esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
The collected environmental data, including initial pavement temperature, average pavement temperature, average circumstance temperature, humidity, radiation, wind speed, and water film thickness, are initially calibrated by RSM to provide the input variables for SVR. After the first calibration process, the input variables in the train set are combined with corresponding icing time to model SVR using Gaussian radial kernel function, predefined penalty coefficient \(C\), and kernel function parameter \(\gamma\). Considering the simplicity of the PSO algorithm, it is used to obtain the optimal parameter combination of penalty coefficient \(C\) and kernel function parameter \(\gamma\) for the SVR model established in the last step. Finally, the experimental results of the hybrid improved SVR are compared with SVR, PSO-SVR, and RSM-SVR in \(R^{2}\), \(MAE\), \(MSE\), and \(MAPE\) to verify the performance of RSM and PSO. The results demonstrate that the hybrid improved SVR outperforms SVR, PSO-SVR, and RSM-SVR in \(R^{2}\) of train set and test set, respectively, 0.9655 and 0.9318, indicating that the combination of PSO and RSM shows better performance in enhancing the prediction capability of SVR. In addition, the hybrid improved SVR and PSO-SVR both converge approximately at the iteration age of 20, suggesting that the prediction accuracy of the proposed model increases as the convergence speed stays relatively constant. The structure of this paper is arranged as follows. Section 2 illustrates three components of the hybrid improved SVR, including SVR, RSM, and PSO, with the framework of \begin{table}
\begin{tabular}{c c c} \hline \hline
**SVR Model** & **Feature** & **Limitation** \\ \hline SVR & The model parameters are set manually & To obtain the optimal is costly \\ \hline \multirow{4}{*}{GA-SVR} & Optimize the parameter combination by & The optimization of prediction \\  & simulating the genetic variation & capability is not always favorable \\  & and crossover & \\ \hline \multirow{2}{*}{GS-SVR} & Optimize the parameter combination by & The optimization \\  & searching every possible solution & is time-consuming \\ \hline \multirow{2}{*}{PSO-SVR} & Optimize the parameter combination by & The local optimal solution may \\  & imitating the collective behaviors & affect the prediction capability \\ \hline \multirow{2}{*}{ FFA-SVR} & Optimize the parameter combination by & The determination of parameters \\  & imitating the accumulation of fireflies & for FFA increases the computation \\ \hline \multirow{2}{*}{RSM-SVR} & The prediction accuracy is improved by & \\  & expanding the number of input variables & Same limitation as SVR \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Different SVR prediction models. the proposed hybrid model diagrammed by figure. Based on that, four prediction models, SVR, PSO-SVR, RSM-SVR, and hybrid improved SVR, are calibrated in Section 3, and the accuracy and convergence speed are used to verify the performance of PSO and RSM. Finally, the conclusions are drawn in Section 4.
# Combining Dilution of Precision and Kalman Filtering for UWB Positioning in a Narrow Space
## 3 Experiments and Results
### Experimental Results of Positioning  
#### 3.3.1 Static Positioning  
In the static positioning experiment, considering that the DOP value of each point in the narrow space was quite different, different points in the space were randomly selected for the experiment. Among them, seven points were selected in Scene A, and eight points were selected in Scene B. The coordinate diagram of the base station and the selected positioning tag is shown in Figure 8.  
Based on UWB ranging, the ranging values of positioning nodes to different positioning base stations can be obtained. Each positioning node was sampled 200 times. Based on the UWB ranging data, different positioning algorithms were used to yield the position coordinates. Evaluating the performance of a positioning solution is an effective way to improve the performance of a navigation system [46]. The current positioning performance evaluation indicators mainly include root mean square (RMS) error, error cumulative distribution function and Cramer-Rao lower bound [47]. The performance evaluation indices selected in this paper were RMS errors. The RMS errors of the three algorithms at each point are shown in Figures 9 and 10. Table 4 lists the detailed error values.  
