esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 System Development
### Crdt
#### 6.3.1 Initial Analysis  
Lets consider a series of users accessing the same text resource. Each user can apply locally one of the following basic operations:Insertion: The user can insert a character at a given position. The operation will be denoted _INSERT_(_C_, _Index_) where \(C\) represents the inserted character and _Index_ is the position where it was inserted. The state of the string will be denoted with \(S\).  
_S = Test. INSERT('I', 4) \(\rightarrow\) TestI_  
Deletion: A user can delete the character on a specific position. The operation will be denoted as _DELETE(Index)_, where _Index_ represents the removed position.  
_S = Test. DELETE(0) \(\rightarrow\) est_  
Every other classic text manipulation operation (group delete, group insertion, etc.) can be reduced to either a delete or insert operation applied to a range (individual characters selected) or a selection of indexes (a sequential block of text selected).  
_S = Test. REPLACE(0,'A') \(\rightarrow\) DELETE(0) \(\rightarrow\) INSERT('A', 0) \(\rightarrow\) Aest_  
Starting from the same resource, users will apply a series of insertion/deletion operations that will modify the shared resource. Each change made by a user needs to be broadcast to all the users that participate in the collaboration session. There is a chance that users can introduce different changes at the same index.  
Figure 14 demonstrates a scenario where two users apply changes to the same resource and, due to the nature of the changes, they obtain different states of the same resource. The system will not converge to a single result in certain scenarios, which is a fundamental requirement for a collaborative system design.  
The problem of convergence can be handled on either the client side or on the server side.
# Sensitivity Analysis of Multi-Temporal Sentinel-1 SAR Parameters to Crop Height and Canopy Coverage
## 2 Materials and Methods  
Within this framework, we have investigated the sensitivity of 10 parameters including linear polarization backscattering coefficients, H-A-\(\alpha\) decompositions, polarization intensity ratios (VH/VV, VV/VH and VV \(-\) VH/VV + VH), Radar Vegetation Index (RVI) and intensity arithmetic calculations (VH \(-\) VV and VV + VH) derived from multi-temporal C-band Sentinel-1 SAR data, to crop height and Canopy Coverage (CC) of maize, sunflower, and wheat. For this purpose, field measurements were carried out simultaneously with SAR data acquisitions. For backscattering analysis four Sentinel-1 SAR images in descending orbit direction were acquired throughout the same growth season of the study area.
## 2 You-Ye-Lin landslide
### Landslide of You-Ye-Lin
#### 2.3.2 Groundwater Levels  
Figure 5 illustrates the variation of groundwater level with the rainfall hyteograph showing the groundwater rise/drawdown in response to the torrential rainfall during typhoons. As shown in Figure 4(a), monitoring point _B09-W_ indicates that the groundwater level gradually rose to the highest level 4-6 days after the attack of Tyhoon Dujuan. Then, the groundwater level gradually decreased to the level before the attack of Tyhoon Dujuan after 10-15 days. The time-lagging response of the  
Figure 4: Monitoring points and drainage systems in the You-Ye-Lin landslide.  
Figure 3: Damages caused by Typhoons Morakot (2009/August/08–09) and Megi (2016/September/27–28) in You-Ye-Lin landslide.  
groundwater level to the rainfall implies that the overall permeability of soil stratum in the landslide area is comparatively low and may be lower than the average rainfall intensity. As a consequence, the infiltrated rainwater is preserved in colluviums (CL-ML) due to the low permeability of fine soil particle. The preserved water becomes an adverse factor to the slope stability and causes the onset of the ground movement. Further on, at the middle to bottom slope of the study area, a thin shale layer of 2.0-4.5 m underlain by the colluvium is also impermeable and the infiltrated rainwater accumulates on top of this layer causing the ground movement of the slope (refer to Figure 6 for inclinometer measurement at _B09-W_ borehole).  
During Typhoon Megi (2016/09/25-09/28), as displayed in Figure 4(b), the groundwater level at monitoring point _B09-W_ was not immediately respond to the principal rainfall until 2016/10/01. This time-lag of the groundwater rise can be attributed to the installation of several groups of parallel shallow horizontal drains (see Figure 4) for subsurface drainage. Comparing the monitoring point of _B12-W_ (Figure 4(c)) with _B09-W_ (Figure 4(b)), it can be found that groundwater responses of these two monitoring points are correlated during Typhoon Megi (2016/09/25-09/28) and both have the time-lag responses.  
