esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Evaluation
### Effect of Malicious Attacks on Positioning Accuracy
#### 4.2.2 Device ID Data Tampering  
Each node that makes up the positioning system receives UWB signals from other nodes. At this time, the UWB message includes the ID of the transmitting device and transmission timestamp information. Nodes in the positioning system are given device IDs starting from Number 1 according to the registration order after system operation. Therefore, all nodes in the system have different device IDs, and each device pair performs DS-TWR-based ranging using the node ID and transmission and reception timestamp data.  
In this subsection, we randomly tampered with the transmitting device's ID. When one specific node out of the total of seven constituting nodes received a UWB message, we randomly reset the transmitting node's ID to one of the remaining six nodes (excluding itself), performed ranging with that distance information, and executed positioning. We implemented this environment in reality and checked the performance.  
Figure 5: CDF of positioning errors in normal and timestamp data tampered situations.  
As a result, the positioning errors of the three tags were 8.85 e + 12 m, 185.95 m, and 7.86 e + 12 m, respectively. The results of the experiment showed how seriously positioning performance can be damaged if the identity of the transmitting device is unknown or the data are damaged due to a malicious attack.  
The basis on which this result was derived is as follows. The device ID data tampering experiment evaluated the positioning performance by randomly altering the transmitting device ID for a specific target among the system nodes each time a signal was received. Therefore, the positioning error value for each tag may significantly vary with each repetition of the experiment. The key point, however, is that the potential positioning error can be quite large. All time data for calculating the ToF based on DS-TWR were obtained within the timeline of each device. Therefore, using this method to determine the ToF did not require a synchronization process for aligning timelines between devices. However, if only the device ID were manipulated, the receiving node may interpret the signal as coming from the same device, while the actual timestamp is from a device with a completely different timeline. This would lead to a distortion of all time data used for the ToF calculation, thereby disrupting the overall ranging performance.
## 2 Methodology
### PSO-XGBoost
#### 2.1.3 PSO-XGBoost  
In this paper, PSO was chosen for hyperparameter tuning in XGBoost due to its specific advantages over traditional methods like grid search CV and random search CV. PSO is more efficient in navigating high-dimensional space, which is particularly beneficial to the given hyperparameters in the XGBoost model. This method often converges faster than gird or random searches in extensive parameters, which is a critical factor when considering computational cost. Additionally, the collaborative search approach of PSO effectively avoids local minima, which could be a limitation of traditional search methods. The balance between the exploration and exploitation of PSO, coupled with its demonstrated success in similar publication references, made it the preferred choice for our study [37,38]. The XGBoost algorithm is known for its ability to handle non-linear relationships, independence from data feature scaling, and suitability for imbalanced class distributions. However, during computation, different hyperparameter values can yield distinct outcomes. Therefore, the PSO optimization algorithm is employed to optimize five XGBoost hyperparameters: learning rate, number of estimators, maximum depth, minimum child weight, and subsampling rate, aiming to create the best-performing hybrid model [39,40,41]. The optimized hyperparameter values are presented in Table 1. Learning rate governs the step size during each iteration towards the minimum of the loss function. A lower learning rate enhances algorithm stability. The number of estimators determines the quantity of decision trees, and opting for a larger value can improve model accuracy. However, it is important to note that excessively increasing the number of estimators can lead to overfitting. Maximum depth is a parameter used to prevent overfitting, but a too-high value of maximum depth can cause overfitting as well. Minimum depth controls the minimum sum of child node weights. The subsampling rate is employed to specify the fraction of data randomly sampled for constructing the training dataset, which can expedite training and address overfitting issues. A five-fold cross validation was conducted to prevent overfitting when selecting hyperparameters.
# SDS: Scrumptious Dataflow Strategy for IoT Devices in Heterogeneous Network Environment
## 1 Introduction
### Smart Homes
#### 1.1.2 IoT-Based Water Systems  
The current level of water shortages in most regions of the world urges critics to effectively manage the water supplies. As a result, most cities are opting for smart solutions that include the installation of a large number of meters on water supply pipes and storm drains. Smart water meters come in a variety of styles. These meters may be used to determine the amount of water entry and outflow as well as potential leaks. Water metering systems based on IoT are also employed in combination with data from meteorological satellites and river water sensors. They can also assist us in forecasting flooding.
