esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Analysis
### Experiment Setup
#### 4.1.3 Evaluation  
The characteristics of network flow reconstructed by the decoder of TCReC will serve as input to the CNN or LSTM model for classification. The classification data consist of various traffic types, and we evaluate the experimental results by using a multiple confusion matrix, precision, recall, and F1-score as comparison metrics. The confusion matrix is presented in Table 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name of Parameter** & **Value** \\ \hline Encoder depth & 3 \\ Decoder depth & 1 \\ Size of data blocks & 16 bytes \\ Mask method of training & Random mask \\ Mask ratio of training & 50\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: The parameters of TCReC.  
\begin{table}
\begin{tabular}{c|c|c|c|} \multicolumn{3}{c}{} & \multicolumn{2}{c|}{Prediction class} \\ \cline{3-4} \multicolumn{1}{c|}{} & \multicolumn{1}{c|}{Positive} & \multicolumn{1}{c|}{Negative} \\ \cline{2-4} \multicolumn{1}{c|}{True class} & Positive & \(TP\) & \(FN\) \\ \cline{2-4} \cline{2-4} \multicolumn{1}{c|}{} & Negative & \(FP\) & \(TN\) \\ \hline \end{tabular}
\end{table}
Table 3: The confusion matrix.  
Recall is true positive rate:  
\[Recall=\frac{TP}{TP+FN} \tag{2}\]  
F1-score is the harmonic mean of precision and sensitivity:  
\[F1\ score=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{3}\]
# Multi-Objective Real-Time Planning of Evacuation Routes for Underground Mine Fires
## 4 Mathematical Models and Algorithms for Emergency Escape Routes
### Mathematical Model of Emergency Escape Routes  
When searching for an underground escape route, feasible exits include the main and secondary shaft exits and in times of emergency, the return air shafts can also be used as an escape route. When the situation is serious and it is not possible to reach the surface exits in time, the underground personnel should enter the refuge in time to escape and wait for rescue. In the event of a disaster, the first step is to wear a self-rescuer. According to the regulations, self-rescuers should be rated for a minimum of 30 min, and when two self-rescuers cannot be relied upon to reach the surface, an emergency refuge system, such as a removable refuge, or a temporary or permanent refuge should be installed underground.  
In general, the downhole shortest emergency escape route problem considering downhole avoidance facilities can be described as follows: for a network \(G=(N,E)\), \(N\) is the set of nodes, and \(E\) is the set of edges. \(|N|=n,|E|=e\). Using \(i\) and \(j\) to represent any pair of nodes, the directed edge from \(i\) to \(j\) is denoted as \((i,j)\). The equivalent weight of the directed edge \((i,j)\) is denoted as \(w_{ij}\) and the time of passage is denoted as \(t_{ij}\). \(w_{ij}\) and \(t_{ij}\) are both non-negative real numbers. For any path \(P\) from the start to the end, the sum of the equivalent lengths of the path is \(W\), and the total time spent traveling through the smoke stream is \(T\). The logic variable \(x_{ij}\) represents whether the directed edge \((i,j)\) is on the path from the personnel position to the goal position, taking 1 if it is and 0 if it is not. At most, each node passes through once. The shortest path that satisfies the requirements that the time in the smoke flow from the start of escape does not exceed the rated protection time of the self-rescuer must be found, and the escape path is relatively short and has the shortest escape time in the smoke flow.  
