esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Control Statement  
### Stalling Behavior of the Wells Turbine  
The stalling behavior in Wells turbines is a phenomenon that restricts the produced power. It happens in the event that the airflow speed \(v_{x}\) rises; however, the rotational velocity \(\omega_{r}\) is slow because the generator is unable to spin quick enough to match the incoming airflow of strong waves. This behavior is visible in Figure 3b which demonstrates when the flow coefficient \(\phi\) surpasses a critical value 0.3, the torque coefficient \(C_{t}\) declines considerably because the rotational speed \(\omega_{r}\) is unable to match the airflow velocity \(v_{x}\).  
The stall effect is explained by operating the uncontrolled OWC plant with different sea states. The first sea condition examines waves with a 10-s period and 0.8-m wave amplitude (Figure 4a,b). The second sea condition examines waves with a 10-s period and a 1.3-m wave amplitude (Figure 4c,d).  
As shown in Figure 4, when the waves are low (i.e., \(A=0.8\) m) the Wells turbine will have a low flow coefficient, which in this case does not exceed the threshold value 0.3 (see Figure 4a). Hence, the resulting turbine torque is not affected by the stalling behavior (see Figure 4b). However, when the waves are high (i.e., \(A=1.3\) m) the Wells turbine will have a higher flow coefficient that exceeds the threshold value 0.3 (see Figure 4c). Hence, the resulting turbine torque is affected by the stalling behavior (see Figure 4d).  
The Wells turbine's stalling behavior can be evaded if the flow coefficient is constantly regulated [24; 25]. From the expression (9), the flow coefficient relies on the airflow velocity in the turbine duct. Thus, adjusting the airflow speed \(v_{x}\) will aid in evading the stall effect; therefore, an airflow control strategy has been suggested.  
The implementation of the airflow control puts to use the air valve set to use within the capture chamber, this device can be used to vary the pressure and airflow in the OWC system. The actuator of the air valve is controlled using a PID controller, as explained by the scheme of Figure 5.  
Figure 4: Uncontrolled OWC operation with two different sea conditions. (**a**) Flow coefficient for wave amplitude 0.8 m. (**b**) Turbine torque for wave amplitude 0.8 m. (**c**) Flow coefficient for wave amplitude 1.3 m. (**d**) Turbine torque for wave amplitude 1.3 m.  
Tuning the PID controller in a complex system such as the OWC often is hard and tedious when using conventional methods and lacking an appropriate systematic design approach. In order to tune the PID controller, the use of optimization theory has been suggested as a promising recourse to easily calculate and optimize all PID gains [21,22].
## 5 Application of Reinforcement XAI Model
### Class-Specific Feature Importance  
The first step for a global explanation involves using SHAP values to compute the relative importance of the features with respect to each of the attack classes, enabling the creation of class-specific feature explanations, which can be particularly useful in the context of an IDS (Intrusion Detection System). To generate these class-level explanations, SHAP values are computed over a large number of random samples from the testing partition, totaling 5000 samples. By analyzing the distribution of SHAP values for a specific class, the most relevant features for recognizing each type of intrusion can be interpreted.  
A high positive SHAP value for a feature indicates a strong contribution towards predicting a particular class, while a negative SHAP value signifies a decreasing influence on that prediction. Figure 3 shows a summary of the importance of a subset of features on the classification of the Analysis class. The selected subset corresponds to the ten features with the highest sum of absolute SHAP values.  
In this figure, color is used to indicate the value of a feature relative to its own typical ranges. It must be considered that some of these features have been separated by one-hot-encoding, meaning that for proto, service and state, a high value of proto_udp indicates a positive (1) instance of the UDP protocol being used. This image allows us to easily draw some conclusions for the Analysis class, it can be inferred that UDP messages with low values of sttl (source to destination time to live) are indicative of a malicious behavior of this class.  
Figure 3: Feature importance interpretation for “Analysis” class.  
Figure 2: Individual explanation for the prediction of a single sample of the “Reconnaissance” class.  
