esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Analysis of Prediction Based on MSC and SNV Preprocess  
The results in Figure 9 show that MSC preprocessing yields slightly tighter clustering around the 1:1 line than SNV for amino acid (AA) prediction, improving the R\({}^{2}\) from 0.690 to 0.700 and reducing the RMSE from 0.384 to 0.377. This indicates that MSC's multiple scatter correction more effectively compensates for particle-size and path-length variations in fresh leaves. For polyphenol (TP) estimation, both corrections produce lower accuracy--R\({}^{2}\) = 0.369 with MSC and 0.346 with SNV--reflecting the greater sensitivity of TP signals to residual water and structural heterogeneity. Nevertheless, MSC still offers a modest gain over SNV. Overall, while neither scatter correction matches the performance of fractional-order derivatives in this study, MSC provides the best balance between noise suppression and feature preservation when only linear preprocessing is applied.  
Figure 9: Test results of the prediction model of predicted vs. observed contents using CARS–PLSR after MSC and SNV preprocessing: (**a**) AA with MSC (R\({}^{2}\) = 0.700, RMSE = 0.377), (**b**) AA with SNV (R\({}^{2}\) = 0.690, RMSE = 0.384), (**c**) TP with MSC (R\({}^{2}\) = 0.369, RMSE = 3.840), (**d**) TP with SNV (R\({}^{2}\) = 0.346, RMSE = 3.796).
# Privacy-Preserving Data Analytics in Internet of Medical Things
## 5 Proposed Method for Privacy Preservation in Healthcare Data
### Experimental Results on the Breast Cancer Prediction Dataset
#### 5.4.2 Time Complexity Analysis with the Breast Cancer Prediction Dataset  
The execution time of queries somewhat increases with an increase in the conditions in the query, as seen in Figure 9. The first query filters the data to include only patients whose age at the time of the mammogramwas between 60 and 70 years. It involves a simple range filter on one attribute. The second query adds another condition to the previous query by checking if the patient has a history of breast biopsy. It involves filtering based on two attributes. The next query is the most complex, combining multiple conditions across several attributes, including logical operations and comparisons. The time increases from 0.01544 s to 0.01999 s, then to 0.03899 s with increasing conditions. A slight rise in execution time observed with each additional attribute, but it is not drastic, suggesting that the filtering operations scale reasonably well with the application of differential privacy and an increase in the number of conditions. The time complexity in practice suggests that the operations are manageable within the given execution times.  
Large-scale healthcare systems, which frequently handle massive volumes of data like patient records and clinical data, can benefit from the use of hierarchical models, which arrange data in layers. Hospitals, for instance, might gather data locally, then aggregate them throughout the region and apply DP to maintain patient privacy. DP algorithms are designed to scale with the dataset. For example, methods such as the Gaussian or Laplace mechanisms introduce controlled noise proportionate to the sensitivity of queries and the quantity of the dataset. This guarantees that personal privacy remains protected, even as data volumes increase. While DP is effective in maintaining privacy, it can introduce latency due to the additional noise that needs to be calculated and added during data processing. Systems can reduce the time taken to respond to user requests by pre-computing commonly requested queries. For instance, if certain aggregate statistics that are frequently needed, they can be computed and stored with the necessary DP adjustments made in advance. Similarly, response times can be greatly decreased by putting in place a caching mechanism for computed results. The system can quickly respond to repeated queries without having to perform all calculations by storing previously computed results with their associated noise.  
Figure 8: Analysis of privacy parameter using the Breast Cancer Prediction Dataset.
## 3 Optomechanical Design
### Receiving Subsystem  
The optoelectronic receiver is an APD110C model (Thorlabs, Newton, NY, USA) which includes an InGaAsP APD photodiode with an intrinsic responsivity of 0.9 A/W (\(M=10\)) and a transimpedance  
Figure 6: CAD 3D model of the optomechanical emission subsystem.  
amplifier with a gain of 10\({}^{5}\) V/A. The photosensitive surface of the APD has a diameter of just 200 \(\upmu\)m and, therefore, its correct positioning requires the use of a precision XY translation stage (Figure 7). The main characteristics of the receiver module are shown in Table 6.  