## 2 Processes of Semiconductor Manufacturing and Measurement Methods
### Processes of Semiconductor Manufacturing
#### 2.1.1 Wafer Fabrication and Photolithography  
The initial wafer fabrication step involves producing high-purity silicon wafers from raw materials. This includes extracting and purifying silicon to create a single-crystal silicon ingot and then sliced into thin wafers. These wafers serve as substrates upon which semiconductor devices are built. Following this, the photolithography process applies a light-sensitive photoresist to the wafer's surface, and precise masking techniques, combined with exposure to light, define the circuit patterns to be formed [22; 23; 24]. This process determines where subsequent material deposition or removal occurs.
# Experimental Investigation of Controlled and Uncontrolled Rectifiers for Low-Power Wind Turbines
## 3 Rectification Types
### Classification on the Basis of Number of Phases  
For higher-power wind energy systems, it is recommended to utilize a three-phase rectifier because the phase angle difference between the generated current or voltage phases is 120 degrees [13]. Three-phase rectifiers are composed of three separate current paths. After every 120 degrees, a new phase starts conducting, thus always keeping the output voltage above 86.6% of the maximum voltage, as shown in Figure 4. One of the significant advantages of three-phase full-wave rectifiers is their extraordinarily low ripple factor of 0.17 [18; 22]. Moreover, the frequency of the ripples is compared to a half-wave rectifier six times higher, while it is three times higher than the full-wave rectifier. Higher frequency means the output filter components can be less bulky and costly. Along with minimum ripple, the three-phase rectifier has the highest utilization factor. The low ripple factor also increases the life and reliability of the switches and capacitors [17]. However, these advantages come at higher costs and complexity.
## 1 Introduction
### Main Contribution  
* A CHS model for safe and energy-conscious routing in WSNs has been created. Distance, energy, security (risk likelihood), latency, appraisal of trust (direct and indirect trust), and received signal strength indicators all favor CHS (RSSI).
* The proposed method predicts energy level using enhanced Deep Convolutional Neural Network (DCNN) to select the best CH in WSN. The BEA-SSA is used to select the best CH in the WSN. High RSSI, PDR, and residual energy are presented by the Bald Eagle Assisted SSA (BEA-SSA) model. The enhanced energy evaluation and prediction utilizing DCNN and the implemented BEA-SSA-based CHS are responsible for these improvements.
* The proposed cluster head selection process aids LEACH to preserve the network lifetime. The literature includes research that similarly aids LEACH. This paper presents an advanced approach that supports LEACH more effectively.
## 3 Maximum Power Point Tracking Algorithms
### Particle Swarm Optimization (PSO)  
The PSO is a metaheuristic algorithm inspired by the social behavior of bird flocks or fish schools employed for maximizing the power harvested from the PV power systems. PSO initializes a population of particles (solutions), which iteratively adjust their positions in the search space based on individual and group experiences to locate the GPP. This method effectively handles non-linear and multi-modal P-V curves, including partial shading, by dynamically optimizing the operating voltage. Its simplicity, adaptability, and high convergence speed make it a robust choice for PV MPPT applications. The tracking of the global peak (GP) is contingent upon the manipulation of particle position and velocity strategically adjusted to discern the optimal trajectory of the GP rather than converging onto a local peak (LP). The updated position of a particle, denoted as \(D_{i}^{k+1}\), can be expressed mathematically as follows [22; 23]:  
\[D_{i}^{k+1}=D_{i}^{k}+v_{i}^{k+1} \tag{2}\]  
The particle velocity \(v_{i}^{k+1}\) is determined through the utilization of the current position \(x_{i}^{k}\), particle velocity \(v_{i}^{k}\), and global best position (\(G_{\text{best}}\)) using [22; 24; 25]:  
\[v_{i}^{k+1}=\omega v_{i}^{k}+c_{1}r_{1}\Big{(}P_{best,i}-D_{i}^{k}\Big{)}+c_{2} r_{2}\Big{(}G_{best}-D_{i}^{k}\Big{)} \tag{3}\]  
In the above equations, \(\omega\) represents the inertia weight governing the exploration region, while \(c_{1}\) and \(c_{2}\) denote the acceleration coefficients [26; 27]. Figure 3: The CSO flowchart based on MPPT. The PSO algorithm begins the GPP search by generating a set of duty cycles (\(D_{i}\)) and applying them to the boost converter. The corresponding output voltage and current are then used to compute the initial personal best (\(P_{best}\)) and global best (\(G_{best}\)) values. Subsequently, the duty cycles are updated iteratively based on the position and velocity update from Equations (2) and (3), refining the search for the optimal operating point. The pseudo code of PSO for tracking the global peak in photovoltaic systems is illustrated in Table 4. On the other hand, Figure 4 presents the flowchart that illustrates the PSO-based MPPT process for a PV energy system. It begins with the initialization of particles, velocities, and parameters followed by iterative updates of duty cycles and velocities based on fitness evaluations. The algorithm identifies the best individual and global fitness values to maximize PV power output. The process continues until all iterations are completed, after which the optimal duty cycle is output.
