esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Nonlinear Switch Characterization
### Interpretation of X-Parameters  
The notation used in X parameters is based on port, harmonic and propagation direction convention, in which \(A_{qn}\) represents the nth harmonic of an incoming signal at port \(q\), while \(B_{pm}\) is the mth harmonic of an outgoing signal at port \(p\).  
In the general X-parameters formulation, outgoing port waves are expressed as a function of the Large Signal Operating Point Stimuli (LSOPS), which is composed of the device DC Stimulus (DCS), and the RF Stimuli, which in turn is composed of the fundamental magnitude \(|A_{11}|\), and the harmonic set of incoming signals \(A_{qn}\) which are conveniently referred to the phase of the fundamental \(P=e^{i\phi 11}\) (\(\phi 11=\angle A_{11}\)).  
A common approximation, valid for most devices, that significantly reduces the computational resources required for measurement, is to consider that the fundamental is the only large RF signal entering the DUT, hence, the only one that can drive it into a nonlinear behavior, while the injected/reflected signals at the remaining ports and harmonics combinations are considered small-signal components. Accordingly, the reference LSOPS is solely defined by \(refLSOPS=\{\{DCS_{q}\},|A_{11}|\}\).  
Then, with the aid of linearization and harmonic superposition assumptions, dropping the dependence on \(refLSOPS\) for the sake of clarity, the port waves can be related [42] through:  
\[B_{pm}=X_{pm}^{(FB)}P^{m}+\sum_{(q,n)\
eq(1,1)}X_{pm,qn}^{(S)}A_{qn}P^{m-n}+ \sum_{(q,n)\
eq(1,1)}X_{pm,qn}^{(T)}A_{qn}^{*}P^{m+n}, \tag{1}\]  
where \(X_{pm}^{(FB)}\), \(X_{pm,qn}^{(S)}\), and \(X_{pm,qn}^{(T)}\) are the X-parameters of type \(FB\), \(S\), and \(T\) respectively, whose interpretation is as follows: the \(X_{pm}^{(FB)}\) represent the large-signal part of \(B_{pm}\), and are a set of mappings from \(A_{11}\) to the output waves at port \(p\) and harmonic \(m\) for a system perfectly matched at the output port and perfectly matched at each harmonic at all ports. The \(X_{pm,qn}^{(S)}\) and \(X_{pm,qn}^{(T)}\) terms determine the sensitivity to mismatch of the system, relating the contributions of the small harmonic-signals from the port \(q\) at harmonic \(n\) to the outgoing wave at port \(p\) and harmonic \(m\).  
The X-parameters model states that the set of outgoing port waves (for all ports and frequencies combinations) result from a linear mapping of the set of incoming port waves (for all ports and frequencies combinations), but in contrast to S-parameters, not only signal ratios are combined, but additionally this modeling considers the contributions depending on the amplitude of the RF Stimulus (RFS) and the relative phases of the remaining incoming signals (harmonics and intermodulation products) with respect to this reference stimulus.
# Quadratic Clustering-Based Simplex Volume Maximization for Hyperspectral Endmember Extraction
## 3 Results and Discussion  
In this section, parameter analysis is first carried out to choose the optimal value for each parameter in CSVM and improve the applicability of CSVM for future use. Then four experiments are performed on three datasets (the [PERSON] dataset, Jasper Ridge dataset, and Cuprite dataset) to evaluate the performance of the proposed method. The comparison algorithms include VCA, SGA, AVMAX, MVSA, AMEE, ESCO, KMEE, FSPM, SENMAV, SPEW. Among them, VCA, SGA and AVMAX are convex geometry-based methods commonly used for comparison; AMEE and ESCO are typical methods that integrate spatial and spectral information; MVSA, KMEE and FSPM are noise-robust algorithms, and the result of FSPM is sent to VCA to determine the final endmembers, as it is a preprocessing module; SENMAV and SPEW combine clustering with a simplex-based algorithm, which is similar to our approach. Since hyperspectral unmixing involves endmember extraction and abundance estimation, the first two experiments aim to compare the performance of all test algorithms on endmember extraction and abundance estimation, respectively. The third experiment verifies the noise robustness of all algorithms by setting the signal-to-noise (SNR) level of the datasets to 15 dB, 20 dB, 25 dB, 30 dB, 35 dB and 40 dB. In the fourth experiment, the processing time of all methods are compared, and the acceleration possibility of the proposed method is analyzed. To ensure the reliability of the experimental results, each experiment is conducted five times and the average values are taken in this paper.
