esa-sceva/satcom-chunk-collection huggingface dataset:
# Effective Use of Flax Biomass in Biorefining Processes
## 2 Materials and Methods
### Biocomposites Production Process
#### 2.5.4 Resistance of Biocomposites to Mold Fungi  
The samples of polylactide biocomposites with 20% biomass content from three varieties of flax: PLA-LS-20, PLA-DP-20 and PLA-FF-20 and samples of pure polylactide--PLA 3251D as references were used for the test of resistance to mold fungi. The tests were carried out in accordance with the methodology specified in the standard ISO 846 [32]. The tested samples were allocated on an agar medium and inoculated with a mixture of five strains of mold fungi, which most often cause the decomposition of cellulose: _Aspergillus niger_, _Chaetomium globosum_, _Penicillium ochrochloron_, _Paecilomyces variotii_ and _Penicillium funcilosum_. The agar medium with mineral salts was used, prepared in accordance with the standard, using Chempur reagents and agar powder from Merck, whereas tested microorganisms originated from the pure culture collection of the Institute of Fermentation Technology and Microbiology, Technical University of Lodz, Poland.  
Then, the inoculated samples were placed in a climatic chamber in optimal conditions for mold development, i.e., at a temperature of 29 \(\pm\) 1 \({}^{\circ}\)C and relative air humidity of 90% for 28 days. These samples were designated as series I. For comparison, samples of biocomposites in each variant, which were allocated on a sterile agar medium and not inoculated with a mixture of mold fungi, were placed in the same climatic conditions. These samples were designated as series S. In the case of samples not inoculated with the test fungi, the degree of infection with microorganisms other than the test fungi was assessed. In addition, biocomposite control samples for each variant, not incubated, were left and stored under standardized conditions of room temperature and relative humidity. These samples were designated as series 0.  
After 28 days, visual assessment of fungal growth on the samples' surfaces was assessed on a scale from 0 to 5, where:  
0--no visible growth under the microscope  
1--growth invisible to the naked eye but clearly visible under the microscope2--growth visible to the naked eye, covering up to 25% of tested surface 3--growth visible to the naked eye, covering up to 50% of tested surface 4--considerable growth, covering more than 50% of tested surface 5--intensive growth, covering all tested surface.  
Next, the samples were cleansed and mechanical properties were evaluated. All the measurements were performed six times.
# A New Affinely Adjustable Robust Model for Security Constrained Unit Commitment under Uncertainty
## 3 Deterministic Model for Security Constrained Unit Commitment  
The deterministic Security Constrained Unit Commitment is formulated as a mixed-integer linear programming (MILP) problem, minimizing the operating cost of the power system subject to system network constraints, under both normal and contingency (\(N-1\)) operation conditions, given by Equations (8) to (19) as shown below. The notation of the model is given in Appendix A:  
\[\min_{x,y,z,g}\quad\sum_{t=1}^{T}\sum_{i=1}^{I}[FC_{i}\cdot x_{i,t}+Csu_{i} \cdot y_{i,t}+Csd_{i}\cdot z_{i,t}]+\sum_{t=1}^{T}\sum_{i=1}^{I}C_{i}\cdot g_{ i,t} \tag{8}\]  
Subject to:  
Binary variable logic:  
\[y_{i,t}-z_{i,t}=x_{i,t}-x_{i,t-1}\quad\forall t\in T,i\in I \tag{9}\]  
\[y_{i,t}+z_{i,t}\leq 1\quad\forall t\in T,i\in I \tag{10}\]  
Initial on-off status of generator \(i\) at \(t=0\) (11), minimum up (12) and down time constraints (13):  
\[x_{i,t}=g_{i}^{on-off}\quad\forall i\in I,t\in L_{i}^{up,min}+L_{i}^{down,min} \tag{11}\]  
\[\sum_{t=t-g_{i}^{low}+1}^{t}y_{i,tt}\leq x_{i,t}\quad\forall i\in I,\quad \forall t\geq L_{i}^{up,min} \tag{12}\]  
\[\sum_{t=t-g_{i}^{lown}+1}^{t}z_{i,tt}\leq 1-x_{i,t}\quad\forall i\in I,\quad \forall t\geq L_{i}^{down,min} \tag{13}\]where:  