To sum up, the mathematical model of the underground emergency escape route considering the underground emergency facilities can be described as follows,  
\[\begin{array}{rl}\min\mathcal{W}=&\sum\limits_{i=1}^{n}\sum\limits_{j=1}^{ n}x_{ij}w_{ij}\\ \min T=&\sum\limits_{i=1}^{n}\sum\limits_{j=1}^{n}y_{ij}x_{ij}t_{ij}\\ s.t.&\sum\limits_{j}x_{ij}-\sum\limits_{j}x_{ji}=&\begin{cases}1&\text{if}i=0\\ -1&\text{if}\ i=D\ \forall i\in N\\ 0&\text{otherwise}\\ \end{cases}\\ &\sum\limits_{j}x_{ij}\leq 1&\forall i\in N\\ &\sum\limits_{(i,j)}x_{ij}t(i,j)\leq T&\forall(i,j)\in E\\ x_{ij}\in\{0,1\}&\forall(i,j)\in E\\ y_{ij}\in\{0,1\}&\forall(i,j)\in E\\ \end{array} \tag{5}\]
# Summary of Lunar Constellation Navigation and Orbit Determination Technology
## 3 Overview of Single-Satellite Navigation and Orbit Determination Methods
### Radio Measurement Orbit Determination Technology
The second integral in Equation (3) is the phase of the transmitted ranging code at the transmission time \(t_{1}(ST)_{T}\) at the transmitting electronics at the transmitting station on Earth. It is equal to the phase of the received ranging code at \(t_{3}(ST)_{R}\). The phases of the transmitter ranging code and the received ranging code at the reception time \(t_{3}(ST)_{R}\) at the receiving electronics at the receiving station on Earth are measured in range units. The difference of these two phases is calculated modulo \(M\) range units, where \(M\) is the length of the ranging code in range units. 3. Very long baseline interferometry (VLBI)  
VLBI technology is an important interferometry technology. It combines multiple radio telescopes hundreds or even thousands of kilometers away into a huge comprehensive aperture telescope. It is currently the highest-resolution astronomical observation technology and is widely used in the astrophysics, astrometry, geodesy and deep space exploration fields. The measurement principle of the VLBI system of two base stations is shown in Figure 1. The distance between the radio source and the Earth is much longer than the distance between the two observation stations on the ground. Therefore, the radio signal emitted from the radio source can be considered parallel light when it reaches the Earth. The signal radiated by the same radio source at a certain time satisfies the interference conditions. In VLBI data processing, according to the radio signals received by each base station, the relative amplitude of the interference fringes, the time delay for radio signals radiated by the same radio source to reach each base station, and the change rate of the delay can be obtained. The correlation amplitude represents the intensity distribution information of the radio source, and the position information between base stations and the position information of the radio source can be calculated using the delay and delay rate. According to the geometric relationship shown in the figure, the time difference \(\tau\) between two measuring stations receiving a certain wave front (for one of the measuring stations, it is the delay value) is [20]:  
\[\tau=\frac{1}{c}d\cos\theta \tag{5}\]  
The distance between the two base stations is \(d\), which can be called the baseline. \(c\) is the speed of light. By performing multiple measurements of \(\tau\) or \(\dot{\tau}=\frac{d\tau}{dt}\) for multiple sources, the three components of the baseline and the location of the source can be solved.
## 2 Materials and Methods
### Simulated Data  
Simulations were performed for the recent reference climate (HIST-986-2005) and for two future climate projections namely for the mid-term (MED-2046-2065) and long-term (LONG-2081-2100) future, respectively. These are the same periods as those considered in the 5 th Intergovernmental Panel on Climate Change (IPCC) Assessment Report [26]. Future simulations considered the RCP8.5 (_Representative Concentration Pathways_) greenhouse gas emission scenario [27]. This is one of the most studied scenarios and represents a radiative forcing of 8.5 Wm\({}^{-2}\) by 2100 and a continuous increase thereafter [28] corresponding to a global averaged warming of about 4.5 \({}^{\circ}\)C in 2100.  
The performed simulations are the following:  
* HIST-Simulation for the 1986-2005 period where the WRF was forced by the MPI-ESM-LR (Max Planck Institute Earth System Model--low resolution) (WRF-MPI) global climate model. This model is fully described by [PERSON] et al. [29] and participated in the Coupled Model Intercomparison Project Phase 5. The MPI-ESM-LR is considered to be one of the best models to simulate European climate [30,31], particularly for Portugal [32].
* MED--Simulation for the 2046-2065 period where the WRF was forced by the MPI model considering the RCP8.5 scenario.
* LONG--The same as MED but for the 2081-2100 period.  
Other studies have used the same simulations to evaluate different aspects of climate over Europe and the Iberian Peninsula [20, 21, 22, 23, 34]. Here, an ensemble of similar simulations performed by other research groups was considered. These participated in the EURO-CORDEX project [35] and are listed in Table 1. The data are obtained with 0.11\({}^{\circ}\) horizontal resolution and are used to evaluate the performance of the WRF and also to obtain an estimate of the uncertainty associated to HW projections. These simulated data are used to estimate the uncertainty of WRF for recent climate and for future projections.
# The Extraction of Maximal-Sum Principal Submatrix and Its Applications
## 4 The Proposed Algorithm
### Parameter \(n\) Is Fixed and Parameter \(k\) Is Variable  
To explore the impact of parameter \(k\) on the time efficiency of the MSPS-RU algorithm, a 30-order (\(n=30\)) real matrix is generated randomly according to the uniform distribution, and the \(k(k\leq n)\) order MSPS is extracted using the Enum method and MSPS-RU algorithm, respectively. The runtime of the two algorithms is listed in Table 3.  
