esa-sceva/satcom-chunk-collection huggingface dataset:
# Artificial Neural Network Model to Predict Final Construction Contract Duration
## 2 Literature Review
### Forecast Model Based on the Causes of the Time Delays  
In this section, the researchers tried to find the significant factors that affected the time delay and integrated them as input data to forecast the final construction contract duration. In a general project, [PERSON] et al. [14] used ten previously discovered criteria to construct a simulation forecasting model for the delay duration in Saudi projects using system dynamics. In order to consider the ten factors that influence project delay, they carried out a systematic, integrated approach using the DEMATEL methodology and system dynamics (SD). This work solved the challenge of methodically creating a causal loop diagram inside the SD modeling process using the DEMATEL technique. Next, consistency and extreme conditions were tested on the generated SD model. Then, it was implemented and validated using three case studies in KSA by contrasting each case study's real and fitted progress curves. In addition, [PERSON] and [PERSON] [2] developed a model to examine the impact of the factors on the final construction project time. The model combined two mathematical decision-making techniques, DEMATEL and SD modeling. The simulation findings highlight the significance of avoiding design errors at the project's beginning (or preconstruction stage) to reduce project delays.  
For highway projects, [PERSON] et al. [15] evolved models to project the final cost and duration of a highway construction project while it was still in the planning stages. Before designing the forecasting models, project data were gathered and examined to determine the variables influencing the project's ultimate budget and duration. The research for these models was based on the ANN. [PERSON] et al. [16] examined the influence of the non-value-adding effort generated from design errors and changes in design on the time delay of the project using system dynamics.  
In order to facilitate reliable project delay risk analysis and forecasting using objective data sources, [PERSON] et al. [17] refined and built machine learning (ML) algorithms (decision tree and naive Bayesian algorithms). As a result, the relevant delay risk sources and components were first found. A multivariate data set of past project timeliness and delay-inducing risk sources was assembled. Exploratory data analysis was then used to reveal the system's intricacy and interconnectivity. In order to anticipate the extent of project delays, the two appropriate algorithms were found and trained using the data set. These models used decision trees and naive Bayesian classification algorithms. Finally, cross-validation tests were performed on both models to assess their predictive abilities. The models were then contrasted using performance metrics pertinent to ML.
###### Abstract  
The tropical Rainfall Measuring Mission TRMM 3B42 V7 product and its successor, the Global Precipitation Measurement Integrated Multi-satellite Retrievals for GPM IMERG high-resolution product GPM IMERG V5, have been validated against rain gauges precipitation in an arid mountainous basin where ground-based observations of precipitation are sparse, or spatially undistributed. This paper aims to evaluate hydro-statically the performances of the TRMM 3B42 V7 and GPM IMERG V50 satellite precipitations products SPPs, at multiple temporal scales, from 2014 to 2017. SPPs are compared with the gauge station and show good results for both statistical and contingency metrics with notable values R \(>\) 0.94. Moreover, the rainfall-runoff events implemented on the hydrological model were performed at 3-hourly time steps and showed satisfactory results based on the obtained Nash-Sutcliffe criteria ranging from 94.50% to 57.50%, and from 89.3% to 51.2%, respectively. The TRMM product tends to underestimate and not capture extreme precipitation events. In contrast, the GPM product can identify the variability of precipitation at small time steps, although a slight underestimation in the detection of extreme events can be corrected during the validation steps. The proposed method is an interesting approach for solving the problem of insufficient observed data in the Mediterranean regions.  