An outline of the receiver optics of the lidar prototype is shown in Figure 7. The backscattered energy is captured by a [PERSON](tm) ETX 80 reflector telescope (Meade Instruments Corp. Irvine, CA, USA) (Figure 5) with an aperture of 80 mm and focal length equal to 400 mm. Three optical elements are used to focus the light collected by the telescope onto the photodiode: a camera lens, a beam reducer and a reflective microscope objective. The camera lens is reversed so that the focal plane corresponds to the real image created at the output of the telescope. This means that light from the real image is collimated into a low-divergence beam. The beam reducer (\(\times\)3) diminishes the beam with a factor of three. This ensures that all of the collected light can enter the aperture of the microscope lens. The reflective microscope objective (\(\times\)15) focuses the light on the photodiode. An interference filter matching the wavelength of the laser source is inserted between the beam reducer and the microscope objective. This is used to remove the ambient light not backscattered by the target, and to increase the signal-to-noise ratio. At this position, the collimated light provided by the beam reducer incises orthogonally on the interference filter surface, avoiding its detuning [23].  
The analogue signal from the photodetector module is digitalized using a Gage(tm) CompuScope 12502 analogue-digital converter (ADC) (DynamicSignals LLC. Lockport, IL, USA) and transmitted to the processing unit (PC). The selected digitizer has 2 channels with a sampling rate of 500 MS/s and 12 bits of vertical resolution. The system has a range resolution of 2.4 m, similar to other 1.5 \(\upmu\)m lidar systems [25]. This high resolution is appropriate for the measurement of thin spray drift clouds.
# Calibration of GA Parameters for Layout Design Optimization Problems Using Design of Experiments
## 1 Introduction  
In general, facility-layout optimization problems are nonlinear, nonconvex, and multimodal in their nature. Facility-layout problems (FLP) can be divided according to types of manufacturing systems into four basic categories, which are product layout, process layout, static layout, and cellular layout [1]. Taking this classification into account, the proposed study addresses the cellular manufacturing problem. In the past, the main objective of a facility-layout problem was to minimize the material handling cost of the manufacturing system [2]. Presently, the main goal of the FLP is to improve manufacturing efficiency. In line with this, a number of authors suggested different objective functions for facility-layout problems, e.g., to maximize the throughput rate and minimize the conveyance time per trip [3] or minimize cycle times in order to increase productivity [4], and so forth.  
An important issue in the design of cellular manufacturing systems (CMS) is the manufacturing-cell-formation problem (MCFP), which is based on group technology principles. Several taxonomies of the MCFP are reviewed in the literature, e.g., by [PERSON] et al. [5], [PERSON] et al. [6] and [PERSON] and [PERSON] [7]. The core of MCFP procedures is that machines are grouped into machine cells and parts into part families. In practical applications, it is not easy to arrange all parts and machines into autonomic cells, and therefore some operations have to be performed on separate machines. The cost of duplicating machines is often high, and therefore, related managerial decisions are usually trade-offs between economic and technological criteria [8; 9; 10]. During previous decades, numerous heuristic and metaheuristic methods and their variations were developed, tested and compared for this problem. The metaheuristics include the genetic algorithm (GA), simulated annealing, ant colony optimization, tabu search, scatter search, particle swarm optimization, GRASP and hybridized metaheuristics. There are several survey papers,such as by [PERSON] et al. [11], [PERSON] and [PERSON] [12] and others, that present and compare the methods from various perspectives.  
In this work, the focus is on application of GAs, and especially on finding out how the solutions of the cell-formation problem are influenced by a set of probability parameters of genetic operators, namely crossover and mutation, including balanced weight factors. In view of this, the presented work attempts to employ the Taguchi approach to find an optimal combination of parameters that impact the efficiency of the genetic algorithm and to explore whether the optimal combination of the genetic operators for the given type of MCFP can be influenced by the magnitude of the noise factors, which is represented by matrix size in our case.
# Flexible Antennas: A Review
###### Abstract  
The field of flexible antennas is witnessing an exponential growth due to the demand for wearable devices, Internet of Things (IoT) framework, point of care devices, personalized medicine platform, 5G technology, wireless sensor networks, and communication devices with a smaller form factor to name a few. The choice of non-rigid antennas is application specific and depends on the type of substrate, materials used, processing techniques, antenna performance, and the surrounding environment. There are numerous design innovations, new materials and material properties, intriguing fabrication methods, and niche applications. This review article focuses on the need for flexible antennas, materials, and processes used for fabricating the antennas, various material properties influencing antenna performance, and specific biomedical applications accompanied by the design considerations. After a comprehensive treatment of the above-mentioned topics, the article will focus on inherent challenges and future prospects of flexible antennas. Finally, an insight into the application of flexible antenna on future wireless solutions is discussed.  