From Table 3, we can find that the time consumption of both algorithms first increases and then decreases with the increasement of \(k\). The maximum value of the time consumption is reached when \(k\) is 15 or so. The reason, perhaps, is that the number of \(k\)-order principal submatrices in a 30-order real matrix is \(C_{30}^{k}\), and when \(k=15\), \(C_{30}^{15}=\underset{k<n}{max}\left\{C_{n}^{k}\right\}\). In addition, the time efficiency of the two methods is not much different when the value \(k\) is relatively close to \(n\); resulting from that, the number of running code Line 8 in Algorithm 1 is small.  
Table 3 also tells us that our method is significantly more time-efficient than the Enum method when \(k\leq 20\). We conducted a significance hypothesis test. Assuming: \(H_{0}\):\(\mu_{1}=\mu_{2}\), \(H_{1}\):\(\mu_{1}\
eq\mu_{2}\), (\(\mu_{1},\mu_{2}\)) is the average time consumption of the two methods. The statistics \(Z=2.014>1.96=Z_{0.05}\), \(p<0.05\), then \(H_{1}\) is accepted at a significance level \(\alpha=0.05\).  
Figure 3 illustrates the comparison of runtime growth trends of extracting \(k\)-order MSPS from a 30-order matrix using different algorithms. The trend lines are generated using the moving average method with 2 period. It also shows that with the increase in \(k\), the time growth rate of our algorithm is lower than that of the Enum algorithm according to the trend lines. Therefore, it indicates that MSPS-RU performs better than that of the Enum algorithm.  
\begin{table}
\begin{tabular}{c c c c} \hline \(k\) & \(T_{Enum}\) & \(T_{ours}\) & \(\eta\) (\%) \\ \hline
2 & 0.0015 & 0.0009 & 62.76 \\
5 & 0.0155 & 0.0099 & 57.02 \\
8 & 1.3105 & 0.7269 & 80.29 \\
11 & 23.2704 & 13.2104 & 76.15 \\
14 & 102.9598 & 64.4397 & 59.78 \\
17 & 143.0534 & 104.1774 & 37.32 \\
20 & 89.3660 & 77.5431 & 15.25 \\
23 & 62.8989 & 61.8635 & 1.67 \\
26 & 64.7635 & 64.9295 & \(-\)0.26 \\
29 & 57.2279 & 57.1216 & 0.19 \\ \hline \end{tabular}
\end{table}
Table 3: The runtime of extracting \(k\)-order MSPS of a 30-order real matrix.  
Figure 3: Runtime trends of extracting \(k\)-order MSPS in a 30-order real matrix.
## 2 Methodology
### Fixed-Bed Gasifiers
#### 2.1.3 Cross-Draft Gasifiers  
In this configuration, the biomass feed is fed into the top part of the gasifier, and the air is introduced in the orthogonal direction. The biomass descends, undergoing drying and eventual gasification, while the resulting product gas emerges from the system's opposite side. The function of a cross-draft gasifier is based on the concept of biomass gasification using a unique cross-current flow arrangement. Solid biomass is horizontally fed into the gasifier. In the initial phase, the biomass undergoes a drying process when heat is utilized to reduce the moisture level. Following this, during the pyrolysis stage, the partially dried biomass undergoes thermal breakdown in the absence of oxygen, generating volatile gases, tar, and char. These gases traverse horizontally across the gasifier and then encounter an airstream for oxidation in the combustion zone. The carefully regulated introduction of air encourages the combustion of volatile gases, leading to the release of additional heat. The resulting mixture proceeds to the reduction zone, where any remaining oxygen reacts with the gases in the presence of a hot carbon bed, ultimately yielding syngas enriched in carbon monoxide, hydrogen, and methane. It exhibits minimal tar generation, offers a relatively quick engine start-up, and can process high-moisture biomass provided that the upper section of the gasifier remains open for venting; however, it is unsuitable for small-scale installations and may face challenges with a high ash content [13]. A model cross-draft, long-stick wood gasifier with a fixed bed was designed and tested to determine the gasification performance of the gasifier as shown in the above Figure 1c [20]. An experiment examining the gasification of wood-based biomass in a cross-draft setup revealed that the gasification efficiency is influenced by various parameters, including the equivalence ratio, biomass characteristics, the type of gasification agent used, and the configuration of the gasifier [21].  
