esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Assessment of the Platform
### Evaluating the Feature Satisfaction Index  
The _Feature Satisfaction Index_ (_FSI_) is a quantitative metric used to evaluate the degree to which a software platform's features meet the set of common functional and non-functional requirements of potential applications that can be built with the platform [38]. This metric takes into account both the importance (weight) of each feature and how well each feature has been implemented (compliance score), providing a comprehensive assessment of overall feature satisfaction. We define a metric, _Feature Satisfaction (FS)_, for the feature satisfaction of the platform as the following:  
\[FS=\frac{\sum_{i=1}^{N_{f}}w_{i}\cdot c_{i}}{\sum_{i=1}^{N_{f}}w_{i}}\]  
where the terms are defined as the following:  
* \(N_{f}\) is the total number of features provided by the platform. * \(w_{i}\) is the weight assigned to each _feature \(i\)_, representing the relative importance of each feature in the context of the platform's overall objectives. Its value ranges between \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Categories of Features** & \
\begin{tabular}{c} **\# of Features** \\ **(Platform)** \\ \end{tabular} &
\begin{tabular}{c} **\# of Features** \\ **(Liver Cancer System)** \\ \end{tabular} & **Functional Coverage (FC)** \\ \hline Functional Components & 12 & 13 & 92.3\% \\ \hline Persistent Object Classes & 16 & 16 & 100.0\% \\ \hline Tasks in Diagnosis Process & 8 & 9 & 88.9\% \\ \hline ML Models for Diagnosis & 6 & 7 & 85.7\% \\ \hline
**Sum** & **42** & **45** & **93.3\%** \\ \hline  & \(\mathbf{N_{f}}\) & \(\mathbf{N_{f}}\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Computing the Functional Coverage (FC) Index. 0 and 1. Features critical to the platform's functionality are assigned higher weights, while less critical features receive lower weights. * \(c_{i}\) is the compliance score of each _feature_\(i\), indicating how well the feature is implemented. The compliance score assesses how well each feature meets its requirements. Its value ranges between 0 and 1. The value 0 indicates that the feature does not meet the requirements at all, and the value 1 indicates full compliance. Through the case study of developing the liver cancer diagnosis system using the LI-RADS standard, we measured the compliance scores for the 42 features of the platform and synthesized their values into the metric _FS_. This result is summarized in Table 6.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Categories** & **List of Features** & **Weight (W\({}_{\text{i}}\))** & **Compliance (C\({}_{\text{j}}\))** & **W\({}_{\text{i}}\)\({}^{*}\)C\({}_{\text{i}}\)** & **Feature Satis.
## 8 Debye Screening, Modified Gluon Propagator  
It is known that the force between two charges, \(e\) and \(-e\), changes when the system is placed in a medium. In an ionized plasma, the \(1/r\) potential turns into Yukawa-Debye screening [13] (see also [14])  
\[V(r)=-\frac{\alpha e^{-\mu r}}{r}=-\frac{\alpha}{r}-\sigma r+\ldots,\;\sigma= \alpha\mu^{2}/2\,, \tag{51}\]  
where we marked with ellipsis the irrelevant constant term \(\alpha\mu\) and terms of order higher than linear in \(r\) (irrelevant for small \(r\)). In expanded form it reminds of the \"Cornell potential\" (1)  
\[V(r)=-\frac{k}{r}+\sigma r,\;\sigma=1/a^{2} \tag{52}\]  
but the sign of the string tension \(\sigma\) is opposite. The positive sign corresponds to the confined phase, negative sign to screened (deconfined) phase. We can take (test) as a confining potential the following one (cf. Figure 7):  
\[V(r)=-\frac{\alpha\cos\mu r}{r}=-\frac{\mu\alpha\cos(x)}{x}=-\frac{\alpha}{r}+ \sigma r+\ldots,\;\sigma=\alpha\mu^{2}/2,\;x=\mu r\;. \tag{53}\]The confining potential turns into a deconfining one when \(\mu^{2}\) changes sign or when exchange particle becomes tachyon.  