According to the topographic and geologic features of the You-Ye-Lin landslide, it is estimated that a sequential rainfall after a principal rainfall with cumulative rainfall more than 800 mm may trigger ground movement locally. The auto-recording rain gauge used for the monitoring program is presented in Figure 4(d). The monitoring results of groundwater during 3 typhoon events are summarized in Table 1.  
Measurements of groundwater levels are directly adopted for the numerical analyses to include the rainfall effect instead of the infiltration and seepage analyses for unsaturated soil strata.  
Figure 5: Groundwater level and rainfall time histories of monitoring points _B09-W_ and _B12-W_.
## 2 Applicability of Blockchain in Sports
### Esports and Sports Betting  
In Esports, blockchain enables secure, transparent transactions through cryptocurrencies and NFTs. Decentralized competition platforms supported by smart contracts mitigate fraud and enhance user trust [30, 31]. In the domain of sports betting, blockchain ensures fairness by automating payouts and securely verifying outcomes via oracles. Augur and Gnosis offer a decentralized betting market.
# An Improved Adaptive Iterative Extended Kalman Filter Based on Variational Bayesian
## 2 Computational Procedure  
### System Model  
Consider the following noisy nonlinear system shown by the state space model:  
\[x_{t}=f_{t-1}(x_{t-1})+w_{t-1} \tag{1}\]  
\[z_{t}=h_{t}(x_{t})+v_{t} \tag{2}\]  
where \(x_{t}\in\mathbb{R}^{n}\) is an observable yet unmeasurable state vector, \(z_{t}=\mathbb{R}^{m}\) is a vector of measurements that may be observed, \(f_{t-1}(\cdot)\) and \(h_{t}(\cdot)\) are recognized nonlinear functions. To simplify computations and ensure the convergence of the Taylor series expansion, assume that \(f_{t-1}(\cdot)\) and \(h_{t}(\cdot)\) are, without loss of generality, analytic functions that are sufficiently smooth and differentiable. The covariance matrices of the random variables \(w_{t-1}\) and \(v_{t}\) are denoted as \(Q_{t}\) and \(R_{t}\), respectively. The process noise \(w_{t-1}\) is a Gaussian process with a mean of zero and an unknown covariance matrix \(Q_{t}\). The measurement noise, denoted as \(v_{t}\), is a type of Gaussian white noise with a mean of zero. The covariance matrix \(R_{t}\) associated with this noise is unknown. First, a Taylor Series expansion (TSE) [34], which can be stated as the following, is used to linearize locally the nonlinear functions \(f_{t-1}(\cdot)\) and \(h_{t}(\cdot)\):  
\[\begin{split} f(x_{t-1})&=f(\hat{x}_{t-1})+\frac{ \partial f(x_{t-1})}{\partial\hat{x}_{k-1}}\bigg{|}_{x_{t-1}=\hat{x}_{t-1}} \quad\times(x_{t-1}-\hat{x}_{t-1})+H.O.T\\ &\approx f(\hat{x}_{t-1})+F_{t}\hat{x}_{t-1}\end{split} \tag{3}\]  
\[\begin{split} h(x_{t})&=h\Big{(}\hat{x}_{t|t-1} \Big{)}+\frac{\partial h(x_{t})}{\partial x_{t}}\bigg{|}_{x_{t-1}=\hat{x}_{t|t -1}}\quad\times\Big{(}x_{t}-\hat{x}_{t|t-1}\Big{)}+H.O.T\\ &\approx h\Big{(}\hat{x}_{t|t-1}\Big{)}+H_{t}\hat{x}_{t|t-1}\end{split} \tag{4}\]  
The requirement for bounded higher-order terms (H.O.T) is crucial to guarantee the convergence and accuracy of the Taylor series expansion. Following the application of the Taylor series expansion, the nonlinear functions \(f_{t-1}(\cdot)\) and \(h_{t}(\cdot)\) are approximated by linear functions with error terms \(F_{t}\) and \(H_{t}\), respectively.  
When there is heavy-tailed noise, the covariance matrix of the process and measurement noise is usually imprecise. To resolve this problem, one can utilize a variational Bayesian (VB) method [19; 35; 36], and subsequently apply an iterative extended Kalman filter to estimate the state vector \(x_{t}\). This work presents an improved variational Bayesian iterative extended Kalman filtering (VBAIEKF) technique designed for tracking estimation.
# Automated Personalized Loudness Control for Multi-Track Recordings
## 2 Materials and Methods
### Neural Network for Loudness Optimization  
In order to test the eligibility of our proposed method, in addition to the customized genetic algorithm, we also implemented a neural network that can solve the automatic loudness control problem in an adaptive way instead of a fixed solution. We chose a Convolutional Neural Network (CNN) architecture because it has the ability to extract useful information from correlated data, in our case, the successive audio samples. Because stereo channels usually have correlated information with many similarities, we simplified the problem by stacking both channels into a single one using a Fortran-like index order (the left sample is followed by its corresponding right sample). This enables us to use 1D convolution instead of the 2D operation, ensuring a better representation for the extracted time features.  
