esa-sceva/satcom-chunk-collection huggingface dataset:
# Mechanical Strength Study of a Cranial Implant Using Computational Tools
## 2 Materials and Methods
### Implant Geometry and Mesh  
The surface obtained after creating the hole in the skull was given a thickness of 4 mm, a decision made based on previous studies [26] where the influence of different thicknesses was evaluated using FEM under three loading scenarios. The implant thickness contained a chamfer to facilitate the assembly in the skull and 28 holes with 2 mm size were incorporated in the implant for the reason mentioned previously.  
The process of meshing the implant was performed with the software Hypermesh. Firstly, a 2D mesh was created on the exterior surface of the implant with square elements of approximately 1.2 mm in size. Next, an offset was made to create 3D elements. This offset consisted of four layers through the thickness of the implant, each layer being 1 mm in size. Since the geometry was not very complex, linear hexahedral elements were used to perform the mesh of the implant, given the fact that they allow for more accurate results. Reduced integration was employed to reduce the computational cost. Overall, the implant mesh contained 20,940 C3D8R elements and 27,285 nodes (Figure 2).
# Partially versus Purely Data-Driven Approaches in SARS-CoV-2 Prediction
## 6 Discussion and Conclusions  
Our study highlights the advantages and disadvantages of different modeling strategies related to the COVID-19 pandemic. With or the aim of forecasting the future number of confirmed, new, and death cases in three selected countries, two machine learning approaches were utilized and compared. The first approach, which is purely data-driven, aims at capturing previous data behavior and assumes that future patterns are just a repetition of the past without considering any human intervention. This approach failed to find any future zero cases for countries that did not experience any decline in their previous COVID-19 number curves. The second approach, which we refer to as partially data-driven, aims to build a model that can predict future cases by allowing some human intervention. This approach assumes that infected cases will reach their peak at specific time and will decrease to reach zero cases at a specific point. Utilizing this approach shows that zero cases are reached even for the countries that did not witness any curve reduction. Linear, exponential, and forecasting using exponential smoothing models are used for the first approach, and the SIR model is used from the second one.  
In the machine learning community, there is a general agreement that there is no one machine learning approach that will work best all the time. Taking into consideration uncertain situations like pandemics and with multivariate factors such as human restrictions, government policies, expert recommendations, and human behavior that are dynamically changeable, we tried to answer the question: \"Which machine learning approach will best predict the growth and trend of the COVID-19 pandemic?\"  
The concepts addressed here are still a work in progress, influenced by the rapid changes that are happening in the epidemiology and trajectory of COVID-19. Our study provides important comparative analytical and modeling approaches to the current pandemic, which would help push the envelope towards narrowing the choice for best modeling strategies for COVID-19.  
Conceptualization, S.A.S. and A.A.; methodology, S.A.S.; software, S.A.S.; validation, S.A.S.; formal analysis, S.A.S.; investigation, S.A.S. and A.A.M.; resources, S.A.S. and A.A.; data curation, S.A.S.; writing--original draft preparation, S.A.S.; writing--review and editing, A.A. and A.A.M.; visualization, S.A.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
[PERSON] received research grants from Pfizer and Akcea (paid to OHSU) and serves on an advisory board with Ionis. None of these have conflicts related to this work, and the other authors have no conflict of interest.
# A Survey of the Effects of Vehicle Configuration on Urban Air Mobility +
## 2 Aerial Urban Vehicle Configurations and Concepts  
Different configurations of AUVs have been introduced so far. Considering the different missions and their requirements, different concepts and configurations are applicable [2]. The most popular configurations in aircraft design are fixed-wings, VTOLs, and hybrids. However, there are about 27 different configurations in aircraft design, including the Magnus effect, moving mass control [15; 16; 17], cyclocopter, flapping wing, Coanda effect, tail-sitter, lighter than air, and others [15]. In the following sections, we review different AUV configurations, the successful examples, their advantages, and disadvantages. We consider three main categories for AUVs: horizontal take-off and landing, vertical take-off and landing, and hybrids. Based on the currently available AUVs, each category is divided into some sub-categories in order to provide a comprehensive and cohesive review. Figure 2 illustrates the classification of aerial urban vehicles.  
