esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Diffractive optical elements (DOEs) have been in development for many years and are an exciting technology with the capability to re-direct light, using diffraction rather than refraction. Holographic Optical Elements (HOEs) are a subset of diffractive optical elements for which the photonic structure is created holographically, i.e., by recording a specific interference pattern in a suitable, photosensitive optical material. Volume HOEs are of particular interest for some applications because of their very high diffraction efficiency and single diffracted order; however, high dispersion and angular wavelength selectivity still present significant challenges. This paper explores a method for producing a compound DOE useful for situations where elements designed for two separate target wavelengths can be advantageously combined to achieve a highly efficient HOE with reduced dispersion. A photopolymer material consisting of two independently sensitized laminated layers is prepared and used in sequential holographic recording at two different wavelengths. The photonic structures recorded are investigated through examination of their diffraction peaks and comparison with the structure predicted by modeling. Finally, the device is illuminated with an expanded diverging beam at both target wavelengths and with white light, and a strong diffracted beam is observed.  
Keywords:diffractive elements; holography; photopolymer; holographic optical elements; volume gratings; compound optical elements; stacked gratings +
Footnote †: journal: Applied Sciences
# Optimization Dubins Path of Multiple UAVs for Post-Earthquake Rapid-Assessment
## 3 HGSA Algorithm  
A hybrid heuristic algorithm for efficiently solving the MURARP will be presented in this section. The GA has excellent ability to find new solutions quickly, while its hill climbing ability is poor and easily falls into local optimum in later iterations. It has been proved that simulated annealing (SA) can converge to the global optimum with a probability of approximately 1 by controlling the cooling process [37]. However, the convergence rate of SA is too slow to meet actual needs. In this paper, we specially develop a two-stage hybrid heuristic algorithm for the characteristics of the MURARP. In the first stage, a better solution is obtained quickly by GA, which is used as the initial solution of the SA algorithm. In the second stage, the optimal solution of the MURARP is obtained through SA operation. To balance the convergence rate and global optimization ability of the HGSA algorithm, a switching criterion from the GA to SA is adaptively designed. The pseudocode of the algorithm is shown in Algorithm 1.  
```
1:Input: \(P\), a population of \(N_{P}\) chromosomes;
2:Output: \(C\). best, a chromosome with the best fitness found;
3:begin
4:initialize the parameters of the HGSA algorithm;
5: initialize the population \(P\) of the GA (see Section 3.1.2);
6: constraint checking and adjustment for each chromosome in the population \(P\) (see Section 3.1.3);
7: evaluate the fitness of the population, and update \(C\). best (see Section 3.1.4);
8:while the switch point between the GA and SA is not reached (see Section 3.5);
9: crossover (see Section 3.2);
10: mutation (see Section 3.3);
11: update population (see Section 3.4);
12:endwhile
13:\(T^{iter}\)=\(T_{0}\);
14:while\(T^{iter}\) > \(T_{md}\)
15: improve \(C\). best through SA iteration (see Section 3.6);
16: T\({}^{iter}\)= T\({}^{iter}\) * R\({}_{T}\);
17:endwhile
18: the chromosome with the best fitness \(C\). best is selected as the final scheme;
19:end
```  
**Algorithm 1**HGSA Algorithm
# A Low-Cost Printed Log-Periodic Dipole Array for DVB-T2 Digital TV Applications
###### Abstract  
A printed log-periodic dipole array (LPDA) for DVB-T2 Digital TV applications, covering the whole DVB-T2 UHF band from Channel 21 to Channel 69 (470 MHz-860 MHz), is presented. The presented antenna offers a compact size and a lower cost compared to both wire and similar printed LPDAs, with a normalized area of only 0.26 \(\lambda^{2}\) (where \(\lambda\) is the free-space wavelength at the central frequency) and a similar (or higher) average gain. It is composed of meandered radiating dipoles, and it is implemented on FR4, the cheapest dielectric substrate available on the market. Moreover, the antenna size has been reduced to an A4 sheet dimension (210 mm \(\times\) 297 mm) to cut down the production cost. The antenna has been designed starting from [PERSON]'s theory and using a general-purpose 3D CAD, CST Studio Suite. The results show that the proposed antenna can be used for broadband applications (\(\approx\)74% bandwidth) in the whole operating frequency band of Digital TV, with a satisfactory end-fire radiation pattern, a stable gain, and a radiation efficiency over the required frequency range (average values 6.56 dB and 97%, respectively).  