flexible antenna; wearable antenna; 3-D printing; specific absorption rate (SAR); Internet of Things (IoT), implantable antennas; ingestible antennas; bending analysis +
Footnote †: journal: International Journal of Electronics and Electronics and Communications  
1  
11 September 2020
## 2 Materials and Methods
### Dataset and Preprocessing
#### 2.2.3 Visualization Method  
Deep learning models consist of multiple hidden layers, making it difficult to analyze how the trained model makes decisions and derives predictions. We use a technique to increase transparency by generating visual explanations for decision-making in large-scale CNN-based modes; to enhance the interpretability of the classification results, this study employs two deep explainers: CAM and guided backpropagation. First, the heatmap (B) drawn by the first attempted CAM tends to have only a focus area and is difficult to observe intuitively. Therefore, the CAM heatmap (B) is integrated with the original image (A) and shown as (C) in Figure 4. The focus area of such a heatmap appears too small, is not clear, and has low sensitivity. To solve this problem, guided backpropagation is introduced. Guided backpropagation is a classic method of model visualization that can respond to high network positions at the pixel level by propagating the gradient backwards through the feature map and localizing the relevant regions within the micrograph by sharpening the focus area of the heatmap as in (D). Finally, for the feature map, the gradients of the target class are computed and combined to form a weighted map, emphasizing the important areas in the image (E) [34].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline RandomResizedCrop & 224 \\ Normalization & [0.485, 0.456, 0.406] \\ Batch size & [0.229, 0.224, 0.225] \\ Number of workers & 8 \\ Optimizer & Adam \\ Criterion & Cross-entropy loss \\ Epochs & 30 \\ Step size & 5 \\ Gamma & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyperparameters and settings for training models.  
Figure 4: The images present the original image (**A**) for class 1 of the dataset, the CAM heatmap image of the VGG model output (**B**), the CAM heatmap (**C**) with (**A**) and (**B**) overlapped, the image with the inductive backpropagation of the VGG model output applied to the original image (**D**), and the superimposed Grad-CAM activation map (**E**).
# Intelligent Healthcare: Integration of Emerging Technologies and Internet of Things for Humanity
## 2 Core Technologies for Smart IoHT Applications
### Cloud, Fog, and Edge Computing  
Cloud computing (CC) has existed for decades. One of the key unique characteristics that make CC successful is its ability to provide everything as a service, including software, infrastructure, and platforms. Over the years, power and flexibility have made CC the dominant computing technology for information and communication systems. The basic CC model consists of two layers: the cloud and end-users. The cloud layer includes servers with powerful configurations, high computing power, and large storage capacity. Cloud servers are connected to the Internet infrastructure via back bolt connections with extremely high throughput. The end-user layer includes end-users such as sensors, IoT devices, actuators, etc. These devices are connected to the cloud layer based on wired and wireless connections. Processing, computation, and storage all take place on cloud servers. One major limitation of CC is its high service response time. Therefore, it is not viable for emergency healthcare scenarios that require real-time computing power.  
In recent years, to solve this problem, several computing models, such as fog computing (FC) [28] and edge computing (EC) [29], have been proposed. Figure 2 presents an all-in-one computing framework for IoT systems.  
Both FC and EC aim to bring database and cloud capabilities closer to the end devices. One key difference between EC and FC is their computational locations. While EC computation is integrated into devices, the edge server is set up in LAN, a few hops away from  
Figure 2: An illustration of all-in-one computing architectures for IoT ecosystems [28].  
end devices; in contrast, FC computation is performed in the dedicated servers of data centers, set up from the Internet gateway to the cloud.  
Each computing model has its advantages and disadvantages. CC has high computing power, but a considerable delay. EC and FC have low service response time, but inadequate computing and storage capacity. However, for each specific scenario and application, we can combine these technologies into an optimally integrated solution. To explain the capabilities of computing technologies, performance comparison studies are presented in Table 2.  
To collect patients' vitals, each patient can carry a variety of sensors, depending on the type of disease and the indicators to be monitored. These sensors assist each other in data acquisition, communication, and the formation of Wireless Body Area Networks (WBANs). The issues with WBAN will be discussed in detail in the next section.