AUVs are classified as HTOL, VTOL, and hybrid configurations based on their flight mechanisms which directly influence their design, performance, and suitability for UAM applications. This classification is a well-known method used in aircraft systematic reviews [15] which offers significant benefits for understanding and studying AUVs. This classification is highly useful for several reasons. It enables performance analysis by allowing clear comparisons of key metrics like speed, range, and payload capacity, helping to identify which vehicle types excel in specific areas, such as HTOL for long-distance efficiency or VTOL for urban maneuverability. It also enhances application suitability by matching configurations to specific UAM needs. Additionally, it supports regulatory and infrastructure planning by clarifying distinct requirements, like vertiports for VTOLs and runways for HTOLs, ensuring that UAM systems are scalable, safe, and well-integrated into urban environments. This framework provides a structured approach to advancing UAM solutions while addressing diverse operational and regulatory challenges. We have tried to consider any available air taxis, personal planes, small aircraft, and conceptual designs of them in order to evaluate the role and importance of configuration in the design of AUVs. This approach was previously considered to analyze unmanned aerial vehicles in other papers [15; 18]. Based on published data, information, and companies' claims,  
Figure 2: Classification of aerial urban vehicles.  
we have tried to summarize the advantages and disadvantages of each configuration in different aspects.
# Variety Identification of Chinese Walnuts Using Hyperspectral Imaging Combined with Chemometrics
## 3 Results and Discussion
### Effective Wavelengths Extraction
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multirow{2}{*}{**Group**} & \multicolumn{4}{c}{**Calibration Set**} & \multicolumn{4}{c}{**Cross-Validation Set**} & \multicolumn{4}{c}{**Prediction Set**} \\ \cline{3-10}  & & **Ningguo** & **Lin’an** & **Total** & **Ningguo** & **Lin’an** & **Total** & **Ningguo** & **Lin’an** & **Total** \\ \hline \multirow{2}{*}{PLS-DA} & Ningguoo & 145 & 5 & 142 & 8 & \multirow{2}{*}{95.33\%} & 44 & 6 & \multirow{2}{*}{92.00\%} \\  & Lin’an & 3 & 147 & & 6 & 144 & & & \\ \hline \multirow{2}{*}{KNN} & Ningguoo & 135 & 15 & \multirow{2}{*}{90.00\%} & 129 & 21 & \multirow{2}{*}{86.67\%} & 43 & 7 & \multirow{2}{*}{79.00\%} \\  & Lin’an & 15 & 135 & & 19 & 131 & & & & \\ \hline \multirow{2}{*}{RBF-SVM} & Ningguo & 150 & 0 & \multirow{2}{*}{99.33\%} & 139 & 11 & \multirow{2}{*}{89.67\%} & 44 & 6 & \multirow{2}{*}{92.00\%} \\  & Lin’an & 2 & 148 & & 20 & 130 & & & & \\ \hline \hline \end{tabular} PLS-DA: partial least squares-discriminant analysis; KNN: \(k\)-nearest neighbor; RBF-SVM: radial basis function-support vector machine.  
\end{table} 
Table 2: Confusion matrices of the optimal PLS-DA, SVM and KNN models based on full spectra. (RMSECV) was used as an evaluation indicator for the number of sampling runs. The RMSECV value continued to decrease until the lowest value reached 296 and thereafter, the value increased. Consequently, the minimum RMSECV value at the 296 th sampling run was used to denote a combination of effective variables. The third subplot shows the regression coefficients path of all the 301 variables at different sampling runs. As the RMSECV values changed, the coefficient of variables that dropped to 0 could be considered as a feature wavelength. Therefore, based on CARS, 15 (402, 456, 458, 472, 474, 650, 812, 842, 868, 932, 952, 956, 962, 968, and 996 nm) out of 301 variables were selected. Further classification of Chinese walnut varieties will be performed using PLS-DA based on SPA- and CARS-selected wavelengths.
