lifecycle of a product, including the procurement, manufacturing, assembly, and use phases. The focus is on mass production where a production run can result in more than tens of thousands of products and where variations in the supply chain, manufacturing and assembly may occur. It excludes the initial design and industrialization/qualification phase of the product lifecycle. The simulation aimed to improve the quality of the product that the manufacturing firm was assembling. The research question for this paper is: \"how can Monte Carlo simulation approaches be used to successfully translate technical risks into a management risk and so, improve decision-making processes?\"
# Optical Turbulence Profile Forecasting and Verification in the Offshore Atmospheric Boundary Layer
## 2 Optical Turbulence Forecasting
### \(C_{n}^{2}\) Estimation Model  
\(C_{n}^{2}\) estimation models that convert standard meteorological data into \(C_{n}^{2}\) vertical profiles are established based on the basic theory of turbulence. According to [PERSON]'s theory, the Tatarski \(C_{n}^{2}\) model is [34]:  
\[C_{n}^{2}=aL_{0}{}^{4/3}M^{2} \tag{11}\]  
where \(a\) is a constant of 2.8 [35], \(L_{0}\) is the outer scale, or rather, the largest scale of inertial range turbulence, and \(M\) is the potential refractive index gradient defined as:  
\[M=-\left(\frac{79\times 10^{-6}P}{T}\right)\frac{\partial{\ln\theta}}{ \partial{h}} \tag{12}\]  
\[\theta=T\left(\frac{1000}{P}\right)^{0.286} \tag{13}\]\(M\) is related to atmospheric temperature \((T)\), atmospheric pressure \((P)\), and potential temperature \((\theta)\). \(h\) is the height above ground. From Equations (11)-(13) it can be noted that the vertical temperature gradient can be estimated from sounding data, so choosing the appropriate \(L_{0}\) is the key to estimating \(C_{n}^{2}\). At present, the HMNSP99 model represents the most widely used outer scale model [36].  
The HMNSP99 model was developed at the Air Force Research Laboratory (AFRL), Holloman Spring 1998 and Holloman Spring 1999 thermosonde campaigns (New Mexico), and the statistical relationships relating \(L_{0}\) parameterization to wind shear \((S)\) and temperature lapse rate \((dT/dh)\)[37]. The relationships are expressed as:  
\[L_{0}^{4/3}=\left\{\begin{array}{ll}0.1^{4/3}\times 10^{0.362+16.7285-192.347 \frac{dT}{dt}},&\text{Troposphere}\\ 0.1^{4/3}\times 10^{0.757+13.8195-57.784\frac{dT}{dt}},&\text{Stratosphere}\end{array}\right. \tag{14}\]  
where \(S\) is the vertical shear of the horizontal velocity:  
\[S=\sqrt{\left(\frac{\partial u}{\partial h}\right)^{2}+\left(\frac{\partial v }{\partial h}\right)^{2}} \tag{15}\]  
where \(u\) and \(v\) are the north and east horizontal wind components, respectively.  
A vertical dependence of refractive index structure parameter was described by several authors with one of the most widely used being the Hufnagel-Valley model:  
\[C_{n}^{2}(h)=8.2\times 10^{-26}W^{2}h^{10}e^{-h}+2.7\times 10^{-16}e^{-h/1.5}+ Ae^{-h/0.1} \tag{16}\]  
where \(h\) is the height above the ground (km). \(W^{2}\) is the square average of the wind speed at a height of 5-20 km. \(A\) is the constant. The typical values are \(W=21\) m/s, \(A=1.7\times 10^{-4}\). Another modification of the Hufnagel model was introduced as the Hufnagel/Andrew/Phillips (HAP) model [38]:  
\[C_{n}^{2}(h)=0.00594\left(\frac{W}{27}\right)^{2}\left(10^{-5}h\right)^{10} \exp\left(-\frac{h}{1000}\right)+2.7\times 10^{-16}\exp\left(-\frac{h}{1500} \right)+C_{n}^{2}(h_{0})\left(\frac{h_{0}}{h}\right)^{4/3} \tag{17}\]  
where \(h\) is the height above ground (m), \(W\) is the wind speed (m/s) and \(C_{n}^{2}(h_{0})\) is the nominal value of refractive index structure parameter (m\({}^{-2/3}\)) at the height \(h_{0}\) (m).
