esa-sceva/satcom-chunk-collection huggingface dataset:
# Rural Fires--Causes of Human Losses in the 2017 Fires in Portugal
## 4 Analysis of the 2017 Rural Fires in Portugal
### Analysis of Fatalities in October 2017  
In the sequence the 15 October fires, the Portuguese government also requested the constitution of the Independent Technical Commission. Also, the Portuguese Government requested to CSFF-ADAI a study of the most relevant facts of these fires, with the country still in the aftermath of the June fires and still in shock from this new disaster.  
Although both reports indicate similar findings, the number of victims identified in both is not coincident: the CSFF-ADAI report identified and analysed 51 fatalities, whereas the ICT only recognised 48 victims. This discrepancy is because the ICT report only considered the direct victims of the fire (and those investigated by the Judiciary Police), while the CSFF-ADAI report analysed victims who were directly and indirectly related to the fire, based on 40 accidents that resulted in these deaths.  
In the fires of June 2017, all victims died in a short time in a limited zone, while in October 2017, the victims were dispersed in different fires. In the October fires, more than 50% of the victims were 50 years of age or older (Figure 6). This fact, important in the following analyses, shows that the victims of these fires were mostly elderly, in many cases with health problems and mobility difficulties. As for gender, in these fires, 65% of the victims were male and 35% female.  
The analysis of the relationship of the victims with the place of death shows that 86% of the victims were residents. In addition, since most victims were old people, they ended up dying either inside their homes or nearby (Figure 7).  
Figure 6: Distribution of the age of the dead victims-October 2017 [17].  
Figure 7: Relationship between the residence of the victims and the place of death-October 2017 [17].  
The distribution of distances from the place of death to the home was more heterogeneous in the October fire (Figure 8), allowing us to conclude that there were different circumstances and decisions by the victims.  
About 44% of victims died inside or near houses, but a large percentage of people, about 46%, tried to run away. It should be noted that, according to the ITC report, 60% of the houses of the victims of this fire did not burn. In this case, 63% of the people tried to run away (Figure 9).  
After the June fires, it was expected that people would have learned to stay in their homes and not choose to run away. Even so, a large percentage of people died while trying to run away.
## 2 Experimental Details
### System Design
It is intentionally designed to be smaller than the OGT for practical mounting purposes onto UAVs. As can be seen from Figure 3b, APD and QPD are mounted on a three-axis stage having a 10 \(\upmu\)m resolution for a precise optical alignment. The QPD with an active diameter of 2 mm consists of a 2 \(\times\) 2 array of p-i-n photodiodes arranged counterclockwise in the \(Q_{1}\), \(Q_{2}\), \(Q_{3}\), and \(Q_{4}\) order, as shown in Figure 3c. The position information can be extracted by monitoring the signal from QPD by using the following equations [29; 30; 31; 32; 33; 34; 35]:  
\[V=V_{Q_{1}}+V_{Q_{2}}+V_{Q_{3}}+V_{Q_{4}} \tag{1}\]  
\[X\ =\ \frac{\big{(}V_{Q_{1}}+V_{Q_{4}}\big{)}-\big{(}V_{Q_{2}}+V_{Q_{3}}\big{)}} {V},\ Y\ =\ \frac{\big{(}V_{Q_{1}}+V_{Q_{2}}\big{)}-\big{(}V_{Q_{3}}+V_{Q_{4}}\big{)}}{V} \tag{2}\]  