In Figure 4, the same explanation visualization has been included for the rest of the classes. This figure can allow a similar inference over each of the attack classifications included on the dataset UNSW-NB15.  
The development of rule-based controls from these global explanations require further individual studies applied over each class, either manually, or by inferring the feature importance by value range following the method described in the next section. However, some evidence can be found just by observing this overview. The selection of protocol seems to be enough of a differentiator to detect DoS, backdoor, fuzzers, and shellcode attacks. Additionally, a low value of sttl, the source to the destination time to live, is indicative of an exploit, while a low mean packet size from the source indicates a possible reconnaissance packet.
# Managing a Smart City Integrated Model through Smart Program Management
## 3 Related Works
Several models were proposed for evaluating, supporting decision assumptions, and planning, such as approaches based on fuzzy logic which offer a more extended comprehension, both for the decision makers as well as citizens, without yielding to competencies and personal subjectivity [1]; an indicator of how small or large the spectrum of vertical domains is in a Smart city which has developed projects and consolidated best practices, and reveals the improvement of Smart city initiatives in different application domains such as social, geographic, demographic, and environmental sectors [38]; a list of indicators to support the triple helix model in order to measure the performances of Smart city components [39]. A review of the literature has highlighted the effort spent in evolving towards the smart concept. However, at the same time, there are difficulties in managing projects in an integrated manner in order to maximize the benefits. Moreover, there is a gap between strategy applied and projects carried out for moving towards a Smart city, as well as a lack of alignment and integration between them. Therefore, it is necessary to manage the interdependence between projects as well as the correlation between strategy and execution, in order to obtain an integrated vision. To manage this additional complexity, the Project Management Institute has over-structured management levels by introducing the concept of program management, whose logic is to increase the overall value of projects thanks to the exploitation of interdependencies between projects. In this case, therefore, the total value generated by the program goes well beyond the sum of the value of the individual projects. In the case of Smart cities, as mentioned above, the integration must be carried out along at least two distinct dimensions, application domains, and technology layers, and the program management must therefore be appropriately specialized.
## 2 Theory and Methods  
### Multi-Factor Interaction Matrix  
All relevant factors are part of a dynamic process of interaction in rock engineering [15]. In the interaction matrix (Figure 4), the main parameters are located on the main diagonal and the interaction values between factors are placed on the off diagonal [16]. The assigning values for the off-diagonal cells are called the coding matrix.  
Figure 3: Steps of risk assessment in this paper.  
Expert semi-quantitative (ESQ) [17; 18] were proposed for the coding of this method. The method provides a basis for calculating constant weights by inviting experts to rate the interrelationship of the indicators using a fixed value from 0 to 4. They are shown in Table 1. The number of experts is five, of which three belong to the construction unit of the project, and two belong to the design unit of the project. They all have 3-5 years of work experience. The score is obtained using a questionnaire survey. They rated the coding value of each factor individually to calculate the constant weight.  
The calculations of the degree of active and passive effects are expressed as follows:  
\[M_{A}(i)=\sum_{j=1}^{n}I_{ij}(i=1,2,\cdots,n) \tag{1}\]  
\[M_{p}(i)=\sum_{j=1}^{n}I_{ji}(i=1,2,\cdots,n) \tag{2}\]  
\[M_{\text{n}}(i)=\sum_{i=1}^{n}M_{\text{A}}(i)=\sum_{i=1}^{n}M_{\text{p}}(i) \tag{3}\]  
where \(n\) is the number of influence factors. \(M_{A}(i)\) and \(M_{p}(i)\) are the sum of the active and passive effect for one factor. \(M_{\text{n}}(i)\) is the sum of the active effect or passive effect for all the factors.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Coding Value** & **Description** \\ \hline
0 & No interaction \\
1 & Weak interaction \\
2 & Medium interaction \\
3 & Strong interaction \\
4 & Critical interaction \\ \hline \hline \end{tabular}
\end{table}
Table 1: Introduction of ESQ method.  