It can be seen from Figures 9 and 10 that the positioning accuracy of the fusion positioning algorithm proposed in this paper was higher than that of the WLS positioning algorithm, and it was much higher than that of the LS positioning algorithm. According to the detailed error values in Table 4, for most points, the proposed fusion algorithm reported the lowest error in both scenes. For Scene A, the mean square error of the LS positioning algorithm was 93.77 cm; the mean square error of the WLS positioning algorithm was 42.94 cm; and the mean square error of the fusion positioning algorithm was 18.67 cm. Compared with the LS positioning algorithm and the WLS positioning algorithm, the fusion positioning algorithm improved the accuracy by 80.1% and 56.5%, respectively. For Scene B, the average positioning error of the LS positioning algorithm was 19.6 cm, and the average positioning error of the WLS positioning algorithm and the fusion positioning algorithm was 10.59 cm, which meets the requirements of high-precision positioning. Compared with the LS positioning algorithm, the fusion positioning algorithm improved the accuracy by 45.9%.
## 4 Implementation
### Picking and Batching Formulation  
The formulation is represented as follows. In this scenario, the travel load is represented according to the number of robots we have. Let us imagine that we have several robots and that each of them makes a single trip. It would be the same as saying that we have a single robot that makes \(n\) trips.  
Let \(N_{0}\) be the origin node, let \(N_{1}\ldots N_{n}\) be the nodes of the products, let \(W_{1}\ldots W_{n}\) be the weights in kg associated with for each product, let \(d_{i,j}\) be the distance from node \(i\) to \(j\), let \(M\) be the maximum load of the qRobots, let \(K\) be the number of qRobots available, let \(t\) be the instant, \(i\) the node (product), \(p\) the robot, and let \(x_{t,i,p}\) be our binary variable (for example, for \(x_{2,3,2}=1\). It means that, at time 2, the qRobot 2 is at node 3). In our formulation, time really tells us the order, i.e., \(t=0\) will be the origin and \(t=1\) the moment in which it goes for the first batch. At \(t=2\) will be the moment of the second, and so on.  
\[\min_{x} \sum_{p=1}^{K}\sum_{t=1}^{n+1}\sum_{i=0}^{n}\sum_{j=0}^{n}x_{t_{t-1,i,p}}x_{t,j,p}d_{i,j},\] (2) s.t. \[\sum_{p=1}^{K}x_{0,0,p}=K\quad, \tag{3}\] \[\sum_{p=1}^{K}x_{n+1,0,p}=K,\] (4) \[\sum_{t=1}^{n+1}\sum_{i=1}^{n}x_{t,i,p}Wi\leq M\qquad\forall p\in \{1,...,K\}, \tag{5}\]  
Figure 9: Scenario 2, Collecting products in the same route from different batches. The robot will calculate a path that includes all the products to optimize their collection in a single journey. For example, if the product from Lot 2 is next to one from Lot 1, the robot will pick it up and store it in the basket from Lot 2.  
\[\sum_{i=1}^{n}x_{i,i,p}=1 \forall t\in\{1,\ldots,n+1\}, \tag{6}\] \[\forall p\in\{1,...,K\}\] \[\sum_{t=1}^{n+1}\sum_{p=1}^{K}x_{i,i,p}=1 \forall i\in\{1,\ldots,n+1\},\] (7) \[x_{t,i,p}\in\{0,1\} \forall t\in\{0,\ldots,n+1\}\] \[\forall i\in\{0,...,n\}.\] (8) \[\forall p\in\{1,...,K\}\]  
Equation (2) is our new objective function. Here, we minimize the total distance. We add the distance of all the robots traveling all the time, and we will check the distances of the nodes. Restriction (3) establishes that all the qRobots start from Depot. Restriction (4) establishes that all the qRobots end at Depot. Constraint (5) establishes any robot \(p\) can carry more load than allowed. Constraint (6) declares that each robot can only be one node at any time. (7) establishes that throughout the entire route, the robots together pass each node only once, and Restriction (8) describes that \(x_{t,i,p}\) are binary variables.  
The number of the qubits to perform this algorithm is equal to \(K(n+1)(n+2)+K\lceil log_{2}M\rceil\). At this point, we can only map our objective function in quantum and then solve it with a VQE.
