esa-sceva/satcom-chunk-collection huggingface dataset:
# An Application of an Unequal-Area Facilities Layout Problem with Fixed-Shape Facilities
## 3 A Genetic Algorithm (GA) for the UA-Flp
### Boundary Search Heuristic (BSH)  
The BSH presented in [PERSON] and [PERSON] (2016) is a construction algorithm used to produce the layout and determine TWD for each chromosome in \(P_{0}\). Given a solution (chromosome) \(\pi\) a placement procedure is used to assign facilities to locations on the plant floor. First, facility in position 1 (i.e., \(\pi(1)\)) of \(\pi\) is placed first on the plant floor, then facility in position 2 (i.e., \(\pi(2)\)) is placed on the boundary of facility \(\pi(1)\). Note, the best location on the boundary is selected for the location of facility \(\pi(2)\) with respect to minimizing TWD. The facility in position \(k\) (i.e., \(\pi(k)\)), is placed on the boundary of already placed facilities (i.e., \(\pi(1)\), \(\pi(2)\),..., \(\pi(k-1)\)) such that TWD is minimized and layout is feasible (i.e., facilities placed are within the boundary of the floor space). See [PERSON] and [PERSON] (2016) for details.  
At the end of stage 2, once BSH is used to generate a layout for each chromosome in \(P_{0}\), the variables for the MILP model (i.e., \(x_{i}\), \(y_{i}\), \(l_{i}\), \(w_{i}\), \(dx_{ij}\), \(dy_{ij}\), \(o_{i}\), \(left_{ij}\), \(below_{ij}\)) are obtained for each layout. In stage 3, the chromosomes are improved using the MILP model presented earlier for the UA-FLP. More specifically, the binary variables for the relative locations (i.e., \(left_{ij}\), \(below_{ij}\)) and orientations (i.e., \(o_{i}\)) obtained by BSH for each layout are used as input data in the MILP for the UA-FLP. It is important to note that if a facility \(i\) is both to the left and below a facility \(j\), either horizontal or vertical separation is used (i.e., either \(left_{ij}=1\) or \(below_{ij}=1\), not both), since this would greatly restrict the solution space. Nevertheless, the resulting formulation is a linear programming (LP) model. Each LP model for each chromosome in \(P_{0}\) is solved efficiently using the dual simplex method. These layouts often improve the layouts constructed in stage 2. Afterward, each layout is improved using the crossover and mutation operations on the best set of solutions from stage 3. Next, the proposed GA is presented for the UA-FLP.
# Semantic Visual SLAM Algorithm Based on Improved DeepLabV3+ Model and LK Optical Flow
## 4 Experimental Results
### Analysis of Experimental Results
#### 4.3.3 Positioning Accuracy Experiments in an Indoor Dynamic Environment
(**a**) The “walking_xyz” sequence; (**b**) the “walking_rpy” sequence; (**c**) the “walking_halfsphere” sequence. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sequence** & **ORB-SLAM2 [10]** & **DynaSLAM [21]** & **Detect-SLAM [17]** & **DS-SLAM [19]** & **Proposed Algorithm** \\ \hline walking\_xyz & 0.7670 & 0.0152 & 0.0241 & 0.0247 & 0.0168 \\ walking\_rpy & 0.6942 & 0.0345 & 0.2959 & 0.4442 & 0.0380 \\ walking\_halfsphere & 0.2736 & 0.0249 & 0.0514 & 0.0303 & 0.0077 \\ walking\_static & 0.3974 & 0.0079 & - & 0.0081 & 0.0062 \\ sitting\_static & 0.0079 & 0.0058 & - & 0.0065 & 0.0066 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of absolute trajectory RMSE (unit: m).
## 4 Results and Discussion
### Performance Evaluation
The proposed SKGC takes a minimum time of 56,942 ms to perform clustering. However, the conventional algorithm obtained an average CT of 91,586 ms. Hence, the proposed work retained better performance in data mapping. The performance analysis of the proposed NE-GO-AC and the existing models are represented in the following Figure 7.  