In paper [15], by proper account of the compact nature of SU(3) gauge group that gives rise to the periodic \(\theta\)-vacuum of the theory, the gluon propagator was modified as  
\[G(p)=(p^{2}+\chi/p^{2})^{-1}=\frac{p^{2}}{p^{4}+\chi}=\frac{1}{2}(\frac{1}{p^{2 }+i\sqrt{\chi}}+\frac{1}{p^{2}-i\sqrt{\chi}}) \tag{54}\]  
which gives the potential (cf. Figure 8):  
\[\begin{array}{l}V(r)=-\frac{\alpha\cosh\mu r\cos\mu r}{r}=-\frac{\mu\alpha \cosh x\cos x}{x}=\mu\alpha(-\frac{1}{x}+\frac{x^{3}}{6}+\dots),\\ x=\mu r,\ \mu=\sqrt[4]{\chi}/\sqrt{2}\end{array} \tag{55}\]  
where \(\chi\) is the Yang-Mills topological susceptibility related to the \(\eta^{\prime}\) mass by the Witten-Veneziano relation,  
\[\chi=\frac{F_{\pi}^{2}}{2N_{f}}(m_{\eta^{\prime}}^{2}+m_{\eta}^{2}-2m_{K}^{2} )\simeq(180\ {\rm MeV})^{4},\ \mu=\sqrt[4]{\chi}/\sqrt{2}=127\ {\rm MeV}. \tag{56}\]  
The topological susceptibility in this formula is the only quantity which is by definition calculable in gluodynamics. Early papers of its calculation are [16, 17, 18], and more recently [19].  
Figure 8: Confining potential (55).  
Figure 7: Potential (53).  
Potential (55) is well motivated and confining. In the minimum of the potential (55) bound states \"bags\" have size of the order of 11 fm,  
\[r=7/\mu=7/0.127\;\text{GeV}^{-1}=11\;\text{fm},\;\text{GeV}^{-1}\simeq 0.2\; \text{fm}\,, \tag{57}\]  
and can give rise to long lived states corresponding to hadronic halos or galactic (in case of gravitational) halos.
## 2 Materials and Methods
### Procedures  
The week before data collection, a visit was made by the researchers to introduce themselves to the adolescents and to show the devices used for the data collection to familiarize them with every procedure. Students aged between 17 and 25 years were interviewed via online questionnaire in the first trimester of their academic year. To reach the aim of the present research, a cross sectional study was developed. The following parameters were analysed.  
_UCLA Loneliness Scale_: This scale has 20 items, and it measures the perceived social isolation. It is answered on a Likert scale where 1 = \"never\" and 4 = \"always\".  
_Acceptance and Action questionnaire-II_: This questionnaire has 7 items, and it measures experiential avoidance and psychological inflexibility.  
_Physical activity_ with a questionnaire in which adolescents were asked about the time per week (h) they spend performing strength-based physical activities, aerobic-based physical activities, daily physical activity, and collective-based physical activities. Each answer was a score ranging from 1 to 5, where 1 = \"less than 30 min \", 2 = \"between 30 and 60 min\",3 = \" between 1 and 2 h\", 4 = \" between 2 and 3 h\", 5 = \"more than 3 h\".  
_Body composition_ by weight, height, and body mass index (BMI) calculated using the classic formula: weight(kg)/height(m)\({}^{2}\).  
_Hours of sleep_ per day were measured on a self-perception scale, indicating the number of hours the student sleep per day.  
_Nutritional habits_ were analysed using an adapted previously used questionnaire. The first 2 questions were related to eating habits. The rest of the questions were related to the consumption frequency of different food groups, including fish, vegetables, legumes, meat, fast food or soft drinks. Each answer was a score ranging from 1 to 6, where 1 = \"I do not consume \", 2 = \"less than three per week\",3 = \"three or more per week\", 4 = \"seven or more per week\", 5 = \"ten or more per week\",6 = \"more than thirteen\"  
_For oral health_, a previously used questionnaire consisting of 4 items related to oral health was used. For the first question, \"How many times a day do you brush your teeth?\", the answers ranged from \"none\" to \"more than four per day.\"
## 3 Results and Discussion
### Synthesis of Evaluation Findings and Analysis  
In order to fully take into account the variation of influencing factors in different years, this paper introduces the data from 2009 to 2018 into the model based on dynamic thinking, which not only meets the systematic requirement of investment climate evaluation, but also effectively reduces the inconsistency of longitudinal comparison brought about by the assignment of cross-sectional data, and realizes the improvement of the accuracy of investment climate evaluation. According to the results in Table 5, among the CEE countries, two countries scored at Level I overall, including Estonia and Slovenia. Six countries in Level II, including the Czech Republic, Hungary, Latvia, Lithuania, Poland, and Slovakia, and six countries in Level III, including Albania, Bulgaria, Croatia, Montenegro, North Macedonia and Romania. Two countries were evaluated at level IV, including Bosnia and Herzegovina and Serbia.  