where \(V_{Q_{1}}\), \(V_{Q_{2}}\), \(V_{Q_{3}}\), and \(V_{Q_{4}}\) are the output voltage values of the four photodiodes \(Q_{1}\), \(Q_{2}\), \(Q_{3}\), and \(Q_{4}\), respectively. \(V\) is the total voltage produced by the four photodiodes. Equation (1) calculates the optical power incident to the QPD by summing up the signals detected by the photodiodes located in four quadrants. As the incident beam spot moves on the photosensitive surface of the QPD, the signal output of the four photodiodes in each quadrant changes according to the displacement of the beam spot. The (X, Y) indicating the position of the incident beam on the QPD can be calculated by using Equation (2). Consequently, pointing, acquisition, and tracking functions were performed by changing the pan and tilt of the PTU using the X and Y values obtained by the position of the incident beam on the QPD. In order to estimate the incident beam size on the QPD [36; 37; 38; 39], the following parameters are defined. \[c_{\rm h}=\frac{\Delta X}{\Delta\rm PAN}\,\ c_{\rm v}=\frac{\Delta Y}{\Delta \rm TILT} \tag{3}\]  
Figure 2: FSO communication system between OGT and OAT. Here, \(c_{\rm h}\) is defined as the ratio of the change in the X value \((\Delta\rm X)\) to the corresponding change in the pan angle \((\Delta\rm PAN)\) moving on the horizontal axis. Similarly, \(c_{\rm v}\) is defined as the ratio of the change in the Y value \((\Delta\rm Y)\) to the corresponding change in the tilt angle \((\Delta\rm TILT)\) moving on the vertical axis. \[d_{\rm h}=d_{\rm QPD}\times\frac{c_{\rm h,ideal}}{c_{\rm h,exp}}\,d_{\rm v}=d_{\rm QPD} \times\frac{c_{\rm v,ideal}}{c_{\rm v,exp}} \tag{4}\]  
In Equation (4), \(d_{\rm h}\) is the horizontal width of the incident beam on the QPD, \(d_{\rm QPD}\) is the diameter of the QPD, and \(d_{\rm v}\) is the vertical length of the incident beam on the QPD. The initially set \(c_{\rm h,\ ideal}\) and \(c_{\rm v,\ ideal}\) are the ideal values when OAT and OGT are well aligned and the diameter of the incident beam matches the QPD diameter. The \(c_{\rm h,\ exp}\) and \(c_{\rm h,\ exp}\) are the experimental values. Using Equations (3) and (4), the incident beam size on the QPD is estimated by comparing the ideal and experimental values.
## 4 Results and Analysis
### Mode Shape Identification  
Taking the second-order torsional mode as an example, test 4 was selected for analysis. The power spectra show multiple peaks and were dominated by first- and second-order modes as shown in Figure 10d. In the VMD method, parameter K was set to 10 to consider more modes, and the convergence parameter \(\varepsilon\) was set to \(10^{-7}\). Performing the VMD, the decomposed second-order torsional modal response and corresponding spectra are shown in Figure 13a, where the Fourier spectra reveal a 3.53 Hz single peak, effectively separating the target modal response from the others. The acceleration signal in Figure 13a was used to amplify the second-order torsional modal components and reduce the energy proportion of the first- and second-order modal components, allowing the VMD method to better retain the second-order torsional modal response component.  
Figure 13: Cont.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mode** & \(f\)**(Hz)** & \(\xi\) **by SH** & \(\xi\) **by HT** \\ \hline
1 & 1.488 & 0.0172 & 0.0155 \\
2 & 1.775 & 0.0065 & 0.0071 \\
3 & 2.980 & 0.0098 & 0.013 \\
4 & 3.526 & 0.0035 & 0.0043 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Identified frequencies and damping ratios.