## 3 Methodology
### Post-Processing Methods
#### 3.1.1 The BMA Method  
For the distribution-based method, BMA, the precipitation PDF for a specific day or period is calibrated by fitting the forecast ensemble based on a historical dataset encompassing EPFs and observations, as follows:  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & \multirow{2}{*}{**Sources**} & **Ensemble** & **Temporal/Spatial** & \multirow{2}{*}{**Temporal Coverage**} \\  & & **Members** & **Resolution** & \\ \hline Observed & & & & \\ meteorological data & 175 meteorological stations & - & Daily/station & 2014-2018 \\ \hline \multirow{5}{*}{Ensemble precipitation forecasts (EPFs)} & CMA (China Meteorological Administration) & 15 & & \\ ECMWF (European Centre for & & & \\ Medium-Range Weather Forecasts) & 50 & Seven lead days & 2014-2018 \\ \cline{1-1} \cline{2-5}  & JMA (Japan Meteorological Administration) & 26 & /0.5\({}^{\circ}\) & summer \\ \cline{1-1} \cline{2-5}  & NCEP (National Centers for & & \\ Environmental Prediction) & 20 & & \\ \hline Observed & & & & \\ streamflow & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The datasets used in this study.  
\[P(y|f_{1},\ldots,f_{k})=\sum_{k=1}^{K}W_{k}g_{k}(y|f_{k}) \tag{3}\]  
where the weight, \(W_{k}\), corresponds to the posterior probability of the selected ensemble member, \(k\). This weight indicates the relative contribution of model \(k\) to the overall predictive skill during the training period. More details can be found in [PERSON], [PERSON] [14].
# Antimicrobial and Odour Qualities of Alkylpyrazines Occurring in Chocolate and Cocoa Products
## 5 Relationship between Odour and Antimicrobial Activities of Pyrazines
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Compound Name**} & \multicolumn{3}{c}{**Pa of Antibacterial Activity from AntiBac-Pred**} \\ \cline{2-4}  & _Yersinia pestis_ & _Mycobacterium tuberculosis_ & _Corynebacterium jeiketium_ & **Log\({}_{\mathbf{10}}\) of Odour Threshold** \\ \hline pyrazine & 0.7689 & & 5.460521 \\ tetramethylpyrazine & 0.7431 & & 4.213065 \\
2,3-dimethylpyrazine & 0.7010 & & 3.091079 \\
2,5-dimethylpyrazine & 0.7010 & 0.6459 & 3.308208 \\
2,6-dimethylpyrazine & 0.6560 & & 3.560305 \\
2-methylpyrazine & 0.6050 & 0.5963 & 4.637609 \\ trimethylpyrazine & 0.6050 & & 2.758911 \\
2,3-diethylpyrazine & 0.5018 & & 0.8195439 \\
2-isobutyl-3- & & & 0.6632 & \(-1.540607\) \\ methoxyppyrazine & & & 0.5242 & 1.544068 \\
2-isobutyl-3-methylpyrazine & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison values for the correlation analysis. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{\(t\)**-Test of Independent Groups**} & \multicolumn{2}{c}{**Spearman’s Rank–Order Correlation Test**} \\ \hline Analysed variables & 2 & Analysed variables & 2 \\ Significance level, \(\alpha\) & 0,05 & Significance level, \(\alpha\) & 0.05 \\ Correction for different variances & No & Number of pairs & 12 \\ Difference of the means & \(-\)2.340587 & r & 0.37895 \\ \(-\)95\% CI for the difference & \(-\)3.496677 & Std. err. of r & 0.292643 \\ +95\% CI for the difference & \(-\)1.184498 & \(-\)95\% CI for r coefficient & \(-\)0.267219 \\ Standard error of the difference & 0.557454 & +95\% CI for r coefficient & 0.790038 \\ Pooled standard deviation & 1.365478 & t-statistic for r & 1.294923 \\ t-statistic & \(-\)4.198709 & Degrees of freedom & 10 \\ Degrees of freedom & 22 & Two-sided \(p\)-value & 0.224445 \\ Two-sided \(p\)-value (\(t\)-test) & 0.000371 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results of \(t\)-test and [PERSON]’s rank–order correlation between log\({}_{\mathbf{10}}\) of odour threshold and probability of antibacterial activity from AntiBac-Pred of pyrazine compounds.