Keywords:TRMM 3B42 V7; GPM IMERG V5; rain gauge; Mediterranean climate; hydrological modeling +
Footnote †: journal: Applied Sciences
# Optical Chirality Determined from Mueller Matrices
## 3 Theory
### Method I: Evaluation of Optical Activity Directly from Mueller-Matrix Elements
With these approximations, Equation (4) reduces to [13]  
\[\mathbf{M}_{\text{C}}\approx\left[ \begin{array}{cccc}1&0&0&C_{\text{D}}\\ 0&1&\mp C_{\text{B}}&0\\ 0&\pm C_{\text{B}}&1&0\\ C_{\text{D}}&0&0&1\end{array} \right]=\left[ \begin{array}{cccc}1&0&0&2 \epsilon\\ 0&1&\mp 2\alpha&0\\ 0&\pm 2\alpha&1&0\\ 2\epsilon&0&0&1\end{array} \right]. \tag{5}\]  
For small optical activity, \(\epsilon\) and \(\alpha\) are, thus, obtained directly from the secondary diagonal in Equation (5). In ORD applied to molecules in solution, the rotation is normalized by the optical path length \(d\) in dm and concentration \(c\) in g/cm\({}^{3}\) and specified at a certain wavelength and temperature \(T\), and also converted from radians to degrees. The specific rotation is, then, obtained as  
\[[\alpha]_{\lambda}^{T}=\frac{\alpha}{dc}. \tag{6}\]  
In standard reflection ellipsometry, the complex reflectance ratio is defined as \(\rho=r_{\text{p}}/r_{\text{s}}=\tan\Psi e^{i\Delta}\), where \(\tan\Psi=|r_{\text{p}}|/|r_{\text{s}}|\) and \(\Delta=\arg r_{\text{p}}-\arg r_{\text{s}}\) with \(r_{\text{p}}\) and \(r_{\text{s}}\) denotingthe complex-valued reflection coefficients for p- and s-polarized light, respectively. We present an analogous chiral transmittance ratio \(\rho_{\rm C}\) defined as in [14]  
\[\rho_{\rm C}=t_{1}/t_{\rm r}=\tan\Psi_{\rm C}e^{\rm i\Delta_{\rm C}} \tag{7a}\] \[\tan\Psi_{\rm C}=\left|t_{1}\right|/\left|t_{\rm r}\right|=e^{- \frac{2\pi d}{\lambda}(t_{1}-k_{\rm r})}\] (7b) \[\Delta_{\rm C}=\arg t_{1}-\arg t_{\rm r}=\frac{2\pi d}{\lambda}(n_{1}-n_{\rm r }), \tag{7c}\]  
where \(t_{1}=e^{\frac{2\pi d}{\lambda}\rm i(n_{1}+\rm i\rm k_{\rm i})}\) and \(t_{\rm r}=e^{\frac{2\pi d}{\lambda}\rm i(n_{\rm r}+\rm i\rm k_{\rm r})}\) are the eigenpolarizations for left-handed and right-handed polarized light, respectively. If we also observe that \(\sec C_{\rm D}=\sin 2\Psi_{\rm C}\), \(C_{\rm B}=\Delta_{\rm C}\) and \(\tanh C_{\rm D}=-\cos 2\Psi_{\rm C}\), we can rewrite Equation (4) as  
\[\mathbf{M}_{\rm C}=\left[ \begin{array}{cccc}1&0&0&-\cos 2\Psi_{\rm C}\\ 0&\sin 2\Psi_{\rm C}\cos\Delta_{\rm C}&\mp\sin 2\Psi_{\rm C}\sin\Delta_{\rm C }&0\\ 0&\pm\sin 2\Psi_{\rm C}\sin\Delta_{\rm C}&\sin 2\Psi_{\rm C}\cos\Delta_{\rm C }&0\\ -\cos 2\Psi_{\rm C}&0&0&1\end{array} \right]. \tag{8}\]  
Equation (8) is applicable for samples exhibiting only optical activity and no linear anisotropic effects and can be used as an alternative to Equation (4).
## 2 Conventional Channel Estimation Methods
### Blind Channel Estimation Method
#### 2.2.3 ML-Based Blind Channel Estimation  
Some researchers have employed the ML algorithm to perform blind channel estimation as well. The ML-based blind algorithm constructs a likelihood function to measure the similarity between the predicted and actual received signals under the given channel parameters. By optimizing this likelihood function, one can obtain the best channel estimation result. In [38], [PERSON] et al. investigated the deterministic ML (DML) and stochastic ML (SML) iterative methods for jointly estimating Doppler frequency shift and channel parameters. These methods offer maximum likelihood solutions and thus perform better than most HOS-based and blind adaptive methods, particularly if only a short data record is available. In [39], [PERSON] et al. developed the first fast converging, fully blind channel estimation algorithm based on the ML principle. This algorithm recovers the absolute phase of the channel transfer function without reference symbols. Compared to the pilot-based method, there is a higher spectrum utilization and a lower mean square error (MSE) and bit error rate (BER).  