# Properties and Model of Pore-Scale Methane Displacing Water in Hydrate-Bearing Sediments
## 4 Results and Analysis
### Flow Characteristics of Methane Flooding at the Pore Scale
#### 4.1.2. Throat Size Heterogeneity  
From the numerical simulation results, it can be seen that the throat size in the nonhomogeneous pore-scale model is an important factor that affects the effective flow channel. To further study the role played by throat width in the process of methane-driven replacement water, we divided the pore model with a contact angle of 90\({}^{\circ}\) into three parts: upper, middle, and lower regions according to its flow pattern and the location of the tail breakthrough and counted the distribution of the throats in the three regions and plotted the openness-frequency histograms, respectively. We used the Log normal function to model the distribution of throat dimensions across various widths. The fitting coefficients were 0.96, 0.96, and 0.94, respectively, aligning with the characteristics of a lognormal distribution. The histograms and fitted curves of the frequency distributions for the three regions are shown in Figure 9.  
Figure 8: The change in water saturation with time in the upper, middle, and bottom regions.  
Figure 7: The volume fraction and pressure distribution of gas drive the water process in porous media.  
In the small size range (10-40 \(\upmu\)m), the percentage of throat width in the upper, middle, and lower regions was 47.83%, 52.97%, and 49.60%, respectively. In the medium size range (40-100 \(\upmu\)m), the percentage of throat width in the three regions was 46.61%, 42.37%, and 44.81%, respectively. In the large size range (100-160 \(\upmu\)m), the percentage of throat width in the three regions was 5.56%, 4.66%, and 5.59%, respectively. The porosity was 46.75%, 40.60%, and 45.83% in the upper, middle, and lower regions, respectively. Under neutral wetting, the process of methane driving off water is only related to the width of the flow channel, and the narrower the flow channel, the more difficult it is for driving off to occur. The central region has the highest number of throats in the small size range, which is 3.7% and 10.75% larger than the lower and upper regions, respectively, but the percentage of the gas phase after breakthrough is much smaller than this value, which shows that the minimum width in the flow channel determines the choice of the flow path. The throat dimensions are consistent with the simulations, and the smaller the porosity at the same pressure, the greater the resistance encountered by the displacement fluid, and the more difficult it is for the tail breakthrough to occur in this part of the region.
## 3 Results
### Impact of Different Preprocessing Methods on Data and Model Accuracy  
#### 3.2.1 Impact of Different Preprocessing Methods on Data  
Extracting spectral feature bands is fundamental to model construction, as it accurately reflects spectral characteristics closely related to soil organic matter (SOM). The preprocessing process highlights spectral information characteristic of SOM, effectively removing redundant information and enhancing the correlation between data and SOM. This strategy simplifies data processing complexity, strengthens the model's ability to invert SOM content, and consequently improves prediction accuracy and reliability [51]. This study employed three denoising methods: no denoising (N), Savitzky-Golay filter denoising (SG), and discrete wavelet transform denoising (DWT). Additionally, nine mathematical transformation methods were used: raw spectral reflectance (R), first derivative (1 DR), second derivative (2 DR), multiplicative scatter correction (MSC), standard normal variate transformation (SNV), logarithmic transformation (logR), first derivative of the logarithm ((LogR'), reciprocal transformation (1/R), and first derivative of the reciprocal ((1/R)'). The combinations are shown in Table 3.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Sample Classification** & **Sample Size** & **Mean (g\(\cdot\)kg\({}^{-1}\))** & **Minimum Value (g\(\cdot\)kg\({}^{-1}\))** & **Maximum Value (g\(\cdot\)kg\({}^{-1}\))** & **Standard Deviation (g\(\cdot\)kg\({}^{-1}\))** & **Coefficient of Variation (\%)** \\ \hline Total Samples & 206 & 25.72 & 4.26 & 80.04 & 13.57 & 52.75 \\ Training Set & 164 & 26.03 & 4.26 & 80.04 & 14.04 & 53.95 \\ Validation Set & 42 & 24.50 & 8.23 & 71.15 & 11.59 & 47.30 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistics on the characteristics of soil organic matter content.  
Figure 5: Different organic matter contents correspond to reflectivity.  