## 3 Results and Discussions
### Fourier Transforms Infrared (FT-IR)  
FT-IR analysis was performed to confirm encapsulation via vibrational deviations formed when the host and guest molecules interact. The absorption spectra of \(\beta\)-CD, GRE, and \(\beta\)-GRE in the 400-4000 cm\({}^{-1}\) regions are depicted in Figure 5. The GRE and \(\beta\)-CD spectra resemble those reported by ([PERSON], 2017; [PERSON], 2017). All samples displayed peaks located at 1024-1070 cm\({}^{-1}\) associated with the phenolic C-O bond, 1606-1646 cm\({}^{-1}\) C=C stretching of aromatic rings, 2929-2939 cm\({}^{-1}\) C-H stretching, and the broad absorption band at 3267-3300 cm\({}^{-1}\) representing the stretching vibration mode of O-H bond associated with the poly hydroxy groups of \(\beta\)-CD and polyphenols in GRE, in particular, the flavonoids being the most abundant polyphenols known to exhibit a broad absorption band between 3200 and 3600 cm\({}^{-1}\). Pure GRE showed specific bands at 1030-1070, 1255, 1606, 2939, and 3267 cm\({}^{-1}\). Meanwhile, the \(\beta\)-CD spectra showed strong bands at 578, 1024, 1078, 1153, 1646, 2946, and 3274 cm\({}^{-1}\). The spectra of the \(\beta\)-CD and \(\beta\)-GRE samples overlapped at certain regions and showed certain spectral differences in comparison with the aqueous extract (GRE). Similarities between GRE and \(\beta\)-GRE were observed at 578, 1025, and 1154 cm\({}^{-1}\). When the \(\beta\)-GRE inclusion complex formed, most characteristic peaks of GRE and \(\beta\)-CD disappeared, reappeared, or shifted bands took place in the newly formed complex. For instance, the 1255 cm\({}^{-1}\) band depicting the C-O stretching of carboxylic acid disappeared following the formation of hydrogen bonds with \(\beta\)-CD. The GRE 1030-1070, 1606, and 2939 cm\({}^{-1}\) bands shifted to 1025, 1615, and 2939 cm\({}^{-1}\) in the \(\beta\)-GRE complex, respectively. [PERSON] et al. (2015) observed similar structural changes when forming a  
Figure 4: Thermogravimetric analysis (TGA) thermograms of betacyclodextrin (\(\beta\)-CD), aqueous green rooibos extract (GRE), and betacyclodextrin-assisted extract of green rooibos (\(\beta\)-GRE).  
rutin-\(\beta\)-CD complex. The above results indicate that successful encapsulation took place, and a new compound with new characteristics was formed.
## 6 Open Challenges, Future Prospects and Research Directions
### Error Correction Codes  
Research in ECC will likely focus on creating more efficient algorithms capable of handling higher data rates and more complex error patterns, especially in real-time use cases.  
Efficient Algorithm Development: The development of these algorithms will be driven by the need to correct errors introduced by AT, scattering, and absorption, which can significantly degrade the quality of FSO links. Future ECC will need to be adaptive, adjusting to the varying error characteristics of the FSO channel in real-time. This could involve ML techniques to predict and correct errors dynamically, thereby improving the efficiency and reliability of data transmission.  
Enhanced Performance in Real-Time Applications: As real-time applications like video streaming and online gaming become more prevalent, ECC must evolve to provide low-latency error correction. Research will focus on reducing the computational complexity and latency of these codes while maintaining high error correction performance. This could involve the use of parallel processing techniques and hardware accelerators to achieve faster error correction without compromising the integrity of the data.
## 3 Results and Discussion
### Effects of Operating Conditions on Reaction Responses
#### 3.4.3 Surface and Contour Plots for Phenol Conversion (\(Y_{1}\)) Response  
As is seen from the analysis of the regression coefficient results in Table 5, all of the variables (temperature (\(A\)), feed flow rate (\(B\)), catalyst weight (\(C\)), Ni-Co ratio (\(D\)), and phenol concentration (\(E\))) and their interactions (\(AE\), \(BE\), \(CE\), and \(DE\)) are statistically significant and affect the phenol conversion (\(Y_{1}\)) response. The response surface plots are useful in understanding the relationship between the main and interaction effects of the variables [61]. The contour plots are curved lines because the model contains the interactions of the factors (\(AE\), \(BE\), \(CE\), and \(DE\)). The surface and contour plots given in Figures 11 and 12 show the relative effects of any two variables when the remaining variables are kept constant. In general, the results of surface and contour plots reveal that the phenol concentration plays an important role in catalyst activity towards phenol conversion. The surface and contour plots for \(AE\) in Figures 11a and 12a show the interactive effect of phenol concentration (\(E\)) and temperature (\(A\)) by keeping \(B\), \(C\), and \(D\) constant. The phenol conversion is relatively high for high levels of \(A\) and \(E\). In the same way, the surface and contour plot show the interactive effect of B and E through keeping \(A\), \(C\), and \(D\) constant. Figures 11b and 12b show the \(Y_{1}\) response of the mutual interactions between the variables. It was found to be high at a low flow rate and high phenol concentration. Furthermore, when the catalyst weight ranged from 0.1 to 0.3 g the effect on the \(Y_{1}\) response was significant (Figures 11c and 12c). On the other hand, Figures 11d and 12d show that the NiCo ratio and phenol concentration (\(D\) versus \(E\)) have a negative effect on \(Y_{1}\) response. In conclusion, surface and contour plots prove the results of the regression coefficient data and the Pareto charts.  