\[L_{i}^{up,min}=min[T,(g_{i}^{up}-g_{i}^{up,init})\cdot g_{i}^{on-off}]\]  
\[L_{i}^{down,min}=min[T,(g_{i}^{down}-g_{i}^{down,init})\cdot(1-g_{i}^{on-off})]\]  
Minimum and maximum generator output constraint:  
\[g_{i}^{min}\cdot x_{i,t}\leq g_{i,t}\leq g_{i}^{max}\cdot x_{i,t}\quad\forall i \in I,t\in T \tag{14}\]  
Ramping constraints:  
\[-ramp_{i}^{down}\leq g_{i,t}-g_{i,t-1}\leq ramp_{i}^{up}\quad\forall i\in I,t\in T \tag{15}\]  
Net power balance constraints:  
\[Pnet_{s,t}=\sum_{i}A_{i}^{s}\cdot g_{i,t}+\sum_{w\in W}A_{w}^{s}\cdot\hat{g}_ {w,t}+\sum_{p\in P}A_{p}^{s}\cdot\hat{g}_{p,t}-\hat{d}_{s,t} \tag{16}\]  
\[\forall s\in S,t\in T\]  
\[\sum_{s}Pnet_{s,t}=0\quad\forall t\in T \tag{17}\]  
Power flow constraint under normal condition (18):  
\[-F_{l}^{\max}\leq\sum_{s}PTDF_{l,s}\cdot Pnet_{s,t}\leq F_{l}^{\max}\qquad \forall l\in L,t\in T \tag{18}\]  
Power flow constraint under contingency (\(N-1\)) conditions (19):  
\[-F_{l}^{max}\cdot TCF\leq\sum_{s}OTDF_{l,s}^{k}\cdot Pnet_{s,t}\leq F_{l}^{max }\cdot TCF\qquad\forall l\in L,k\in K,t\in T \tag{19}\]  
\(OTDF_{l,s}^{k}\) parameter is the sensitivity factor between line \(l\) and generator bus \(s\) when line \(k\) was opened. It is computed as follows:  
\[OTDF_{l,s}^{k}=PTDF_{l,s}+LODF_{l,k}\cdot PTDF_{k,s}\qquad\forall l\in L,k\in K,s\in S \tag{20}\]  
In this equation, \(PTDF_{l,s}\) is the power transfer distribution factor of the line \(l\) given a power injection in bus \(s\). Likewise, \(PTDF_{k,s}\) is a power transfer distribution factor of the opened line \(k\) given a power injection in bus \(s\). Finally, \(LODF_{k,s}\) is line outage distribution factor of the line \(l\) when line \(k\) is opened.
## 3 Results  
### Study Selection  
The automatic search of electronic databases selected 540 studies (219 from the Web of Science and 321 from Scopus, respectively). A preliminary analysis of the selected studies identified 209 duplicates, which were subsequently removed. Next, we screened the remaining 331 studies by abstract to verify that they were relevant to our review's purpose: after this evaluation, an additional 117 studies were manually removed. Notably, these studies were out of the scope of the review because they used other technologies (e.g., optical fibers, thermal cameras, micro cameras), methodologies (e.g., acoustic emission analysis, vibrational signals, piezoelectric signals), or were applied to specific components of the targets (e.g., cables, wind turbines, polymers). The remaining articles (214) were considered suitable for retrieval of detailed information from the full text: for 67 articles, the full text was not available (only the abstract was directly accessible), and thus were discarded.  
Therefore, only 147 articles were further screened for eligibility according to the established criteria: of these, 3 were discarded because they were published in a non-English language; 21 because of the type of publication (e.g., review, book, data report); 40 because they had a different focus (e.g., traffic load estimation, load forces, performance comparisons between cameras) or did not provide results; and 10 because more comprehensive and extended work was already published. In the end, after the automatic selection process and manual screening, 73 articles were eligible and included in this review. The PRISMA flow diagram summarizes the automatic selection and manual screening process (Figure 1).  
Figure 1: PRISMA flow diagram related to the overall screening procedure.  
The general analysis of the articles selected and included in this review confirmed the use of computer vision approaches and image processing techniques for various SHM applications while revealing a preliminary breakdown into two macro-categories: studies that implemented vision systems (71%) and studies using image datasets available from other studies (29%). The following sections present and detail the selected studies according to this primary categorization, while Figure 2 shows their distribution per year.
