esa-sceva/satcom-chunk-collection huggingface dataset:
# Integration of Functional Link Neural Networks into a Parameter Estimation Methodology
## 3 Proposed Dual-Response Model Based on Functional Link NNs
### Learning Algorithm  
The learning or training process in NNs helps determine suitable weight values. The learning algorithm back-propagation is implemented in training feed-forward NNs. Back-propagation means that the errors are transmitted backward from the output to the hidden layer. First, the weights of the neural network are randomly initialized. Next, based on presetting weight terms, the NN solution can be computed and compared with the desired output target. The goal is to minimize the error term \(E\) between the estimated output \(\hat{y}_{out}\) and the desired output \(y_{out}\), where:  
\[E=\frac{1}{2}\sum(y_{out}-\hat{y}_{out})^{2} \tag{9}\]  
Finally, the iterative step of the gradient descent algorithm modifies \(w_{j}\) refers to:  
\[w_{j}\to w_{j}+\Delta w_{j} \tag{10}\]  
where  
\[\Delta w_{j}=-\eta\frac{\partial E(w)}{\partial w_{j}} \tag{11}\]  
The parameter \(\eta\) (\(>0\)) is known as the learning rate. While using the steepest descent approach to train a multilayer network, the magnitude of the gradient may be minimal, resulting in small changes to weights and biases regardless of the distance between the actual and optimal values of weights and biases. The harmful effects of these small-magnitude partial derivatives can be eliminated using the resilient back-propagation training algorithm (trainrp), in which the weight updating direction is only affected by the sign of the derivative. In addition, the Marquardt-Levenberg algorithm (trainlm), an approximation to [PERSON]'s method, is defined such that the second-order training speed is almost achieved without estimating the Hessian matrix.  
One problem with the NN training process is overfitting. This is characterized by large errors when new data are presented to the network, despite the errors on the training set being very small. This implies that the training examples have been stored and memorized in the network, but the training experiences cannot generalize new situations. To avoid the overfitting problem, efficient researchers can use an additional technique of \"early stopping\" to improve the generalization ability. In this model, the dataset is separated into three subsets, which are specialized to train, validate, and test the database. The process weight and bias terms of the network can be updated in the training set, in which the gradient is estimated as well. Then, the error, which is supervised during the training process, has to be evaluated in the validation set. While in the testing set, the capability to generalize the supposedly trained network can be examined. The accurate proportion of the learning algorithm among training, testing, validation data is determined by the designer; typically, the ratios of training:testing:validation are 50:25:25, 60:20:20, or 70:15:15.
## 2 Theory  
### Variational Mode Decomposition  
The VMD algorithm is a new-developed non-recursive method that converts the problem of signal decomposition into a variational problem. Through continuous iterative search, the optimal solution to the variational problem is finally determined, which is regarded as modes of a signal. Modes in the method are assumed into an ensemble of IMFs with a certain center frequency and limited bandwidth. For the best decomposition result, the variational problem can be described as minimizing the sum of the estimated modal bandwidths and its constrained condition is that the sum of modes is equal to the original signal, just as follows [35]:  
\[\min_{[u_{k}],[\alpha_{k}]}\left\{\sum_{k}\|\partial_{t}\left[ \left(\delta(t)+\frac{j}{\pi t}\right)*u_{k}(t)\right]e^{-j\omega_{k}t}\|_{2} ^{2}\right\} \tag{1}\] \[\text{s.t}\sum_{k}u_{k}=x \tag{2}\]  
where \(u_{k}(t)\) is the \(k\)-th variational modal component, \(x\) is the original noisy signal, \(\omega_{\text{k}}\) is the center frequency of the \(k\)-th component, \(*\) denotes convolution, and \(\delta(t)\) is the Dirac distribution. To get the corresponding solution, the Lagrangian multiplier \(\lambda\) and a quadratic penalty term \(\alpha\) are introduced to render the problem unconstrained. The augmented Lagrangian function \(L\) is expressed as follows:  
\[\begin{split} L(u_{k},w_{k},\lambda)=\alpha\underset{k}{ \sum}\|\partial_{t}\left[\left(\delta(t)+\frac{j}{\pi t}\right)u_{k}(t)\right] \times e^{-j\omega_{k}t}\|_{2}^{2}\\ +\|x(t)-\underset{k}{\sum}u_{k}(t)\|_{2}^{2}+\left\{\lambda(t),x (t)-\underset{k}{\sum}u_{k}(t)\right\}^{2}\end{split} \tag{3}\]  