# FFNN-TabNet: An Enhanced Stellar Age Determination Method Based on TabNet
## 4 Construction of the FFNN-TabNet Determination Model
### Data Preprocessing  
The data acquired post-cross-matching is subjected to preprocessing. This includes conducting anomaly detection to identify and eliminate extreme outliers. Since the dataset contained only a few missing values, these incomplete entries were excluded to ensure the quality and consistency of the data. Subsequently, the data undergo normalization, compressing it into the \([-1,1]\) range. The formula is delineated in Equation (9).  
\[\hat{X}^{i}=\frac{X^{i}-X^{i}_{\text{min}}}{X^{i}_{\text{max}}-X^{i}_{\text{ min}}} \tag{9}\]  
Here, \(X^{i}\) and \(\hat{X}^{i}\) correspond to the values of the red giant data before and after normalization, respectively, while \(X^{i}_{min}\) and \(X^{i}_{max}\) indicate the minimum and maximum values present in the red giant data, respectively.  
Following the data preprocessing phase, a comprehensive selection of 6470 red giant stars was finalized to serve as input data. The distribution of red giant samples in \(T_{\text{eff}}\) (from LAMOST DR8), mass (from [PERSON] et al. [51]) and age (from SDSS DR17) is represented in Figure 6.  
In this study, the Python sklearn library was used to divide the dataset of 6470 entries into a training set and a test set in an 8:2 ratio. Specifically, out of the 6470 entries, 5176 entries (which is 80%) were allocated to the training set for model learning and adjustment. The remaining 1294 entries (20%) were designated as the test set for evaluating the model's performance and generalization ability.  
Figure 5: Comprehensive process diagram of the FFNN–TabNet model.
# A Deep Learning Method for Lightweight and Cross-Device IoT Botnet Detection +
## 6 Limitations and Threats to Validity
For example, we made sure to test our autoencoder-based IDS by means of held-out data, i.e., not used at all for training and hyperparameters optimization. The reference dataset used to conduct the experiments contains attacks that follow up BASHLITE and Mirai botnet infections. Overall, the attacks are well-established in the literature and have been used to validate many existing IDS proposals. The traffic is based on several devices; more importantly, the training of both separate and all-in-one autoencoders--semi-supervised--is not biased by the specific attack types. The use of such a diverse mixture of controlled conditions and techniques aims to mitigate internal validity threats. **Conclusion validity**. Conclusions have been inferred through a careful design of the experiments. The conclusions of the study are consistent along the different dimensions of our experiments, which makes our finding perfectly reasonable and technically sound. For example, we assess the sensitivity of the IDS performance with respect to different configurations of the autoencoders, i.e., in terms of the number of hidden layers and neurons. We present an extensive discussion of the results. The key findings of the study are consistent across the devices. The proposed approach detects the attacks at hand irrespective of devices, which means the results are not biased by a specific attack or device. More importantly, the IDS performance achieved is in line with the related literature, which provides a reasonable level of confidence in our analysis. **External validity**. The cross-device model can be applied to other similar systems, types of neural networks and attacks. Nowadays, there exist many public datasets and attack tools, which make our approach definitively feasible in practice. Our approach does not interfere with system operations: as only passive tracing is required, the approach is inherently _nonintrusive_. More importantly, there exist many products for capturing network packets and generating fixed-length records, which allow porting our method to other systems. In fact, in this paper we successfully applied the method to the network traffic--transformed into a data format suited for machine learning experiments--related to several IoT devices and attack types to mitigate external validity threats. We are confident that the experimental details provided in the paper would support the replication of our study by future researchers and practitioners.
# Data Aggregation Based on Overlapping Rate of Sensing Area in Wireless Sensor Networks
## 5 Performance Evaluation
### Experiment Results
The main reason is that member nodes send data to their gathering nodes by just one hop both in AggOR and AggOR-CP. By contrast, there exists multi-hop routing in clusters of EEUC, and in HEER the members transfer their data to cluster heads following Hamilton Paths, which increase hops to the sink and prolong the end-to-end delay. As Figure 7d illustrates, in AggOR the ratio of information accuracy is the highest (around 88%) among those four schemes and very similar to AggOR-CP, which is higher than EEUC and HEER by 38% and 48% respectively. This is because aggregating lots of data into a small fixed amount of data and aggregating the same data for several times both affect the information accuracy. In HEER, the end node of Hamilton Path transmits its data to its neighbor which is closer to the cluster head, and the neighbor aggregates data into one packet of a constant size. This process continues until the data reaches the cluster head. Therefore, an original data may be aggregated for several times. In EEUC, the clusters aggregate all member data into one packet with fixed size, regardless of the specific redundancy ratios. By contrast, AggOR aggregates one data only once, and does not lose any information. Note that the ratio of information accuracy in AggOR is not 100% because of the redundant data. In conclusion, AggOR scheme achieves an energy-efficient and quick data collection, while ensuring a high data accuracy. Moreover, AggOR has a greater advantage over other schemes when the network scale rises, because the efficiency of redundancy clearing within only one-hop forwarding is apparent in a large scale scenario.