## 2 System Model
### System Model
Therefore, according to [32], the probability density function(PDF) of \(h_{ss}\) is formulated as:  
\[f_{h_{ss}}\left(x\right)=\alpha\exp\left(\beta x\right){{}_{1}F_{1}}\left(m_{ ss},1,\lambda x\right) \tag{4}\]  
where \({{}_{1}F_{1}}(\cdot,\cdot,\cdot)\) is the confluent hypergeometric function, and \(\alpha,\beta\) and \(\lambda\) are formulated respectively as:  
\[ \begin{array}{l}\alpha{=}\frac{1}{2b_{ss}}\Big{(}\frac{2b_{ss}m_{ss}}{2b_{ss }m_{ss}+\Omega_{ss}}\Big{)}^{m_{ss}}\\ \beta=-\left(\frac{1}{2b_{ss}}\right)\\ \lambda=\frac{\Omega_{ss}}{2b_{ss}(2b_{ss}m_{ss}+\Omega_{ss})},\end{array}  \tag{5}\]  
where \(2b_{ss}\) is the average power of the scatter component, \(m_{ss}\) denotes the Nakagami fading parameter, and \(\Omega_{ss}\) is the average power of the line-of-sight (LOS) component. Similarly, the power of the interference link \(P_{i}\) that is between the satellite user to the terrestrial base station can be calculated as:  
\[P_{i}{=}P_{t}G_{t}\left(\theta^{\prime}\right)G_{BS}L_{sp}h_{sp}, \tag{6}\]  
where \(G_{t}\left(\theta^{\prime}\right)\) is transmit antenna gain, the off-axis angle \(\theta^{\prime}=\arccos\left(\cos\left(\theta\right)\cos\left(\psi\right)\right)\), and \(\psi\) represents the angle between the projected main lobe of the satellite user and the BS [33]. In addition, \(G_{BS}\) is the gain of base station receiver antenna, and \(L_{sp}\) is free space loss. In addition, the Nakagami fading model is adopted to present the interference link, and the PDF of \(h_{sp}\) is as follows:  
\[f_{h_{sp}}\left(x\right)=\frac{\left(\frac{m_{sp}}{h_{sp}}\right)^{m_{sp}}x^{m_ {sp}-1}}{\Gamma\left(m_{sp}\right)}\exp\left(-\frac{m_{sp}}{\Omega_{sp}}x \right), \tag{7}\]  
where \(\Gamma\left(m_{sp}\right)\) is the Gamma function [20], \(\Omega_{sp}\) is the signal average power, and \(m_{sp}\) is the Nakagami fading parameter. Moreover, the Additive Gauss White Noise (AWGN) is considered as independent and zero mean, and its distribution is denoted as \(CN\left(0,N_{0}\right)\). Additionally, due to the large distance between the primary terrestrial user to the satellite and the limited power control of the terrestrial user, the interference caused by primary terrestrial user to the satellite can be neglected [20]. In addition, it is assumed that it is possible to obtain the perfect Channel State Information (CSI) of \(h_{ss}\) and \(h_{sp}\) by satellite user [34], which can be accomplished through the database or the training model [26].
## 2 A Proposed ASPC Approach for OCRs in a DG-Integrated Distribution Network  
### Modelling of OC Relay Characteristics  
In this study, the definite-time OC functions (e.g., 50P for the phase-to-phase fault protection; 50G for the ground fault protection) are deployed as primary protection, while the inverse-time OC functions (e.g., 51P for phase-to-phase fault protection and 51G for ground fault protection) are used for both primary and backup protection. Parameter settings of definite-time and inverse-time OC functions are separate; however, it always allows that the primary protection is activated before the backup protection. The IEEE C37.112 TM-2018 standard characteristics are considered for the relay coordination problem [57; 58]. The operating time equation of the standard inverse-time OC function is given by Equation (1):  
\[T_{F51\_ik}=TDS_{i}\left[\frac{A}{\left(I_{f\_ik}/I_{pu\_i}\right)^{B}-1}+C\right] \tag{1}\]  
where \(T_{F51\_ik}\) is the operation time of the \(i\)-th relay (\(R_{i}\)) for a \(k\) fault location in the protected line/zone of the distribution network; \(TDS_{i}\) and \(I_{pu\_i}\) are the time dial setting (\(TDS_{i}\)) and pick-up current (\(I_{pu\_i}\)) parameters of the \(i\)-th relay, respectively; \(I_{f\_ik}\) is the fault current seen by the \(i\)-th relay for the fault at \(k\) location; and \(A\), \(B\), and \(C\) are the coefficients of the inverse-time protection curve. Figure 1 illustrates the protection characteristic curves of definite-time (50) and inverse-time (51) OC functions.