The optimal solution of the original variational problem is considered as the saddle point of the augmented Lagrangian function. By alternate direction method of multipliers (ADMM), the mode \(u_{k}(t)\) in the time domain and the center frequency \(\omega_{k}\) of the estimated mode can be given by the following equation:  
\[\hat{u}_{k}^{n+1}(\omega)=\frac{\hat{f}(\omega)-\underset{i<k}{ \sum}\hat{u}_{i}^{n+1}(\omega)-\underset{j\geq k}{\sum}\hat{u}_{i}^{n}(\omega )+\frac{\hat{u}^{n}(\omega)}{2}}{1+2\alpha(\omega-\omega_{k}^{n})^{2}} \tag{4}\] \[\hat{u}_{k}(t)=\Re\left[ifft(\hat{u}_{k}(\omega))\right] \tag{5}\]\[\omega_{k}^{n+1}=\frac{\int_{0}^{\infty}\omega\big{|}\hat{u}_{k}^{n+1}(\omega) \big{|}^{2}d\omega}{\int_{0}^{\infty}\big{|}\hat{u}_{k}^{n+1}(\omega)\big{|}^{2 }d\omega} \tag{6}\]  
where \(\hat{u}_{k}(\omega)\), \(\hat{f}(\omega)\), and \(\hat{\lambda}(\omega)\) are the Fourier transforms of \(u_{k}(t)\), \(f(t)\), and \(\lambda(t)\), respectively, \(ifft(\cdot)\) denotes the inverse Fourier transforms of modes \(\hat{u}_{k}(\omega)\), and \(\Re(\cdot)\) means obtaining the real part of the filtered analysis signal.
## 4 Signal Models
### Uplink Signal Model for Different Satellite Systems
Notably, the Doppler model used in this paper pertains to a stationary (or slowly moving) target, where the ground velocity is considerably lower than the satellite's motion concerning the Earth. Thus, the LEO satellite is considered to be moving along the orbit with a velocity \(V_{\mathrm{sat}}\), while the ES is static. Note that when \(f_{\mathrm{d}}=0\), then, Equation (13) can be defined as the steer vector component \(a_{n}\) for a GEO satellite. It is essential to note that,  
\[\Delta t_{n}=\frac{nD\sin\theta}{c};\theta\in[-90^{\circ},90^{\circ}], \tag{7}\]0 Uniform linear array geometry at satellite systems. in the uplink scenario, the channel matrix \(\mathbf{H}\) is set to unity, as the uplink transmission lacks environmental influences due to the terrain and infrastructure in the receiver environment. Therefore, the multiplication involving \(\mathbf{A}\), \(\mathbf{H}\), and \(\mathbf{s}\) is carried out through matrix multiplication, resulting in a \([N\times\xi]\) matrix. Consequently, due to this configuration, the received signal \(\mathbf{r}\) is also a \([N\times\xi]\) matrix. Accordingly, the received signal in the uplink scenario can be characterized as an AWGN channel with a steering vector.
## 7 Implementation and Evaluation
### Workflow Validation  
For modeling Web service choreographies and compositions, a number of description languages exist, like Web Service Modeling Ontology (WSMO) and Business Process Execution Language (BPEL), which include the means to define the required message protocols. Using the existing IOPE (Inputs, Outputs, Preconditions and Effects) annotation together with composition process elements, like Sequence, Split, Any-Order and Choice, it is possible to represent the events as described in Section 5 in many of these languages. At the current stage, we specify protocol roles already at the workflow level, using elements from WSCDL [35] for simplicity.  
Describing the policy, _i.e._ the behavior of a service, is somewhat more difficult. Though OWL-S basically supports the IOPE annotation of process models the sending and receiving of messages only corresponds to the invocation of individual Web service operations. For the sending of messages to other services, an unbound process is needed, as there is no grounding for the receiving service available. In its current state, checking interoperability is done according to the description in [33], which is a variant of bi-simulation [32].  
The protocols we currently use are still simple, so a Java based approach was chosen to check compliance of a policy against the protocol role. When protocols get more complicated and interactions between services increase, transformation of the protocols and policies into finite state automata (FSA) might be needed and checking requires specialized tools, e.g. Spin [42].  
Figure 14 shows the time needed for validating the policies of a service against the protocol specification of a role. As we can see, the validation time stays in the area of hundred milliseconds. However, the policies which are stored as part of the OWL-S service description need to be retrieved from the database and transformed into Java objects, which takes considerably longer then the check itself (around 500-700 ms). Note that this materialization can already be performed for several selected services at an earlier step, e.g., during service selection.