Figure 4: Multi-factor interaction matrix. (**a**) Influence of each factor; and (**b**) the principle of the interaction matrix.  
In the evaluation system, the determination of the constant weights is particularly important. It can be expressed as:  
\[{w^{\prime}}_{i}=\frac{\left[M_{A}(i)+M_{p}(i)\right]}{\sum\limits_{j=1}^{n} \left[M_{A}(i)+M_{p}(i)\right]}\left(i=1,2,\cdots,n\right) \tag{4}\]  
where \({w^{\prime}}_{i}\) are the constant weights of factor \(i\).
## 2 Forward Problem Description (Mathematical Formulation)  
The two-dimensional steady-state heat transfer system (domain \(\Omega\)) is shown in Figure 1, which was based on heat transfer from the walls of industrial equipment in real operational environments. The shape of the accessible boundary \(\Gamma\) was given but the inaccessible inner boundary \(\Gamma_{I}\) was unknown.  
The boundary conditions at \(x=0\) and \(x=L\) are adiabatic; the boundary condition of the unknown boundary \(\Gamma_{I}\)\((y=f(x))\) is the robin boundary condition with a fluid temperature of \(T_{I}\) and a heat transfer coefficient of \(h_{i}\). The boundary condition of \(\Gamma\)\((y=0)\) is a combination of convection and radiation, where the ambient temperature is \(T_{o}\), the heat transfer coefficient is \(T_{o}\), and the absorptivity is \(\varepsilon\). The points marked \"\(\Delta\)\" are temperature sensor positions, assuming a uniform distribution of \(M\) measurement points set on \(\Gamma\). For the domain \(\Omega^{\prime}\), which is the effective absorptivity model of the physical environment (surface radiation), the boundary condition at \(\Gamma_{r}\) is constant temperature \(T_{r}\) and the effective absorptivity is \(\varepsilon^{\prime}\).  
Assuming that the material properties of the problem domain \(\Omega\) are a constant thermal conductivity \(k\), \(\Omega\) and \(\Omega^{\prime}\) are gray bodies, and \(\varepsilon=\varepsilon^{\prime}\), the governing equation of this two-dimensional steady-state heat conduction system can be expressed as  
\[\frac{\partial^{2}T}{\partial x^{2}}+\frac{\partial^{2}T}{\partial y^{2}}=0 \text{ }in\text{ }\Omega \tag{1}\]  
The boundary conditions are as follows:  
When \(x=0\) and \(x=L\), the heat conduction equation is  
\[\frac{\partial T}{\partial\boldsymbol{n}}\bigg{|}_{x=0,x=L}=0. \tag{2}\]  
When \(y=0\), the heat conduction equation is  
\[-k\frac{\partial T}{\partial n}\bigg{|}_{y=0}=h_{o}(T_{o}-T_{xi})+\varepsilon \sigma^{\prime}\big{(}{T_{r}}^{4}-{T_{xi}}^{4}\big{)}. \tag{3}\]  
Namely,  
\[-k\frac{\partial T}{\partial n}\bigg{|}_{y=0}=h_{o}(T_{o}-T_{xi})+h_{r}(T_{r}- T_{xi})\text{ },i=0,\dots,M\text{ }xi\text{ }\in\Gamma \tag{4}\]  
where the equivalent radiative heat transfer coefficient \(h_{r}=\varepsilon\,\sigma^{\prime}\big{(}{T_{r}}^{2}+{T_{xi}}^{2}\big{)} \ast T_{r}+T_{xi}\). The Stefan-Boltzmann constant \(\sigma^{\prime}\) is \(5.669\times 10^{-8}\) W/(m\({}^{2}\)k\({}^{4}\)).  
Figure 1: Physical model of inner wall boundary identification of thermal equipment.  
When \(y=\ f(x)\), the heat conduction equation is  
\[-k\frac{\partial\mathbf{T}}{\partial\mathbf{n}}\Big{|}_{y=f(x)}=h_{i}(T-T_{I}),\ x\in \Gamma_{I} \tag{5}\]  
The FVM was used to solve the forward problem of Equations (1) and (2). To simplify the forward problem and remeshing process for IGPs, the mesh grid of FVM was fixed to a small value such as 0.001 m.