# Wind Speed Spectrum of a Moving Vehicle under Turbulent Crosswinds
## 2 Wind Speed Spectrum Models
### Wind Speed Spectrum of a Moving Vehicle
According to \(S_{u}(f)=4\sigma_{u}^{2}\int\limits_{0}^{\infty}f(\widetilde{r})\cos 2\pi f \widetilde{r}d\widetilde{r}\) and \(S_{v}(f)=4\sigma_{v}^{2}\int\limits_{0}^{\infty}\mathrm{g}(\widetilde{r})\cos 2 \pi f\widetilde{r}d\widetilde{r}\), thus:  
\[ \begin{array}{l}4\sigma_{u}^{2}\int\limits_{0}^{\infty}f(\widetilde{r})\cos 2 \pi f\tau d\tau=(\frac{L_{u}}{\alpha V_{R}})S_{u}(\hat{f})\end{array}  \tag{17}\]  
\[ \begin{array}{l}4\sigma_{v}^{2}\int\limits_{0}^{\infty}g(\widetilde{r})\cos 2 \pi f\tau d\tau=(\frac{L_{u}}{\alpha V_{R}})S_{v}(\hat{f})\end{array}  \tag{18}\]  
where \(\hat{f}=fL_{u}/\alpha V_{R}.\sigma_{v}\) is the root mean square of the fluctuating wind speed in the lateral direction. When Equation (17) and Equation (18) are put into Equation (16), Equation (16) becomes as follows:  
\[ \begin{array}{l}4\sigma_{u}^{2}c_{u}\int\limits_{0}^{\infty}f(\widetilde{r })\cos(2\pi f\tau)d\tau+4\sigma_{u}^{2}(1-c_{u})\int\limits_{0}^{\infty}g( \widetilde{r})\cos(2\pi f\tau)d\tau\\ =c_{u}(\frac{L_{u}}{\alpha V_{R}})S_{u}(\hat{f})+(1-c_{u})(\frac{L_{u}}{\alpha V _{R}})S_{v}(\hat{f})\end{array}  \tag{19}\]  
For the von Karman wind spectrum, \(\alpha=0.747\). The parameter values of the power spectral density function of a moving point are as follows:  
\[\widetilde{r}=\alpha\frac{\tau V_{R}}{L_{u}} \tag{20}\]  
\[L_{u}={}^{x}L_{u}\Big{[}c_{u}+(1-c_{u})(2^{y}L_{u}/{}^{x}L_{u})^{2}\Big{]}^{1/2} \tag{21}\]  
\[c_{u}=\big{[}(V_{T}/V_{R})\cos\phi+(\overline{V}/V_{R})\big{]}^{2} \tag{22}\]When the wind speed spectrum at a static point is the von Karman wind spectrum, the corresponding wind spectrum of a moving point can be obtained. \[S_{uu}(\hat{f})=c_{u}(\frac{L_{u}}{\alpha V_{R}})S_{u}(\hat{f})+(1-c_{u})(\frac{ L_{u}}{\alpha V_{R}})S_{v}(\hat{f}) \tag{23}\]  
where \(\hat{f}=fL_{u}/\alpha V_{R}\). Therefore, based on the theoretical derivation process of the wind velocity spectrum of a static point and the corresponding moving point, a general equation of the wind speed spectrum for a moving point can be obtained. For the von Karman wind speed spectrum of a static point, the corresponding wind speed spectrum of a moving point can be obtained. The derivation process of the transverse wind velocity spectrum at the moving point is similar to that of the longitudinal wind velocity spectrum. The difference is mainly related to \(c_{i}\) (\(i=u,v\)), \(L_{u}\) and \(V_{R}\). In this study, the investigation mainly focuses on the longitudinal wind velocity spectrum of a moving point.
## 4 Design Optimization
### Optimization Methodology

Table 6: Latin hypercube sampling matrix and measured responses from finite element analysis. The correlation matrix \(R\) is constructed using Equation (14), and \(Y\) contains the observed responses at sampling points. By maximizing this function, we identified correlation parameters that best capture the data patterns, thus completing the kriging model. This sequence establishes a clear relationship between the three equations: Equation (13) defines the model structure, Equation (14) provides the correlation function needed to calculate \(Z(x)\), and Equation (15) optimizes the parameters of Equation (14). The model demonstrated high prediction accuracy during 5-fold cross-validation, with \(R^{2}\) = 0.91 (RMSE = 5.42 MPa) for contact pressure and \(R^{2}\) = 0.88 (RMSE = 12.75 MPa) for equivalent stress. This validated metamodel was then used to identify optimal designs balancing the competing objectives.