Correlation analysis is a statistical method used to explore the degree of association between two or more variables [52]. The Pearson correlation coefficient, as an indicator of correlation analysis, reflects the relationship between adjacent spectral bands and soil organic matter (SOM) content. Based on this, a correlation heatmap is constructed to reveal the relationship and degree of influence between band reflectance and SOM content under different preprocessing methods. Using the data preprocessed according to the combinations in Table 3, a Pearson correlation coefficient heatmap of spectral band reflectance and SOM content is shown in Figure 6. In the heatmap, Pearson correlation coefficients are represented by different colors. The heatmaps for the mathematical transformations R, logR, and 1/R show relatively uniform correlation changes. In contrast, MSC and SNV exhibit diverse correlation changes, but their color distribution is concentrated, indicating less noticeable changes in individual spectral data. Observations reveal that the heatmaps for the four mathematical transformations 1 DR, 2 DR, (LogR
# Effects of Fractional Time Delay as a Low-Power True Time Delay Digital Beamforming Architecture
## 6 Analysis
This will become increasingly critical as technology moves past 6G into future 7G technologies, currently being developed [29]. Optimizing this architecture further will be the key to enabling these products.
## 3 ML Application in O-RAN
### Network Slicing
\begin{table}
\begin{tabular}{c c l} \hline \hline
**Year** & **Ref** & **Contribution** \\ \hline
2021 & [22] & Provides an initial O-RAN standard the terminology, workflow, and requirements, related to AI/ML model training, distribution and deployment. \\ \hline
2021 & [36] & Proposed ML pipeline automation technique to manage ML training in O-RAN RIC. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Existing studies on ML applications for AI/ML implementation in O-RAN. The admission control decides upon the slices to be served over future time instants, based on different criteria, e.g., resource availability, resource efficiency, operator revenue, etc. It can also be built on the outcome of the traffic forecasting for refining admission decisions in an anticipatory manner. Once a slice/user is admitted, the resource allocation block assigns the resources to each slice/user by avoiding over-provisioning and under-provisioning of the resources and ensuring the SLAs are respected. O-RAN is a promising RAN architecture that inherits all the necessary features, such as intelligence, open and standard interfaces, and closed control loops, to facilitate resource management in a network shared among verticals. In [38], AI techniques are used to perform predictions of future SLA violations and perform corrective actions in advance. Specifically, a recurrent neural network model is utilized to predict the amount of resources required over each slice, given the volume of traffic it carries. E2E O-RAN setup has been used for evaluation of the intelligent closed control loop and resource provisioning scheme, for network slicing and control of the radio and cloud resources of slices, respectively. In O-RAN, distinct network slices must be dynamically controlled to avoid SLA variation caused by rapid changes in the environment. A novel framework is introduced in [39] to manage network slices through provisioned resources intelligently. Due to diverse heterogeneous environments, the intelligent machine learning approaches require sufficient exploration to handle the harsh situations in a wireless network and accelerate convergence. To tackle this issue, a solution based on evolutionary-based DRL (EDRL) is proposed to accelerate and optimize the slice management learning process in the RIC modules. An elastic O-RAN slicing problem is addressed in [40] for industrial monitoring and control in the industrial internet of things (IIoT) networks. This work aims to reduce the age of information (AoI) penalty of fresh information updates from different IIoT devices while considering the energy consumption of the IIoT devices. A matching game for solving the IIoT association problem is introduced and an actor-critic-based DRL model applied for O-RAN slicing-based resource allocation.