Figure 10: Full interaction plot matrix for (**a**) \(Y_{1}\) and (**b**) \(Y_{2}\).
# A Scalable Framework for Sensor Data Ingestion and Real-Time Processing in Cloud Manufacturing
## 4 Materials and Methods
### Case Study
#### 4.2.2 Data Processing and Analytic Systems  
The tested data processing frameworks included both Apache Spark Streaming and Hadoop, with the former configured with a 5 s sliding window and a 1 s batch interval to compute rolling averages for temperature and humidity. In-memory processing enables low-latency responses for real-time decision making in manufacturing.  
Figure 4: Experimental setup outlining the operational workflow for the considered case study.  
Apache Hadoop was used as a batch-processing baseline for calculating temperature and humidity averages. Although it has a higher latency than Spark Streaming, it serves as a reference point for assessing real-time capabilities. The analyzed metrics included the processing latency, resource utilization, and scalability under increased data loads.
## 3 Experiments and Discussions
### Accuracy and Convergence of RT-PPP in MGEX Stations
#### 3.3.1 Dual-Frequency PPP
As shown in Figure 8, the convergence times of BDS-3 PPP based on PPP-B2b products are within 15 min. The PPP-B2b-based GPS PPP presents the slowest convergence time, with an average convergence time of 52 min and the longest time of 109 min. The reason for the longer convergence time of JFNG may be caused by the inconsistency of the satellites' orbit errors, as shown in Figure 4, in which the fluctuations of B2b corrected orbit is more significant than that of B2b corrected BDS.
## 4 The Optical Kerr Effect
This process is known as self phase modulation (SPM), which results in modulation of the instantaneous frequency around the central frequency, \(\omega_{0}\), given by: \(\omega(t)=\dot{\phi}=\omega_{0}+\Delta\omega(t)\). The deviation, \(\Delta\omega(t)\), from the central frequency is given by:  
\[\Delta\omega(t)=\epsilon t\exp\left(-\frac{t^{2}}{\sigma_{t}^{2}}\right) \tag{3}\]  
where \(\epsilon=(2\omega_{0}Ln_{2}I_{0})/(c\sigma_{t}^{2})\) is the modulation coefficient and a Gaussian pulse was assumed with duration \(\sigma_{t}\) (from here on, the linear refractive index, \(n_{0}\), will be denoted as \(n\), unless specifically mentioned otherwise). From Equation (3), one can see that the instantaneous frequency is red-shifted (to lower frequencies) in the leading edge of the pulse and blue-shifted (to higher frequencies) in the trailing edge. This process of self phase modulation (SPM) [37] continuously broadens the spectrum of the pulse. Thus, SAM and SPM are the mechanisms for pulse shortening and spectral broadening,respectively. Eventually, both mechanisms will be limited by dispersion compensation and the finite gain bandwidth, which leads to the formation of stable, soliton-like pulses.