The blind channel estimation method can effectively utilize spectrum resources and adaptively track the changes in CSI, without requiring extra pilots. Nevertheless, the blind method is not without its limitations. These include the method's high computingcost, relatively slow convergence speed, extra observation data, and the presence of phase ambiguity [33; 35]. Thus, these challenges pose difficulties for the practical use of such a method.
# Experimental Validation and Evaluation of a Coupled Twist-Camber Morphing Wing Concept
## 2 Morphing Wing Concept
### Experimental Procedure  
The experimental determination of the deformed shapes assumes that the position of a point in the wing surface could be determined based on video footage from two different cameras rotated around a fixed axis parallel to the wing's straight LE. With reference to Figure 6, the position of a point P on the wing surface can be described either relative to the reference frame xyz or reference frame x'y'z'. A simple rotation of an angle \(\theta\) around the x-axis transforms the coordinates of P from one frame to the other.  
Equation (1) relates the two sets of coordinates and describes the formula to obtain the coordinate z of a point knowing the (y, y') pair. Therefore, from two projections in different planes, one can obtain the full set of coordinates of a point P.  
\[\begin{array}{l}x^{\prime}=x\\ y^{\prime}=y\cos\theta+z\sin\theta\ \ \Rightarrow\ \ z=\frac{y^{\prime}-y\cos\theta}{\sin \theta}\ \,\\ z^{\prime}=z\cos\theta-y\sin\theta\end{array}\ \, \tag{1}\]  
Figure 6: Camera positions, views, and reference frames for determination of coordinates of wing surface points.  
While for camera view #1, parallel to the wing planform, the resulting footage corresponds to a projection on the xy plane, the same did not apply for camera view #2. Figure 6 shows the distortion of the image when the view plane is not parallel to the wing planform caused by the depth variation for the points on the surface. Knowing the wing dimensions, a scaling factor function of \(y^{\prime}\) could be generated to correct the distortion and obtain the desired projection in the plane \(x^{\prime}y^{\prime}\). One could then proceed with the z coordinate calculation for a spatial description of the wing surface using Equation (1). The same procedure could be applied to the lower wing surface.
## 4 Sensitivity Analysis of RPC-FST  
Irreversible RPC-FST damage will emerge during the explosion process. Meanwhile, the accumulation of the damage process will decrease the durability of the RPC-FST [22]. Furthermore, the characteristic parameters for estimating the damage of the RPC-FST are random due to manufacturing and measurement errors [34; 35]. Therefore, the quantification and propagation of randomness of the explosion process are critical in RPC-FST design. In addition, the low calculation efficiency of the modified K&C model under explosion load is considered. The surrogate Kriging model is adopted to replace the modified K&C model for sensitivity analysis of RPC-FST under random explosion load.  
For probabilistic analysis, the reliability of RPC-FST under explosion load can be defined as a multi-dimensional integral:  
\[P_{s}=\int\cdots\int_{G(x)>0}f_{X}(x)dx \tag{5}\]  
where  
\[G(x)=\left[\left(\frac{H}{2}\right)\times\tan(\theta)\right]-\varepsilon(x) \tag{6}\]  
where \(G(x)\) is the limit state function, which can be established based on Section 2; \(x\) = (\(H\), \(W\), \(t\), \(L\), \(D\))\({}^{\text{T}}\) is a vector of independent random variables used to calculate the dynamic response of the RPC-FST system; \(X\) is a random parameter vector corresponding to \(x\); \(H\) is the height of RPC-FST; \(W\) is the weight of TNT; \(t\) describes the thickness of steel tube; \(L\) represents the distance between TNT and RPC-FST; \(D\) is the section diameter of RPC-FST; and \(\theta\) denotes the deflection angle of RPC-FST at the upper damage limit [36]. According to TM5-1300 [3], the damage levels of concrete-filled steel tube columns can be divided into low damage, moderate damage, and high damage (Table 8). Therefore, the failure probability is calculated for the low damage of a 0-2\({}^{\circ}\) column, as shown in Figure 6. In order to calculate the sensitivity of the allowable deflection angle between 0 and 2\({}^{\circ}\), we decided to choose 1.4\({}^{\circ}\) as the allowable deflection angle. \(\varepsilon(x)\) is the maximum displacement based on FSI theory. However, due to the high dimensionality and complicated integrand of Equation (5), both analytical and direct numerical methods cannot be applied directly.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Damage Level** & **Low Damage** & **Moderate Damage** & **High Damage** \\ \hline \(\theta\) & 0–2\({}^{\circ}\) & 2–5\({}^{\circ}\) & 5–12\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Damage level of concrete-filled steel tube column.