# Lightweight Attention Pyramid Network for Object Detection and Instance Segmentation
## 4 Results and Discussion
### Experiment on the 2018 WAD Dataset
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{7}{c}{**(a)**} \\ \hline  & **AP** & \(\mathbf{AP_{00.5}}\) & \(\mathbf{AP_{00.75}}\) & \(\mathbf{AP_{S}}\) & \(\mathbf{AP_{M}}\) & \(\mathbf{AP_{L}}\) \\ \hline FPN\({}_{car}\) & 35.7 & 59.8 & 36.8 & 11.7 & 48.1 & 77.5 \\ APN\({}_{car}\) & 39.8 & 61.3 & 42.4 & 15.7 & 53.5 & 79.2 \\ \hline FPN\({}_{moto}\) & 23.5 & 46.8 & 20.3 & 7.1 & 26.2 & 47.1 \\ APN\({}_{moto}\) & 28.1 & 51.6 & 27.9 & 10.0 & 31.3 & 52.0 \\ \hline FPN\({}_{like}\) & 12.6 & 30.5 & 8.2 & 1.6 & 14.5 & 31.1 \\ APN\({}_{like}\) & 15.7 & 34.0 & 11.1 & 3.5 & 17.5 & 36.2 \\ \hline FPN\({}_{person}\) & 16.4 & 36.2 & 12.1 & 4.9 & 31.6 & 58.2 \\ APN\({}_{person}\) & 18.3 & 38.3 & 14.7 & 6.3 & 35.3 & 57.0 \\ \hline FPN\({}_{track}\) & 44.7 & 69.3 & 50.6 & 17.0 & 50.1 & 65.4 \\ APN\({}_{track}\) & 48.8 & 73.4 & 58.1 & 21.9 & 54.9 & 66.0 \\ \hline FPN\({}_{bus}\) & 41.0 & 66.7 & 44.3 & 10.7 & 47.4 & 67.2 \\ APN\({}_{bus}\) & 46.0 & 71.7 & 51.3 & 17.3 & 56.4 & 68.8 \\ \hline FPN\({}_{tricycle}\) & 34.7 & 61.6 & 36.4 & 29.2 & 36.2 & 47.7 \\ APN\({}_{triple}\) & 37.3 & 65.3 & 40.1 & 31.9 & 37.8 & 50.9 \\ \hline \hline \multicolumn{7}{c}{**(b)**} \\ \hline  & **AP** & \(\mathbf{AP_{00.5}}\) & \(\mathbf{AP_{00.75}}\) & \(\mathbf{AP_{S}}\) & \(\mathbf{AP_{M}}\) & \(\mathbf{AP_{L}}\) \\ \hline FPN\({}_{all}\) & 30.7 & 49.7 & 29.2 & 12.8 & 34.4 & 55.7 \\ APN\({}_{all}\) & 32.4 & 54.7 & 33.8 & 15.4 & 39.4 & 59.2 \\ \hline  & +1.7 & +5.0 & +4.6 & +2.6 & +5.0 & +3.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Object detection results using Mask R-CNN [18] evaluated on the 2018 WAD test-dev set: (**a**) object detection results on separate class of 2018 WAD test-dev; and (**b**) object detection results of 2018 WAD test-dev. Figure 7: Images in each row are the object detection and instance segmentation results.
# Numerical Analysis of Fatigue Life of Wind Turbine Blades Reinforced with Graphene Platelets
###### Abstract  
The rapid growth of wind energy has necessitated the development of advanced materials to address the increasing structural demands of wind turbine blades. Graphene platelets (GPLs) have garnered attention as a promising reinforcement material due to their outstanding mechanical properties, such as high strength and low density. This study investigates the fatigue life of wind turbine blades reinforced with GPLs, benchmarking their performance against conventional fiberglass blades. A finite element model of a 5 MW wind turbine blade was developed to evaluate stresses within the blade structure. The traditional fiberglass blade was modeled based on the SNL 61.5 m design by Sandia National Laboratories, while the GPL-reinforced composite (GPLRC) blade was designed by substituting fiberglass with GPLRCs. Material properties of the GPLRCs were determined using the rule of mixtures and the Halpin-Tsai micromechanics model. Wind speed data were randomly sampled following the probability distribution observed at European wind farms, and corresponding aerodynamic loads were computed using blade element momentum theory. Finite element analyses were performed to derive stress time histories, and fatigue life was predicted using the S-N curve approach, incorporating the Goodman diagram and the Palmgren-Miner rule. The results reveal that while GPLRC-reinforced blades exhibit some limitations in fatigue performance compared to traditional fiberglass blades, potential solutions for improving their durability are proposed, highlighting avenues for further research and optimization in the application of GPLRCs to wind turbine blades.  
wind turbine blades; graphene platelets (GPLs); fatigue damage; fatigue life assessment; finite element structural analysis; blade element momentum theory (BEMT)
# In Situ Neutron Radiography Investigations of Hydrogen Related Processes in Zirconium Alloys
## 3 Examples of In Situ Neutron Radiography Investigations  
In this section, three examples of in situ investigations of the hydrogen behavior in nuclear fuel cladding materials are given. First, in situ measurements of the hydrogen diffusion into a solid cylinder made of the zirconium alloy Zircaloy-4 (Zry-4) were described, followed by investigations of the hydrogen uptake of cladding tube segments made of Zry-4 during steam oxidation and during oxidation in various nitrogen-containing atmospheres. In all in situ experiments, LiF scintillators were applied.  
Figure 4: The INRRO furnace mounted at the ICON beamline and a radiograph of the empty furnace.  
Figure 5: Sample holder constructed by CNEA, Buenos Aires, for a mechanical load machine to perform tensile tests at higher temperatures.