# Prediction of Truck Fuel Consumption Based on Crossformer-LSTM Characteristic Distillation
## 3 Methodology
### Cross-LSTM Multi-Feature Distillation
#### 3.3.3 Feature Distillation  
Multi-layer feature distillation [20] is a transfer learning technique that employs Crosformer and LSTM as the teacher and student models, respectively. During the pre-training phase, the Crosformer model transfers its hidden layer feature knowledge to the LSTM model, providing it with rich prior knowledge. In the feature distillation process, both the LSTM student model and the Crosformer teacher model receive complete data inputs, which helps ensure that the teacher model provides high-quality guidance. The final error consists of two key components: the student model prediction error and the feature distillation error. This combination of teacher and student models through feature distillation allows the student model to maintain high performance while achieving a more lightweight structure and faster inference speed.  
The student model prediction error, which reflects the accuracy of the model's predictions, is given by the following formula:  
\[\text{Pre Loss}=\frac{1}{N}\sum_{i=1}^{N}(\hat{y}_{i}-y_{i})^{2} \tag{4}\]  
where \(N\) represents the total number of samples; \(\hat{y}_{i}\) denotes the predicted value for the \(i\)-th sample; and \(y_{i}\) represents the true value for the \(i\)-th sample.  
The feature distillation error (FD Loss) measures the consistency of feature representations between the teacher and student models and is expressed as  
\[\text{FD Loss}=\frac{1}{M}\sum_{i=1}^{D}\|\phi_{i}(T)-\psi_{i}(S)\|_{2}^{2} \tag{5}\]  
where \(M\) denotes the number of hidden layers used in the distillation; \(\phi_{i}(T)\) represents the output of the \(i\)-th hidden layer of the teacher model (Crossformer); and \(\psi_{i}(S)\) represents the output of the \(i\)-th hidden layer of the student model (LSTM). A smaller distillation error indicates that the student model has better learned the knowledge from the teacher model.  
Thus, the total loss for the student model during prediction is  
\[TLoss=\text{Pre Loss}+\text{FD Loss} \tag{6}\]
# Connectivity Analysis in VANETs with Dynamic Ranges
## 3 Analytical Procedure
### Vehicle Velocity Distribution in Traffic  
Work in [28; 29] indicate that the velocities of the vehicles are Gaussian distributed with PDF as;  
\[f_{V}(v)=\frac{\exp(-\frac{(v-\mu)^{2}}{2\sigma^{2}})}{\sqrt{2\pi \sigma}},\text{ for }\sigma>0, \tag{24}\]  
where \(\sigma^{2}\), \(\mu\) are the variance \(\mathbb{V}ar[V]\) and expectation \(\mathbb{E}[V]\) respectively.  
**Remark 1**: _It can be noted that this velocity distribution caters to traffic scenarios where overtaking is possible in the microscopic traffic model or a free-flow traffic model for which slower moving vehicles can be overtaken in a multi-lane road such as a highway or urban roads. Thus, vehicle velocities tending towards zero can be captured by this standard Gaussian distribution. On the other hand, the practicality of traffic on multi-lane urban roads or highways in a free-flow model [30], bounds vehicle velocities away from zero and cannot be negative such as in a congested traffic where vehicles can stop (due to traffic signals). Moreover, to analyse free-flow traffic with overtaking, a modified Gaussian distribution (truncated version) is considered for vehicles whose velocities are non-zero and positive. In this section, and overall in the work, we consider truncated Gaussian velocity distribution. For a modified (truncated) Gaussian velocity, the variable \(V\sim TN(\mu,\sigma^{2};\dot{\mu},\dot{\sigma}^{2})\)[20; 24], \(V\in(V_{min},V_{max})\) where \(V_{min}=\mu-3\sigma\) and \(V_{max}=\mu+3\sigma\) is given by PDF;_  
\[\dot{f}_{V}(v)=\frac{f_{V}(v)}{\int_{V_{min}}^{V_{max}}f_{V}(v) dv}=\frac{2f_{V}(v)}{\Phi\Big{(}\frac{V_{min}-\mu}{\sqrt{2\sigma}}\Big{)}-\Phi \Big{(}\frac{V_{min}-\mu}{\sqrt{2\sigma}}\Big{)}}, \tag{25}\]  
