esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Preliminaries
### Safety Barriers  
Safety barriers (SBs) have not had a universally accepted definition so far because they cover a broad range of subjects in areas like economics, management, traffic, aeronautics and astronautics, nuclear power, and the petrochemical industry. As far as process systems are concerned, [PERSON] [28] recommended an appropriate definition of SBs. The definition implies three fundamental elements of SBs as follows:  
* Barrier systems: physical and/or non-physical means;
* Barrier functions: for prevention, control, or mitigation;
* Barrier objects: undesired events or accidents. Furthermore, they can be categorized into several types according to different dimensions. Table 1 summarizes some typical classifications of SBs in process systems. There are different requirements for SB functions and implementation methods in safety management, leading to different classification methods and principles. In addition, considering the specific application scenarios, industry standards, regulatory requirements,and specific requirements of safety management, different classification systems emphasizes different aspects. Therefore, although the classification principle is the same, classification results will be different. \begin{table}
\begin{tabular}{p{128.0 pt} p{128.0 pt} p{128.0 pt} p{128.0 pt} p{128.0 pt}} \hline
**Classification Principle** & **Classification** & **Description** & **Examples** & **References** \\ \hline \multirow{8}{*}{Barrier functions in accident management and emergency response} & \multirow{8}{*}{Prevention} & \(\bullet\) & Reduce the probability of an event by suppressing some of the underlying causes or reducing their intensity. & \(\bullet\) & non-flammable products \\  & & & \(\bullet\) & Monitor the operation process of the system or the changing process of events. & \(\bullet\) & pressure detection system \\  & & & & \\  & & & & \\  & & &
# Structural Information Reconstruction of Distorted Underwater Images Using Image Registration
## 2 Methods
### Image Registration  
Similar to [28], a nonrigid B-spline based registration is applied to yield fine local alignment and deskew the distortion. On the basis of this algorithm, an underlying mesh of control points is driven to deform the input frames into reference. For each point \((x,y)\) in the image, the free-from deformation can be expressed by a mesh of control points \(\phi_{i,j}\) of size \(n_{x}\times n_{y}\):  
\[\mathrm{T}(x,y)=\sum_{m=0}^{3}\sum_{l=0}^{3}B_{m}(v)B_{l}(u)\phi_{i+l,j+m} \tag{8}\]  
where \(i=\lfloor x/n_{x}\rfloor-1,j=\left\lfloor y/n_{y}\right\rfloor-1,u=x/n_{x}- \lfloor x/n_{x}\rfloor,v=y/n_{y}-\left\lfloor y/n_{y}\right\rfloor\) and \(B\) is the standard cubic B-spline based functions, are defined as \(B_{0}(t)=(1-t)^{3}/6,B_{1}(t)=(3t^{3}-6t^{2}+4)/6,B_{2}(t)=(-3t^{3}+3t^{2}+3t+ 1)/6\) and \(B_{3}(t)=t^{3}/6\), where \(0\leq t<1\). According to the distance to the point \((x,y)\), every control point is assigned a weight to free-form deformation by these B-spline based functions. Besides, the coarse-to-fine method, which determines the resolution of controlled mesh, is employed to achieve the optimal balance between the model flexibility and computational cost [29; 30]. The optimal solution of transformation can be calculated by the formulas given in the previous publication [21].
# Doped Zinc Oxide Nanoparticles: Synthesis, Characterization and Potential Use in Nanomedicine
## 4 Use of Doped ZnO NPs in the Biomedical Field
### Nanotools for Photobiomaging
#### 4.3.2 Exploiting Room Temperature Ferromagnetism for Imaging  
In recent years, diluted magnetic semiconductors (DMS) materials have attracted great research interest due to their potential combination of both semiconductive and magnetic properties. The application of ZnO-DMS, exploiting the electron charge associated with the intrinsic spin of the electron, has been noticed in spintronic devices [107,247]. It has been predicted in theory as well as proven in practice that ZnO nanoparticles doped with transition metals (TMs) or rare earth (RE) elements can result in a DMS material with a high Curie temperature, comprising ferromagnetic properties at room temperature [248, 249, 250]. The magnetic properties of TM or RE elements are implemented by the magnetic moments generated due to the presence of unpaired electrons in the outermost 3d or 4f orbital, respectively, although the nature of the magnetic moments of TM and RE elements are quite different. For instance, TMs have a small total magnetic moment per atom, as the outermost 3d electrons in TMs are exterior and delocalized, and their orbital momentum is frequently zero [248]. On the other hand, in RE elements, the peripheral 4f electrons are localized, having indirect exchange interactions among 5d or 6s conduction electrons. Therefore, RE elements can be expected to have a high total magnetic moments per atom owing to its high orbital momentum than that of TMs [113,251]. Various TMs such as Fe, Co, Cr, Mn, and Ni, and RE elements such as Eu, Tb, La and Gd have been exploited as dopants for creating doped ZnO NPs DMS. The magnetic parameters of the doped ZnO NPs are summarized in Table 3. Besides, there are few reports showing an enhanced ferromagnetism by exploiting Co-doped ZnO NPs [252, 253, 254]. The doped ZnO NPs can also be exploited as magnetic resonance imaging (MRI) contrast agents either to increase the signalintensity on T1-weighted images or to reduce the signal intensity on T2-weighted images [255; 256; 257]. Moreover, some research groups have reported Gd-doped ZnO quantum dots (QDs) as efficient dual modal fluorescence and MRI nanoprobes [163; 258].