###### Abstract  
The purpose of this study was to investigate the effects of patient-related factors such as anteroposterior and vertical skeletal patterns and alveolar bone density on the success rate of micro-implants. Cases of orthodontic micro-implants (\(t=404\); diameter, 1.6 mm; length, 6 mm) were investigated in 164 patients (127 women, 37 men; mean age, 23.6 \(\pm\) 5.8 years). Cortical bone thickness and alveolar bone density were measured using diagnostic cone-beam computed tomography to examine their effects on the micro-implant's survival. Moreover, anteroposterior and vertical facial patterns were considered as independent variables for the success of micro-implants. Marginal survival analysis was performed by analyzing the time from implant placement to the removal of the failed micro-implants, or to the end of treatment for successful micro-implants. Variables including age, sex, implantation side, implantation site, root proximity, and type of loading (immediate vs. delayed) were also assessed. In total, 347 (85.9%) of the 404 micro-implants were successful. The mean loading time was 12.4 \(\pm\) 4.3 months. Marginal survival analysis showed that the effects of the anteroposterior and vertical facial patterns on the risk of failure were not statistically significant. The factors significantly associated with the micro-implant loading time were cortical bone density, root proximity, and micro-implants replanted in the same site. In conclusion, our findings indicate that anteroposterior and vertical skeletal patterns are not associated with the success of orthodontic micro-implants. Cortical bone density may be associated with the micro-implant's success.  
micro-implants; survival analysis; success rate; facial patterns Article
# Reversed Lempel-Ziv Factorization with Suffix Trees +
## 2 Preliminaries
The string label of a node \(v\) is defined as the concatenation of all edge labels on the path from the root to \(v\); \(v\)'s string depth, denoted by \(\operatorname{str\_depth}(v)\), is the length of \(v\)'s string label. The operation \(\operatorname{suffix}(v)\) returns the node with string label \(S[2\dots]\) or the root node, given that the string label of \(v\) is \(S\) with \(|S|\geq 2\) or a single character, respectively. sufixlink is undefined for the root node. The leaf corresponding to the \(i\)-th suffix \(R[i\dots]\) is labeled with the suffix number \(i\in[1\dots 2n]\). We write \(\operatorname{sfun}(\lambda)\) for the suffix number of a leaf \(\lambda\). The leaf-rank is the preorder rank (\(\in[1\dots 2n]\)) of a leaf among the set of all \(\mathsf{ST}\) leaves. For instance, the leftmost leaf in \(\mathsf{ST}\) has leaf-rank \(1\), while the rightmost leaf has leaf-rank \(2n\). To avoid confusing the leaf-rank with the suffix number of a leaf, let us bear in mind that the leaf-ranks correspond to the lexicographical order of the suffixes (represented by the leaves) in \(R\), while the suffix numbers induce a ranking based on the text order of \(R\)'s suffixes. In this context, the function \(\operatorname{suffix}(\lambda)\) returns the leaf whose suffix number is \(\operatorname{sfunnum}(\lambda)+1\). The reverse function of sufixlink on leaves is \(\operatorname{prev\_leaf}(\lambda)\) that returns the leaf whose suffix number is \(\operatorname{sfunnum}(\lambda)-1\), or \(2n\) if \(\operatorname{sfunnum}(\lambda)=1\) (We do not need to compute \(\operatorname{suffix}(\lambda)\) for a leaf with \(\operatorname{sfunnum}(\lambda)=2n\), but want to compute \(\operatorname{prev\_leaf}(\lambda)\) for the border case \(\operatorname{sfunnum}(\lambda)=1\).) In this article, we focus on the following two \(\mathsf{ST}\) representations: the compressed suffix tree (CST) (23; 38) and the succinct suffix tree (SST) (21) ((Section 2.2.3)). Both can be computed in \(\mathcal{O}(\epsilon^{-1}n)\) time, where the former is due to a construction algorithm given by [PERSON] et al. (27) ((Section 6.1)), and the latter due to (21) ((Theorem 2.8)), see Table 2. These two representations provide some of the above described operations in the time bounds listed in Table 3. Each representation additionally stores the pointer smallest.leaf to the leaf with suffix number \(1\), and supports the following operations in constant time, independent of \(\epsilon\):  
* returns the leaf-rank of the leaf \(\lambda\);
* returns the depth of the node \(v\), which is the number of nodes on the path between \(v\) and the root (exclusive) such that root has depth zero;
* returns the level-ancestor of the \(\lambda\) on depth \(d\); and
* returns the lowest common ancestor (LCA) of \(u\) and \(v\). As previously stated, we implicitly represent nodes by their pre-order numbers such that the above operations actually take pre-order numbers as arguments.