broadband antennas; log-periodic arrays; DVB-T2; digital TV applications Article
## 5 Drive System Simulation
### Overall Configuration  
The overall configuration of the proposed system is shown in Figure 10. It consists of two parts, which are the drive system circuit and the drive system controller. The drive system circuit has the same structure as the system shown in Figure 1, equipped with current sensors measuring the phase currents of OW-PMSM, and voltage sensors measuring DC-bus voltages of dual inverter. The capacitors C1 and C2 are used to filter the voltage fluctuation of the power sources and provide reactive power needed.  
Figure 9: Flowchart of the voltage vector distribution method selection strategy.  
In the drive system controller, the desired torque \(T_{e}^{*}\) and the optimal output power of inverter1 \(P_{inv1\_opt}\) are inputted from vehicle control unit. In torque regulator, the desired stator current \(\overset{\rightharpoonup}{i_{s}^{*}}\) is calculated by MTPA (maximum torque per ampere) method in constant torque region and CBE (constant back EMF) method in flux weakening region [26; 27]. Then the desired stator voltage vector \(\overset{\rightharpoonup}{u_{s}^{*}}\) is generated by the current regulator, which is actual a PI controller. The desired power sharing calculation method introduced in sector 3 is executed in the desired power calculator, outputting desired output power of inverter1 \(P_{inv1}^{*}\). Afterwards, the voltage vector distribution method expounded in sector 4 is carried out in the voltage vector distributor. After the desired stator voltage vector is partitioned, the desired voltage vectors of inverter1 and inverter2, known as \(\overset{\rightharpoonup}{u_{s1}^{*}}\) and \(\overset{\rightharpoonup}{u_{s2}^{*}}\), are sent to the corresponding SVPWM modulators respectively. Where \(GatesL\) and \(GatesR\) are the gate control signals for inverter1 and inverter2 respectively.
# A Survey on Penetration Path Planning in Automated Penetration Testing
## 4 Planning Models in Penetration Path Planning
### Graphical Security Model
#### 4.1.2 Attack Graph  
Compared to the attack tree, the attack graph offers a more potent and adaptable representation of attack paths [34]. Structurally, attack graphs can be supposed as directed graphs that encompass multiple attack trees. [PERSON] and [PERSON] [35] were the pioneers in proposing the first comprehensive framework for modeling attack graphs. They assume a target network consisting of N hosts, each with M vulnerabilities, with each vulnerability being characterized by its state of being exploited or not. Consequently, the attack graph can have up to 2 mn possible states. In this model, each node corresponds to one of the 2 mn potential states, and each edge signifies the exploitation of a vulnerability, representing the attacker's behavior. This model of attack graph is referred to as a state-based attack graph. Several researchers [36; 37] utilized state-based attack graphs in their research but encountered significant scalability issues.  
Subsequently, [PERSON] [38] introduced an exploit-dependence graph as a solution to address scalability issues. The exploit-dependence graph is structured as a directed graph comprising two types of nodes: exploit nodes and condition nodes. Edges extend from condition nodes to exploit nodes, indicating the prerequisites for the exploit. Conversely, edges from exploit nodes to condition nodes represent the postconditions of the exploit, which are the privileges an attacker can gain by exploiting the vulnerability. Based on the definition of exploit-dependence graphs, developers have implemented various automated tools for generating attack graphs, including MulVAL, NetSPA, and TVA [39]. Among these, MulVAL [40] has been widely used in practical applications due to its high accuracy and good scalability in generating attack graphs. An example of an attack graph generated with MulVAL is presented in Figure 3.  
Based on attack trees and attack graphs, [PERSON] et al. [41] explored the integration of attack graphs and attack trees and proposed the hierarchical attack representation model (HARM). [PERSON] et al. [42] identified potential penetration paths through HARM, the upper layer captures network topology reachability with an attack graph, while the lower layer concentrates on detailing attack action sequences with an attack tree. This hierarchical architecture not only enhances the model's scalability for larger network environments but also facilitates in-depth analysis of the specific steps and interdependencies within a particular attack path.
