esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Bias Compensation Estimator for Localization in 3D Space  
### Bias Compensation Localization in 2D Space  
In [28], a bias compensation method for bearing-only localization is proposed, which can effectively solve the problem of nonlinear localization. Dividing \(\cos\hat{\phi}_{i}\) on both sides of (5) gives:  
\[\tan\hat{\phi}_{i}x_{t}-y_{t}=\tan\hat{\phi}_{i}x_{s_{i}}-y_{s_{i}}+\frac{|| \mathbf{t}-\mathbf{s}_{i}||\sin e_{i}}{\cos\hat{\phi}_{i}}. \tag{10}\]  
Rewrite (10) into the matrix form as:  
\[\mathbf{Z}_{n}=\mathbf{\hat{W}}_{n}\mathbf{t}+\mathbf{C}_{n}\mathbf{N}_{n}, \tag{11}\]  
where  
\[\mathbf{Z}_{n}= \begin{bmatrix}\tan\hat{\phi}_{1}x_{s_{1}}-y_{s_{1}}\\ \vdots\\ \tan\hat{\phi}_{n}x_{s_{n}}-y_{s_{n}}\end{bmatrix} ,\mathbf{\hat{W}}_{n}= \begin{bmatrix}\tan\hat{\phi}_{1}&-1\\ \vdots&\vdots\\ \tan\hat{\phi}_{n}&-1\end{bmatrix} ,\]  
\[\mathbf{C}_{n}= \begin{bmatrix}-\frac{||\mathbf{t}-\mathbf{s}_{1}||}{\cos\hat{ \phi}_{1}}&0&0\\ 0&\ddots&0\\ 0&0&-\frac{||\mathbf{t}-\mathbf{s}_{n}||}{\cos\hat{\phi}_{n}}\end{bmatrix} , \mathbf{N}_{n}= \begin{bmatrix}\sin e_{1}\\ \vdots\\ \sin e_{n}\end{bmatrix} . \tag{12}\]  
Based on  
\[\hat{\sigma}^{2}=\min\lambda, \tag{13}\] \[s.t.\ \det\left(\frac{1}{n}\big{[}\mathbf{\hat{W}}_{n}\quad \mathbf{Z}_{n}\big{]}^{\prime}\big{[}\mathbf{\hat{W}}_{n}\quad\mathbf{Z}_{n} \big{]}-\lambda \begin{bmatrix}1&0&-\frac{1}{n}\sum_{i=1}^{n}x_{s_{i}}\\ 0&0&0\\ -\frac{1}{n}\sum_{i=1}^{n}x_{s_{i}}&0&\frac{1}{n}\sum_{i=1}^{n}x_{s_{i}}^{2} \end{bmatrix} \right)=0\]  
of \(\sigma^{2}\), the Bai estimator is:  
\[\hat{t}_{\phi^{2}}=\left(\frac{1}{n}\mathbf{\hat{W}}_{n}^{\prime}\mathbf{\hat {W}}_{n}-\sigma^{2} \begin{bmatrix}1&0\\ 0&0\end{bmatrix} \right)^{-1}\left(\frac{1}{n}\mathbf{\hat{W}}_{n}^{\prime} \mathbf{Z}_{n}+\sigma^{2} \begin{bmatrix}\frac{1}{n}\sum_{i=1}^{n}x_{s_{i}}\\ 0\end{bmatrix} \right). \tag{14}\]  
Inspired by [28], a corresponding bias compensation estimator is designed for (5). Firstly, the matrix \(\mathbf{A}_{n}\) and \(\mathbf{B}_{n}\) are processed as:  
\[\frac{1}{n}\big{[}\mathbf{A}_{n}\quad\mathbf{B}_{n}\big{]}^{\prime}\big{[} \mathbf{A}_{n}\quad\mathbf{B}_{n}\big{]}=\frac{1}{n}\sum_{i=1}^{n} \begin{bmatrix} a_{11}^{i}&a_{12}^{i}&b_{1}^{i}\\ a_{12}^{i}&a_{22}^{i}&b_{2}^{i}\\ b_{1}^{i}&b_{2}^{i}&c^{i}\end{bmatrix} , \tag{15}\]where  
\[a_{11}^{i} =\sin^{2}\phi_{i}\cos^{2}e_{i}+2\sin\phi_{i}\cos\phi_{i}\cos e_{i} \sin e_{i}+\cos^{2}\phi_{i}\sin^{2}e_{i}, \tag{16}\] \[a_{12}^{i} =-\sin\phi_{i}\cos\phi_{i}\cos^{2}e_{i}-\cos^{2}\phi_{i}\cos e_{i} \sin e_{i}\] \[\quad+\sin^{2}\phi_{i}\cos e_{i}\sin e_{i}+\cos\phi_{i}\sin\phi_{i }\sin^{2}e_{i},\] (17) \[a_{22}^{i} =\cos^{2}\phi_{i}\cos^{2}e_{i}-2\cos\phi_{i}\sin\phi_{i}\cos e_{i }\sin e_{i}+\sin^{2}\phi_{i}\sin^{2}e_{i},\] (18) \[b_{1}^{i} =a_{11}^{i}x_{s_{i}}+a_{12}^{i}y_{s_{i}},\] (19) \[b_{2}^{i} =a_{12}^{i}x_{s_{i}}+a_{22}^{i}y_{s_{i}},\] (20) \[c^{i} =a_{11}^{i}x_{s_{i}}^{2}+2a_{12}^{i}x_{s_{i}}y_{s_{i}}+a_{22}^{i} y_{s_{i}}^{2}. \tag{21}\]  
To derive the bias compensation estimator in detail, two lemmas are proposed. **Lemma 1**.
