esa-sceva/satcom-chunk-collection huggingface dataset:
# Aromatic Potential of Diverse Non-Conventional Yeast Species for Winemaking and Brewing
## 3 Results and Discussion
### Microwine
#### 3.1.1 Fermentation Performance
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Strain 1** & **Strain 2** & **Ratio** & **Sugars (g/L)** & **Ethanol\% (v/v)** \\ \hline \hline \multicolumn{1}{l}{Lalvin T73} & & \(11\pm 4.4\,^{f}\) & \(20\pm 0.0\,^{\text{b}}\) \\ CBS 772.71 & & & \(163\pm 7.8\,^{\text{a-d}}\) & \(13\pm 0.3\,^{\text{d}}\) \\ CBS 772.71 & & \(1.1\) & \(63\pm 1.7\,^{\text{d-f}}\) & \(17\pm 1.1\,^{\text{b,c}}\) \\ CBS 2567 & & & \(170\pm 1.4\,^{\text{a-c}}\) & \(11\pm 0.1\,^{\text{d,e}}\) \\ CBS 2567 & & & \(30\pm 1.3\,^{\text{e-f}}\) & \(21\pm 0.8\,^{\text{a,b}}\) \\ CBS 5552 & & & \(20\pm 2.1\,^{f}\) & \(18\pm 1.1\,^{\text{b,c}}\) \\ CBS 5552 & & & \(32\pm 0.6\,^{\text{e-f}}\) & \(18\pm 0.3\,^{\text{b,c}}\) \\ CBS 6641 & & & \(157\pm 2.7\,^{\text{a-d}}\) & \(12\pm 0.7\,^{\text{d}}\) \\ CBS 6641 & & & \(34\pm 1.4\,^{\text{e-f}}\) & \(24\pm 0.2\,^{\text{a}}\) \\ CBS 7692 & & & \(235\pm 20\,^{\text{a}}\) & nd \\ CBS 7692 & & & \(59\pm 7.3\,^{\text{c-f}}\) & \(19\pm 0.5\,^{\text{b,c}}\) \\ CBS 8031 & & & \(173\pm 5.7\,^{\text{a,b}}\) & \(13\pm 0.3\,^{\text{d}}\) \\ CBS 8031 & & & \(73\pm 2.3\,^{\text{b-f}}\) & \(18\pm 0.8\,^{\text{b,c}}\) \\ CBS 8849 & & & \(135\pm 1.7\,^{\text{b-f}}\) & \(13\pm 0.0\,^{\text{d}}\) \\ CBS 8849 & & & \(30\pm 3.2\,^{\text{e-f}}\) & \(21\pm 1.2\,^{\text{a,b}}\) \\ CBS 8860 & & & \(250\pm 6.2\,^{\text{a}}\) & nd \\ CBS 8860 & & & \(75\pm 5.4\,^{\text{b-f}}\) & \(22\pm 0.3\,^{\text{a,b}}\) \\ CBS 9716 & & & \(138\pm 24.3\,^{\text{a-e}}\) & \(15\pm 1.0\,^{\text{c}}\)d \\ CBS 9716 & & & \(78\pm 2.0\,^{\text{b-f}}\) & \(22\pm 0.6\,^{\text{a}}\)b \\ CBS 9716 & & & \(170\pm 4.5\,^{\text{a,b}}\) & \(11\pm 0.4\,^{\text{d,e}}\) \\ CBS 10400 & & & \(74\pm 2.0\,^{\text{b-f}}\) & \(18\pm 0.1\,^{\text{b,c}}\) \\ \hline \multicolumn{5}{l}{Letters indicate grouping according to ANOVA.} \\ \end{tabular}
\end{table} 
Table 3: Residual sugars and ethanol in the resulting microwines. ferentations in wine, as well as when fermenting other substrates such as cashew apple juice [3; 21]. Nevertheless, sequential and mixed fermentations can result in a different outcome and cashew apple juice present high quantity of phenolic compounds, which can exert microbial inhibitory effects.