A big advantage when using a neural network is its ability to generalize by learning on a batch of data, instead of one single example. In our case, since the number of audio mixes in the dataset is limited, we chose our batch size as the entire train data size (15 songs), allowing the model to extract information from all musical pieces available for training. The output of our architecture will be represented by a vector of 66 coefficients, ensuring that all the tracks have a corresponding scaling factor (the same as the genetic algorithm output). Usually, augmenting operations for extending the training dataset are applied, e.g., overlapping windows for audio are treated as different examples in the input of the model. However, because we decided to use only the best segment of 1024 stereo samples in the genetic algorithm, we kept the same input for our neural network as well, therefore, only one segment was used from each track. The only difference is that the input for the genetic algorithm is stereo, while the neural network receives the stacked mono signal. The architecture of the model is represented in Figure 1.  
Note that we used a simple model in order to learn the track coefficients, because this problem does not require very complex architectures, since this is only a scaling task. However, we mention that any type of architecture can be used here, and more large models might be required for bigger datasets. The loss function used is the same one presented in Equation (5). This means that we did not apply the MAE function between real and predicted coefficients, but rather on the input samples multiplied with the predicted coefficients and compared with the ground truth samples. An explication for this will be the fact that the MAE function between audio signals can give better results than an error between only 66 values. This neural network was implemented only to test its functionality for this problem and can be considered as a baseline for all the next tasks in automatic mixing, e.g., EQ, panning, DRC or even artificial reverberation.
# Effects of Turbulence Modeling on the Simulation of Wind Flow over Typical Complex Terrains
## 4 Results and Discussions
### Case 3: Bolund Island Field Experiment (270\({}^{\circ}\) Wind Direction)
The Mod RSM model produces a relatively larger TKE, but this is still inadequate at M6 and M2 near the ground. The prediction of the turbulent kinetic energy behind the cliff is always a challenge for turbulence models, and similar conclusions were found in the literature [10,48]. Through a comprehensive analysis of the TKE statistical errors for all anemometers, it can be seen from Table 4 that the numerical results of the TKE with the two RSM models are better, and the modified RSM model is better than the standard RSM model, while the poorest performance is found in the standard SST \(k\)-\(\omega\) model.
## 3 Results
### Spatiotemporal Distributions of Annual Radiation in China
The annual average diffuse radiation fraction in China increased markedly between 1981 and 2012 by 0.0002 y\({}^{-1}\) (\(p<0.05\)) (Figure 3c). The spatial distribution of the variation in annual \(R_{\rm g}\), \(R_{\rm d}\), and diffuse radiation fraction in China over the period 1981-2012 is shown in Figure 4. Decreases in \(R_{\rm g}\) mainly occurred in North China, Hainan, and on the Qinghai-Tibet Plateau (Figure 4a), which is consistent with the findings of [PERSON] et al. [45] and [PERSON] et al. [46]. Conversely, \(R_{\rm g}\) increased only over a small part of China during the period 1981-2012. Generally, the changes in \(R_{\rm g}\) over most parts of China were not significant, predominantly because China experienced \"dimming\" prior to 1990, whereas \(R_{\rm g}\) increased slightly over large parts of China post-1990 [23]. Increases in \(R_{\rm d}\) predominantly occurred over the Qinghai-Tibet Plateau, Shanghai-Hangzhou-Wuhan region, Guangdong, and Guangxi and its surrounding areas (Figure 4b). Decreases in \(R_{\rm d}\) occurred in the northwestern part of Xinjiang, western parts of Inner Mongolia, and Shenyang. The national average diffuse radiation fraction increased by 0.2% decade\({}^{-1}\) during the period 1981-2012 (Figure 4c). Initially, the diffuse radiation fraction increased and then decreased during the 1980s. In 1993, after the eruption of Mount Pinatubo, the diffuse radiation fraction attained its peak during the study period, and then quickly returned to its average value. Figure 4: Spatial distribution of the changing rate in annual mean global radiation ((a), unit: MJ m\({}^{-2}\) y\({}^{-1}\)), diffuse radiation ((b), unit: MJ m\({}^{-2}\) y\({}^{-1}\)), and diffuse radiation fraction (**c**) in China for the period 1981–2012 (white areas show no significant trends; \(\alpha=0.05\)) with a spatial resolution of 0.0727\({}^{\circ}\).