_where \(\text{erf}(x)=\Phi(x)=\frac{2}{\sqrt{\pi}}\int_{0}^{x}e^{-t^{2}}dt\) and whose equivalent speed \(\mu_{eq}\) is given as;_  
\[\mu_{eq}=\frac{1}{\int_{V_{min}}^{V_{max}}\frac{f_{V}(v)}{v}dv}. \tag{26}\]
## 2 Materials and Methods
### Hplc-Qqq-Ms/Ms Analysis of Phenolic Compounds  
The analyses of the phenolic compounds were performed on an Agilent 1200 series high-performance liquid chromatographer (HPLC) equipped with an Agilent 6410B triple-quadrupole (QqQ) mass spectrometer (MS) (Agilent Technologies, CA, USA), based on our previous studies [19; 20]. The chromatographic separation was carried out using a Poroshell 120 EC-C18 column (150 mm \(\times\) 2.1 mm, 2.7 \(\upmu\)m; Agilent Technologies, Santa Clara, CA, USA). The mobile phases were: A = water solution containing 0.1% (\(v/v\)) formic acid;B = acetonitrile/methanol solution (50:50, \(v/v\)) containing 0.1% (\(v/v\)) formic acid. The gradient elution was: (1) from 10% to 46% B in 28 min, and (2) from 46% to 10% B in 1 min. The post time was 5 min. A wine sample of 1 mL was filtered using 0.45 \(\upmu\)m inorganic membranes (polyether sulphone). The injection volume was 5 \(\upmu\)L and the flow rate was 0.4 mL/min. The column was thermostatically controlled at 55 \({}^{\circ}\)C. An electrospray ionization source was used with 4 kV voltage in the negative mode and the positive mode for the non-anthocyanin phenolics and the anthocyanin compounds, respectively. The temperatures of the ion source and gas (N\({}_{2}\)) were 150 \({}^{\circ}\)C and 350 \({}^{\circ}\)C, respectively. The gas had a flow rate of 12 L/min, while the nebulizer pressure was 35 psi. The [M + H]+ and [M + H]\(-\) ions were selected as the precursors for the anthocyanin and non-anthocyanin compounds, respectively. The multiple reaction monitoring mode was used for both identification and quantification. The identification was achieved by comparing the retention times and qualitative transition ions with the phenolic compounds in an in-home phenolic compound library [19; 20]. The quantification process was carried out according to reliable methods established in our previous studies [19; 20]. All anthocyanins were quantified on the basis of the calibration curve of malvidin-3-O-glucoside and non-anthocyanins were quantified according to the calibration curves of their own reference compounds.
## 2 Methodology Overview
### Cable System Structure Represented in the Tool
The \"left\" and \"right\" labels in Figure 2, as well as the directions of the counterflows in the two ducts, are purely conventional. The function of the cooling stations is to restore the temperature and pressure of the fluid to its original conditions, which have been altered during passage through the preceding cable segment. As will be elaborated upon in Section 6.2, the solver selects the designs complying with the limits on the pressure drop and temperature rise set by the user. In particular, precautions must be taken to prevent a phase transition in the liquid nitrogen, whose boiling could compromise the quality of the heat exchange. For sufficiently long lines, termination cooling stations may not be sufficient to maintain the cryogen within the fluid dynamic constraints. Thus, a number \(n_{stat}\) of intermediate cooling stations may be necessary. As illustrated in Figure 2, these intermediate stations must manage both counterflows from the former and the annular gap simultaneously, restoring the initial thermodynamic conditions in both streams. These intermediate stations are assumed to be uniformly spaced at a distance \(d_{stat}\), meaning that \(d_{stat}=L_{line}/(n_{stat}-1)\). By adopting this simplified hypothesis, the system can be approximated as modular, meaning that each cable segment between adjacent cooling stations is assumed to be identical in design, as well as in the heat produced and then absorbed by the two fluids within that segment. Hence, the optimization can focus on a single cable segment \(d_{stat}\) while considering all intermediate cooling stations as equally sized.