# Automatic Extraction of Material Defect Size by Infrared Image Sequence
## 5 Discussion and Analysis
### Influence of Size of Sensitive Region on Detection Results
#### 5.1.1 Influence of Size of Sensitive Region in the Primary Detection  
The setup of a single sensitive region during the primary detection is shown in Figure 13. We adopt a random method to locate a point on the extracted edge; then, we fix the point and sample the sensitive region size from \(3\times 3\) pixels, generally expending to \(15\times 15\) pixels around the point. That is, both the width and the height are changed from 3 pixels to 15 pixels. The step size change in both the width and height is 1 pixel, so there are 169 test areas in total.  
The standard deviations are calculated for every sensitive region in each image in the sequence, and the time history curves of standard deviation are obtained similar to the curves in Figures 14 and 19. All peaks of the curve are collected to obtain the maximum standard deviation surface of different defects related to the sensitive regions with different widths and heights, as shown in Figure 22. Figure 22a,b are the surfaces corresponding to defect #1 and #2, respectively, and they have same shapes except that the values of standard deviation in Figure 22a are larger than those in Figure 22b. From these two figures, it can be seen that the values of standard deviation varying with height are larger than with width. It shows that the sensitive region change in height is more sensitive than in width, i.e., the direction of perpendicular to edges is much more sensitive than along the edges.  
Due to each maximum of standard deviation corresponding to a frame, the selected frames are shown in Figure 23a,b for defect #1 and defect #2, respectively. For defect #1, all the selected frames are in the interval of (165, 196), which are similar to the results shown in Figure 17 and can be used as a suitable frame to carry out the primary detection. However, not all of the selected frames are suitable for defect extraction, and 5.92% of the results (10 data) should be eliminated because defects cannot be fully displayed. Frame 347 and Frame 646 in the middle and late stage of thermal diffusion are frequently chosen when the height of the size of the sensitive region is the minimum of 3. As mentioned before, the gray value of defect #1 plays a dominant role in the binary image segmentation of the entire image, leading to a complete extraction of defect #1 but incomplete extraction of defect #2. The results show that the higher the precision of defect boundary extraction, the less affected by the size of the sensitive area.  
Figure 22: The maximum standard deviation surface related to width and height: (**a**) The maximum standard deviation surface for #1; (**b**) The maximum standard deviation surface for #2.
# Enhancing Railway Network Safety by Reproducing Wheel-Rail Electrical Contact on a Laboratory Scale
## Appendix A Area of an Elliptical Sector
### Calculation of \(A_{relative}\) renewed contact  
Figure 11 illustrates the elliptical wheel-rail contact surface at instant \(t_{0}\) and \(t_{0}+\Delta t\) when the train is running at speed \(v\). Depending on \(v\), there may be overlapping between these two ellipses. Let \(A_{renewed\ contact}(t_{0}+\Delta t)\) be the renewed contact surface during \(\Delta t\).  
The calculation of \(A_{renewed\ contact}(t_{0}+\Delta t)\) as a function of the displacement speed \(v\) is presented hereafter. Let \(O\) and \(O^{\prime}\) be the centers of the two contact ellipses at instants \(t_{0}\) and \(t_{0}+\Delta t\), respectively. Also, let \(M\) be the intersection point of these two ellipses.  