# Performance Enhancement of Consumer-Grade MEMS Sensors through Geometrical Redundancy
## 4 Realized Prototype of Redundant IMU  
The first prototype of redundant configuration has been designed in such a way as to be exploited as a data acquisition system; in particular, raw data acquired by each inertial sensor are transmitted to an external Personal Computer (PC) running the navigation algorithm.
# High-Fidelity OC-Seislet Stacking Method for Low-SNR Seismic Data
## 1 Introduction
Despite all this, OC-seislet transform is applied after NMO, inaccurate velocities can affect the correction of the OC operator, and the effectiveness of stacking under these non-stationary conditions requires further verification. Based on the theoretical foundation of the OC-seislet transform, this paper explores the theory and implementation of applying seislet transform to high-fidelity stacking for low-SNR seismic data. The OC-seislet transform has been shown to have advantages in non-stationary feature analysis [30]. Additionally, it operates with high computational efficiency after the \"logarithmic stretching\" of the time axis and double Fourier transformation. The basic idea is that we can use the OC-seislet transform based on the high-order CDF 9/7 coefficients in the frequency-wavenumber-scale domain to simultaneously compress both diffraction and reflection waves, achieving high-fidelity DMO stacking. In Section 2, we introduce the combination of wavelet coefficients and phase prediction, as well as the use of coefficient normalization and soft thresholding [36] techniques to ensure signal fidelity and effective denoising. Finally, the inverse transform is applied to return to the time-space domain to obtain high-order OC-seislet stacking results. In Section 3, synthetic data tests are conducted to verify the effectiveness of the high-order OC-seislet stacking method when compared with conventional DMO stacking methods. The results demonstrate that the high-order OC-seislet stacking method effectively balances fidelity and SNR in the stacking results. A further application to low-SNR field seismic data also confirms the method's suitability for high-fidelity stacking in low-SNR seismic data.
## 4 Online Learning of Repeated Games
### Online Shortest Path Problem Reformulation  
For the convenience of expression, we refer to the problem of multi-channel power allocation under a repeated confrontation scenario as the semi-combinatorial Blotto Game (SCBG) problem. Inspired by the works in [10; 11; 12], we transform the SCBG problem into an online shortest path (OSP) problem on a graph structure, which makes the solving process more intuitive. The schematic diagram of converting the SCBG problem into the OSP problem is shown in Figure 5.  
Figure 4: Schematic diagram of the approximate Nash equilibrium of \(G(\mathbf{R},\mathbf{M},u)\) under asymmetric game conditions. The three-dimensional coordinates of each point in the figure represent the power allocation strategy, and the color represents the probability assignment of the strategy.(**a1**,**a2**) \(S_{r}/S_{m}=0.9\); (**b1**,**b2**) \(S_{r}/S_{m}=0.7\); (**c1**,**c2**) \(S_{r}/S_{m}=0.5\); (**d1**,**d2**) \(S_{r}/S_{m}=0.3\).  