# SCADA System Testbed for Cybersecurity Research Using Machine Learning Approach
## 5 Attack Scenarios, Features Selection, and Evaluation Scenarios
### Evaluation Scenario  
After defining the dataset, the features were extracted as discussed in the previous subsection. Then, the data was labeled either as normal traffic or attack traffic. Following that, the dataset was split into training and test datasets. The training dataset was composed of 80% of the total data, and it was used to train our ML model. The test dataset consists of the remaining 20% of the data, and it was used to evaluate the performance of our trained ML model. We call this training and test phase as \"offline evaluation\", because the ML models were trained and tested offline. Figure 7 shows our evaluation scenario.  
After training and testing, the trained ML models were created and deployed in the network. Then, their performance was analyzed using real network traffic. This phase was called \"online evaluation\". We compared the results obtained from the two phases (offline and online). This is described next.
## 2 Materials and Methods
### Calculations
Nowadays, several mathematical models have already been developed, which, with some approximation, provide the required accuracy to define this phenomenon. Still, each of them has some limitations (level of turbulence, altitude, and distance). To summarizing, the effect of scintillation cannot be eliminated but can be minimized [35]. The OWC system availability depends on weather conditions and parameters, such as transmitted optical power, beam divergence, receiver sensitivity, or link path distance [36]. To visualize the influence of scintillation on the OWC system operating at two attractive wavelengths (1.5 \(\upmu\)m and 4 \(\upmu\)m), a mathematical model of such data link based on the analytical dependence described in [37; 38] was used. In Figure 2, data link ranges are shown for OWC systems providing SNR = 10 at different turbulence levels (\(C_{n}^{2}\) values). The parameters of these systems and beam propagation conditions were the same (a laser power of 600 mW, the four-inches diameter of receiving optics, beam divergence of 1 mrad, 3 \(\times\) 10\({}^{9}\) cm\(\sqrt{\mathrm{Hz}}\)/W-detector detectivity with 1 \(\times\) 1 mm\({}^{2}\) active surface, 700 MHz-signal bandwidth, and an extinction coefficient of 0.25 km\({}^{-1}\)). The estimated data range is above 1 km for both wavelengths with the advantage of 4 \(\upmu\)m, and it increases commonly if \(C_{n}^{2}\) parameter is lower than 10\({}^{-15}\) m\({}^{-2/3}\) (weak turbulence). The practical limitation of an OWC system is also a pointing error generated by moving its transceivers from the line of sight. This misalignment results from building sway and mechanical vibration caused by thermal expansions, strong wind, and weak earthquakes [39]. This effect decreases the average received signal and, consequently, increases the bit-error rate (BER). However, it is a common limitation for OWC systems.
# The Holistic Perspective of the INCISIVE Project--Artificial Intelligence in Screening Mammography
## 4 Results to the Existing Data Repository
### Federated Sharing
When authorized, INCISIVE users can perform queries on the data within the data-federated repository thanks to specific search modalities, such as the gender, age range, cancer type, cancer stage, months of observation, image modality, genomic data, treatment/therapy, datasets with full cases, data providers/research group, and dataset country of origin. The results retrieved from each database source in the federation are then aggregated and returned to the users who submitted the query. The data never leave the physical entities that hold it. Instead, the data are \"visited\" and only the computed answers to the query are brought back via the INCISVE search engine API to the user (see Figure 8). If the need arises, it is also possible for the data consumers to access and examine the data stored in each federated node, provided that all relevant legal and ethical issues are previously addressed. At the time of writing this article, the INCISIVE repository includes data from 7391 patients and 13,976 imaging examinations, covering four cancer types e.g., breast, lung, prostate, and colorectal cancer. The data come from nine data providers and five different countries, with the goal of reaching an amount of 20,000 imaging examinations over the next 2 years and much more in the long term. The first phase of the data collection in INCISIVE was the retrospective data collection. All nine partners with the role of providing data in the project collected and prepared their data based on the data preparation procedure described in the previous session. The current status of the data stored in the infrastructure per partner is described in Table 2. The table depicts the numbers in terms of patients provided, studies provided (study is defined as an imaging examination), annotated studies provided, and the actual DICOM images. The repository will constantly be populated with more data until the end of the project. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Partner** & **No of Patients** & **No of Studies** & **No of Annotated Studies** & **No of Images** \\ \hline AUTH & 25 & 85 & 71 & 36,345 \\ DISBA & 11 & 26 & 19 & 14,886 \\ GOC & 67 & 297 & - & 43,391 \\ HCS & 1950 & 2326 & 1309 & 10,326 \\ UNITOV & 11 & 615 & - & 30,331 \\ UNS & 1392 & 4704 & 666 & 908,580 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Current data storage status.