The area of the ONM triangle, denoted \(S\), can be expressed as:  
\[S=\frac{1}{2}\,x_{M}\cdot y_{M}\]  
With:  
\[\text{M}\left\{\begin{matrix}x_{M}=\frac{1}{2}v\Delta t\\ y_{M}=b\sqrt{1-\frac{v^{2}dt^{2}}{4a^{2}}}\end{matrix}\right.\]  
Then:  
\[S=\frac{1}{2}v\cdot b\cdot\Delta t\sqrt{1-\frac{v^{2}\Delta t^{2}}{4a^{2}}}\]  
Which leads to the expression of \(\alpha\), the angle between [ON] and [OM]:  
\[\alpha=\arctan\left(\frac{2b\sqrt{1-\frac{v^{2}\Delta t^{2}}{4a^{2}}}}{v\Delta t }\right)\]  
The renewed contact surface \(A_{renewed\ contact}\) after a track circuit signal period \(\Delta t\) (_hatched surface_) is:  
\[\frac{1}{2}A_{renewed\ contact}(t_{0}+\Delta t)=\frac{\pi ab}{4}+[A]_{\delta} ^{\frac{a}{2}}-([A_{renewed\ contact}]_{0}^{\frac{a}{2}}-2S)\]  
Based on the calculation of the area of an elliptical sector in the Section 1 of the appendix:  
\[A_{renewed\ contact}(t_{0}+\Delta t)=\pi ab-2 ab\cdot arctan\left(\frac{a}{b} \tan(\alpha)\right)+4S\]  
And the relative renewed surface is given by:  
\[A_{relative\ renewal\ contact}(t_{0}+\Delta t)=\frac{A_{renewed\ contact} \ (t_{0}+\Delta t)}{\pi ab}\]  
Then:  
\[A_{relative\ renewal\ contact}(t_{0}+\Delta t)=1-\frac{2}{\pi}\arctan an\left( \frac{a}{b}\tan(\alpha)\right)+\frac{4S}{\pi ab}\]
# Effects of Movement for High Time-Bandwidths in Batched Pulse Compression Range-Doppler Radar
## 1 Introduction
Common emitter waveforms are digital broadcasting channels like DAB, DVB-T or DVB-T2 based on orthogonal frequency-division multiplexing (OFDM) with an instantaneous bandwidth up to 7.77 MHz at UHF frequencies. More advanced illumination sources include also radar emissions and originally non-classical radar waveforms [8]. The high DPI and stationary clutter levels 20 to 25 dB over the noise level are typically addressed by either the signal orthogonality of OFDM [9; 10], particular orthogonal MIMO waveforms [11] or e.g., ECA cancellation [12]. Since the actual detection process requires some threshold margin to eliminate false alarms, the common batched range-Doppler implementation [8] has to provide the remaining 60 to 70 dB for target detection by coherent pulse compression in its range-Doppler processing. This publication is focused on the effects that arise particularly for relatively fast moving targets starting with the particular case of a constant effective velocity. It thereby includes a fundamental analysis of a high integration based detection capability that aims to highlight the influence of large instantaneously processed bandwidths. This leads to a better understanding of mostly degradations and their causes in particular if high instantaneous bandwidth waveforms are considered. After an overview about the stationary principle of batched range-Doppler processing in bistatic radars and the relation of the coherent integration gain to the time-bandwidth product are given (Section 2), the limits for not anymore stationary considerations are introduced (Section 2.5). This is followed by examples of the processing with an exceeded limit and a modelling of the Doppler effect in its original scaling relations (Section 3). After this, the implication of mainly a constant effective velocity in a common batched pulse compression implementation is shown (Section 4) and the linear movement impacts are quantified (Section 5). The article proceeds with a discussion of time-scale analysis compensation approaches under practical considerations of target motion and in relation to the results shown (Section 6). Although this analysis tries to be as generic as possible, most effects are given in the context of cyclic prefix OFDM waveforms and findings from DVB-T based passive radar measurement sets.
# Bioelectrical Signals for the Diagnosis and Therapy of Functional Gastrointestinal Disorders
## 5 Large Intestine
### Therapies  
A number of electrical therapy methods, such as sacral nerve stimulation (SNS), colonic electrical stimulation (CES), and TES, have been introduced for treating FBDs such as FI and constipation. SNS is performed with a stimulator and electrodes implanted on sacral nerves 2-4 which provide continuous stimulation to the nerve. SNS implants were initially introduced to the market by Medtronic as InterStim, and since then, Axonics Modulation Technologies Inc (Irvine, CA, USA) have developed smaller and rechargeable implants [136]. SNS has been trialed in a number of studies to improve symptoms and quality of life in constipation, but the evidence that SNS is effective for constipation is limited [137; 138]. On the other hand, several controlled studies have shown that the objective andsubjective measures, functional outcomes and quality of life for FI patients improved significantly with SNS compared to controls. In addition, a significant reduction in weekly incontinence episodes and incontinence scores was observed, while the ability to defer defecation was increased [139; 140]. The clinical outcomes suggest that SNS can be a safe and effective alternative treatment for FI patients, but adverse events and cost are still an important factor in practice.  
Since the SW activity pattern along the colon is non-uniform, electrical stimulation or pacing is difficult to design and execute. A limited number of animal studies have trialed CES to improve colonic contractions and motility patterns, and reported that CES accelerated colonic transit and induced colonic contractions [141; 142]. Similarly, TES was applied to patients with constipation, and it was reported that there was a significant improvement in the quality of life, colonic transit time and laxative use [143; 144]. However, more controlled studies are required to confirm the efficacy of both CES and TES.