Table 5 shows the ranking of CEE countries in terms of agricultural investment climate scores, average values and relative change from 2009 to 2018. The agricultural investment climate in CEE countries is generally high in the three Baltic countries, significantly higher than in the Visegrad Group and Slovenia, as well as in the Balkans. Among the 16 countries, Estonia has a stable rating and is the long-term leader. Slovenia and the Czech Republic follow closely behind. In terms of the overall improvement of the investment climate, Albania's agricultural investment climate score was only 0.2954 in 2009, but after 10 years of optimization, its overall index has increased by 41.6%, showing great enthusiasm for improving the investment climate. After Albania, the agricultural investment climate in Serbia and Estonia also shows positive trends. Although the Balkans lags behind among the 16 countries in terms of its investment climate score, the increase in the score over the past 10 years reflects a particular potential for optimization, and there is still room for further improvement in the future.  
On the one hand, the performance is closely related to stable political climate, free and open economic climate, large market size, relatively sound investment regulations, and generally better infrastructure conditions in different countries. On the other hand, it is also benefited by the export structure of international agricultural capital. This finding also reflects the differences in economic development, openness to the outside world, infrastructure construction, public services, and agricultural resources of the 16 target countries evaluated, which are the main aspects that determine the agricultural investment climate.
# Severity Prediction of Traffic Accidents with Recurrent Neural Networks
## 2 Recurrent Neural Networks (RNNs)  
Recurrent Neural Networks (RNNs) are neural networks with feedback connections specifically designed to model sequences. They are computationally more powerful and biologically more reasonable than feed-forward networks (no internal states). The feedback connections provide a RNN the memory of past activations, which allows it to learn the temporal dynamics of sequential data. A RNN is powerful because it uses contextual information when mapping between input and output sequences. However, the traditional RNNs have a problem called vanishing or exploding gradient. To handle this problem, [PERSON] and [PERSON] [PERSON] and [PERSON] (2013) proposed the Long Short-Term Memory (LSTM) algorithm.  
In LSTM, the hidden units are replaced by memory blocks, which contain one or more self-connected memory cells and three multiplicative units (input, output, forget gates). These gates allow writing, reading, and resetting operations within a memory block, and they control the overall behavior internally. A representation of a single LSTM unit is shown in Figure 1. Let \(c_{t}\) be the sum of inputs at time step \(t\), then LSTM updates for time step \(i\) at given inputs \(x_{t}\), \(h_{t-1}\), and \(c_{t-1}\) are [PERSON] and [PERSON] (2013):  
\[i_{t}=\sigma(W_{xi}.x_{t}+W_{hi}.h_{t-1}+W_{ci}.c_{t-1}+b_{i}) \tag{1}\]  
\[f_{t}=\sigma\left(W_{xf}.x_{t}+W_{hf}.h_{t-1}+W_{cf}.c_{t-1}+b_{f}\right) \tag{2}\]  
\[c_{t}=i_{t}.tanh(W_{xc}.x_{t}+W_{hc}.h_{t-1}+b_{c})+f_{t}.c_{t-1} \tag{3}\]  
\[o_{t}=\sigma(W_{x0}.x_{t}+W_{ho}.h_{t-1}+W_{co}.c_{t}+b_{o}) \tag{4}\]  
\[h_{t}=o_{t}.\text{tanh}(c_{t}) \tag{5}\]  
where \(\sigma\) is an element-wised non-linearity such as a sigmoid function, \(W\) is the weight matrix, \(x_{t}\) is the input at time step \(t\), \(h_{t-1}\) is the hidden state vector of the previous time step and \(b_{i}\) denotes the input bias vector.