# Research on Data Transmission of Laser Sensors for Reading Ruler
## 5 Engineering Experimental Testing  
The engineering application site is shown in Figure 7. The length of the scale track is 300 m. Continuous production data for 150 h. Sampling period: 10 ms. In the experimental tests, the key performance indicators of two-dimensional coding and hybrid coding are shown in Table 7. Dynamic performance analysis as shown in Table 8. Scanning recognition efficiency (average of 1500 tests), Performance comparison test data as shown in Table 9.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline \multirow{2}{*}{**Indicator**} & **Traditional** & **Hybrid** & **Improvement** \\ \cline{2-4}  & **Coding** & **Coding** & **Rate** \\ \hline Positioning Accuracy (mm) & \(\pm 3\) & \(\pm 2\) & 33.3\% \\ \hline Repeat Positioning Accuracy (mm) & \(\pm 1.6\) & \(\pm 0.8\) & 50\% \\ \hline Response Time (ms) & 45 & 28 & 37.8\% \\ \hline Failure Rate (times/thousand  hours) & 6.4 & 2.2 & 65.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Key Parameter Indicators of the System.  
\begin{table}
\begin{tabular}{l l l} \hline \hline \multirow{2}{*}{**Speed (m/s)**} & **Traditional Coding Error** & **Hybrid Coding Error (mm)** \\ \hline
0.5 & 0.8 & 0.18 \\ \hline
1.0 & 1.8 & 0.29 \\ \hline
1.5 & 2.7 & 0.70 \\ \hline
2.0 & 3.8 & 0.69 \\ \hline \hline \end{tabular} Remarks: Tracking error under the condition of acceleration 1.5 m/s\({}^{2}\).  
\end{table}
Table 8: Dynamic Performance Analysis.  
Figure 7: The engineering application site.
# Carrier Phase Dual One-Way Ranging Method Based on a Frequency Hopping Signal
## 1 Introduction
[18] studied and analyzed the dual and triple frequencies of GPS signals and analyzed the integer linear combination of the GPS triple frequency [18]. [PERSON] et al. analyzed the BDS triple-frequency carrier phase combination in detail [19]. In addition to the linear combination method, researchers solve for integer ambiguity by means of search methods, such as the Lambda algorithm [20; 21]. This approach reduces the correlation between variance components via the integer Gauss transform to improve the search efficiency. Based on the traditional Lambda algorithm, researchers have adopted prior information such as multiantenna attitude and antenna baseline length to improve the probability of success with regard to ambiguity resolution [21; 22]. In this paper, an improved time synchronization method based on the original DOWR synchronization method is proposed for the ISL application environment. We adopt a frequency hopping (FH) signal to achieve carrier phase DOWR. Frequency hopping signals receive/transmit signals at multiple frequencies through the same radio frequency channel, reducing resource consumption. Furthermore, the frequency hopping signal exhibits superior anti-jamming performance compared to the BPSK signal, thereby effectively enhancing the anti-jamming performance of the ISL. The structure of the paper is as follows. First, we describe the basic principle of the ISL measurement and transmit/receive signal model. Second, we introduce the frequency hopping carrier phase measurement system. At the receiving end, we adopt signal extrapolation and phase fitting to improve the continuity of the signal and implement the correct rate for the carrier phase integer ambiguity solution. Finally, the period of frequency hopping, frequency hopping performance, and continuous signal performance are analyzed theoretically and simulated.
## 3 Methodology  
The proposed review study is based on scientific publications published from 2015 to March 2022 in the manufacturing domain. A systematic literature review (SLR) was performed by following the steps shown in Figure 2[22]. SLR has three stages:  
* Planning the review,
* Conducting the review,
* Reporting the review.  