# Hardware Prototyping and Validation of a W-\(\Delta\)DOR Digital Signal Processor
## 3 W-\(\Delta\)DOR Hardware Implementation
### W-\(\Delta\)DOR DSP Core Architecture Description
#### 3.1.5 Subcarrier Generation  
The W-\(\Delta\)DOR DSP core must generate two subcarriers at \(\frac{F1}{2}\) and \(2F1\). Two Numeric Controlled Oscillators (NCOs) are used: the phase accumulator is 32-bit wide, while the phase is truncated to 16 bits. The phase-to-amplitude conversion is performed by a 14-iteration CORDIC. Each iteration is computed in parallel to maximize the throughput. The NCO output register is 16-bit wide. Additional blocks in the RRC section are: two switches for selecting the narrowband/wideband configuration, attenuators (named \(\phi\)#) to implement modulation indexes, multipliers to modulate the subcarriers, and an adder to mix the two waveforms.
# GNSS Signal Acquisition Algorithm Based on Two-Stage Compression of Code-Frequency Domain
## 4 Simulation Results
### Results on Algorithm Complexity  
Figure 9 gives the complexities of the four acquisition algorithms with different \(FCR\) and \(N\). As we can see, the complexity of the proposed CFC acquisition algorithm is less than that of the other three algorithms. This advantage becomes more obvious with the increase of parameters \(FCR\) and \(N\). Specifically, as shown in Figure 9a, when the \(FCR=2\), the code length \(N=1024\), the complexities of the serial, the PMF-FFT, the CC, and the CFC acquisition algorithms are \(~{}1.049\times 10^{7}\), \(1.114\times 10^{6}\), \(1.638\times 10^{6}\), \(8.192\times 10^{5}\), respectively, and the proposed CFC acquisition algorithm is 92.19%, 26.46%, and 50% lower than the other three algorithms. As shown in Figure 9b, when the code length \(N=2048\), the \(FCR=1\):2, the complexities of the four acquisition algorithms are \(~{}6.711\times 10^{7}\), \(4.325\times 10^{6}\), \(3.965\times 10^{6}\), \(1.982\times 10^{6}\), respectively. Compared with the other algorithms, the CFC acquisition algorithm reduces 99.7%, 54.17%, and 50%. Thus, the frequency domain compression preprocessing can reduce the resource overhead, which makes the proposed CFC acquisition algorithm better adapt to high dynamic conditions.
## 3 Results and Discussion
### Comparison of Emission Performance  
Figure 14 shows comparisons of NO\({}_{\rm x}\) emission for different variable valve control strategies. It was found that the CIC V1 case, with no-full lift and duration of intake and exhaust valve, shows the lowest NO\({}_{\rm x}\) emission with minimum temperature, due to using recompression internal EGR. If it is difficult to control both valves, as shown in Figure 14, the CIC V3 case, with no-full lift and duration of exhaust valve only and post injection, is an effective strategy to reduce NO\({}_{\rm x}\) emission at part load operating conditions in a compression ignition combustion. In the case of post injection at 320\({}^{\circ}\) in CIC V3, it shows the lowest NO\({}_{\rm x}\) emission under variable exhaust valve control without a variable intake valve. As shown in Figure 15, the lowest volume efficiency occurred in the case of CIC V1 with full control of both the intake and exhaust valve. Whereas, it was found that CIC V2 and CIC V3 have a similar volume efficiency at each maximum combustion temperature.  
Figure 16 shows the comparison results for NO\({}_{\rm x}\) emission versus indicated mean effective pressure (IMEP) for all variable valve control strategies applied in this study. This shows a noticeable reduction in NO\({}_{\rm x}\) emission with a reduction in IMEP. Especially, CIC V1 is a moderate case with regard to emission reduction, but this strategy was worse with regard to thermal energy. As the post injection timing in CIC V3 was retarded, it caused a decrease of NO\({}_{\rm x}\) and an increase in IMEP linearly. The result was derived from effective work due to increased burnt fuel through post fuel injection. Figure 17 shows the comparison result for NO\({}_{\rm x}\) emission versus indicated specific fuel consumption (ISFC) for all variable valve control strategies. This shows that the lowest fuel consumption with NO\({}_{\rm x}\) emission was in the case of CIC V1. Furthermore, the post injection at 320\({}^{\circ}\) in CIC V3 is the best case with regard to the level of NO\({}_{\rm x}\) emission under fuel penalty. Figure 18 shows the compar  
Figure 14: Comparison on NO\({}_{\rm x}\) emission for different variable valve control strategies.  
Figure 15: Comparison on volume efficiency for different variable valve control strategies.