## 3 Cognitive Mobile BTS
### Uplink Channel Control  
The uplink channel may also experience the hidden node phenomenon. For example, in Figure 6, MS3 may be blocking the uplink channel of MS2, but not the downlink channel. Therefore, the cognitive BTS must use information about signal quality in the uplink. Reference [32] describes how quality measurements are performed in the uplink. They are calculated on each SACCH multi-frame whenever a dedicated connection exists, associated with either a Traffic Channel (TCH) or a Standalone Dedicated Control Channel (SDCCH). The reported parameter (Rx-Qual) is the received signal quality, averaged over the reporting period of an SACCH multi-frame, defined in [33]. As in the downlink, we considered a \(C/I\) ratio of 9 dB. If signal quality drops below this threshold for any active communication, the cell reselection procedure is performed as described above.
## 3 Estimation of Lateral Stiffness Ratio
### Generic Building Models and Analyses  
Discrete systems were utilized to estimate the lateral stiffness ratio (\(\alpha\)) defined for the continuous beam model. Ten building model sets consisting of moment-resisting frames with different Blume values were considered. In each model set, buildings between five and 20 stories are considered. The frame models were constructed while considering the beam-to-column stiffness ratio \(\rho\) (Equation (18)), corresponding to Blume's coefficient. Various group models were generated by adjusting the element sizes of beams and columns. All building models consisted of a six-span frame system with a 5 m beam length and 3 m column height. The modulus of elasticity was uniformly assigned as 2.85 \(\times\) 10\({}^{7}\) kN/m\({}^{2}\)for all models. Different numbers of stories (N = 5, 8, 10, 12, 15, and 20 stories) with the same floor plan were obtained, as presented in Table 1. Rigid diaphragms were defined at floors, and lumped masses were assigned at floor levels. Column axial stiffnesses were considered, with column dimensions proportional to the axial load.  
The UBC97 response spectrum curve (Figure 1) was used in the dynamic analysis of the sample models. The seismic parameters, such as seismic zone factor (Z) of 0.3, local soil type (S\({}_{\text{B}}\)) of rocky, and seismic coefficients \(\text{Ca}\) and \(\text{C}_{\text{v}}\) of 0.3, were considered.
## 3 General Formulation
### Generic Transmitter  
The block diagram of the generic transmitter is shown in Figure 3a. The \(N\)-length block of the points belonging to \(M\)-ary constellations is represented by the symbol vector \(\mathbf{X}_{l}\in\mathbb{C}^{N\times 1}\) in the discrete frequency domain. In the sequel, \(\mathbf{X}_{l}\) is properly mapped to be used in a baseband data communication system, such as the PLC. The mapping process results in the mapped vector \(\mathbf{X}_{\zeta,l}\in\mathbb{C}^{2N\times 1}\) being in the discrete frequency domain. The details of this mapping process can be found in [8] for the HS-OFDM scheme, [21] for the OTFDM schem, and [16] for both the OCDM scheme and SCCP.  