# Application of Lightweight Convolutional Neural Network for Damage Detection of Conveyor Belt
## 2 Principles and Methodology
### Network Structure and Improvement Methods
On this basis, a lightweight attention model based on the squeeze and excitation structure in SENet was also added to adjust the number of channels, as shown in Figure 3; Besides, h-swish was used as the activation instead of the swish to reduce the amount of calculation and improve the performance. As an improved version of Yolov3, Yolov4 has made many improvements on the basis of Yolov3. The network structure is shown in Figure 4. CSPDarknet53 is used instead of Darknet53 as the backbone feature extraction network. The use of CSPnet enables the fusion of high-level and low-level semantic information and reduces the loss. SPPnet and PANet are used to expand the receptive field and repeatedly extract image features, which greatly improves the feature extraction capability. Same as Yolov3, Yolov4 also uses the extracted feature information to make predictions through YoloHead. It can be seen from Figure 4 that three feature layers from the backbone feature extraction network were extracted for feature enhancement of SPPnet and PANet in Yolov4, and then were passed to YoloHead for prediction. In order to realize the lightweight Yolov4 network structure, the paper proposed to replace the backbone feature extraction network CSPDarknet53 of Yolov4 with MobileNet, continue to use the feature fusion and feature enhancement strategy in the original YoloV4, and make predictions through YoloHead. In this paper, the structure of MobilenetV1-YoloV4 is used to explain the replacement operation, and the structure or replacement principle of MobilenetV2-YoloV4 and MobilenetV3-YoloV4 is the same. In YoloV4, the feature layers  
Figure 3: The structure of the bneck in MobilenetV3. Figure 2: The structure of the inverted residual connection in MobilenetV2. PAnet are the original images compressed 3, 4, and 5 times. Similarly, in the MobilenetV1 network, we pass the feature layers which compressed 3, 4, and 5 times into the subsequent feature enhancement, as shown in Figure 5; At the same time, we also change the number of channels of the layers by adjusting the value of \(\alpha\) to achieve different degrees of lightness. In addition, we have taken a small trick, that is replacing the convolution in PAnet with a depthwise separable convolution, to better reduce the number of parameters.
# \(K\)-Means Cloning: Adaptive Spherical \(K\)-Means Clustering
## 2 K-Means Cluster Cloning
### K-Means Colony Growth
#### 2.3.2 Split/Merge Decision  
A crucial step is how to select the next cluster(s) to split or merge. Our splitting and merging procedures issue criterion functions to select cluster(s) to be split or merged according to fitness assessments on the changing cluster structure. It is instructive to see how clustering objective functions change with respect to the change of the number of clusters \(K\). The idea of performing split and merge procedures has been successfully applied to calculating the cluster silhouettes \(S(\kappa),\kappa=1,\ldots,K\). If the decision was splitting, the cluster which has the minimum cluster silhouette is the one to be split into two sub-clusters. On the other hand, if the decision was merging, the two clusters with minimum cluster silhouettes are the two to be merged.  
As an illustrative example, Dataset 3-2 in Figure 1a has the largest value of \(SF(3)=0.86\), with the optimal number of cluster \(K=3\), as also shown in Figure 2a. We computed two appropriate clusters for Dataset 3-2, which \(S(1)=0.7017\) and \(S(2)=0.8963\), as shown in Figure 3a. Similarly, four appropriate clusters for the same Dataset 3-2, which are \(S(1)=0.6003\), \(S(2)=0.7075\), \(S(3)=0.8072\) and \(S(4)=0.9167\), see Figure 3b. According to the \(K\)-Adaptation Procedure, if the comparison was between \(K=2\) and \(K=3\) and \(SF(2)<SF(3)\), then splitting procedure must be applied to the cluster which have the minimum dissimilarity \(S(1)=0.7017\), as shown in Figure 3a. In the case of which the comparison was between \(K=4\) and \(K=3\) and \(SF(3)>SF(4)\), merging procedure must be applied to the two clusters whose the minimum dissimilarities \(S(1)=0.6003,S(2)=0.7075\), as shown in Figure 3b.  
For Dataset 10-2, Figure 4 describes how the split/merge decision is taken for the clusters that need to be split are determined, as in Figure 4a, or merged, as in Figure 4b.  
Figure 3: The cluster silhouettes for Dataset 3-2 to determine clusters must be split or merge for Dataset 3-2.  
Many attempts were performed on other datasets and proved the veracity of this method and adoption of it as a cluster merging and splitting method to detection an appropriate clusters.