## 3 Numerical Validation Examples
### Three-Dimensional Three-Story Shear Building
#### 3.2.2 Probability of Damage Detection  
The MPVs of mass and stiffness under the damaged condition and corresponding uncertainties are utilized to compute the probability given a certain damage level. Figure 11 illustrates that, for damage case No.1, the mass parameter \(\beta_{1}\) and the stiffness parameter \(\theta_{3,+y}\) have a high probability (61.22% and 85.62%, respectively) of having possible damage--20% and 25%. For damage case No. 2, the mass parameter \(\beta_{2}\) and the stiffness parameter \(\theta_{3,+y}\) have a possible reduction of 20% and 40% with a high probability of 83.62% and 76.38%, respectively.  
In practice, damage can be detected by probabilistic curves, because the curves related to damage locations are generally easily distinguished from the ones related to healthy locations. For example, the curves of \(\beta_{1}\), \(\theta_{2,+x}\) and \(\theta_{3,+y}\) in damage case No.1 are clearly separated from the others, indicating that the location corresponding to \(\beta_{1}\), \(\theta_{2,+x}\) and \(\theta_{3,+y}\) may have certain damage. A similar observation is found in damage case No. 2. The proposed method can detect damage in mass and stiffness along with location and severity. Engineers can be informed that some repairing work may be necessary at a certain location.  
Figure 10: Updated parameters by two approaches. Damage case No. 1: (**a**) mass; (**b**) stiffness. Damage case No. 2: (**c**) mass; (**d**) stiffness.
## 3 Methods  
### Multi-Layer Neural Networks  
We want to use the synthetic dataset to retrieve snow parameters based on the TOA radiances. For this purpose, we created a neural network that takes as input the sun-satellite geometry angles as well as the TOA radiances. The output will be the corresponding snow grain size, snow impurity concentration, visible albedo (VIS, 380-700 nm), near-infrared (NIR, 700-2500 nm) albedo, and visible shortwave infrared (VSWIR, 380-2500 nm) albedo. A multi-layer neural network (MLNN) is a feedforward artificial neural network used for pattern classification, recognition, prediction, and function approximation. It has been demonstrated that MLNNs with one or more hidden layers and a non-linear activation function can approximate nonlinear functions [36; 37]. Therefore, it is suitable for solving our inverse problem, which is to derive the snow parameters and the VIS, NIR, and VSWIR albedo from the TOA radiances measured at a given set of wavelength bands for a specific sun-satellite geometry configuration, i.e., the solar zenith angle, the sensor observation angle, and the relative azimuth angle between the sun and the satellite sensor. When constructing an MLNN, one important issue is to find the optimum number of hidden layers and neurons. This determination depends on many variables, such as the number of neurons of the input and output layer, the number of training samples, the complexity of the function to be approximated, the type of activation function, and the training algorithm. This circumstance makes it difficult to find the best solution in many cases. Drawing on previous experience [38], we were able to find a suitable MLNN configuration, as described below.
# Unmanned Vehicles' Placement Optimisation for Internet of Things and Internet of Unmanned Vehicles
## 2 Unmanned Aerial and Surface Vehicle Networks: Applications and Advancement in Their Optimisation
### USV Network Applications  
Water communications began as a response to the necessity of connecting boats and ships to exchange information related to their identity and position. Moreover, there were situations in which the safety of ships and crews was in danger. Nowadays, water communications are critical for use in search and rescue missions, and they were also extended for aquatic environment monitoring applications [36; 37] (especially for marine fauna research [38; 39] for the localisation of rich fish populations or seismic activity detection [40].  
Meanwhile, a new class of unmanned vehicles emerged: unmanned water vehicles, which have roles in hydrographic monitoring, facilitate oceanographic research and maritime or river missions. There are two categories of unmanned water vehicles: underwater vehicles and surface vehicles [41]. Both types of vehicles integrate technologies and intelligent devices such as sensors and wireless networks, thus becoming critical components for the IoT landscape. Compared to human-crewed ships and boats, unmanned vehicles can be often used in missions that imply the inspection of rugged areas.