# Fragility Analysis Based on Damaged Bridges during the 2021 Flood in Germany
## 1 Introduction
In this regard, FEMA P-58 seismic performance assessment methodology can be used to produce fragility curves for bridges subjected to floods [36]. The P-58's analytical formulation can be generalized, accounting for adjustments to hazards other than the seismic one. Metrics similar to those adopted in seismic fragility analysis are intended to be used, such as displacements of chosen structural elements [27; 37]. However, the absence of such information for the majority of damaged structures, required the devising of original metrics. This approach is common in flood fragility analysis, as the literature indicates a variety of intensity measures--as opposed to seismic hazards--depending on available data and the investigated failure mode [38; 39; 40]. Recently, a seismic risk approach was used to assess structural damage caused by hydraulic force during the 2021 flood in a portfolio of buildings situated in the Ahr valley. The method employed a seismic damage classification as the basis for the flood damage model [41]. In the present study, evidence was collected from local authorities in the aftermath of the 2021 flood in Germany, classifying damage with the help of surveys, photos and bridge condition ratings. Then, a calibrated hydraulic model representative of the 2021 flood was employed to reconstruct the hazard magnitude at each bridge location [42]. Ultimately, the damage level is linked to a metric representative of the hydraulic force to produce fragility curves. Specifically, Section 2 illustrates the flood event; the bridge database, including recurrent failure modes; and the fragility generation. Section 3 is dedicated to the presentation and discussion of results. Section 4 illustrates the lessons learned and the limitations and briefly discussesshortcomings in existing codes, while Section 5 is dedicated to the concluding remarks and future perspectives for this research.
# Cyberattack on Flight Safety: Detection and Mitigation Using LoRa
### Flight Safety  
The ADS-B system uses an alternative way of displaying aircraft traffic compared to the use of the commonly used traditional radar. ADS-B messages are built from information collected from the aircraft system, as well as from the GPS mounted on the aircraft [5]. Typically, when aircraft are airborne, the ADS-B Out subsystem transmits extended squitters (long Mode S messages) twice per second for airborne position and airborne velocity [11]. The ADS-B In subsystem, which is located at a ground station or installed on the aircraft (ADS-B In for aircraft), receives and processes these messages and builds a map of the nearby aircraft that it detects (Figure 1). ADS-B messages may include additional information such as an urgency code entered by the pilot.  
In addition to the ADS-B system, there is another system related to flight safety mounted on aircraft. The Traffic Collision Avoidance System (TCAS) is a safety system designed to prevent or reduce the severity of a collision between aircraft [1]. The interrogating TCAS receives the target aircraft altitude, and from the measured time delays, it computes the slant range to the target. Since the system uses a time delay, it has to use an accurate clock to calculate the Doppler shift effect. The system calculates the time when the two aircraft will be at the minimum distance from each other, including the height difference if any, and issues an alert to the pilots. Advanced TCASs have additional features such as coordinated actions for the pilots to climb or descend to maintain safe separation [12].
# Recurrent Neural Network Based Short-Term Load Forecast with Spline Bases and Real-Time Adaptation
## Appendix A Spline Bases Functions
### Cubic B-Spline Bases  
\(B\)-splines are a clean, flexible way of making long splines with arbitrary order continuity. \(B\)-splines of order d are connected piecewise polynomial functions of degree \(d-1\) defined over a grid of knots \(\{s_{1},\ldots,s_{i},\ldots,s_{n}\}\). The \(B\)-spline bases for all orders d, \(d\geq\ 0\), may be defined through Cox-deBoor recurrence formula. A spline curve \(S_{d}(s)\) of order \(d\), built from a linear combination of cubic B-spline basis functions \(B_{i,d},\ i=1,\ldots,m\), can be expressed as  
\[S_{d}(s)=\sum_{i}c_{i}B_{i,d}(s) \tag{4}\]where  
\[B_{i,0}(s)=\left\{\begin{array}{cc}1&\text{if }s_{i}\leq s\leq s_{i+1},\\ 0&\text{otherwise}.\end{array}\right. \tag{10}\]  
and \(d\geq 1\)  
\[B_{i,d}(s)=\frac{s-s_{i}}{s_{i+d}-s_{i}}B_{i,d-1}(s)+\frac{s_{i+d+1}-s}{s_{i+d+1 }-s_{i+1}}B_{i+1,d-1}(s) \tag{11}\]  
with \(d\) = 4 corresponding to the cubic B-spline basis function. Note that \(B_{i,d}(s)\) are non-zero only for s in interval \([s_{i},s_{i+d+1})\).
# Satellite Attitude Determination and Map Projection Based on Robust Image Matching
## 3 Method
### Finding Matched Feature Points from Satellite and Base Images
#### 3.2.2 Rough Matching
Finally, following a previous study [26], we discarded the unconfident pairs; that is, pairs whose distance between the VIS feature descriptor and its nearest neighbor in the OLI descriptor exceeded a specified threshold percentage of the distance to the second-nearest neighbor. In this study, we set the threshold to 75%. Because the above screening procedure does not include the geometric constraints, incorrect feature pairs are often not rejected perfectly, as seen from the non-orderly lines in Figure 4. As evident in Figure 4c, correct pairs are rather difficult to find by visual inspection. If all pairs from the rough matching are input to the registration (Equation (7) in Section 3.1), the registration errors would amplify by more than 100 times, relative to a registration using only correct pairs. Therefore, to achieve satisfactory accuracy, we must impose geometric constraints in the robust estimation procedure.