In Figure 7, the proposed NE-GO-AC is compared to traditional techniques like ACO, Whale Optimization Algorithm (WOA), Particle Swarm Optimization (PSO), as well as Genetic Algorithm (GA). The proposed work establishes elite guidance, along with non-linear transition parameters to optimize the convergence rate. The proposed NE-GO-AC attained a Normalized Fitness Function (NFF) of 584, whereas the traditional algorithms reached an average NFF of 1319. The proposed NE-GO-AC acquired optimal results in the reducing process.
# A Novel Hybrid-Copy Algorithm for Live Migration of Virtual Machine
## 3 Novel Hybrid-Copy Algorithm
### Switched Decision Factor(SDF)
Based on Formulas (1) and (2), we can easily get Formula (5) as follow:  
\[\mathrm{F}(0)=(1-\ \alpha)\times N \tag{5}\]  
If \(n>0\), then we can get Formula (6) through Formulas (2)-(4),  
\[ \begin{array}{ll}\mathrm{F}(n)&=\alpha\times V_{1}(n)+(1-\ \alpha)\times V_{2}(n)\\ &=\alpha\times\sum_{i=1}^{n}T(i)+(1-\ \alpha)\times V_{2}(n)\\ &=\alpha\times\sum_{i=1}^{n}(S(n)-(V_{2}(n-1)-V_{2}(n)))+(1-\alpha)\times V_{2 }(n)\\ &=\alpha\times\sum_{i=1}^{n}S(n)-\alpha\times V_{2}(0)+\alpha\times V_{2}(n)+(1 -\alpha)\times V_{2}(n)\\ &=\alpha\times\sum_{i=1}^{n}S(n)-\alpha\times N+V_{2}(n)\\ &=\alpha\times R(n)+V_{2}(n)-\alpha\times N\end{array}  \tag{6}\]  
We hope the value of \(\mathrm{F}(n)\) is as small as possible to save bandwidth resources and reduce the number of page faults. Under ideal condition, the \(V_{2}(n)\) should tend to 0 and the \(V_{1}(n)\) would not increase. As a result, the value of \(\mathrm{F}(n)\) would tend to 0 with the increase of n. However, in write-intensive workloads, a large number of invalid transmissions are produced during the VM migration process. Therefore, the value of \(\mathrm{F}(n)\) may actually increase rather than decrease. At this point, the iteration phase should be stopped, and the pre-copy algorithm should be switched to the post-copy algorithm because more iterative copies are just wasting network resources and time. If the \(n^{\mathrm{th}}\) round iterative copy has a positive effect, the \(\mathrm{F}(n)\) should be smaller than \(\mathrm{F}(n\text{-}1)\). Then, Formula (7) can be derived as follow:  
\[ \begin{array}{c}\mathrm{F}(n)-\mathrm{F}(n-1)<0\\ \alpha\times(R(n)-R(n-1))+(V_{2}(n)-V_{2}(n-1))<0\\ \alpha\times\mathrm{S}(n)+(V_{2}(n)-V_{2}(n-1))<0\\ \frac{V_{2}(n-1)-V_{2}(n)}{S(n)}<\alpha\end{array}  \tag{7}\]  
We define \((V_{2}(n-1)-V_{2}(n))/\mathrm{S}(n)\) as the switched decision factor (SDF). SDF(\(n\)) represents the value of SDF in \(n\)th round. If the SDF(\(n\)) is smaller than \(\alpha\), the iteration phase will be stopped, and the post-copy algorithm kicks. According to formula (2), \(\alpha\) represents the tradeoff between the number of page faults and bandwidth resource consumption. As \(\alpha\) increases, a few iterative copies are executed.