## 2 Islanding Detection Methods
### Hybrid Methods  
The primary goal of active approaches is to reduce the potential NDZ that can arise when employing passive methods, which subsequently improves the precision of islanding detections. However, adopting active approaches will result in issues with system power quality due to the gradual introduction of disturbance signals into the system. Therefore, hybrid approaches are created by fusing the benefits of both passive and active methods in order to solve these issues. Below is a discussion of the hybrid islanding techniques.
## 3 System Framework Overview
### Middleware Layer
#### 3.2.3 Context Storage  
The storage of context is highly required since context history can be essential for process planning, constituting a good source of knowledge for prediction of future actions to be undertaken and inference processes. Taking into consideration the rather extended volume of data, a distributed cloud repository is involved in the system model, to guarantee the conservation of contextual information. Up-to-date data and respective context are stored into the context-aware middleware cloud as an expandable knowledge base of ontologies while all attributes are stored as key-value pairs in various ontology repositories, simplifying the query versions [100]. All the system components as well as the devices have at disposal a unique id for the distinction of incoming contextual data.  
In the proposed system model the entity database as well as the entity properties, tables and workflows are initially developed in Structured Query Language (SQL) which is a standard programming tool for storing, managing, and retrieving data, yet afterwards they are upgraded into a graphical environment inside the cloud platform. A screenshot representing part of an indicative entity database is illustrated in Figure 11. Let as note that while workflow and tables are optional for the entity database, properties are obligatory. In addition, screenshots representing some indicative (a) entity properties, (b) an entity table and (c) an entity workflow are illustrated in Figure 12. Regarding the issue of storage and retrieval of sensitive data into the context-aware middleware cloud [101], a sufficient authentication mechanism for access control is applied.  
Figure 10: Mapping example in terms of the proposed system’s entities.
## 3 Materials and Methods
### Volatile Profile  
#### 3.4.1 SPME Fiber Coating Selection  
Data on the quantitative profile of IWG volatiles have not yet been reported. Therefore, the optimal conditions, including the fiber coating material, for extracting volatiles from IWG were evaluated. Three SPME fiber coatings were tested, aiming to select the one with the highest volatile extractability. The coatings tested were PDMS (polydimethylsiloxane, 1 cm, 7 \(\upmu\)m), DVB/PDMS (divinylbenzene/polydimethylsiloxane, 1 cm, 65 \(\upmu\)m), and DVB/CAR/PDMS (divinylbenzene/carboxen/polydimethylsiloxane, 1 cm, 80 \(\upmu\)m). Before use, all fibers were properly conditioned according to the manufacturer's recommendation (Supelco, Bellefonte, PA, USA).  