## 2 Materials and Methods
### SAR Image Processing  
The Sentinel-1 mission is a two satellite constellation (i.e., Sentinel-1A and Sentinel-1B) with the same reference orbit [27]. Sentinel-1A and -1B were launched on 3 April 2014 and on 25 April 2016, respectively. In this study, 120 images were acquired from an ascending orbit on path 127 for the years 2014-2020. The data were Ground Range Multi-Look Detected (GRD) level-1 products acquired in Interferometric Wide Swath (IW) mode with High Resolution (HR) and VV polarization. The image processing was automated through multiple batch programs with a Graph Processing Tool (GPT). In general, the SAR data are provided as a digital number (DN), which are relative to the incidence angle and the highest intensity value. Radiometric correction was required to facilitate the comparative analysis with other studies. In this study, we converted the DN values to gamma naught (\(\gamma^{0}\)) as dB values. Multi-look processing was not applied any further so as to preserve the intrinsic spatial resolution of the Sentinel-1 GRD HR products, which were generated by averaging five looks in range and one look in azimuth in the Singe Look Complex (SLC) product. A [PERSON] filter with a 3 \(\times\) 3 window size was adopted to reduce the speckle noise without losing spatial resolution. Terrain correction was applied to the Sentinel-1 images using the Shuttle Radar Topography Mission (SRTM) DEM data. The radar backscattering coefficient (\(\gamma^{0}\)) images were converted into geographic coordinates with respect to the World Geodetic System (WGS84) reference system.
# Spam Review Detection Techniques: A Systematic Literature Review
## 3 Assessment and Discussion of Research Questions  
This section discusses the answers of each RQ, and assessment of RQs is based on the analysis of 76 primary selected studies. Facts of each research question are extracted after the analysis of the selected studies.  
Assessment of RQ1: Which Feature Engineering Techniques Are Used for Construction or Extraction of Features from Review Datasets?  
This section discusses publicly available datasets and existing feature extraction techniques in the area of spam review detection. The construction or extraction of features from data is called feature engineering. Many studies have employed different types of feature engineering techniques to extract the most common features or words in reviews. The most common feature extraction technique is the linguistic approach, and this technique is applied by the bag of words [13] approach. In a bag of words method, features for each review contain single words or small group of words found in a review text. Another feature engineering approach is based on individual spammer's behavioral characteristics. Further, spammer features can be classified into two types: review centric and reviewer centric features [14]. Features that are constructed using information contained in a single review are called review centric features. In reviewer centric features, this study not only considers all reviews written by an author on the same platform, such as Amazon.com or Tripadvisor.com, but also information about the author, such as review rating and time of posted review. Further data collection for one author from multiple platforms is at times not possible (due to change of user identity, non-availability of data, and disperse nature of data).  
Figure 3: Distribution of selected studies with respect to time of publication.
## 2 Materials and Methods
### Interval Type-2 Possibilistic Fuzzy C-Means (IT2-PFCM)  
It is known that the synthesis of Fuzzy C-Means (FCM) and T2 FS gives more room to handle the uncertainties of belongingness caused by noisy environment. In addition, a Possibilistic C-Means (PCM) clustering algorithm was presented, which allocates typicality using an absolute distance between one pattern and one central value. However, the PCM algorithm also has a problem that clustering results respond to the initial parameter values sensitively. To address this sensitivity problem, PFCM algorithm combining FCM and PCM by weighted sum was proposed. However, the PFCM method also has the uncertainty problem of determining the value of the purge constant. It is an important issue to control the uncertainty of the fuzzy constant value because the fuzzy constant value plays a decisive role in obtaining the membership function. To control the uncertainty of these fuzzy constants, hybrid algorithms are suggested, which includes the general type-2 FCM [30; 31; 32], Interval Type-2 FCM (IT2-FCM) [33], kernelized IT2-FCM [34], interval type-2 fuzzy c-regression clustering [35], interval type-2 possibilistic c-means clustering [36; 37], interval type-2 relative entropy FCM [38], particle swarm optimization based IT2-FCM [39], interval-valued fuzzy set-based collaborative fuzzy clustering [40], and others. These T2 FS based algorithms have been successfully applied to areas like image processing, time series prediction and others. Interval Type-2 FCM (IT2-FCM): In fuzzy clustering algorithms, like FCM, the fuzzification coefficient \(m\) plays an important role in determining the uncertainty of partition matrix. However, the value of \(m\) is usually hard to be decided upon in advance. IT2-FCM considers the fuzzification coefficient as an interval (\(m_{1}\), \(m_{2}\)) and solves two optimization problems [41].