Then, the mapped vector is processed through a precoding and unitary matrix, \(\mathbf{Q}\), such that \(\mathbf{Q}^{\dagger}\mathbf{Q}=\mathbf{I}_{2N}\), where \(\mathbf{I}_{C}\) is an identity matrix of size \(C\) and \((\cdot)^{\dagger}\) is the conjugate transpose operator. This precoding matrix defines which one of the multiplexing modulation schemes is being used, following  
\[\mathbf{Q}=\begin{cases}\mathbf{I}_{2N}&\text{for the HS-OFDM scheme}\\ \mathbf{\Gamma}_{\text{HS}}^{\dagger}\mathbf{F}&\text{for the OCDM scheme}\\ \mathbf{F}&\text{for the SCCP scheme}\\ \overline{\mathbf{D}}^{\dagger}&\text{for the OTFDM scheme}\end{cases}. \tag{3}\]  
The final step in the generic transmitter comprises the inverse discrete Fourier transform (IDFT) matrix, \(\mathbf{F}^{\dagger}\), and the cyclic prefix matrix, which is equal to  
\[\mathbf{\Psi}_{\text{t}}=\begin{bmatrix}\mathbf{0}_{L_{\text{CP}}\times(2N-L _{\text{CP}})}&\mathbf{I}_{\text{LCP}}\\ \mathbf{I}_{2N}&\end{bmatrix}. \tag{4}\]  
Therefore, we can define a generic transmission matrix as  
\[\mathbf{T}\triangleq\mathbf{\Psi}_{\text{t}}\mathbf{F}^{\dagger}\mathbf{Q}, \tag{5}\]  
and, consequently, the vector representation of the \(l\)th transmitted block is given by  
\[\mathbf{x}_{l}=\mathbf{T}\mathbf{X}_{\zeta,l}. \tag{6}\]
# Effects of Atmospheric Turbulence on Optical Wireless Communication in NEOM Smart City
###### Abstract  
The foundation of any smart city requires an innovative and robust communication infrastructure. Many research communities envision free-space optical communication (FSO) as a promising backbone technology for the services and applications provided by such cities. However, the channel through which the FSO signal travels is the atmosphere. Therefore, the FSO performance is limited by the local weather conditions. The variation in meteorological variables leads to variations of the refractive index along the transmission path. These index inhomogeneities (i.e., atmospheric turbulence) can significantly degrade the performance of FSO systems. Thus, a practical implementation of the FSO link must carefully consider the atmospheric turbulence effect. This paper aims to investigate the feasibility of FSO communication for NEOM, a promising smart city in Saudi Arabia. We study the effect of weather conditions on FSO links using the micrometeorology model, taking into account actual weather data. The FSO performance in winter and summer was compared in terms of the bit error rate, signal-to-noise ratio (SNR), link availability, and transmission distance. The study shows that the atmospheric turbulence strength is moderate and strong in winter and summer, respectively. The temperature has the biggest impact on the FSO system when compared to the other meteorological elements included in this study. Furthermore, at transmission distances less than 300 m, atmospheric turbulence does not significantly affect the FSO for the operating wavelength of 1550 nm. Furthermore, it has been shown that at transmission distances greater than 300 m, the SNR in summer is more than 18% higher than in winter. The findings of this research enable understanding of the effect of turbulence caused by NEOM weather on the FSO link, thus assisting engineers in establishing a reliable FSO backbone link by adjusting the relevant parameters.  
s +
Footnote †: journal: _Photonics_  
_2022_, 9, 262. [[https://doi.org/10.3390/photonics9040262](https://doi.org/10.3390/photonics9040262)]([https://doi.org/10.3390/photonics9040262](https://doi.org/10.3390/photonics9040262))
## 2 Numerical Simulation Methods
### Numerical Simulation Models and Parameters
#### 2.1.2 Numerical Simulation Parameters
\begin{table}
\begin{tabular}{c c c} \hline \(\mathbf{\Gamma}\) & \(\mathbf{S}\) & \(\mathbf{C}\) (km/s) \\ \hline \(1.86\) & \(1.29\) & \(3.96\) \\ \hline \end{tabular}
\end{table} 
Table 3: Parameters of steel for shock equation of state. Ignition growth model parameters of reactive fragment are shown in Table 5. The ignition growth model [41; 42] with three parts (ignition, growth, and the completion reaction rate) is stated as  
\[\frac{\partial F}{\partial t}=I(1-F)^{b}\left(\frac{\rho}{\rho_{0}}-1-a\right)^ {x}+G_{1}(1-F)^{c}F^{d}P^{y}+G_{2}(1-F)^{d}F^{g}P^{z} \tag{11}\]  
The material failure is influenced by the temperature softening and strain rate strengthening in situations of high temperature, pressure, and strain rate. A J-C failure model that takes stress triaxiality, temperature, and strain rate effects into account was proposed by [PERSON] et al. [43]. The damage parameters are defined by the model, which uses the cumulative damage criteria:  
\[D=\sum\frac{\Delta\varepsilon_{eq}}{\varepsilon_{f}} \tag{12}\]  
where \(\Delta\varepsilon_{eq}\) is the equivalent plastic strain increment for a calculated cycle, and \(\varepsilon_{f}\) is equivalent fracture strain [12; 44; 45]. The failure model parameters of Q345S, 304 SS, and reactive fragment are shown in Table 6.  