# A Smart and Mechanized Agricultural Application: From Cultivation to Harvest
## 3 Materials and Methods
### Cultivation Phase
#### 3.1.2 Planting Crops Based on SCSO Algorithm  
The SCSO is used for economical product planning and efficient resource consumption. Hereby, crops can be planted in the most optimized places in a mechanized way without human intervention during the cultivation (planting) phase. In this regard, the SCSO algorithm is used instead of an analytical solution to plant each crop with an optimized method. As mentioned earlier, since this action is an NP-Hard problem type, metaheuristic-based approaches can lead to fruitful results. In addition, restrictions and requirements were taken into account so that it can be applied in real farmlands. These criteria are, for example, biodiversity indices based on landscape ecology measures, diversity of land uses, and soil erosion. The criteria to be optimized here will mostly have a positive effect on the farmer's income from the harvest and the type and amount of employment. In addition, efficient land use will lead to optimum allocation of water resources. An optimized planting stage will also be useful for an autonomous robot to accurately position itself in crop fields to perform precision farming tasks effectively.  
In this study, tomato (_Lycopersicon esculentum_ Mill.) is taken as a case study. In this arrangement, the row spacing can be selected as 80 cm, and the seedling spacing as 40 cm ((3.1 m\({}^{2}\)/unit)), 60 cm (2.1 m\({}^{2}\)/unit), and 80 cm (1 m\({}^{2}\)/unit) in tomato planting. It is recommended to choose 40 cm seedling spacing for pollination, temperature preservation, and to grow more product [51; 52]. In addition, it is recommended that the soil's pH level be 6.0-6.5 when planting tomato seeds [53]. Single-row arrangement and the hanging method are preferred in tomato cultivation [51; 52]. In line with the reasons and objectives mentionedabove, the places of the products relative to each other are determined algorithmically, considering the distance and other requirements. Here, the process will be the planting of the products with the least error rate. The crops planted at the end of this phase were equipped with IoT devices in the next phase. The places where the products should be placed was given to the autonomous robots as a map with this algorithm. They would perform the mechanized planting work (saving manpower and other natural resources). This was the first mission of the autonomous robots. Their second and final task was in collecting (harvesting) items.
# Mode-Matching-Based Sound Field Recording and Synthesis with Circular Double-Layer Arrays
## 1 Introduction
### Generalized Singular Value Decomposition Approach  
In the GSVD approach [39], \(\mathbf{G}(k)\) is also decomposed into interior component \(\mathbf{G}_{\text{int}}(k)\) and exterior component \(\mathbf{G}_{\text{ext}}(k)\):  
\[\mathbf{G}(k)=\left[\begin{array}{c}\mathbf{G}_{\text{ext}}(k)\\ \mathbf{G}_{\text{int}}(k)\end{array}\right]. \tag{7}\]  
GSVD is applied to Equation (7). \(\mathbf{G}_{\text{int}}(k)\) and \(\mathbf{G}_{\text{ext}}(k)\) are then decomposed:  
\[\mathbf{G}_{\text{ext}}(k) =\mathbf{U}(k)\mathbf{C}(k)\mathbf{X}^{H}(k), \tag{8}\] \[\mathbf{G}_{\text{int}}(k) =\mathbf{V}(k)\mathbf{S}(k)\mathbf{X}^{H}(k), \tag{9}\]  
where \(\mathbf{U}\) and \(\mathbf{V}\) are complex unitary matrices, \(\mathbf{X}\) is a complex square invertible matrix, and \(\mathbf{C}\) and \(\mathbf{S}\) are pseudo-diagonal matrices containing singular values:  
\[\mathbf{C}^{H}(k)\mathbf{C}(k)+\mathbf{S}^{H}(k)\mathbf{S}(k)=\mathbf{I}, \tag{10}\]  
Figure 2: Arrangements: (**a**) double-layer microphone array for recording a primary sound field; and (**b**) double-layer arrays of loudspeakers and control points for synthesizing recorded primary sound field at a secondary field.  