# Continuous Wavelet Transform and CNN for Fault Detection in a Helical Gearbox
## 4 Proposed Defect Detection Models
### Methodology  
The methodology applied in the current study is depicted in Figure 7 and contains the following steps: (1) data acquisition, described in Section 3.1; (2) CWT data processing and generation of signal representations as 2D grayscale images, presented in Section 3.2; (3) development of 2D-CNN-based defect detection models; and (4) models evaluation and comparison.  
In the third step, four 2D-CNN-based defect detection models are built. Three models are trained with the 2D CWT-based representation of the information acquired by the triaxial accelerometer on individual axes. We denote the models as Mcwt_x (X-axis), Mcwt_y (Y-axis), and Mcwt_z (Z-axis). The model Mcwt_xyz is generated by taking as input the fusion of the CWT-based representations of the signals on individual axes. In Section 4.2, the proposed architecture of the models is presented, while in Section 5, the experiments performed with these models help to evaluate and compare their performances.  
Figure 6: 2D grayscale CWT-based images of 102 \(\times\) 7710 pixels for all three axes in the 400 Hz-4 kHz frequency interval.
## 2 HSI Procedure
### Preprocessing
#### 2.2.9 Log (1/R)  
The log(1/R) transformation is one of the spectral data processing methods used to linearize the relationship between wavelength and the response variable. This technique is helpful if the data in the spectra have a nonlinear relationship; it enhances absorption spectra and sharpens important features. The reflectance can be logarithmically transformed, whereby the sum of a small constant avoids undefined logarithmic values stemming from zero or negative reflectance values; therefore, it does not change the stability and maintains the relative differences [39]. Logarithmic transformation, Log(1/R), has been one of the most widely used transformations in HSI for monitoring moisture content along with other quality attributes of food items during processing, such as binary mixtures of food powders [74], fat content [75], and classification [76]. [PERSON] et al. 2021 used this transformation in tandem with visible and near-infrared (Vis-NIR) hyperspectral imaging to monitor the moisture decay of apple slices during hot-air drying. Logarithm transformation significantly cleaned the spectral noise, and hence it was helpful for selecting wavelengths that carry information on moisture content [77]. Similarly, [PERSON] et al. (2021) applied a logarithmic transformation log(1/R) to assess drying processes on beef jerky. Along with NIR HSI and chemometric models, the method increased the precision of predicting moisture content in the drying process [78]. Due to the improved spectral features, it was possible to develop superior process control and a more complete prediction of moisture content, therefore proving the efficiency of transformation to improve model performance in meat quality monitoring. In such a case, it is suggested to ensure its efficacy by applying it to assess different quality aspects in different meat products. Consequently, the preprocessing algorithms have significantly enhanced the feature extraction and generated more accurate predictive models while retaining much of the increased clarity and interpretability in hyperspectral data. The proper application of preprocessing techniques not only optimizes model performances but also provides more reliable outcomes in the assessment of food quality and safety. Some pre-processing techniques of the spectrum are summarized below in Table 1. Since hyperspectral imaging is an advancement in a continuous manner, embedding the steps of preprocessing will remain important in realizing how to exploit this premium technology in a wide range of applications related to food industries.
## 2 Materials and Methods
### Procedure and Experimental Design  
#### 2.4.1 Batch Adsorption  
In this study, batch adsorption was optimized using a minimal number of experimental runs through the Box-Behnken design (BBK) and response surface methodology (RSM). STATISTICA 10.0(r) software was utilized for this purpose. The effects of the variables and their interactions were assessed using analysis of variance (ANOVA). RSM was employed to predict the optimal conditions based on the F-value and p-value. The experimental design considered three factors: pH (x\({}_{1}\)), adsorbent mass (x\({}_{2}\)), and contact time (x\({}_{3}\)), each evaluated at three levels, resulting in a total of 15 experiments (see Table 1). Two response variables were selected: the removal efficiency of the compounds (R%--Equation (2)) and the adsorption capacity of the adsorbent (Q\({}_{\rm e}\)--Equation (3)).  
\[\rm R\%=\frac{C_{i}-C_{e}}{C_{i}}\times 100 \tag{2}\]  
\[\rm Q_{e}=\left[\frac{(C_{i}-C_{e})\times V}{m}\right] \tag{3}\]  
where C\({}_{i}\) is the initial concentration of the pharmaceuticals (mg/L), C\({}_{\rm e}\) is the equilibrium concentration (mg/L), Q\({}_{\rm e}\) is the adsorption capacity of the carbon activated (mg/g), V is the volume of the solution (L), and m is the mass of the carbon activated (mg).  