# Wideband Channel Characterization for 6G Networks in Industrial Environments
## 6 Measurement Results and Analysis
### K-Factor  
Figure 9a-c present the K-factor plots against the Tx and Rx separation distance at the Manufacturing hall at Dokka Fastners AS, the Test room at Dokka Fastners AS, and the NTNU MANULAB environments, respectively. It is shown that the K-factor varies from 1.9 dB to 21.6 dB, from 0.9 dB to 19.9 dB, and from \(-\)0.3 dB to 26 dB at the Manufacturing hall at Dokka Fastners AS, the Test room at Dokka Fastners AS, and the NTNU MANULAB environments, respectively. It can be observed that the K-factor is particularly high when the Tx-Rx separation distance is small. This implies that when the Tx-Rx separation distance increases, the LoS signal component becomes weak. However, it is accordingly clear that this decreasing trend of the K-factor is not monotonic with the distance, which means that with strong NLoS MPCs, the K-factor becomes small regardless of the Tx-Rx separation distance. The K-factor in dB becomes smaller than 0 dB when the total power acquired from NLoS MPCs is greater than the received power of the LoS component that can be observed at a 2.5 m Tx-Rx separation distance at the NTNU MANULAB environment, as shown in Figure 9c. The minimum, maximum, and average values of K-factor are listed in Table 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Environment**} & \multirow{2}{*}{**Model**} & **PLE for CI** & **FI (\(\mathbf{a}\))** & \(\mathbf{\sigma}\) \\  & & \(\mathbf{\beta}\) **for FI** & **(dB)** & **(dB)** \\ \hline \multirow{2}{*}{Manufacturing hall at Dokka Fastners AS} & CI & 1.7 & - & 0.4 \\  & FI & 1.7 & 73.3 & 0.4 \\ \hline \multirow{2}{*}{Test room at Dokka Fastners AS} & CI & 1.6 & - & 0.3 \\  & FI & 1.6 & 73.1 & 0.3 \\ \hline \multirow{2}{*}{NTNU MANULAB} & CI & 1.9 & - & 0.7 \\  & FI & 2 & 71.5 & 0.7 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Path loss models’ parameters. PLE, path loss exponent.  
Figure 8: Path loss models in (**a**) the Manufacturing hall at Dokka Fastners AS, (**b**) the Test room at Dokka Fastners AS, and (**c**) NTNU MANULAB. FSPL, free space path loss; CI, close-in free space reference distance; FI, floating intercept.
## 5 Conclusions  
In this paper, a novel biomimetic design and modeling method based on L-systems distributed along the PSLs' directions has been proposed. Numerical simulations and parametric optimization schemes based on an L9 DOE sensibility study were conducted to prove the effectiveness of this method in adapting mechanical structures to various loading cases as well as ensuring a good stiffness-to-weight ratio. The structures studied were all fixed at their lower boundary. Although the proposed method could be applied to any load cases (flexural, torsional, and impact), only three loading cases were considered: compression (CL), shear (SL), and combined compression/shear (CSL). Each load case presented two structures derived from the two significant PSLs' directions. Indeed, the DOE sensibility study helped us identify the optimal structure for each load case:  
* For the compressive load case (CL-2), the weight reduction was estimated between 74 and 84% with respect to the initial rough geometry, and the maximal von Mises stress was equal to 5.61 MPa, a value much lower than the yield stress of VeroWhite material (49.9 MPa).
* For the shear load case (SL-1), the weight reduction was in the same range as the one of the compressive load case, and the maximal von Mises stress was equal to 17.04 MPa.
* For the combined compressive/shear load case (CSL-2), the weight reduction was between 75 and 84%, and the maximal von Mises stress was equal to 32.22 MPa.  
The latter results of the conducted numerical simulations have demonstrated that this method helped reinforce the structure, thus obtaining high specific strength while reducing its weight. This study also highlighted the need of producing lightweight and stiff structures by AM since it was developed in a way to respect AM constraints and build orientations. In future horizons, this generative design method will be matured in terms of designing and optimizing 3D complex models and in conducting a combined parametric and topology optimization scheme. In addition, future work will focus on different loading conditions, especially impact study cases.  
Conceptualization, M.A.K., N.L., F.D. and S.R.; methodology, M.A.K., N.L., F.D. and S.R.; software, H.B.; writing--original draft preparation, M.A.K.; writing--review and editing, N.L., F.D. and S.R.; supervision, N.L., F.D. and S.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This study did not report any data.  
The authors declare no conflict of interest.