## 3 Group Communication Requirements
A very powerful method for limiting the number of messages inside the LLN is using efficient caching techniques. This not only limits the number of messages and energy consumption, but it also decreases response latency. CoAP transactions and options are thus well optimized to support caching whenever possible. Group communication should not be an exception and should not hinder the use of caches. 5. _Validation and Error Handling_: since a group might include heterogeneous members, it should be possible to validate the group in order to make sure that the group works as intended. The group should have mechanisms for reporting and handling error conditions such as node or route failures. 6. _Reliability_: sometimes it is not essential to get reliable replies from all group members (e.g., it might be enough to get the temperature measurements from just one of the many temperature sensors in a room). However in many other cases, it can be important to have reliable communication with all group members. For example, one would expect that all lights in the room would go on when one flips on the light switch. 7. _Ease of Group Manipulation_: the needs of the user might change with time and thus group membership might also change. In dynamic environments the changes might be frequent. It is important to be able to handle such changes easily. One should avoid node reconfigurations, as this might be a tedious task. Also it should be possible for nodes to be part of different groups at the same time or at different times. 8. _Expressiveness/Control_: there are several results that one might want to achieve by interacting with a group of objects/object resources. In some cases one might be interested in all the individual results of all members as in the case of turning the lights on. In many other cases the individual values might not be of interest at all. In these cases one might be interested in an aggregated value (e.g., min, max, avg,...) of all, or even of just a subset of, the group members. Thus it is desired to have support for processing of individual group member results and replying to the requester with aggregated results. For example it should be possible to query a certain subset of the members and compute the average, or reliably update all members. 9. _Security_: secure communication might be of little interest inside a shielded and controlled environment. However, by exposing sensors and actuators to the Internet, security becomes a major concern. In some scenarios having an end-to-end security is a strict requirement. Communicating with a group of resources is no exception. In fact it is even more sensitive than communicating with an individual resource, since compromising the group means compromising all the individual members.
# Investigating Coherent Magnetization Control with Ultrashort THz Pulses
## 3 Results
To characterize this pulse shape, we fitted our data with the following equation which is often used to describe ultrafast demagnetization [7, 18]:  
\[\frac{A-A_{0}}{A_{0}}=\left[\left(K_{1}-\frac{(K_{2}\tau_{E}-K_{1}\tau_{M})e^{ \frac{-t}{\tau_{M}}}}{\tau_{E}-\tau_{M}}-\frac{\tau_{E}(K_{1}-K_{2})e^{\frac{-t }{\tau_{E}}}}{\tau_{E}-\tau_{M}}\right)H(t)\right]*\Gamma(t), \tag{1}\]  
where \(A_{0}\) is the unpumped asymmetry, \(K_{1}\) and \(K_{2}\) are constants, \(\tau_{E}\) and \(\tau_{M}\) are the electrons-phonons and spins relaxation times, \(H(t)\) the Heaviside function and \(\Gamma(t)\) the temporal intensity profile of the pump beam. To simplify the analysis, we considered that the pump beam consisted of 10 cycles (given the number of period of the THz undulator) of a single THz frequency of either 3 or 6.7 THz (corresponding to pulse durations of about 3.3 ps and 1.5 ps respectively). This ignores the fact that the higher harmonics will distort the shape of the THz pulse [11] but a more accurate description would not match the time resolution of our data. The results of the fits are shown in Figure 6. The most evident observation is that the intensity is not uniformly spread among cycles for both THz pump wavelengths. This was to be expected by observing the demagnetization curves on Figure 5 which presented broken lines demagnetization slopes. Moreover, the most intense cycles are not situated at the same position within the pulse for 45 and 100 \(\upmu\)m fundamental wavelengths: respectively in the middle and at the end of the THz pulse. This observations are in rough agreement with proper measurements of the THz pulse shape which showed that the exact shape of the THz pulse is highly dependent on the XFEL parameters and can drastically change upon variation of these parameters during an experiment [5]. Figure 5: Normalized asymmetry as a function of time delay for THz pump of fundamental wavelengths of 45 \(\upmu\)m (red) and 100 \(\upmu\)m (blue) as a function of time delay. The unusually long demagnetization time is a consequence of the THz pulse length of about 1.5 and 3.3 ps respectively. The exact shape of the pump pulse gives rise to the varying slope in the demagnetization trace.