## Multi-Layer Approach for the Detection of Selective Forwarding Attacks  
**[PERSON] *** and [PERSON] ***  
Computer Science and Engineering Department, University of Bridgeport, Bridgeport, CT 06604, USA  
*** Authors to whom correspondence should be addressed; E-Mails: [PERSON]ADDRESS] (N.A.); [EMAIL_ADDRESS] (K.E.); Tel.: +1-203-576-4703 (K.E.); Fax: +1-203-576-4766 (K.E.).  
Academic Editor: [PERSON]  
_Received: 22 September 2015 / Accepted: 16 November 2015 / Published: 19 November 2015_  
**Abstract:** Security breaches are a major threat in wireless sensor networks (WSNs). WSNs are increasingly used due to their broad range of important applications in both military and civilian domains. WSNs are prone to several types of security attacks. Sensor nodes have limited capacities and are often deployed in dangerous locations; therefore, they are vulnerable to different types of attacks, including wormhole, sinkhole, and selective forwarding attacks. Security attacks are classified as data traffic and routing attacks. These security attacks could affect the most significant applications of WSNs, namely, military surveillance, traffic monitoring, and healthcare. Therefore, there are different approaches to detecting security attacks on the network layer in WSNs. Reliability, energy efficiency, and scalability are strong constraints on sensor nodes that affect the security of WSNs. Because sensor nodes have limited capabilities in most of these areas, selective forwarding attacks cannot be easily detected in networks. In this paper, we propose an approach to selective forwarding detection (SFD). The approach has three layers: MAC pool IDs, rule-based processing, and anomaly detection. It maintains the safety of data transmission between a source node and base station while detecting selective forwarding attacks. Furthermore, the approach is reliable, energy efficient, and scalable.  
**Keywords:** wireless sensor networks; selective forwarding attacks
# Deep Learning for Medical Image Cryptography: A Comprehensive Review
## 2 Deep Learning-Based Medical Image Analysis
### Detection  
In addition to classification, medical image analysis techniques aim to detect specific objects, regions, or abnormalities within medical images. It enhances the accuracy, efficiency, and consistency of image interpretation and aids in early diagnosis, treatment planning, and disease monitoring. Accurate and rapid object localization of anatomical or diseased objects, such as organs and landmarks, is crucial for image registration and segmentation tasks [49; 50; 51]. [PERSON] et al. [52] used two stage task-oriented CNN-based regression model for anatomical landmarks extracted from MRIs and CT images. As a result, 1200 brain landmarks from MRIs while 7 prostate landmarks from CT images were detected. The goal of LUNA16 [53] challenge is to improve the ability to detect lung nodules on CT scans, which is essential for pulmonary cancer diagnosis [54]. On the same LUNA 16 dataset, deep ensemble 2D CNN approach [55] comprising three different CNNs with different layers, pooling techniques and kernels is applied and it predicted the outcome with more accuracy. [PERSON] et al. [56] used an AI model designed in a cascade structure using deep learning for detecting abdominal hemorrhage lesions in real time. The developed method could achieve reasonably good results in terms of sensitivity and specificity. [PERSON] et al. [57] explores various deep learning techniques such as ResNet152, ResNet50, DenseNet121 and VGG16 for detecting COVID-19 and pneumonia on CT and radiography images. The suggested model outperforms other reported approaches in the literature in diagnosing COVID-19 and pneumonia. [PERSON] et al. [58] presents the survey on distinct mammography task, i.e., breast density classification, calcification detection, mass detection and classification along with breast density classification, using CNN-based approaches. A comparison is provided with advantages and disadvantages of each CNN-based approach applied to these performed tasks.