where \(I\) is the \(L\times L\) identity matrix. The optimal driving signals of the GSVD method are obtained:  
\[\mathbf{D}_{\text{GSVD}}(k)=(\mathbf{X}^{H})^{-1}(k)\mathbf{S}_{K}^{+}(k)\mathbf{V}^{H}(k)\mathbf{P} _{\text{org}}(k), \tag{11}\]  
where \(\mathbf{S}_{K}(k)\) retains the first \(K\) largest coefficients of \(\mathbf{S}(k)\) along the diagonal and \(\mathbf{S}_{K}^{+}(k)\) is the generalized inverse of \(\mathbf{S}_{K}(k)\). The tradeoff between the interior field synthesis accuracy and the exterior field propagation reduction can be controlled by singular value truncation parameter \(K\). As in a previous work [39], \(K\) was automatically chosen to satisfy that all diagonal terms of \(\mathbf{C}_{K}(k)\) are smaller than 0.01 where \(\mathbf{C}_{K}(k)\) also retains the first \(K\) smallest coefficients of \(\mathbf{C}(k)\) along the diagonal. When \(K=L\), the GSVD method just corresponds to the LS approach. The detailed derivation of the GSVD approach was previously described [39].
## 2 Materials and Methods
### Methodological Quality Assessment  
Each manuscript was independently assessed by two randomly chosen authors from a set of three authors (AGS, AIM, and NPR). Disagreements were solved by consensus or discussion with the third author. The assessment of the eligible studies' methodological quality was performed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool (QUADAS-2). QUADAS-2 is a validated tool used to evaluate the quality of diagnostic accuracy studies [45], and comprises four domains: patient selection, index test, reference standard, and flow and timing. Each domain is assessed in terms of the risk of bias through signaling questions that can be answered with \"yes\", \"no\", or \"unclear\". The first three domains (i.e., patient selection, index test, and reference standard) are also assessed based on applicability concerns. Overall concerns about the risk of bias and applicability per domain are then rated as \"high\", \"low\", or \"unclear\". These results are defined for each domain [45]. A pilot test for the bias risk assessment was conducted, using studies that were not eligible for this review.
## 5 The Evolution towards a Global Smart Grid Deployment: Challenges and Perspectives
### Data Communication and WSANs
#### 5.1.2 Quality of Service  
Assuring the system reliability is a crucial issue in power grids. However, the evolution from a classical grid to a SG involves a number of new technologies to be applied that may affect the Quality of Service (QoS) of the system. Taking into account the different applications envisaged for the SGs, several levels of QoS will be required, in terms of reliability, latency and throughput [89]. In this context, the deployment of faster and more robust control devices and embedded intelligent systems (IEDs) in the grid, in parallel with secured communication and information technologies and protocols, will have an important role in order to increase the system robustness. Concerning the impact of the chosen communication protocol, wired technologies offer a good level of reliability and security, but they are in general costly. On the other hand, wireless technologies with a certain level of security constitute an interesting choice because of the reduced installation costs, in spite of their lower robustness level.
## 5 Algorithm Analysis
### Time Complexity of PSA  
At the stage of network layering and clustering, the nodes broadcast layer by layer for network layering, and the number of layers is not better than \(size/R_{c}^{\min}\). In each layer, the nodes select the cluster head node synchronously. The worst situation in this process is that a node waits for all its neighbor nodes to give up the competition one by one. The time complexity is \(N_{a}\). Thus, this stage's time complexity, \(complexity_{3}\), is \(N_{a}\)-\(size/R_{c}^{\min}\).  
At the stage of optimizing the positions of the cluster-in nodes, for each cluster, the algorithm firstly selects the node one by one to calculate their CRR, and then calculates the deployment location of each cluster-in node by using the PSOA iteratively, that is, the algorithm uses PSOA to calculate the deployment positions of cluster-in nodes one by one. Thus, for each cluster, the time complexity is \(N_{ci}\) (\(complexity_{\text{PSOA}}+N_{ci}\)). Assuming that network has \(N_{c}\) cluster head nodes, this stage's time complexity \(complexity_{4}\) is formulated as follow:  
\[complexity_{4} =N_{c}\cdot N_{ci}\cdot\left(complexity_{\text{PSOA}}+N_{ci}\right) \tag{25}\] \[=N\cdot\left(complexity_{\text{PSOA}}+N_{ci}\right)\]  
Therefore, the time complexity of PSA, \(complexity_{\text{PSA}}\), is formulated as follows:  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Symbol** \\ \hline Target area size & _size_ \\ Population number & \(N_{p}\) \\ Iteration number of the map and compass operator & \(N_{1}\) \\ model & \(N_{2}\) \\ Iteration number of the landmark model & \(N_{a}\) \\ Average number of neighbor & \(N_{a}\) \\ Average number of non-cluster head node & \(N_{ci}\) \\ Minimum communication radius & \(R_{c}^{\min}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Symbol Table.  