# The Feature Selection Effect on Missing Value Imputation of Medical Datasets
## 2 Literature Review  
### Types of Missing Values  
According to [9], there are three types of missing values or missing data mechanisms: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR).  
In MCAR, the missing values occur entirely at random, which means that the data are missing independently of both observed and unobserved data. As an example of two attributes, represented by \(x\) and \(y\), missing value \(y\) neither depends on \(x\) nor \(y\). In MAR, whether a data point is missing or not is not related to the missing data, but rather related to some of the observed data; that is, given the observed data, data are missing independently of the unobserved data. For example, the missing value \(y\) depends on \(x\) but not \(y\). In MNAR, the probability of a missing value depends on the variable that is missing.  
Although real world data are rarely MCAR, it has often been assumed in past studies that the data are MCAR or MAR. However, for MNAR data, imputation methods that assume MAR data can often produce only small biases, and this depends on how well a MAR mechanism can approximate the MNAR mechanism.
## 2 Materials and Methods
### Steps of Weather Disaster Emergency Response Capability Feature Extraction
#### 2.4.3 Interpretation Methods for Feature Extraction  
The results of competency feature extraction provide a reference for competency evaluation but need to be able to be recognized and accepted by managers and evaluated subjects. For this reason, the feature extraction results need to have a good interpretability. By interpretability, we mean the choice to express the model in a way that humans can understand based on their perception of reality. In data science, it is often difficult to balance the performance and interpretability of a model. In recent years, with the development of computing power and the increase of data volume, deep learning models based on neural networks have become more and more important in the field of data mining, but these models are \"black boxes\", i.e., they cannot explain the prediction results of the models, which greatly limits the credibility of the models.  
To address this shortcoming, [PERSON] et al. [25] proposed a model interpretation framework, the Shapley Additive Explanations (SHAP) method, based on game theory, which achieves model interpretation by calculating the importance of each feature in the sample.  
The SHAP method is an additive explanatory model, i.e., each feature is considered as a contributor and its marginal contribution is calculated, and the final prediction of the model is equal to the sum of the marginal contributions of all features.  
\[SHAP_{feature}(x)=\sum_{set:feature\in set}\biggl{[}|set|\times\binom{F}{|set|} \biggr{]}^{-1}\biggl{[}Predict_{set}(x)\ Predict_{set\in feature}(x)\biggr{]} \tag{32}\]  
where \(x\) is a sample, _Feature_ is a feature of the sample, and _set_ is the set of features containing feature; _Predict_ is the prediction result of the model about the set of features, and \(Predict_{set}(x)-Predict_{set/feature}(x)\) denotes the marginal contribution of feature to the model with set as the feature. \(SHAP_{feature}(x)\) is equal to the sum of the marginal contributions of the feature to the model.  
If no features are known, then the output of the model can be interpreted as the average prediction over the dataset as a baseline, with each feature having a corresponding contribution value that explains how the model goes from the baseline to the new prediction of the model containing this feature; the sum of the SHAP values for each feature of the sample is the difference between the model prediction and the dummy model without any feature.  
The comprehensive analysis above shows that, compared with traditional machine learning interpretation methods, such as random forest, the SHAP method can not only give the importance ranking of each feature, but also illustrate the specific impact of a feature on the prediction results, effectively unifying the global and local interpretation of machine learning models.
# Deep Reinforcement Learning-Based Video Offloading and Resource Allocation in NOMA-Enabled Networks
## 4 Deep Reinforcement Learning-Based Algorithm
### Deep Reinforcement Learning Model  
The deep reinforcement learning process reformulates the computational offloading problem as a Markov Decision Process (MDP) model. A typical MDP model consists of a tuple \(\{S,A,P,R,\gamma\}\) with five elements, where \(S\) represents the state space, \(A\) represents the finite action space, \(P\) is the state transfer probability, \(R\) represents the reward function, and \(\gamma\in[0,1]\) is the discount factor for future rewards. Each element of the MDP model tuple corresponds to the following meaning.