# Weighted-CAPIC Caching Algorithm for Priority Traffic in Named Data Network
## 6 Simulation Results and Analysis
The LCD+Sharing cache hit ratio increases along with the rise in request rate difference, up to 12% of the average for the largest request gaps. Weighted-CAPIC provides the lowest cache hit ratio compared to other comparison schemes for the third class. This happens because, in Weighted-CAPIC, the third class is the lowest priority class; therefore, it is given the minor cache portion compared to the first and second class. This is different from the Static-CAPIC and LCD+Sharing schemes, which provide greater performance for this third class. Weighted-CAPIC provides a lower cache hit ratio than Dynamic-CAPIC in this third class as compensation for delivering greater performance to the classes with higher priority. The  
Figure 6: Cache hit ratio and path stretch of class 1 for various request rate difference. The simulation scenario with variations in the request rate difference gives the largest network cache hit ratio value for Weighted-CAPIC, followed by Dynamic-CAPIC, LCD+Sharing, and Static-CAPIC. As in Figure 9, Weighted-CAPIC provides an average network cache hit ratio of 9.7% higher than Dynamic-CAPIC, 22% higher than LCD+Sharing, and 24% higher than Static-CAPIC scheme. The network cache hit ratio is the total cache hit ratio for the system. Figure 9 shows that Weighted-CAPIC not only provides the higher performance to the priority class but also increases the total system cache hit ratio, compared to the Dynamic-CAPIC, Static-CAPIC, and LCD+sharing schemes. Figure 2.0 Cache hit ratio and path stretch of class 3 for various request rate difference. Figure 2.1 Cache hit ratio and path stretch of class 2 for various request rate difference. From the simulation results, Weighted-CAPIC gives the higher cache hit ratio to the priority class. Weighted-CAPIC improved the method in the Dynamic-CAPIC algorithm. Dynamic-CAPIC can dynamically allocate cache portions for each traffic class based on consumer demand. This is done using the Dynamic-CAPIC formula, which provides the cache portion based on the variations in content for certain classes. However, based on our derived multiclass-content utility function, we must treat priority classes differently besides just considering the traffic variation. The Weighted-CAPIC formula is also as dynamic as the Dynamic-CAPIC formula, which can provide different portions at any time, according to consumer demand patterns. However, Dynamic-CAPIC has not provided the most extensive resource for the highest priority class. The highest priority is given to the class which has the highest opportunity to be requested by consumers at one time. For this reason, Dynamic-CAPIC was improved into Weighted-CAPIC, which focused more on providing larger resources to the appropriate class. The simulation results for Weighted-CAPIC show that this algorithm successfully provides a higher cache hit ratio for the priority class and provides a higher cache hit ratio for the network system. This performance is supported while the algorithm still provides the same path stretch value as Dynamic-CAPIC.
## 2 Sonar Target Strength Prediction Method Based on the Multiresolution SBR  
### Multiresolution Grid Algorithm  
All points on the target are projected onto a plane which are perpendicular to the incident direction, and the plane passes through an incident point to determine the shape of the equiphase surface. The incident plane waves consist of an infinite number of rays which are parallel to each other. After the corner ray-tracing the ray tubes can be divided into three types, including valid ray tubes, as shown in Figure 1a, invalid ray tubes, as shown in Figure 1b, and divergent ray tubes, as shown in Figure 1c.  
In a conventional SBR algorithm, the sound field calculation is only for valid ray tubes, as shown in Figure 2a. Figure 2b shows the process of ray tubes subdividing. The multiresolution grid algorithm splits a ray tube bundle into four sub-ray tube bundles through the ray tube bundle splitting process, instead of directly discarding it, allowing the initial division step size of the ray tube bundle to be large. By setting the threshold of the split times, the ray tube bundle can be subdivided into smaller steps and realize the \"grading\" operation of the sound tube bundle, saving memory and runtime. Further, under the condition of satisfying maximum parallelism, more regions on each triangular patch will be involved in the scattered sound field integration.  
Figure 1: The schematic diagram of attribute information judgment of sound ray tube bundle. (**a**) valid ray tube; (**b**) invalid ray tube; (**c**) divergent ray tube.