# Fraud Detection Using Neural Networks: A Case Study of Income Tax
## 4 Results and Discussion
### Data Description and Pre-Processing  
The used data in this research was obtained from the Rwanda Revenue Authority, the national body in charge of tax collection in Rwanda. The dataset consisted of 7840 audited taxpayers from across the country, with 1655 (21.1%) found guilty of fraud and 6185 (78.9%) cleared of any tax fraud. Each taxpayer was identified by features such as Province, Business scale, Sector, Business origin Department, Operation time and others. For feature exploration, Table 14 provides a comprehensive view of the dataset and the important features in it. There were no missing or duplicate values in our data. There was no outliers since our data set was composed of categorical variables except one variable that indicates the difference in time from when a business was registered to the time of audit that was composed of integers.  
As some of the features, such as scale, department, and others, are categorical, it was necessary to create a dummy variable that would allow those features to be used in a model. Dummy variables are variables derived from qualitative or logical propositions that are not numerical in nature. They can only hold numerical values of 0 or 1, representing qualitative values by switching various parameters on and off [37]. Example: The Business Scale (Scale) feature holds values such as Large, Medium, Small, and Micro, which result in four dummy variables, namely Scale_large, Scale_medium, Scale_small, Scale_micro, of which only one is on (1) and the other three are off (0) based on business Scale. Dummy variables representing each category were created for the categorical variables, increasing the number of features from 11 to 54.  
There was an imbalance in our data because, out of 7840 data points of taxpayers, 6185, or 78.8%, were labeled as non-fraudulent, while only 1655, or 21.2%, were labeled as fraudulent. To address such issues, a combination of both under-sampling and over sampling methods were used, namely the Random Under sampling(RUS) and the synthetic minority over-sampling technique (SMOTE). This is because the dataset is quite small and dropping a lot of majority observations would result in a significant loss of information needed in a model while only oversampling would increase the likelihood of overfitting.  
RUS is straightforward under-sampling method that randomly removes data points from the majority class in order to balance the dataset [38]. While using SMOTE, each minority class sample is over-sampled by introducing synthetic examples similar to \(k\) minority class nearest neighbors. Neighbors from the k nearest neighbors are chosen at random depending on the amount of oversampling required [39].  
After resampling, the remaining dataset was of 4000 data points which was divided into training and testing ratios of 80% and 20%, respectively.
# Mid-State Kalman Filter for Nonlinear Problems
## 4 Nonlinear Filter Performance Evaluation
### Normalized Estimation Error Squared  
Normalized Estimation Error Squared (NEES) gives a more accurate quantitative evaluation of the consistency of the filtering algorithm. Literature [22] gives a relatively standard definition: for the confidence level \(\alpha(0\leq\alpha\leq 1)\), when the difference between the real error information and the error information calculated by the filter is not statistically significant, the filter can be considered to be consistent at the level \(\alpha\). The definition is as follows:  
\[\epsilon_{k}=\text{NEES}=(x_{k}-\hat{x}_{k|k})^{T}P_{k|k}^{-1}(x_{k}-\hat{x}_ {k|k}) \tag{34}\]  
When \(P_{k|k}\) is equal to the actual mean square error matrix, \(\epsilon_{k}\) follows the distribution \(\chi^{2}\) with degree of freedom \(n_{x}\), and \(n_{x}\) is the state dimension. In this case, the consistency of state estimation is transformed into a \(\chi^{2}\) test problem. Most of the existing filter performance evaluation is based on the Monte Carlo simulation and the Arithmetic-mean Normalized Estimation Error Square (ANEES) using \(N_{m}\) simulation results defined as follows:  
\[\zeta_{k}=\text{ANEES}=\frac{1}{N_{m}}\sum_{i=1}^{N_{m}}e_{k}^{i}/n_{x}=\frac{ 1}{N_{m}n_{x}}\sum_{i=1}^{N_{m}}e_{k}^{i} \tag{35}\]
# Design and Implementation of Virtual Security Function Based on Multiple Enclaves
## 4 System Design
### System Structure
#### 4.1.1 Trusted Part  
The trusted part is mainly composed of an overall VNF module, configure and local attestation. The configuration file stores the configuration information of the virtual network function. The Manager can read the configuration file through call and parse the file according to the file. The local attestation module is mainly responsible for the authentication between multiple enclaves to ensure that it runs on the same platform, which is also responsible for the transmission of information between multiple enclaves. This module also provides a cryptographic function library to encrypt and decrypt sensitive packets.  