The fibers were exposed to the HS of the samples (600 mg) under the same conditions for comparison purposes: pre-incubation for 10 min at 50 \({}^{\circ}\)C under stirring at 300 rpm, followed by extraction at the same temperature for 60 min [52] using a PAL System RSI 120. After extraction, the fibers were immediately introduced into the GC injector for the desorption of the analytes at 280 \({}^{\circ}\)C, in splitless mode, for 1 min. All the extractions were performed in triplicate, and the fiber with the largest total area of the chromatogram was selected for the further steps of extraction optimization.
# A Study on Assimilation of CYGNSS Wind Speed Data for Tropical Convection during 2018 January MJO
###### Abstract  
The National Aeronautics and Space Administration (NASA) Cyclone Global Navigation Satellite System (CYGNSS) mission was launched in December 2016. CYGNSS provides ocean surface wind speed retrieval along specular reflection tracks at an interval resolution of approximately 25 km. With a median revisit time of 2.8 h covering a \(\pm\)35 deg latitude, CYGNSS can provide more frequent and accurate measurements of surface wind over the tropical oceans under heavy precipitation, especially within tropical cyclone cores and deep convection regions, than traditional scatterometers. In this study, CYGNSS v2.1 Level 2 wind speed data were assimilated into the Weather Research and Forecasting (WRF) model using the WRF Data Assimilation (WRFDA) system with hybrid 3- and 4-dimensional variational ensemble technology. Case studies were conducted to examine the impact of the CYGNSS data on forecasts of tropical cyclone (TC) Irving and a westerly wind burst (WWB) during the Madden-Julian oscillation (MJO) event over the Indian Ocean in early January 2018. The results indicate a positive impact of the CYGNSS data on the wind field. However, the impact from the CYGNSS data decreases rapidly within 4 h after data assimilation. Also, the influence of CYGNSS data only on precipitation forecast is found to be limited. The assimilation of CYGNSS data was further explored with an additional experiment in which CYGNSS data was combined with Global Precipitation Mission (GPM) Integrated Multi-satellite Retrievals for GPM (IMERG) hourly precipitation and Advanced Scatterometer (ASCAT) wind vector and were assimilated into the WRF model. A significant positive impact was found on the tropical cyclone intensity and track forecasts. The short-term forecast of wind and precipitation fields were also improved for both TC Irving and the WWB event when the combined satellite data was assimilated.  
CYGNSS; data assimilation; tropical cyclone; tropical convection; MJO +
Footnote †: journal: Remote Sensing, 2020, 12, 1243; doi:10.3390/rs12081243
## 2 Results and Discussion
### Aroma Compounds of 15 Brands of EVOOs Extracted by SPME and SAFE
#### 2.1.1. Alcohols  
In this study, 17 alcohols were identified from the 15 brands of EVOOs (Table 1). Among them, hexanol, (_E_)-3-hexenol, (_Z_)-3-hexenol, and phenylethyl alcohol existed in all the 15 EVOOs. Compared to previous studies, these four compounds were also detected in virgin olive oils produced in Iran, Turkey, Spain, Greece, Tunisia, and Italy [2, 8, 11, 12, 15, 16, 20, 21, 22, 23]. (_E_)-3-Hexenol and (_Z_)-3-hexenol are derived from the reduction of (_E_)-3-hexenal and (_Z_)-3-hexenal by the action of an alcohol dehydrogenase (ADH). They present the most obvious green, fresh, and grass note. However, these aroma compounds were not thought to be responsible for the significant effect on olive oil odor, owning to their high odor threshold values in oil [19, 24]. 2-Ethyl hexanol, linalool, octanol, 1-nonanol, undecanol, and benzyl alcohol were found in most of the 15 brands of EVOOs. 2-Ethyl hexanol, octanol, 1-nonanol, and benzyl alcohol were also reported in the previous studies about EVOOs from Turkey, Spain, Greece, Tunisia [2, 8, 16, 23]. Nonanol presents fat or green note, which also derives from lipoxygenase pathway of unsaturated fatty acids [19]. Linalool with the flower and lavender note was detected in the samples of Turkey [2], while undecanol was not found in previous studies on EVOO flavor.