Figure 1: (**a**) Updraft gasifier, (**b**) downdraft gasifier, and (**c**) cross-draft gasifier.
# Enhancing Smart Cities' Resilience Through Competency Assessment and Open Data Utilization
## 9 Design Principles
The tool is a single-page application presenting one competency per page, allowing users to navigate between pages (DF2). This design supports user focus on the current competency (DP1). A navigation button advances to the next competency, and a back button, which leads back, allows users to adjust previous answers. The tool includes a progress bar to enhance progress transparency (DF4) [1; 65] and ensures only one statement is selected (DP5) using radio buttons. Users are informed if no answer is chosen before proceeding (DF3). Moreover, a default option (\"I am not sure about that\") is available for each question to prevent distortion from false statements (DF5). An intuitive visualization of the self-assessment results allows users to understand their training needs to achieve SCRO proficiency (DP6) [64]. The results are presented in the form of a radar chart, which is drawn based on analyzed answers (DF6). Furthermore, the tool recommends modules for advancing proficiency levels through learning journeys (DF7) and allows users to document results by saving and printing them as PDFs (DF8) (Table 4). \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Design Principles** & **Design Features** \\ \hline DP1: The tool will list 20 core competencies related to anticipation, absorption, recovery from crises, and adaptation to new conditions. & \\ \hline DP2: To allow the user to identify their proficiency level in each of the 20 competencies, the tool will be designed as a questionnaire that will contain 80 statements in total. & DF1: Personalized language support \\ \hline DP3: To avoid inconsistencies between competencies, the tool will provide simple and standardized introductions for the four proficiency levels of each competency, which points to a certain attitude specific to that level. & DF3: Fault awareness \\ \hline DP4: To ensure the clarity of the included statements, the introduction to each proficiency level will be followed by a statement focused on the specific knowledge and skills associated with that level. & DF4: Progress transparency \\ \hline DP5: To ensure proper performance evaluation for each of the 20 competencies, the tool will allow only one statement to be selected among the five (foundation, intermediate, advanced, specialized, and not sure) available for each of the 20 competencies. & DF6: Drawing of the competence maturity graph \\ \hline DP6: The tool will provide an intuitive visualization of the self-assessment results to allow the user to understand their training needs in order to achieve good performance as an SCRO. & DF7: Learning journey recommendation based on the competence maturity graph \\ \hline DP7: The recommended modules will allow the user to evolve knowledge and skills to an advanced level of proficiency. & DF7: Learning journey recommendation based on the competence maturity graph \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Connecting the design principles with the design features of the self-assessment tool.
# Advantages of Corrosion-Resistant Overlay Welding on Steel S355J2N
## 3 Experimental
### Theoretical Background of the Welding Process  
Applying the appropriate welding conditions is crucial for controlling dilution, especially for the first layer (to avoid hot cracking). Thus, for using the D = 1.2 mm wire electrode, the welding current and welding speed were set within the recommended range, near the upper limit, in order to achieve better welding power [17; 30]. This was later taken into account during the fusion of the buffer layer and the cladding layers.  
The composition of the filler materials indicates that the first layer contains more chromium and nickel alloying elements than the second and third layers. This is necessary to prevent corrosion from starting at the boundary from the base plate, as the S355J2N steel is strongly deficient in chromium, and we need to ensure corrosion resistance even at the boundary areas. Moreover, the higher alloy content makes the filler material more expensive, so from a cost-effectiveness perspective, it is also advisable to use a cheaper electrode for the second and third layers.  
The filler materials selected for the experiment are chemically resistant and fall into the 'highly suitable for welding' category. Thanks to their extremely low carbon content, they cannot be hardened, meaning that no hardening occurs in the heat-affected zone (HAZ), and there is no significant grain coarsening. By using the selected shielding gas (82% Ar + 18% CO\({}_{2}\)), we managed to ensure arc stability, minimize sputtering, and provide effective protection against oxidation.  
Certain difficulties may arise from choosing an inappropriate technology, which include the following:  
* Sensitization, i.e., a decrease in corrosion resistance due to the formation of chromium carbides (Cr\({}_{23}\)C\({}_{6}\)), so the goal when forming the buffer layer is to achieve a high ferrite number (FN > 5).
* Hot cracking, i.e., the formation of a liquid layer along the grain boundaries in the heat-affected zone. To prevent this, the Cr\({}_{\text{(E)}}\)/Ni\({}_{\text{(E)}}\) ratio must be maintained above 1.6 [30].