\[complexity_{PSA} =N_{a}\cdot size/R_{c}^{\min}+N\cdot\left(N_{2}N_{p}\log N_{p}+2N_{p} (N_{1}+N_{2})\right) \tag{26}\] \[\leq\left(N_{2}N_{p}\log N_{p}+2N_{p}(N_{1}+N_{2})+size/R_{c}^{ \min}\right)N\]
## 2 Theoretical Backgrounds of Damage Detection with Modal Information  
### Modal Curvature Difference  
In beam-type structures (Figure 2), by considering the force balance and deformation coordination at the interface of micro-segments, the curvature function at location \(x\) in flexural vibration is given as:  
\[\frac{M}{EI(x)}\ =\ k(x) \tag{1}\]  
where \(M\), \(E\), and \(I(x)\) are the moment, modulus of elasticity, and moment of inertia of the section; \(k(x)\) is the curvature.  
The curvature \(k(x)\) can be expressed as the reciprocal of the radius of curvature \(\rho(x)\):  
\[\frac{1}{\rho(x)}\ =\ \frac{\partial^{2}u(x)}{\partial x^{2}}\ =\ \sum_{n\ =\ 1}^{\infty}\phi_{n^{\prime\prime}}(x)q(x) \tag{2}\]  
where \(u(x)\) is the displacement, \(\phi_{n^{\prime\prime}}(x)\) is the \(n\)th curvature mode, and \(q(x)\) is the external load.  
From Equations (1) and (2), the reduction in stiffness due to damage leads to an increase in modal curvature. Researchers have reported that the modal curvature is far more sensitive than other modal-based indicators such as the MAC and COMAC [17]. For beam-type structures, mainly subjected to bending deformation, the modal curvature can be calculated using the quadratic difference of normalized modal shapes. For the \(n\)th mode, the curvature at the \(i\)th node can be expressed as:  
\[k(x)\ =\ \frac{\phi_{n,i-1}-2\phi_{n,i}+\phi_{n,i+1}}{l^{2}} \tag{3}\]  
where \(\phi_{ni}\) is the modal displacement at coordinate point \(i\), for the \(n\)th mode, and \(l\) is the distance between two nearby coordinate points.  
Figure 2: Force and deformation at the interface between profiled steel and concrete slab: (**a**) force balance; (**b**) deformation coordination.  
To calculate the curvature changes with respect to the undamaged state, the modal curvature difference can be calculated as in Equation (4):  
\[\Delta V\ =\ \sum_{i\ =\ 1}^{n}\left|\kappa^{d}(x)-\kappa(x)\right| \tag{4}\]  
where \(\Delta V\), \(n\), and \(\kappa(x)\) represent the absolute value of curvature difference, the number of modal shapes, and modal curvature, respectively; superscript \(d\) refers to the damaged state.
## 3 Proposed Crypto Processor Design
### Control Unit and Clock Cycles Calculation
Recalling again, in the Itoh-Tsujii inversion algorithm, \(m-1\) squares followed by ten modular multiplications are needed to compute each inversion operation. As we reported earlier, we utilized an iterative schoolbook modular multiplier for both polynomial multiplication and square computations; hence, the Itoh-Tsujii inversion algorithm is implemented using the multiplier unit of Figure 3. For \(m-1\) squares computation, our design utilizes 54,056 clock cycles, and for ten modular multiplication computations, it takes 2330 cycles; overall, 56,386 clock cycles are needed to implement the Itoh-Tsujii inversion algorithm for one modular inverse computation. As noted, the last two lines of Algorithm 1 involve two modular inverse computations; therefore, 2 \(\times\) 56,386 clock cycles are needed to implement two modular inverse operations. Instead of the modular inverse computations, several square, multiplication, and addition operations are required to implement the last line of Algorithm 1; hence, an additional 5340 clock cycles are needed to execute these operations. Subsequently, 118,112 clock cycles are needed to implement the Lopez Dahab projective to affine conversions. As summarized, Algorithm 1 bears a total of 716,459 clock cycles to compute one PM in \(GF(2^{233})\). The distribution of these clock cycles comprises 469 cycles for affine to Lopez Dahab conversion, 597,878 cycles for PM computation in Lopez Dahab projective coordinates, and 118,112 clock cycles for re-conversion (from Lopez Dahab projective to affine coordinates).