## 3 Numerical Simulations  
To validate the developed solver, some typical 2D unsteady simulations of internal and external flows were performed. For the former, lid-driven cavity flows were chosen, whereas flows around a single cylinder and two circular cylinders in tandem arrangement were analyzed for the latter. The nondimensional coefficients related to the present study for all cases are the **Re** and Strouhal number (**St**) for the external flows.  
\[\textbf{Re}=\frac{UL}{\
u}=\frac{UD}{\
u},\ \ \textbf{St}=\frac{fD}{U}, \tag{24}\]  
where \(U\), \(L\), and \(D\) are the representative speed, length, and diameter, respectively, whereas \(f\) is the frequency of vortex shedding.  
The pressure, drag, and lift coefficients used for the analysis of external flows are defined by Equations (25)-(27).  
\[C_{P}=\frac{p-p_{ref}}{(1/2)\rho DU^{2}} \tag{25}\]  
\[C_{D}=\frac{F_{D}}{(1/2)\rho DU^{2}},\ F_{D}=\oint_{S_{\text{body}}}((-p\textbf{I }+\boldsymbol{\tau}_{vis})\!\cdot\!\textbf{n})\!\cdot\!\textbf{e}_{x}ds, \tag{26}\]  
\[C_{L}=\frac{F_{L}}{(1/2)\rho DU^{2}},\ F_{L}=\oint_{S_{\text{body}}}((-p\textbf{ I}+\boldsymbol{\tau}_{vis})\!\cdot\!\textbf{n})\!\cdot\!\textbf{e}_{y}ds, \tag{27}\]  
where **I** is an identity matrix, \(\boldsymbol{\tau}_{vis}\) is the viscous stress tensor, and \(\textbf{e}_{x}\) and \(\textbf{e}_{y}\) are the unit vectors in the \(x\) and \(y\) directions, respectively.  
If vortex shedding occurs, the values during the last 20 periods within the simulation times are averaged, and the mean values are presented for **St**, \(C_{D}\), and \(C_{L}\).
# Analysis of Clinical Profiles, Deformities, and Plantar Pressure Patterns in Diabetic Foot Syndrome
## 2 Materials and Methods
### Experimental Protocol 1: Data Collection and Analysis  
Multi-instrumental gait analysis was performed by exploiting an established pressure-force-kinematics technique [17] so as to obtain highly reliable plantar regions of interest (ROIs). While ROI identification is often based on the geometry of the acquired pressure footprint, whenever proper stereophotogrammetry for foot kinematics and a suitable multi-segmental foot model are reliably used, ROIs can be better identified by exploiting the position of foot and ankle anatomical markers. This reliable anatomical masking allows the best matching between plantar loaded areas and anatomical structures of the foot to be achieved, even in the presence of abnormal or incomplete footprints [18; 19]. In the present study, the Rizzoli Foot Model [20] and the anatomical masking were used to separate the hindfoot from the midfoot on the basis of the midtarsal joint (Chopart's joint), the midfoot from the forefoot on the basis of the Liskranc joint, the metatarsal area of the forefoot from the toes based on the arch of the metatarsal heads and the positions of the first metatarsal head and the base of the hallmark phalanx, and the hallux from the other toes on the basis of the alignment of the first metatarsal. The experimental protocol of this study relied on pressure patterns and kinematic data acquired during barefoot level walking at a self-selected speed. A capacitive sensor platform (EMED(r) q-100, novelGmHH, Munich, Germany; 4 sensors/cm2; range 0-1270 kPa; 100 Hz) was synchronized with an eight-camera 3D motion tracking system (Vicon(r), Oxford, UK) by off-line aligning the signal from the pressure platform to the signal from a force plate rigidly fixed underneath and acquired simultaneously with the tracking system. The established marker-set of the Rizzoli Foot Model [20] was used to track the multi-segment foot kinematics. Five consistent full footprints were collected for each patient and foot. The anatomical masking procedure was automatically applied to each foot (EMED-associated software packages by novelGmphH, Munich, Germany) to identify the five ROIs, namely the hindfoot, the midfoot, the forefoot, the big toe, and the other toes [17]. Main pressure-related parameters were extracted for each ROI by means of the same dedicated software packages and averaged over repetitions within each foot. Regional Peak Pressure (PP, kPa) and Pressure-Time Integral (PTI, kPa-s) were included in the analysis for a total of 10 parameters for each foot. The dominant (D) and non-dominant (ND) limbs were analyzed separately.