# Regional Crop Gross Primary Productivity and Yield Estimation Using Fused Landsat-MODIS Data
## 2 Materials and Methods
### Statistical Metrics  
The correlation coefficient (r) was used to characterize the correspondence between the model results (Mod) and validation (Val) datasets at a 0.05 \(p\)-value significance threshold. Bias, calculated as the difference between Mod and Val (bias = Mod-Val), and the root mean square error (RMSE) were used to evaluate model performance in relation to the GPP and yield observations. The model bias and RMSE metrics were also expressed as a relative percentage of the validation datasets.
## 2 Study Area and Data Used  
### Svalbard Islands  
The Svalbard archipelago (10\({}^{\circ}\) E-35\({}^{\circ}\) E, 74\({}^{\circ}\) N-81\({}^{\circ}\) N), which is under the sovereignty of Norway, is located in the Arctic Ocean within the Arctic Circle. It consists of nine main islands, including Spitsbergen Island, Northeastern Land Island, Edgoya Island, and Barents Island, and many small islands, with a total area of about 62,700 km\({}^{2}\) (Figure 1). The largest of these, Spitsbergen, covers an area of 39,044 km\({}^{2}\). The islands are mountainous, with three landforms: moraine landform, ice erosion landform, and ice water accumulation [24]. In total, 60% of the land is covered by huge glaciers, 30% is wasteland, 10% is covered by lichen and moss vegetation, and there is a permafrost layer of up to 500 m thick, and a large ice-free valley in Spitsbergen. The coastal Dorfords, Spitsbergen, and Northeasters have deep landlocked bays. The east coast of the Northeast Indies is covered with many glacier fronts, most of which extend into the sea. The rest of the islands have vast ancient coastal plains. The highest peak on the island is Newton Peak in West Spitsbergen, at 1717 m above sea level.  
Svalbard has a typical tundra and maritime climate, with a cold climate and alternating polar day/polar night. The archipelago has been affected by transport, accumulation, and erosion for a long time, and has developed many unique types of glaciers. Among them, Monacobreen is the largest and fastest moving glacier on Spitsbergen Island. Affected by the North Atlantic Current flowing through the islands, the temperature of the islands is higher than that of other areas at the same latitude, with an average annual temperature of 4 \({}^{\circ}\)C. The temperature in summer is between 4 \({}^{\circ}\)C and 6 \({}^{\circ}\)C, and the temperature in January in winter is between \(-\)16 \({}^{\circ}\)C and \(-\)12 \({}^{\circ}\)C. The influence of the warm current is more obvious in winter. Due to the wide range of Svalbard across the north and south, the average winter temperature difference between the southern and northern ends is about 5 \({}^{\circ}\)C, and the average summer temperature difference is about 3 \({}^{\circ}\)C. Moreover, mountainous areas are 3 \({}^{\circ}\)C colder in winter than coastal areas, and about 2 \({}^{\circ}\)C warmer in summer. In general, the southeast of the archipelago is warmer than the northwest, and the east has more precipitation than the west.  
Due to the significant factors affecting the glacier flow patterns in Svalbard, such as temperature fluctuations caused by the high latitude and environmental impacts from the North Atlantic Current, POT monitoring requires higher spatial resolution. Sentinel-1's superior spatial resolution facilitates the precise capture of detailed glacier changes, yielding more accurate measurements of glacier flow velocities. Additionally, high-resolution imagery contributes to effective noise suppression, thereby enhancing the reliability of the results. Therefore, in this study, Sentinel-1 data were chosen as the data source.