# Investigating the Effects of Fish Effluents as Organic Fertilisers on Basil (_Ocimum basilicum_)
## 3 Results
### Plant Growth and Yield
Different letters indicate statistically significant differences between treatments (\(p<0.05\)), using Tukey’s Honest Significant Difference (HSD) test. height (24.56 mm vs. 23.24 mm). For the leaf biomass measurement, the effects of tap water and compost substrate (CT) were in a very close range to the ones from the compost-free substrate and fish water (IF), at 23.5 g vs. 23.75 average weights. Overall, the fish sludge treatment performed the best. The degrees of freedom (df), F-values, and \(p\)-values are reported in Table 4.  
Stem weights were also measured (Figure 5); however, given the lack of sufficient replicates in the measurements (the measurements were taken per treatment batch), complex statistics with reliable results were not achievable. For stem weight and for all fertiliser types, compost substrate (C) performed better than compost-free substrate (I), except for the fish sludge treatment (FS), where the values were equal (9.88 g). In both substrate types, tap water (T) produced the lowest yields, at an average of 3.13 g for IT and 9.25 g for CT; however, the highest yield was produced by fish water for compost substrate (CF), with an average weight of 11.38 g, and by fish sludge for compost-free substrate (IFS), with an average weight of 9.88 g.
# A Study on Cyber Target Importance Quantification and Ranking Algorithm
## 4 Design of Experimental Scenario and Experiment Results
### Experiment Result  
By substituting the scenario data listed in Table 12 into the TIR algorithm (Equation (4)) written in 3.2 and the priority selection method, the target priority was derived as listed in Table 13.  
For target connectivity, according to the Section 2.2.1 cyber target definition, the number of edges between hosts (excluding switches among the parts grouped in the green box in Figure 12) was used. The target criticality was quantified according to Table 6 and replaced with qualitative items, and weights were selected as MI: 1, MV: 0.5, and ACA: 1 when calculating the target exposure. When calculating up to the process in Figure 6, the target connectivity and target criticality scores were synthesized by selecting the initial PR value as 0.1 and performing power iteration four times to make the vector values similar. Finally, the TIR score was calculated by multiplying the target exposure value and sorted in descending order to finally derive the results listed in Table 13. As a result, the S7-315 PLCs were selected as the most important targets. This will allow commanders to easily understand target priorities and issue correct operational guidelines.
# Design and Implementation of a Smart Traffic Signal Control System for Smart City Applications
## 4 EVSP Process
### Traffic Signal Control Algorithm
Another important issue is that the minimum green constraint (\(G_{min}\)) should be followed for the pedestrian protection; this period is the traffic signal guarding mode shown in Figure 10. After traffic signal guarding, the control algorithm switches to the \(Y_{mode}\), which includes a short blinking yellow followed by an all red interval, which is the interaction clearance interval for clearing the intersection to ensure safety. In EV preemption control and blinking stage, the algorithm controls the signal in EV directions with green and blinking yellow to signal to other vehicles, and red for crossing directions to stop vehicular conflict. Once the RSU detects that the EV is leaving the intersection (discussed later in Section 4), it switches back the signal and conducts the compensation process (discussed in Section 5). * Traffic Signal Mode: \(G_{mode}\)  
In \(G_{mode}\), the traffic signal in EV directions is originally green, the task of the control algorithm is to maintain or extend the green period, clearing the vehicles in front of the EV to facilitate it moving through the intersection. The signal control process of \(G_{mode}\) is illustrated in Figure 11, it extends the current green phase and controls the signal in green and blinking yellow in EV directions (S to N, N to S), and stays red in crossing directions (E to W, W to E). The extending green period that should be compensated after EV leaves the intersection is discussed in Section 4.5.