# Experimental Study on Dynamic Response Characteristics of RPC and RC Micro Piles in SAJBs
## 3 Experimental Results
### Deformation of Piles
#### 3.4.2 The Influence of Seismic Waves  
The horizontal deformations of micro piles under different seismic loads in cases 6, 7, and 8 (EI-Centro wave, Kobe wave, and artificial wave with amplitude of 0.15 g, respectively) were plotted in Figure 12. It can be clearly seen from Figure 12 that the horizontal deformation distribution laws of the pile along its buried depth are consistent basically for each pile. The maximum deformations of piles under different seismic wave loads are observed at the pile head. It indicated that the horizontal deformations of piles above the soil surface decrease linearly from the pile head to the soil surface. Moreover, the horizontal deformations of piles body decrease significantly with the increase of buried depth. Then, the horizontal deformations of piles reduce to 0 mm at the buried depth of 0.4 m (4D) and then change into the negative value. The maximum negative deformations of the piles are observed at the buried depth of 0.8 m (8D), and it gradually diminishes to 0 mm as the buried depth increases.  
As seen in Figure 12, it is also found that the horizontal deformation of piles under artificial wave load is greater than that of the Kobe wave or the EI-Centro wave. Especially, the maximum deformation of RPC and RC piles reach 5.5 mm and 6.3 mm at the pile head, but the deformations of piles are 3.1 mm and 3.2 mm, respectively, under the Kobe wave load. Therefore, the horizontal deformation of the RC pile is larger than that of the RPC pile, which is increased by 15% when piles are subjected to the artificial wave. Besides, comparing Figures 11 and 12, it indicates that the negative deformations of piles under seismic wave loads are more significant than that under the sinusoidal wave loads. The reason is that the frequencies of the sinusoidal wave were monotonous whereas the frequencies of seismic waves were abundant. The frequency of the seismic wave with abundant frequency includes the frequency component corresponding to the higher-order vibration modes of the micro pile. Although the horizontal displacement of the RC pile is larger than that of the RPC pile, but the increment is slight, at only 15%. It is noteworthy that the RPC micro pile has better crack resistance, higher ductility, and flexural rigidity than that of the RC pile.  
Figure 12: Horizontal deformations of piles under different seismic wave loads. (a) RPC micro pile. (b) RC micro pile.
## 1 Introduction
Reactive species with relatively long lifetimes, e.g., hydrogen peroxide, are generated by, in particular, the interaction of CAP with liquids [37; 40; 41]. Interestingly, plasma-treated cell culture media or buffers achieve similar effects on cells, as reported for direct plasma treatment of cells in liquids [42; 43; 44; 45; 46; 47]. Plasma-activated media (PAM) can be made in advance and retain their biological efficiency during storage for up to 21 days [48; 49; 50; 51; 52; 53]. In vitro experiments have revealed effects like reduced viability, detachment and apoptosis of cells, and DNA damage, as well as openings of the tight junction protein ZO-1 in cell-cell contacts [54; 44; 45; 55; 56; 57; 58; 44; 54; 58]. Promising results have been shown regarding the selective killing of tumor cells alongside normal cells using CAP or PAM [57; 59; 60; 61; 62; 63; 64; 65]. Clinically used buffer solutions like [PERSON]'s lactate solution are also interesting targets for plasma activation. Plasma-treated [PERSON]'s lactate solution has shown antitumor effects on different types of cancer both in vitro and in vivo [66; 67; 68; 69; 70]. To investigate the detachment and impaired adhesion of cells, other groups have examined the influence of CAP/PAM on molecules in adhesion complexes, e.g., integrins and cadherins or ZO-1 as a protein in tight junctions [44; 45; 48; 71; 72; 73; 74]. We assumed that, in cells that express HA at their surface, e.g., tumor cells, the RONS generated during plasma therapy first reaches the cells HA coat before they can contact the molecules located in the cell membrane. This idea was further nourished by works showing that HA is degraded by free radicals [75; 76; 77]. This made it interesting to investigate whether, in addition to the antiproliferative and apoptosis-inducing effects described so far for plasma therapy, the influence of CAP/PAM on the HA pericellular coat could be another important target in tumor therapy and if it could influence tumor development and metastasis. To our knowledge, findings on these interesting research questions have not yet been published. Here, we described the first in vitro investigations on the influence of plasma-activated medium (PAM) on the hyaluron pericellular coat of HaCaT keratinocytes. The PAM was generated by argon-plasma treatment of Dulbecco's modified Eagle's Medium (DMEM) via the kINPen(r)9 plasma jet [44, 78]. The question arose whether PAM is able to reduce the HA pericellular coat in a manner similar to enzyme hyaluronidase.