## 3 Results and Discussion
### Optical Analysis  
The transmittance spectra of the fluorinated hybrid coatings (achieved through the deposition of fluorinated silica materials on glass substrates) were measured by spectroscopic ellipsometry (SE) in the 250-800 nm wavelength range. The spectra are presented in Figure 8. All samples presented a good transmission of more than 85% in the visible spectral range. An interesting small increase was observed in the transmission of the F1 sample in comparison with the uncoated glass substrate, but a decrease in the transmission was also observed for sample F2, which could be correlated with the increase in F2's sample thickness leading to an increase in the absorption. It can also be seen that the F1 spectrum starts at 85% transmittance, while the F2 spectrum starts at 65% transmittance, indicating that the addition of more \(-\)CH\({}_{3}\) groups from HDTMES effectively utilized more light. In previous reports, it has been shown that films of lower thicknesses always have a lower optical gap [49].  
\begin{table}
\begin{tabular}{c c c c} \hline
**Sample** & **d\({}_{\rm film}\) (nm)** & **Roughness (nm)** & **MSE** \\ \hline F1 & 1052 & 0.93 & 1.804 \\ F2 & 1570 & 2.34 & 1.468 \\ \hline \end{tabular}
\end{table}
Table 2: Thickness (d\({}_{\rm film}\)), roughness, and fit error (MSE).  
Figure 7: Root-mean-square roughness for the uncoated glass and the fluorinated hybrid coatings (TEOS/MTES/FS (F1) and TEOS/MTES/HDTMES/FS (F2)), at the scales of 2 \(\upmu\)m \(\times\) 2 \(\upmu\)m, and 4 \(\upmu\)m \(\times\) 8 \(\upmu\)m.  
Figure 9 reveals the diffuse reflectance spectra of fluorinated hybrid coatings, recorded in the range of 380-780 nm.  
As can be seen in Figure 9, the diffuse reflectance spectrum of uncoated glass was found to be \(\sim\)10.5% (\(l\) = 550 nm). In the case of glass substrates coated with the fluorinated silica materials (samples F1 and F2), the reflectance was \(\sim\)9.7%. Both fluorinated hybrid coatings presented lower reflectance than the uncoated glass. It can also be observed that the reflectance of the fluorinated hybrid coatings decreases as the wavelength range increases. From the obtained results, it can be concluded that the silanization of the glass substrates with fluorinated silica materials is helpful in lowering reflectivity.  
Figure 8: Transmittance spectra of the uncoated glass and the fluorinated hybrid coatings (TEOS/MTES/FS (F1) and TEOS/MTES/HDTMES/FS (F2)).  
Figure 9: Diffuse reflectance spectra of the uncoated glass and the fluorinated hybrid coatings (TEOS/MTES/FS (F1) and TEOS/MTES/HDTMES/FS (F2)).  
In previous papers, it has been shown that to produce electricity (for example, in photovoltaic systems), coatings with antireflective effects play a significant role in reducing losses, owing to the reflection of sunlight [50].
# Large-Scale Experimental Static Testing on 50-Year-Old Prestressed Concrete Bridge Girders
## 2 Case Study  
### Corso Grosseto Viaduct  
The Corso Grosseto viaduct, built in Turin (Italy) in 1970, was a multilevel road interchange developed along two main lines, the Corso Grosseto West-Corso Grosseto East route and the Corso Potenza-Corso Grosseto East route. Each route was linked by two structurally independent decks, one for each carriageway. The viaduct consisted of 80 simply supported spans, ranging from 16 to 24 m, for a total length of about 1.4 km (Figure 1).  
The deck of each span consisted of 10 precast PC I-beams and 2 U beams all connected by a cast in situ slab, with transverse beams at third points of each span. Figure 2 shows a technical drawing of the top view and cross-section of the viaduct. The elements retrieved for the project are depicted in Figure 2a in green, whereas the elements tested, whose results are reported in this paper, are painted in red.  