## 4 Conclusions and Future Works  
In this study, we develop a deep learning method for wearable sensor-based fall detection in a real-time setting. First, we implement and evaluate the impact of different types of sliding-window segmentation on deep-learning-based fall-detection methods. To our knowledge, there are no studies on the evaluation and implementation of different sliding-window segmentation methods other than the conventional sliding window for wearable sensor-based fall detection. Using multi-window segmentation helps us to detect falls in real time (0.2 s). Using a gyroscope in conjunction with an accelerometer as part of the detection process can potentially lead to incremental benefits compared to merely using an accelerometer for the detection process. Another important finding is that the use of convolution-based learned features extracted with different kernels tends to increase the performance of the classifier. Moreover, from the performance presented above, it is better to use the customized loss function than resampling techniques and data augmentation for handling the imbalanced nature of the fall datasets. Balancing the sample dataset using upsampling of the minor class or downsampling of the major class did not help.  
Although using wearable sensors has the advantage of flexibility and privacy over camera-based fall detection, camera video recording is still used for manual data annotation. Data labeling for supervised machine learning methods is expensive, as we use video recording and playback of each patient's video to label the data. Future work in this study will employ self-supervised and unsupervised machine learning algorithms. We will create a large database by segmenting the input sequences from different positions with different sensors using multi-window or peak-detection windowing techniques and feed it to a variational autoencoder with LSTM and a transformer to perform unsupervised clustering.
# Bioconversion of Apple Pomace to _Meyerozyma guilliermondii_ and _Scheffersomyces stipitis_ Biomass
## 3 Results and Discussion  
### The Basic Parameters of the Apple Pomace  
The basic parameters of the apple pomace used in our experiments are presented in Table 1.  
The apple pomace contains residual sugars from the original apples. However, the critical components for the overall bioconversion into SCP are the polarised forms of sugars such as cellulose and hemicellulose. The resulting dry matter content of the raw material (90.68 \(\pm\) 2.7%) is close to that reported in the literature [17; 18; 45; 46]; however, it must be noted that these values are specific to the climate, soil, apple cultivar, processing method, and storage conditions. The cellulose and hemicellulose content in the AP used in our experiments was 15.42 \(\pm\) 0.3 g/L and 12.45 \(\pm\) 0.09 g/L, respectively. According to the literature [17; 67; 68], the cellulose content in apple pomace varies from 12.7% to 21.22%. In comparison, the hemicellulose content falls within the range of 7.2-14.7%, and these results support the data presented in Table 1. The lignin content obtained by us (15.23 \(\pm\) 0.12%) is lower than reported by [PERSON] et al. (19.8%) [69] but correlates with the values presented by others (15.2-23.5%) [67; 68; 70; 71]. The sum of the assayed cellulose, hemicellulose, and lignin makes up less than half of the dry matter of the analysed apple pomace. Therefore, based on the literature sources mentioned earlier, it can be inferred that the remaining portion includes pectin and some simple sugars like fructose and glucose, as well as organic acids and mineral constituents from the apple tissue. These compounds were not specifically assayed at this stage of our experiment due to its design; however, they were analysed in more detail in the apple-pomace-derived hydrolyses (e.g., pectin derivative--galacturonic acid was assayed).