In addition, an overall VNF consists of multiple modules. In our architecture, the sensitive modules of a VNF should run in the different enclaves. This architecture has three advantages. Firstly, it can keep sensitive modules secure and prevent sensitive data leakage. Secondly, it can increase the scalability of the system. Thirdly, since our architecture decomposes an overall VNF into several separately deployable and smaller elements, different elements can be reused to reduce code redundancy, which can further reduce the size of enclave usage. When enclaves are larger than the total memory available to the enclave page cache (EPC), EPC paging provided by SGX can evict the rarely used memory pages to DRAM pages outside the PRM (processor reserved memory) range with encrypted mode. As shown in Figure 3, the sensitive modules of IDS, such as DNSAnalyzer, HTTPAnalyzer, FTPAnalyzer, and packet classifier, are running in different enclaves. These enclaves are freely combined through the Click configuration language and stored in the configure module. The enclave also includes a Handler module. The Handler includes a read Handler and a write Handler. The read Handler can be used to read the information in the enclave. The write Handler can be used to dynamically modify the configuration parameters inside the enclave.
# A Review of Test Stimulus Compression Methods for Ultra-Large-Scale Integrated Circuits
### Increase in Test Power Consumption
Compared to previous reviews, as shown in Table 1, the contributions of this paper are as follows:  
1. Based on the existing classification structure, this paper summarizes the latest research and proposes a new category, Enhancing Encoding Compression Efficiency, for subsequent scholarly research and reference. 2. The paper analyzes the four mainstream compression methods from five aspects--Principle, Dominance, Limitation, Application Scenarios, and Compressed Objects--providing a reference for subsequent scholars in selecting compression methods for testing. 3. From the perspective of the interplay between the four methods, as shown in Figure 2, the paper organizes the application scenarios and compressed objects of these methods, offering a reference for scholars to adopt a hybrid approach of multiple compression methods. 4. The paper thoroughly reviews and discusses the latest research under this classification method and presents the advantages and development of various sub-methods within this classification framework. Figure 2: Schematic diagram of test stimulus compression techniques.
## 3 Methodology
### Data Preprocessing
#### 3.2.2 One-Hot Encoding  
NSL-KDD and UNSW-NB15 contain nominal features that are not directly compatible with classification models, such as \"protocol_type\", \"service\", and \"flag\". Therefore, it is imperative to convert these nominal features into numerical features. One common approach to achieve this conversion is through one-hot encoding. In our study, we utilize the \"get_dummies\" function from the Python pandas library to facilitate the one-hot encoding for the nominal features. By using the \"get_dummies\" function, binary vectors are generated for each nominal feature, representing each potential feature value with a distinct binary vector.
## 2 Results and Discussion
### XRD Analysis  
Figure 2 shows the XRD patterns for pristine TiO\({}_{2}\), TP0 (immobilized TiO\({}_{2}\) DSAT) and TP2 (immobilized TiO\({}_{2}\)/PEG DSAT) samples. All peaks were detected as anatase and rutile phases. There is no phase transformation occurring in TP0 and TP2, since the immobilization processes of TP0 and TP2 were prepared under low temperature (120 \({}^{\circ}\)C). Phase transformation in the presence of organic binder was observed when the calcining temperature happened at 900 \({}^{\circ}\)C [42]. [PERSON] et al. [43] also found that the phase transformation for TiO\({}_{2}\) calcined without organic binder occurred at a temperature of 700 \({}^{\circ}\)C onwards. All peaks for all samples correspond to the characteristic peak of the anatase and rutile phases of TiO\({}_{2}\) nanoparticles detected at 20 from 15\({}^{\circ}\) to 65\({}^{\circ}\) by using low angle XRD. The XRD pattern in all samples shows different crystallinity as the sharp diffraction peaks displayed the good crystallinity of the prepared nanoparticles [44]. Pristine TiO\({}_{2}\) has the highest crystallinity as compared to TP0 and TP2, where TP2 is the lowest. This is due to the presence of DSAT and DSAT + PEG in TP0 and TP2, respectively. The increased porosity in both immobilized samples is the main factor for low crystallinity and broad peaks, as reported by [PERSON] [45]. In short, no phase transformations occurred for any of the samples prepared under low heat temperature.  
Figure 1: FTIR spectra of different percentages of polyethylene glycol (PEG) in immobilized TiO\({}_{2}\)-PEG.  
Figure 2: XRD patterns of pristine TiO\({}_{2}\), TP0 and TP2 samples.