The girders had an average span length of 19.2 m. According to the design documentation [26], the girders were pretensioned using 17 strands along the bottom flange, distributed on two rows, and 3 strands on the top flange with a nominal diameter of 12.7 mm, as reported in Figure 3. In the bottom flange, three strands from 0 to 5 m and two from 0 to 3 m starting from the end sections were unbonded using ducts. The shear reinforcement consisted of \(\diameter 88\) mm stirrups with 250 mm spacing. The stirrups were also used to connect the precast beam with the cast in situ top deck slab.  
Figure 1: Historical view of the Corso Grosseto viaduct.  
Figure 2: (a)Top view; (b) cross-section of the PC deck (in meters).  
The edge beams (Figure 2b) with a box section (U-beam with slab) consisted of two I-girders coupled together in terms of resisting section and reinforcement. The piers were T-shaped and reached a maximum height of 12.40 m. The pier caps were post-tensioned with multiple groups of smooth wires along a curved profile. A group of tendons was released before placing the beams, and secondary prestressing was induced before the application of non-structural loads.  
Construction reports specified a characteristic tensile strength of prestressing steel of 1638 MPa and a concrete compressive strength of 30 MPa at 28 days. The maximum allowable stress for the strands immediately after tensioning was 1400 MPa. The total prestress loss was estimated at the design stage as 36.8% of the initial stress after tensioning. Based on the original design data, the material properties were chosen according to the allowable stress design, assuming an acting bending moment on the most stressed girders of 824 kNm due to the live loads.  
The exposure to aggressive atmospheric agents linked to a massive use of de-icing salts has led, over the years, to evident problems of durability and several conservative maintenance operations. In July 2017, the complete closure to vehicular transit was settled to start the deconstruction process following new urban redevelopment and different mobility needs.
# Recognition of the Mental Workloads of Pilots in the Cockpit Using EEG Signals +
## 4 Workload Recognition
### Extracting Input Data from EEG Signals  
For EEG recording, an EMOTIV EPOC+ headset [40] was used, which has 14 electrodes placed according to the 10/20 system. This sensor provides both raw data and power spectra for the main brain frequencies (\(\theta\), \(\alpha\), \(\beta_{low}\), \(\beta_{high}\), and \(\gamma\)). Given that proposed n-back tasks are memory demanding stressing games and baseline phases consist in watching a relaxing video, the theta wave [41] is the best candidate for discriminating the different mental loads of our experimental phases. In this work, we used the power spectrum of theta wave (4-8 Hz) sampled at 8 Hz.  
Eye blinking and sudden head movements introduce abrupt sharp peaks of large amplitude in the power spectra wave that should be filtered before using them as predictors of a mental state [21]. In particular, we used an interquartile range (IQR) [42] filtering strategy to detect outlier values associated with muscular movement wave peaks. Our IQR filtering was based on setting the value of the 99% percentile of the distribution to all points above it.  
To ensure a high quality of signals, we further filter data according to the quality of the EEG during recordings provided by the headset itself. For each sensor and recorded sample, Emotiv reports the quality of the recording in a discrete scale with values in the range 0-4 indicating how good the contact between sensor and head is: 4 for optimal--0 for none. For the sake of data with the highest possible quality while keeping a reasonable sample size, signals with a 25% of bad recordings were discarded (<3). Further, since there is no evidence about what are the most discriminating sensors that best correlate with the detection of mental workload, the whole phase was discarded if the signals of two or more of the sensors were low quality. Finally, a subject was discarded if either all its base line or its workload phases were discarded, since, in this case, there were not enough data to define the binary classification. After this quality filtering, only 16 of the 20 subjects were selected for models training and testing.  
In order to feed data to models, \(\theta\) signals were cut in temporal windows. Notice that the size and overlap of the temporal windows might be a critical issue in order to properly include workload peaks [43]. For that we have used several window widths with different overlaps, obtaining the best results with 40 s windows overlapped 30 s. Thus, the input data of the networks were the concatenations of 40 s windows for the 14 EEG sensors (14 \(\times\) 40 = 560-dimensional feature space). In order to account for the difference in units and magnitudes, input data were standardized using the mean and standard deviation of the training set.