The necessity of a systematic literature review was identified early in the planning of the review. The research questions were defined in later stages. In the following stage, the review was conducted by accumulating recent publications, extracting data, performing a questionnaire survey, and analysing the data extracted from articles. This stage was followed by the reporting stage, where the outcome of the data analysis was reported. The final review report was obtained in the last stage.  
The strategy provided in [23] was used to identify the primary studies. It comprises two steps: (a) automatic search, and (b) manual search. A search string was defined based on the research questions of the automatic search. The search string consists of the following: the primary keywords: (\"Manufacturing\" or \"Production\", or \"Operation\") AND (\"Digital Twin\" or \"DT\") AND (\"Machine Learning\" or \"ML\"). These keywords were used with shifting positions and replacing them with synonyms. The databases used in the proposed study are: Scopus, Web of Science, Springer, IEEE, ScienceDirect, and ACM Digital Library. In the manual search, a backward citation search strategy was used. The proposed study is based on publications published between 2015 and March 2022. This period was chosen to include the most recent studies in this domain. The process to identify primary studies (for data extraction) is shown in Figure 3.  
The search string described in the previous section resulted in 1050 publications. After several screenings, 71 publications were found to be most relevant based on the inclusion and exclusion criteria. In the next stage, the primary publications were carefully read and data were extracted carefully. The derived data were saved in an Excel file for detailed analysis.  
Figure 2: Systematic Literature Review steps. Systematic Literature Review steps shown with the help of four boxes and connecting arrows.
# Robust CNN Compression Framework for Security-Sensitive Embedded Systems
## 4 Experiments
### The Convergence Behavior  
To investigate the effect of the knowledge distillation on the convergence behavior of the adversarial pruning, we traced both original accuracy and adversarial accuracy of AP and APD on every epoch. The results on the epoch 0 indicate the initial performance of the currently pruned model where the weight parameters were initialized with the baseline model. We focused on the original accuracy of the early stage of the optimization  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Network (Dataset)**} & \multirow{2}{*}{**Comp Rate**} & \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Org Acc (\%)**} & \multicolumn{2}{c}{**PGD5**} & \multicolumn{1}{c}{**PGD10**} \\ \cline{5-6}  & & & & **Adv Acc (\%)** & **Adv Acc (\%)** \\ \hline \multirow{5}{*}{VGG16 (CIFAR10)} & \multirow{2}{*}{\(\times\)1.5} & AP & 79.91 & 49.02 & 41.10 \\ \cline{3-6}  & & APD & **81.01** & **50.18** & **42.62** \\ \cline{2-6}  & & AP & 73.69 & 47.11 & 40.56 \\ \cline{2-6}  & & APD & **76.88** & **48.61** & **41.22** \\ \cline{2-6}  & \multirow{2}{*}{\(\times\)2.5} & AP & 69.30 & 45.10 & 39.61 \\ \cline{3-6}  & & APD & **74.53** & **47.19** & **40.21** \\ \hline \multirow{5}{*}{ResNet18 (CIFAR10)} & \multirow{2}{*}{\(\times\)1.5} & AP & 84.57 & 51.42 & 42.35 \\ \cline{3-6}  & & APD & **86.70** & **54.29** & **45.42** \\ \cline{2-6}  & & APD & 83.37 & 51.27 & 42.90 \\ \cline{1-1} \cline{2-6}  & & APD & **85.55** & **53.32** & **45.59** \\ \cline{1-1} \cline{2-6}  & & APD & 82.09 & 51.65 & 43.21 \\ \cline{1-1} \cline{2-6}  & & APD & **84.02** & **52.54** & **44.63** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of filter-wise pruning results of APD (ours) and AP on CIFAR10.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Network (Dataset)** & **Comp Rate** & **Method** & **Org Accuracy (\%)** & **Adv Accuracy (\%)** \\ \hline \multirow{5}{*}{LeNet (MNIST)} & \multirow{2}{*}{\(\times\)1.5} & AP & 98.91 & 95.26 \\ \cline{3-5}  & & APD & **99.18** & **96.32** \\ \cline{2-5}  & & AP & 98.79 & 94.95 \\ \cline{2-5}  & & APD & **99.17** & **96.21** \\ \cline{2-5}  & & AP & 98.68 & 94.58 \\ \cline{2-5}  & & APD & **99.04** & **96.02** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of filter-wise pruning results of APD (ours) and AP on MNIST.  
to show how well APD preserved the original accuracy of the baseline model during the adversarial pruning.