\[\varepsilon_{f}=[D_{1}+D_{2}\exp(D_{3}\sigma^{*})]\left(1+D_{4}\ln\frac{ \dot{\varepsilon}_{eq}}{\dot{\varepsilon}_{0}}\right)(1+D_{5}T^{*}) \tag{13}\]  
\[T^{*}=\frac{T-T_{R}}{T_{m}-T_{R}} \tag{14}\]  
In the formula above, \(D_{1}\), \(D_{2}\), \(D_{3}\), \(D_{4}\), and \(D_{5}\) are the material parameters; \(\sigma^{*}\) is the stress triaxiality; \(\dot{\varepsilon}_{eq}\) is the actual strain rate; \(\dot{\varepsilon}_{0}\) is the reference strain rate; \(T^{*}\) is the dimensionless temperature; \(T\) is the instantaneous temperature; \(T_{R}\) is the reference temperature; and \(T_{m}\) is the melting temperature.
# _Separtial system innovation_
## 9 Results
### Research Questions
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Technique** & **Count** & **Reference** \\ \hline SHapley Additive exPlanations (SHAP) & 38 & [174] \\ \hline Local Interpretable Model Agnostic & 13 & [175] \\ Explanation (LIME) & 8 & [174] \\ \hline Class Activation Maps (CAM) & 8 & [176] \\ \hline Layer-wise Relevance Propagation (LRP) & 3 & [176] \\ \hline Feature Importance (others) & 2 & [175] \\ \hline Integrated Gradients (IG) & 2 & [177] \\ \hline Abductive Logic-Based Explanations (ALBE) & 1 & [175] \\ \hline Classification and Regression Trees (CART) & 1 & [159] \\ \hline Local Explanations for deep Graph & 1 & [86] \\ networks by Input perturbaTion (LEGIT) & 1 & [106] \\ \hline Local Explanation Method using & 1 & [106] \\ Nonlinear Approximation (LEMNA) & 1 & [178] \\ \hline Mixed Integer Linear Problem (MLP) & 1 & [179] \\ \hline Object Saliency Maps (OSM) & 1 & [179] \\ \hline Statistical Fault Localization (SFL) & 1 & [102] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: RQ.7.1 Post-hoc XAI Tools. Figure 9: Used dataset type. For XAI tools, Table 5 confirms both the presence of prevalent approaches (i.e., CAM, LIME, and SHAP) and the richness of research directions, confirming the general findings of this SLR about the current early phase. For the sake of brevity, a full description of all methodologies in Table 5 is not reported here, but references have been provided for each of them. This is also confirmed by an analysis of the presence of performance-oriented evaluation of XAI solutions answering the research questions RQ.8, RQ.9 and RQ.10. Regarding RQ.8, only 55% (55 out of 100 papers) of the analyzed papers make assessments on the performance of AI techniques combined with XAI techniques in the context of process engineering applications when considering generalization, it is well known that explainability and accuracy represent a typical cutoff. An even smaller percentage of only 38% (38 out of 100 papers) make assessments of the computational costs involved in implementing the above techniques, often justifying any high cost by maintaining high system explainability and transparency (RQ.9). Finally, only 29% (29 out of 100 papers) of the papers address the problem of improving the current performance of the XAI algorithms (RQ.10). In summary, we argue that as XAI techniques continue to evolve, their application within process engineering can further enhance transparency and reliability, as demonstrated by post-hoc methods such as SHAP and LIME, which provide insight into model decisions.