# Assessing the Sustainability of Alternative Shaft Construction Methods
## 2 The WA2 Shaft  
The construction of the DPT, completed in 2006, was a major engineering infrastructure projects in Ireland and aimed to establish a quick connection between the Dublin harbor and the C-ring (M50) and M1 motorways, allowing for a quick transfer of heavy goods, while reducing heavy traffic congestion in the city center, contributing to a more sustainable environment with better air quality and noise reduction [32]. The project comprised the construction of twin-bored tunnels with 12.5 m of external diameter and 2.6 km long. To speed up the construction process, it was decided to build shaft WA2 (concluded in 2001), which served as a launching platform for the twin tunnel excavation. As explained by [PERSON] et al. [34], after early design optimization studies, the final geometry of the circular shaft was established, consisting of an internal diameter of 56.6 m and a depth of around 29.0 m. The construction followed the SBE method [35], with the support consisting of a 1.5 m thick diaphragm wall. A total of 26 panels were constructed, each being slightly over 7 m wide and approximately 32.5 m deep, with this dimension depending on the position of the top of the bearing stratum, a limestone layer. In addition to the capping beam at the top of the shaft, a ring beam, made of concrete with a section of 2.5 \(\times\) 2.5 m\({}^{2}\) with its top at a depth of 10.5 m, was also executed. The concrete base slab was 1 m thick in the general section and 2 m thick around the breakouts of the tunnels [34]. The geometry and support system installed in the WA2 shaft are illustrated in Figure 1.  
At the shaft location, the geology comprised the well-known Dublin boulder clays (DBCs) overlying a carboniferous limestone layer [36]. A detailed assessment of the geological features and geotechnical characteristics of that formation and its units can be found in [PERSON] et al. [36] and [PERSON] [37], respectively. The stratigraphy comprised 2 m of the weathered Upper Brown (UBrBC) unit, followed by the Upper Black (UBkBC) unit, which had a thickness of around 12 m. The third layer was the Lower Brown (LBrBC) unit, with a thickness of 11 m, and, finally, the 7.5 m thick Lower Black (LBkBC) unit was found (see Figure 1). Approximately hydrostatic conditions with a groundwater table located about 2 m below ground level (bottom of UBrBC) were measured by piezometers installed at the site.
# Performability Evaluation of Autonomous Underwater Vehicles Using Phased Fault Tree Analysis
## 3 Performability Evaluation Method Based on Phased Fault Trees
### Performance Evaluation Method
#### 3.2.2 Weighting Method  
In order to define the weight for each performance, the weight definition method of approximate methodologies was taken into consideration based on the FT design results. Approximation approaches and ratio allocation comprise the majority of weighting methods. Each element is given a score via the ratio assignment technique, which takes into account the relative or absolute importance of each element in relation to the other elements. Each element's weight is determined by dividing the total number of points by its ratio. Without information such as relative comparisons, approximation algorithms determine weights for each component by taking into account ordinal statistical principles [65]. The rank summed weighting (RS) method uses important information based on the ranking order of each element to estimate the weight of each element [66]. Prior to calculating the ranking summed weight for each element, the RS approach ranks each element. Expert views are used to determine the ranking of each element in the general RS methodology, which uses a mechanism like pairwise ranking [67]. In this work, the ranking for every performance was determined using an FT. The ranking of each element is the only factor for which the RS approach may determine relevant weights. Using this rank, each element's rank summed weight is calculated. Equation (8) illustrates how the RS approach weights each \(N\) ranking element.  
\[wi=\frac{N-i+1}{\sum_{i=1}^{N}N-i+1}=\frac{2(N-i+1)}{(N(N+1)} \tag{8}\]  
where the elements are listed in order of importance (\(i=1\)) to (\(i=N\)) (least important). The rank exponent weighting approach can be described as follows (Equation (9)) by generalizing the rank summed weighting technique.  
\[wi=\frac{(N-i+1)^{p}}{(\sum_{k=i}^{N}N-i+1)^{p}} \tag{9}\]  
Every element has the same weight if \(p\) is 0, and the outcome is the sum of the ranks if \(p\) is 1. Each element's weight distribution spreads as the \(p\) value rises.
# An Efficient Carrier Synchronization Scheme for Demodulation Systems
## 2 Conventional Carrier Synchronization Algorithm  
### Conventional Carrier Synchronization Scheme  
As shown in Figure 1, the physical layer (PL) frame of the VM system consists of three parts: the frame header, the data block, and the pilot block. The physical layer header (PLHEAD) consists of 26 symbols of start of frame (SOF) code and 64 symbols of physical layer signaling (PLS) code (PLSCODE). The data block consists of 16 slots, each with 90 symbols. The pilot block consists of 36 pilot symbols, and each unmodulated pilot symbol can be defined as \(I=\frac{1}{\sqrt{2}}\) and \(Q=\frac{1}{\sqrt{2}}\). The conventional carrier synchronization scheme is illustrated in Figure 2, which consists of three parts: coarse CFR, fine CFR, and CPR, and the scheme is more complicated [13].  
Figure 1: Frame structure.