# Uncertainty Quantification Approach on Numerical Simulation for Supersonic Jets Performance
## 2 CFD Model  
The fluid domain consists of a 2D axisymmetric De Laval nozzle with an \"expansion volume\" (Figure 1) at the exit, which represents the discharge environment at constant pressure. The axisymmetric RANS equations were solved using an 'axis' boundary condition at the nozzle axis, as common practice [21]. A structured grid was used for the spatial discretization. As shown in Figure 2, a grid refinement was performed near the nozzle wall, in order to ensure a y+ value close to one. The Reynolds Averaged Navier Stokes Equations (RANS) in the steady form are numerically solved with the addition equation for the turbulence closure. The two equation k-\(\omega\) SST turbulence model is selected based on previous experience on the application case; it allows a good accuracy both in proximity and far from the walls. A second order upwind scheme was selected for the spatial discretization, because if the flow is not aligned with the structured mesh (e.g., oblique shock waves) the first-order convective discretization is not appropriate.  
Table 1 and Figure 1 summarize and show the set of boundary conditions used in the CFD simulations:  
* total pressure and total temperature at the nozzle inlet;
* static pressure on expansion volume (outlet domain) side walls.  
Figure 1: Fluid domain.  
Figure 2: Nozzle mesh.  
In the first part of the work, the operating fluid is air as an ideal compressible gas with constant Cp. [PERSON]'s law with three coefficients (Equation (1)) is adopted to model the variation of viscosity with temperature.  
\[\mu=\mu_{\text{ref}}\bigg{(}\frac{T}{T_{\text{ref}}}\bigg{)}^{\frac{3}{2}}\bigg{(} \frac{T_{\text{ref}}-\text{S}}{T-\text{S}}\bigg{)} \tag{1}\]  
In the second part of the article, the working fluid is natural gas treated as an ideal and compressible gas and the viscosity is computed through Sutherland's law with suitable coefficients for methane.
## 5 IEEE 802.15.4 Technology
### IEEE 802.15.4 Physical Layer  
The 802.15.4 core system consists of an radio frequency (RF) transceiver and the protocol stack, depicted in Figure 5.  
The 802.15.4 physical layer operates in three different unlicensed bands (and with different modalities) according to the geographical area where the system is deployed. However, spread spectrum techniques are wherever mandatory to reduce the interference level in shared unlicensed bands.  
IEEE 802.15.4 specifies a total of 27 half-duplex channels across the three frequency bands and is organized as follows:  
* the 868 [MHz] band: only a single channel with data rate 20 [kbps] is available; \(-92\) [dBm] RF sensitivity required and ideal transmission range approximatively equal to 1 [km];
* the 915 [MHz] band: ten channels with rate 40 [kbps] are available; the receiver sensitivity and the ideal transmission range are the same of the previous case;
* the 2.4 [GHz] ISM band: sixteen channels with data rate 250 [kbps] available; minimum \(-85\) [dBm] RF sensitivity required and ideal transmission range equal to 220 [m].  
The ideal transmission range is computed considering that, although any legally acceptable power is permitted, IEEE 802.15.4-compliant devices should be capable of transmitting at \(-3\) [dBm].  
According to the energy efficiency issue, low rate and low duty cycle are provided. IEEE 802.15.4-compliant devices are active only during a short time and the standard allows some devices to operate with both the transmitter and the receiver inactive for over \(99\%\) of time.  
Figure 5: ZigBee protocol stack.
# Quaternion Signal Analysis for Detection of Broken Rotor Fault Degrees in Induction Motors
## 2 Methodology
### QSA Method
\[q(t)=V_{h}(t)+V_{d}(t)i+V_{a}(t)j+V_{s}(t)k \tag{2}\]5  