## 5 Scattering of Surface Waves by a Cavity of Arbitrary Shape  
A plane surface wave propagating in the \(x\)-direction is incident on a three-dimensional cavity of arbitrary shape at the surface of an isotropic homogeneous elastic half-space. The incidence generates a scattered field that is of interest in this article. This section introduces two approximations to the theoretical analysis of the scattered field. Consider a cavity on the surface of a half-space as given in Figure 2. In the coordinate system \((x,y,z)\), suppose that the surface of the cavity \(S\) is defined by \(z=h(x,y)\). Then the outward normal vector \(\mathbf{n}\Big{(}n_{x},n_{y},n_{z}\Big{)}\) of \(dS\) can be found using:  
\[\mathbf{n}=\frac{\Big{(}h_{x}^{\prime},h_{y}^{\prime},-1\Big{)}}{\sqrt{\Big{(}h_{ x}^{\prime}\Big{)}^{2}+\Big{(}h_{y}^{\prime}\Big{)}^{2}+1}}, \tag{5}\]  
where \(h_{x}^{\prime}=\frac{\partial h(x,y)}{\partial x}\) and \(h_{y}^{\prime}=\frac{\partial h(x,y)}{\partial y}\).  
From the linear decomposition principle, as stated in Section 2, the scattered field is equivalent to the field generated by the application of the distribution of tractions on the surface of the cavity, as shown in Figure 1c. These tractions are equal in magnitude but opposite in sign to the corresponding tractions due to the incident wave on a virtual cavity in the half-space without the cavity, as shown in Figure 1b. Thus, they are equal to \(-\mathbf{t}\) where \(t_{i}\) (\(i=x,y,z\)) are determined from the stress components of the incident Rayleigh wave and the outward normal vector as \(t_{i}=\tau_{ji}n_{j}\). The tractions, in turn, generate the scattered field, which is of concern in this paper. It should be noted that these tractions generate body waves, as well as surface waves radiating from the cavity. In the three-dimensional scattering, the surface waves attenuate with the distance from the points of excitation on the cavity according to \(r^{-1/2}\) while the body waves decay according to \(r^{-1}\), where \(r\) is the distance from the cavity. Therefore, the surface waves dominate at a sufficiently large distance from the cavity.  
Figure 2: A cavity in a half-space.
## 2 Literature Review
### Chaos Function  
The application of chaos functions in LSB steganography continues to be relevant due to its simplicity and effectiveness in generating pseudo-random sequences [19]. A function is considered chaotic if it demonstrates sensitive dependence on the initial conditions, is topologically transitive, and has periodic points that are dense in its domain [20].  
Sensitive dependence on the initial conditions means that small differences in the initial conditions lead to significantly divergent outcomes over time [21; 22]. If two slightly different initial values lead to subsequent values that increasingly diverge over time, this condition is called chaotic. The rate at which these two values diverge from each other over a given period is represented by a value known as the Lyapunov exponent [23].  
In addition to sensitive dependence, a chaotic function must also be random or topologically transitive. This characteristic can be illustrated using a bifurcation diagram, which visually represents the qualitative changes in the dynamics of a system as control parameters are varied. According to [PERSON] [24], bifurcation is defined as a qualitative change in the dynamics of a dynamical system given a change in the control parameters. Thus, a bifurcation diagram is a visual representation of these changes in the behavior of a dynamical system as its control parameters are altered.  
Several chaos functions can be used to generate key sequences for steganography, including the logistic map, tent map, quadratic map, Bernoulli map, sine map, and Chebyshev map [25]. Each of these functions has specific intervals where they exhibit chaotic behavior, which can be determined using methods like the Lyapunov exponent and bifurcation diagrams. This research focuses on the logistic map due to its well-documented chaotic properties and effectiveness in steganographic applications [25]. It was initially used to model population dynamics but was later found to exhibit chaotic behavior [26]. The map is represented by the equation:  
\[x_{n+1}=r\cdot x_{n}(1-x_{n}) \tag{1}\]  
where \(x_{n}\) represents the population size at time \(n\), and \(r\) is the growth parameter.