We represent the set of actions of \(r\) as a set of paths (from initial vertex to terminate vertex) on a directed acyclic graph (DAG). DAG has the following properties--there are two special vertices, the initial vertex and the terminate vertex, they are called \(S\) and \(D\), respectively. \(\mathcal{P}\) represents the set of all paths starting from \(S\) and ending with \(D\), the vertex set and edge set of the DAG are denoted by \(\mathcal{V}\) and \(\mathcal{E}\), respectively. Setting \(V=|\mathcal{V}|\geq 2\) and \(E=|\mathcal{E}|\geq 1\), and each edge \(e\in\mathcal{E}\) belongs to at least one path \(p\in\mathcal{P}\). Let \(n\) denote the longest path length in \(\mathcal{P}\) (\(n\) is the number of channels in \(G(\boldsymbol{R},\boldsymbol{M},u)\)), that is, \(\|p\|_{1}\leq n,\forall p\in\mathcal{P}\). Figure 5 is the DAG in the case of \(S_{r}=S_{m}=3\) and \(n=3\). There are 10 paths from the initial vertex \(S\) to the terminate vertex \(D\), which represent the resource allocation strategy, where each edge represents the number of resources allocated to the current stage. For example, the red path in the Figure 5 represents the allocation of 2 resources in stage 1, the allocation of 0 resources in stage 2, and the allocation of 1 resource in stage 3, that is, the strategy is (2,0,1). Given the time horizon \(T\in N\), the online shortest path (OSP) problem is defined as, in time \(t\in[T]\), \(r\) choose a path \(p\) to represent its power allocation strategy without knowing the power allocation strategy of \(m\). Each edge on this path corresponds to a scalar loss determined by Formula (1). At the end of a time period \(t\), \(r\) observe the loss of all edges on the selected path by themselves. The goal of \(r\) is to minimize the expected regret, which is defined by Equation (11).
# Development of the User Requirements for the Canadian WildFireSat Satellite Mission
## 3 Definition of the User Requirements
### Precision Requirements
#### 3.3.1 Sensitivity and Noise Requirements  
The goal for all spectral bands is to minimize noise (i.e., maximize signal to noise ratio; SNR), and achieve the highest degree of sensitivity possible (Table 6). However, such requirements have limited value in conducting trade-off analysis and identifying candidate systems and approaches. Given that the primary aim of the mission is to support monitoring activities and only provide detection capacity in remote regions, the sensitivity requirements were not driven by detection requirements. Wildfire managers described satisfaction with the detection capacity of MODIS active-fire products [74] for use in remote regions. Initial screening of candidate pixels in wildfire detection products with comparable sensitivity to MODIS was used to define the thresholds for the MWIR and LWIR bands. The noise and sensitivity requirements in the MWIR also govern FRP accuracy, which was similarly derived with a threshold of achieving the minimum detector performance required to replicate MODIS quality detections. Keeping in mind that the measurement requirements (Table 5) include significantly finer spatial resolution than MODIS, the sensitivity and noise requirements could be relaxed further and still meet the user requirements (Table 6). Additionally, FRP integrity is known to be linked to viewing geometry (e.g., [85]), warranting specification of constraints for off-nadir FRP accuracy (Table 6).  
VIS and NIR bands were also defined in terms of their minimum functionality. In this case the threshold criteria were the performance required to adequately generate cloud masks and support contextual wildfire detection calculations. For reference, the threshold SNR values described for the VIS and NIR are comparable to the lower end of what Landsat ETM+ produced [86]. At this performance level the requirement to support active-fire detections will be achieved and would be able to provide data for entry level mapping products.
###### Abstract  
Techniques of automatic medical image segmentation are the most important methods for clinical investigation, anatomic research, and modern medicine. Various image structures constructed from imaging apparatus achieve a diversity of medical applications. However, the diversified structures are also a burden of contemporary techniques. Performing an image segmentation with a tremendously small size (\(<\)25 pixels by 25 pixels) or tremendously large size (\(>\)1024 pixels by 1024 pixels) becomes a challenge in perspectives of both technical feasibility and theoretical development. Noise and pixel pollution caused by the imaging apparatus even aggravate the difficulty of image segmentation. To simultaneously overcome the mentioned predicaments, we propose a new method of medical image segmentation with adjustable computational complexity by introducing data density functionals. Under this theoretical framework, several kernels can be assigned to conquer specific predicaments. A square-root potential kernel is used to smoothen the featured components of employed images, while a Yukawa potential kernel is applied to enhance local featured properties. Besides, the characteristic of global density functional estimation also allows image compression without losing the main image feature structures. Experiments on image segmentation showed successful results with various compression ratios. The computational complexity was significantly improved, and the score of accuracy estimated by the Jaccard index had a great outcome. Moreover, noise and regions of light pollution were mostly filtered out in the procedure of image compression.  
Keywords:data density functionals; denoising; image segmentation; image compression +
Footnote †: journal: Medical Image Segmentation with Adjustable Computational Complexity Using Data Density Functionals