This means that if we know the values of \(q\) and \(q_{d}\), we can obtain \(q_{r}\) through the product of general rotation matrix and \(q\) presented in (12) [21] using the coefficients values shown in (2) and (4). This value contains the two-sample quaternion model, which helps us obtain statistical values of multiple models to quantify BRBs later. \[q(t)=V_{h}(t)+V_{d}(t)i+V_{a}(t)j+V_{s}(t)k \tag{2}\]6 Rotation of \(q(t)\) to obtain \(q_{d}(t)\). \[q_{r}= \begin{bmatrix}1-2V_{ad}^{2}-2V_{sd}^{2}&2(V_{dd}V_{ad}+V_{hd}V_{sd})&2(V_{ dd}V_{sd}-V_{hd}V_{ad})\\ 2(V_{dd}V_{ad}-V_{hd}V_{sd})&1-2V_{dd}^{2}-2V_{sd}^{2}&2(V_{ad}V_{sd}+V_{hd}V_{dd} )\\ 2(V_{dd}V_{sd}+V_{hd}V_{ad})&2(V_{ad}V_{sd}-V_{hd}V_{dd})&1-2V_{dd}^{2}-2V_{ad} ^{2}\end{bmatrix}\begin{bmatrix}V_{d}\\ V_{a}\\ V_{s}\end{bmatrix}  \tag{12}\]  
Next, we compute and organize the rotational models for the signal based on the pure quaternion \(q_{r}(t)\) as defined in (13) [23]. Here, \(q_{r0}\) is set to zero, reflecting that the rotation is confined to the three-dimensional space of the imaginary components. \[q_{r}(t)=q_{r1}(t)i+q_{r2}(t)j+q_{r3}(t)k \tag{13}\]  
We have vibration signals containing 29,999 samples, and the quaternion \(q_{r}\) is calculated for each sample with the next one, resulting in 29,998 pure quaternions. After this, we calculate the modulus of each quaternion using Equation (14), which generates a constant value for each one. \[|q_{r}|=\sqrt{q_{r1}^{2}+q_{r2}^{2}+q_{r3}^{2}} \tag{14}\]  
Using these values of consecutive models from \(q_{r}(t)\), we can assemble a vector **w**[n] by selecting \(N\) samples from the signals as in (15) [15], where \(t+N\Delta t\) refers to the sample of the rotation obtained. \[\textbf{w}[n]=[|q_{r}(t)|,|q_{r}(t+\Delta t)|,|q_{r}(t+2\Delta t)|\dots|q_{r}(t +N\Delta t)|] \tag{15}\]  
Statistical analysis of **w**[n] is essential for quantitatively characterizing the models and defining diagnostic value ranges for each BRB condition. Following the workflow in Figure 1, the mean (\(\mu\)) was selected as the statistical measure with the best-fitting performance. The mean of **w**[n] reflects the evolution of signals over time and is calculated according to (16). \[\mu=\frac{1}{N}\sum_{n=1}^{N}\textbf{w}[n] \tag{16}\]  
Each test file for each BRB condition contains 29,999 samples, providing 29,998 moduli of \(q_{r}(t)\). The window **w**[n] utilizes \(N\) values from these 29,998 moduli to compute \(\mu\). By shifting the window **w**[n] across the test file, multiple values of \(\mu\) can be calculated. This approach enables calculating and evaluating polynomial and Fourier models that describe BRB behavior, as shown in Section 2 of Figure 1.
## 2 Problem Formulation
### Problem Formulation  
For Student's t distribution, if the variable \(s_{t}\) follows a t distribution, it is denoted as \(s_{t}\sim St(\mu,\delta,\eta)\), where \(\mu\) is the mean, \(\delta\) is the scale matrix, and \(\eta\) is the degrees of freedom (DOFs). According to [20], the probability density function (pdf) of \(St(\mu,\delta,\eta)\) is given by Equation (1).  
\[\begin{array}{rcl}p(s)&=&\frac{\Gamma\left(\frac{\pi+2}{2}\right)}{\Gamma \left(\frac{\pi}{2}\right)}\frac{1}{\left(\eta\pi\right)^{\frac{1}{2}}}\frac{ 1}{\sqrt{\det(\delta)}}\bigg{\{}1+\frac{(s-\mu)^{T}s^{-1}(s-\mu)}{\eta}\bigg{\}} ^{-\frac{\eta+2}{2}}\\ &=&\frac{\Gamma\left(\frac{\pi+2}{2}\right)}{\Gamma\left(\frac{\pi}{2}\right) }\frac{1}{\left(\eta\pi\right)^{\frac{1}{2}}}\frac{1}{\sqrt{\det(\delta)}} \bigg{\{}1+\frac{\Delta^{2}}{\eta}\bigg{\}}^{-\frac{\eta+2}{2}}\end{array}, \tag{1}\]  
where \(m\) represents the size of the \(s_{t}\) dimension. The equation representing the state for the \(i\)th local adaptive Student's t EKF is as follows:  
\[\underbrace{\left[\begin{array}{c}x_{t}^{(i)-}\\ px_{t}^{(i)-}\\ y_{t}^{(i)-}\\ y_{t}^{(i)-}\end{array}\right]}_{\mathbf{s}_{t}^{(i)-}}=\underbrace{\left[ \begin{array}{cccc}1&0&\Delta t&0\\ 0&1&0&\Delta t\\ 0&0&1&0\\ 0&0&0&1\end{array}\right]}_{\mathbf{A}}\underbrace{\left[\begin{array}{c}x _{t}^{(i)}\\ px_{t}^{(i)}\\ y_{t}^{(i)}\\ y_{t}^{(i)}\end{array}\right]}_{\mathbf{s}_{t}^{(i)}}+\omega_{t}^{(i)},i\in[1,N], \tag{2}\]  
Figure 2: The structure of the UWB localization with the adaptive distributed student’s t extended Kalman filter with Allan variance.  