# Performance Analysis of Rate Splitting in Massive MIMO Systems with Low Resolution ADCs/DACs
## 6 Conclusions  
In this paper, we analyzed the performance of a rate-splitting multiple access scheme in a massive MIMO system with low-resolution ADCs and DACs. Under imperfect CSI, we characterized the SE and EE derived by the asymptotic SINRs of the common and private messages, respectively. Our results are verified by Monte Carlo simulations. We showed that the proposed RS scheme achieves higher SE than that of the non-RS scheme for low-resolution ADCs and DACs (i.e., 2-3 bits) with an even lower number of antennas. The SE performance is more degraded under the effects of low-resolution DACs compared to that of low-resolution ADCs. Finally, we showed that the best SE-EE trade-off is obtained for the 4-bit resolution ADCs/DACs. For future work, rate-splitting can be considered in the context of mixed ADCs/DACs systems. To extract further gains, hierarchical rate-splitting can also be investigated for massive MIMO with low-resolution ADCs/DACs.  
Conceptualization, methodology, and original draft preparation, R.K.A.; supervision, validation, writing--review and editing, K.C. Both authors have read and agreed to the published version of the manuscript.  
This work was partly supported by the Technology development Program of MSS [S2967489], the National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIT) under Grant 2021R1A2C1010370, and the 2021 Yeungnam University Research Grant.  
The authors declare no conflict of interest.
## 4 Results and Discussion
The defect-assisted Shockley-Read-Hall (SRH) recombination rate is reduced by more than half and the Auger recombination rate is estimated to be negligible in comparison to the R-LED. The comparatively reduced SRH recombination and Auger recombination rates of W-LED is supported by earlier experimental and theoretical reports [13; 35]. The rate of Auger recombination has been reported to be higher in rectangular wells than softened or graded wells, hence reducing the device efficiency [35]. The internal quantum efficiency comparison is shown in Figure 5. By using W-LED, we are able to increase the internal quantum efficiency of the device significantly. Improved IQE peak and droop ratio in W-LED is observed in comparison to R-LED i.e., droop ratios of R-LED and W-LED are \(\sim\)65% and \(\sim\)57%, respectively. The IQE peak of R-LED and W-LED occurs at \(\sim\)0.18 uA\(\cdot\)cm\({}^{-2}\) and \(\sim\)0.17 A\(\cdot\)cm\({}^{-2}\) respectively. The IQE peak in W-LED is shifted to higher current density, which is typically a case in graded LEDs [8; 13]. This shift of IQE peak or droop onset is desirable in improving the device  
Figure 4: Comparison of recombination rates in the two devices: (**a**) Radiative; (**b**) Shockley-Read-Hall (SRH); and, (**c**) Auger. Insets show the magnified recombination rates. performance [2]. Similarly, the light output power of W-LED is improved by \(\sim\)116% than that of the R-LED at 100 A\(\cdot\)cm\({}^{-2}\). The reason of the droop in W-LED can be understood by the built-in field comparison of the two structures in Figure 6. It can be observed that the average built-in field in W-LED is \(\sim\)24% higher than the R-LED. Despite the reduced Auger recombination rate, the internal quantum efficiency roll-off in Figure 5 can be associated with the comparatively higher built-in field in the proposed W-LED. The emission spectra of the two devices is shown in Figure 7. The peak wavelength of R-LED and W-LED is \(\sim\)518 nm and \(\sim\)513 nm, respectively. The broad emission spectrum is the characteristic of the indium fluctuations in the quantum well region [50]. The full width half maximum (FWHM) of R-LED and W-LED is 25 nm and 28 nm, respectively at the peak wavelength. With increasing injection current and higher indium fluctuations, FWHM, is reported to be broader in green light-emitting diodes [51]. In addition, indium-rich regions, regardless of high dislocation density, have been attributed to improve radiative recombination because of localization of carriers [51; 52; 53]. At peak wavelength, the emission of W-LED is \(\sim\)93% improved in comparison to the R-LED. Figure 5: Internal quantum efficiency and light output power of R-LED and W-LED. Figure 6: Built-in field comparison of R-LED and W-LED.