# Functional Outlier Detection by Means of h-Mode Depth and Dynamic Time Warping
## 5 Industrial Test-Case Study
### Functional Outlier Detection  
The previously presented outlier detection technique is applied to this set of curves. Both the h-mode depth and the DTW are the selected features to obtain the degree of outlyingness of each functional datum. The curves presenting a degree \(\hat{\theta}\) of outlyingness over 95% are shown in Figure 3.  
The first apparent result is that the main magnitude outlier is easily detected, since the curve that acts as the upper envelope of temperature in most points of the domain is the one presenting the highest value of \(\theta\) (\(\theta=1.0\) actually in this case). This curve is not only anomalous in the magnitude sense, but also in the shape one, similar to Model 4 that was presented in the previous section (these are sometimes called _phase_ outliers).  
Two other magnitude outliers have been identified, and one of them is also a shape outlier, presenting an anomalous peak of temperature after about 120 s of simulation (physical time). The final main outlier is a pure shape one, remaining zones with high density of data during the whole domain of simulation, but presenting two peaks of temperature. Especially notorious is the first peak, which occurs around the 100 s of transient, in a time interval that does not match most curves.
## 3 Methodology
### Insertion Strategy of New Neurons  
Representing a large amount of data with a few neurons is an essential idea of GNG models. As described in Algorithm 1, the original GNG creates a new neuron periodically, the strategy of which cannot adapt to a sudden change in the distribution of streaming data with a new data topology. If there has been no new pattern for a long time, the algorithm will create a lot of redundant neurons. On the other hand, a threshold \(\mu\) is defined in our previous work (GNG-U) to determine whether an input is distinctive enough to insert new neurons, and thus, the fixed threshold \(\mu\) may limit its ability to adapt to dynamically changing data streams. In this paper, we further optimized the threshold-setting strategy.  
When a new instance \(x\) of the data stream is coming, we find the winner \(s\) by following line 6 of Algorithm 1. If \(s\) has directly connected neighbors, then the new threshold \(T_{s}\) is calculated using the maximum distance between \(s\) and its neighbors following Formula (1).  
\[T_{s}=\max\lVert\omega_{s}-\omega_{n}\rVert \tag{1}\]  
where \(\omega_{s}\) is the weight of \(s\), \(\omega_{n}\) is the weight of \(n\), \(n(s)\) is the set of neurons that connect to the neuron \(s\), and \(\lVert\omega_{s}-\omega_{n}\rVert\) is the Euclidean distance between neuron \(s\) and neuron \(n\). If \(s\) has no neighbor, the threshold \(T_{s}\) is calculated using the minimum distance between \(s\) and other neurons:  
\[T_{s}=\min\lVert\omega_{s}-\omega_{n}\rVert \tag{2}\]  
where \(A\) is the set of all neurons. During the learning process, if the distance between a new instance \(x\) and its winner neuron \(s\) is larger than the threshold, the algorithm adaptively inserts new neurons to support the new topology. The original GNG inserts the new neuron between the two neurons with the largest and second-largest cumulative error (line 14 of Algorithm 1), overlooking the role of the new neuron. However, the new neuron describes a new inputting pattern for the model. Therefore, the insertion location in this paper is  
Figure 1: The dynamic topology learning of Growing Neural Gas (GNG) algorithm. The blue bullets are neurons of GNG. The red bullet represents newly inserted neuron as the topology grows.  
between \(x\) and \(s\), taking into account both the winner neuron and the new neuron, and the weights of the new neuron can be calculated as follows.  
\[\omega_{i}=0.5\times(\omega_{s}+x) \tag{3}\]  
The purpose of optimizing the neuron insertion is to improve the model's adaptability in online anomaly detection scenarios for streaming data. When the data distribution is stationary, fewer neurons are created, and it is necessary to create neurons in a timely manner in order to handle changes.
## 4 Performance Evaluation  
### Simulation Settings  
Table 1 presents the key simulation parameters used in this study to evaluate the performance of the proposed model. The simulation environment is based on NS-3/5G-LENA integrated with SUMO for mobility modeling. Various parameters, such as packet size, transmission power, and noise figure, have been carefully selected to reflect realistic vehicular network conditions. The scenario considered for evaluation is the Houghton, Michigan urban environment, where vehicle speeds are variable to account for diverse traffic conditions. The simulations operate within the ITS Band at 5.9 GHz, with a channel bandwidth of 10 MHz, ensuring compliance with standardized vehicular communication settings. Additionally, the LOS fading model has been employed to simulate fast fading effects in the wireless channel, and beacon sizes are set to 300 bytes to facilitate periodic message exchanges. These parameters collectively contribute to a comprehensive and realistic assessment of the performance of the proposed systems.