where \(\left(x_{t}^{(i)},y_{t}^{(i)}\right)\) denotes the target object's position information at the time index \(t\) estimated by the \(i\)th local filter, \(\left(vx_{t}^{(i)},vy_{t}^{(i)}\right)\) denotes the target object's velocity information at the time index \(t\) estimated by the \(i\)th local filter, and \(\omega_{t}^{(i)}\sim St\Big{(}0,\mathbf{Q}_{t}^{(i)},\eta_{1,t}^{(i)}\Big{)}\) is the system noise under Student's t distribution. Here, \(\mathbf{Q}_{t}^{(i)}\) is the scale matrix for the \(i\)th local filter, and \(\eta_{1,t}^{(i)}\) is the DOFs.  
\[r_{t}^{(i)}=\underbrace{\sqrt{\left(x_{t}^{(i)-}-\left(x_{R}^{(i)}\right) \right)^{2}+\left(y_{t}^{(i)-}-\left(y_{R}^{(i)}\right)\right)^{2}}}_{g\left( \mathbf{s}_{t}^{(i)-}\right)}+\mathbf{v}_{t}^{(i)},i\in[1,N], \tag{3}\]  
where \(r_{t}^{(i)}\) means the range from the \(i\)th UWB BS to the target object, \(\left(x_{R}^{(i)},y_{R}^{(i)}\right)\) is the \(i\)th UWB BS's position, and the \(\mathbf{v}_{t}^{(i)}\sim St\Big{(}0,\mathbf{R}_{t}^{(i)},\eta_{2,t}^{(i)} \Big{)}\) is the measurement noise under Student's t distribution, Here, \(\mathbf{R}_{t}^{(i)}\) is the scale matrix for the \(i\)th local filter, and \(\eta_{2,t}^{(i)}\) is the DOFs.
# Evaluation of Orbital Drift Effect on Proba-V Surface Reflectances Time Series
###### Abstract  
Multi-temporal consistency of space-borne observations is an essential requirement for studying inter-annual changes and trends of satellite-derived biophysical products. The Proba-V mission, launched in 2013, was designed to ensure the continuity of the SFOT-VEGFATION long-term data record of global daily observations for land applications. The suitability of Proba-V to provide a temporally consistent data record is, however, potentially jeopardized by the orbital drift effect, which is known to induce spurious trends in time series. The aim of this paper is therefore to evaluate, for the first time, the orbital drift effect on Proba-V surface reflectance time series at 1 km resolution. In order to reliably identify such an effect, a two-fold approach is adopted. A simulation study is first defined to predict the temporal anomalies induced by the drifting illumination conditions. The numerical simulations are used as a benchmark to predict the impact of the drift for a range of sun-viewing angles. Real observations are then analyzed over a large set of land sites, globally spread and spanning a wide range of surface and environmental conditions. The surface anisotropy is characterized using the Ross-Thick Li-Sparse Reciprocal (RTLSR) Bidirectional Reflectance Distribution Function (BRDF) model. Both the simulation and the analysis of real observations consistently show that the orbital drift induces distinct and opposite trends in the two sides of the sensor across-track swath. Particularly, a positive drift is estimated in backward and a negative one in the forward scattering direction. When observations from all angular conditions are retained, these opposite trends largely compensate, with no remaining statistically significant drifts in time series of surface reflectances or Normalized Difference Vegetation Index (NDVI). As such, the Proba-V archive at 1 km resolution can be reliably used for inter-annual vegetation studies.  
Proba-V; BRDF; orbital drift; time series; surface reflectances Article
# An Unequal Clustering and Multi-Hop Routing Protocol Based on Fuzzy Logic and Q-Learning in WSNs
## 4 Proposed Method  
The FQ-UCR protocol employs an unequal clustering technique, which is executed in two major phases: clustering and data transmission. During the clustering phase, the algorithm leverages an FIS to determine the probability of nodes becoming CHs and their competition radius, prioritizing nodes with higher probabilities for CH selection. In the data transmission phase, Q-learning optimizes the multi-hop routing process, with relay CHs being chosen based on their updated Q-values to relay data to the BS.  
Figure 3 presents a flowchart that outlines the steps of the proposed method.  
Figure 3: Flow chart of proposed FQ-UCR protocol.