* Secondary precipitates, such as the formation of the sigma phase, which should be minimized in the second and third weld layers to adjust the amount of maximum allowable FN [31].  
These issues can be avoided by selecting the appropriate welding materials and the optimal technology. The delta-ferrite content of the welding seams must be in the 3-15 FN range [30]. This provides adequate resistance to hot cracking, as delta-ferrite better dissolves the impurities (S and P) primarily responsible for crystallization cracking, while not compromising corrosion resistance.
## 2 O-RAN Architecture and Development
### O-RAN Architecture
#### 2.2.1 O-RU  
The major O-RU hardware and software components in Figure 4 highlight the internal and external interfaces that are required. The O-RU terminates the O-RAN Fronthaul (FH) interface, known as Lower Layer Split, as well as Low-PHY functions of the radio interface towards the UE. This is a physical node. The O-RU terminates the O-RAN Fronthaul M-Plane interface towards the O-DU and SMO. The O-RU termination of the O1 interface towards the SMO is under study under the O-RAN Operations and Maintenance Architecture. A single split point, known as \"7\(-\)2x\", but which allows a variation, with the precoding function located either \"above\" the interface in the O-DU or \"below\" the interface in the O-RU. For the most part, the interface is not affected by this decision, but there are some impacts, namely to provide the necessary information to the O-RU to execute the precoding operation. O-RU\({}_{(7-2)}\) within which the precoding is not done (therefore of lower complexity) are called \"Category A\" O-RUs, while O-RU\({}_{(7-2)}\) within which the precoding is done are called \"Category B\" O-RUs, as in Figure 5.  
Figure 4: O-RU high-level architecture showing the main hardware components, and internal and external interfaces.  
Figure 3: Logical architecture of O-RAN, its associated interfaces, and the three control loops.
## 3 River Runoff into the Sea  
[PERSON] et al. [9] focused on the geochemical and sedimentological characterization of recent sediments from two marine sites located in the North Adriatic Sea, between the Po River prodelta and the Rimini coast. Major and trace metal concentrations reflect the drainage area of the Po River and its tributaries, considered one of the most polluted areas in Europe [4].  
High values of Cr, Ni, Pb, and Zn were detected in sediments collected from the Po River prodelta. This suggests a Po River supply. Meanwhile, lower levels of these elements in sediments collected in front of the Rimini coast were measured, indicating the Apennines provenance of these.  
The historical trends of Pb and Zn reconstructed from the sedimentary record around the Rimini coast document several changes that can be correlated with the industrialization prior to World War II.  
[PERSON] et al. [10] examined the transport of sediments and their surficial pathways from the mouth of Neretva River toward the Eastern Adriatic Sea. Sediment dynamics were evaluated using several proxies such as organic matter, radiochemical isotopes, selected metal concentrations, and physical parameters. The data analysis showed that the influence of the river on particle distribution decreases northward, with an estimated sediment accumulation rate ranging from 1.9 to 8.5 mm/yr. We speculate that either the dispersion or accumulation of sediments is driven by an eddy in the waters of the Neretva River, triggered or intensified seasonally by the interaction of karstic springs, river input, and Eastern Adriatic Sea waters.  
The Huangmao Island dumping area is adjacent to the Pearl River Estuary in the South China Sea. From its first dumping activity in 1986 to 2017, 6750 \(\times\) 104 m\({}^{3}\) dredged materials were dumped in this dumping area. The evaluation results reported by [PERSON] et al. [11] for the heavy metals revealed that the dumping area with a large dumping amount was more severely polluted. The dumping of dredged materials seemed to have a negative impact on the benthic community in the dumping area.
## 3 Reducing the Uncertainty at the Output of Kalman Filter by Post Processing
### Reducing the Uncertainty at the Output of Kalman Filter  
By analyzing Equations (7) and (8), we can understand why, despite its wide use and success, practitioners using the Kalman filter must resort to manually tuning its parameters. As a result of noise presence, possible missing data, and imperfections of models, at the output of the Kalman filter, a certain uncertainty appears. As already mentioned, this uncertainty can be reduced by transforming the Kalman filter into an adaptive filter or by post processing. A blind post processing approach, which is faster than the adaptive approaches already mentioned, because it is not iterative, and more robust because the blind estimation approaches are more robust than the informed estimation approaches, based on data fitting, was proposed in [16] and developed in [6]. This method supposes to fit the data obtained at the output of the Kalman filter to a polynomial.