The batch adsorption tests were carried out using 100 mL solution volumes. An initial solution containing a mixture of pharmaceuticals at a concentration of 20 mg/L was added to an Erlenmeyer flask, followed by the addition of the carbon activated solid, pH adjustment, and agitation at 150 rpm on an orbital shaker (New Lab, model NL 161-04, Sao Paulo, Brazil) maintained at a controlled temperature of 25 \({}^{\circ}\)C. This methodology is consistent with previous studies [38; 39]. The process duration for each assay was determined according to the experimental design. The standard deviation was calculated based on the central point.  
The second-order polynomial model (Equation (4)) was used to approximate the mathematical relationship between the independent variables.  
\[\rm Y=\beta_{0}+\sum_{i=1}^{k}\beta_{i}x_{i}+\sum_{i=1}^{k}j=1^{k}\beta_{ii}x_ {i}x_{j}+\sum_{i=1}^{k}\beta_{ii}x_{i}^{2}+\varepsilon \tag{4}\]  
where Y refers to the predicted response, while \(\beta_{ij}\) shows the interaction coefficient, \(\beta_{ii}\) represents the quadratic, \(\beta_{i}\) shows the linear coefficient, and \(\beta_{0}\) is the constant coefficient. In addition, x\({}_{i}\) and x\({}_{j}\) represent the independent variables, and \(\varepsilon\) and k, respectively, refer to the residual error and the number of independent variables.
# Highly Sensitive SPAD-Based Receiver for Dimming Control in LiFi Networks +
## 5 Numerical Results and Discussion  
In this section, BER performance and achievable data rate metrics for the previously discussed modulation techniques are presented. The common parameters used in the simulation are given in Table 2. Because of the differences in their system design and to demonstrate the superiority of our receiver, a relatively small number of SPADs are used for single-carrier systems. Such systems can be implemented in IoT devices that benefit from high power efficiency and do not require high spectral efficiency [51]. For OOK and VOOK modulations, the SPAD detector has \(N_{\rm a}=1024\) micro-cells, \(\tau_{\rm d}=40\) ns, and \(\rm Y_{\rm PDE}=0.1\). For the OFDM ones, we aim to explore the application of SPAD receivers in high-speed OWC, hence larger array sizes and less dead time are considered. Therefore, the SPAD detector has \(N_{\rm a}=8192\) micro-cells, \(\tau_{\rm d}=10\) ns, and \(\rm Y_{\rm PDE}=0.35\). Because the spectral efficiency of ACO-OFDM is half that of DCO-OFDM, a fair comparison of the schemes should be \(M\)-QAM ACO-OFDM versus \(\sqrt{M}\)-QAM DCO-OFDM. Therefore, in the simulation, 16-QAM is used for ACO-OFDM and 4-QAM is considered for DCO-OFDM. We assume that the whole available LED bandwidth \(B_{\rm max}\) is utilized for signal transmission and hence a time-domain symbol duration of \(T_{\rm s}=8.35\) ns is considered for all modulation schemes. We also consider different ambient light scenarios. Despite using optical filtering, ambient optical power from sources such as sunlight, skylights, and incandescent bulbs can still be a significant factor. To account for this, we have categorized the ambient light power into two levels based on the aperture of our receiver: 1 nW for situations with relatively low ambient light, and 10 nW (approximately 500 lux) for situations where the user is locatednear a window [52]. It is noted that we assumed that a proper narrowband optical filter with a centre wavelength aligned with the wavelength of the transmitted optical signal is employed at the receiver.  
The adaptive change of the VOA transmittance with the change of the received optical power is the key feature of the proposed adaptive SPAD receiver. The relationship between the VOA transmittance, \(\xi\), and the received optical signal power is shown in Figure 2 for the considered systems at \(\gamma=20\%\) and \(P_{b}=1\) nW. A similar figure or a look-up table can be implemented to cover any desired dimming level and certain background power so that in practical implementation when the environment changes the optimal VOA transmittance can be quickly determined for the proposed receiver. The transmittance of the VOA is limited up to 1, and it can be seen how this transmittance will decay as the optical power increases to keep the SPAD array in optimal performance.