# LoRaWAN Path Loss Measurements in an Urban Scenario Including Environmental Effects
## 2 LoRaWAN Outline
### Spectrum Usage  
LoRaWAN operates in the industrial, scientific, and medical (ISM) bands, which are unlicensed in most countries, so users do not have to pay any fee for their utilization [34]. In the case of Europe, the accepted frequency ranges are from 863 MHz to 870 MHz, divided into 16 upload/download channels of BW equal to 125 kHz. In the case of America, the accepted frequency ranges are from 902 MHz to 928 MHz, divided into 64 uplink channels of BW of 125 kHz, eight uplink channels of BW of 500 kHz, and eight downlink channels of a BW of 500 kHz. Our dataset was generated in the approved band for America (US902-928). Because the ISM bands are intensively used, designers must attend duty cycle policies that demand to wait a period until a new transmission. For instance, in Europe, the duty cycles must be under 1%, and in America, there are no duty cycle limitations, but a transmission must not last more than 400 ms.
# Multivariate Analysis as a Tool for Quantification of Conformational Transitions in DNA Thin Films
## 2 Materials and Methods
### Multivariate Analysis
#### 2.2.1 Principal Component Analysis  
PCA is a multivariate statistical method used to reduce the dimensionality of a dataset to a new set of variables called principal component (PC) scores and loadings [21]. A PCA model is built so that the maximum variance of the dataset is captured: each principal component is calculated in a way to capture as much of the variation (within the dataset) as possible with the first PC capturing most of the variance, while each successive PC accounts for decreasing proportion of the variance. With the PC scores calculated, each sample within the dataset can be presented in the principal component space (PC score plot) with similar samples having similar PC scores and, consequently, similar positions in the PC score plots. Thus, intra-sample relationship and clustering of the data can be observed with the help of the PCA modeling. This is especially useful in dealing with spectra as they tend to comprise hundreds of variables; in our case, entire spectral range extends to 1150 wavenumbers, i.e., 1150 variables. However, with the PCA model hundreds of spectral features related to the variance within the dataset can be reduced to a set of several variables, for example scores on PC1 and PC2, and presented in a two-dimensional PCA score plot.  
The decomposition of data in the principal component space was done on pre-processed spectra in the entire spectral range from 1800 to 650 cm\({}^{-1}\); the data array includes absorbance values of all the wavenumbers in the named region. The used parameters for PCA analysis were: the singular value decomposition (SVD) algorithm, cross validation venetian blinds with 10 splits, and the generalized least squares weighting (GLSW) filter declutter threshold set to 0.02 (program default setting). GLSW is often used as a multivariate filter capable of removing background and interference signals by identifying unwanted covariance structures [22]. In preliminary studies, the number of principal components varied from 1 to 20; however, only the results for the models obtained using first four and five PCs were used in further analysis in order to avoid contributions from noise and overfitting. The obtained principal component scores were then presented in the PC1 versus PC2 score plot generated within the PLS toolbox without any alterations and enhancements.
# Vision-Based Surface Inspection System for Bearing Rollers Using Convolutional Neural Networks
## 4 Experiment
### Performance of the Surface Inspection System  
As the detection of a defect does not mean that a bearing roller fails, it is necessary to determine whether the roller is qualified according to the category, position, and area of the defect. In the following experiments, we inspected three different sized bearing rollers. We used the F1 score to evaluate the performance of the entire bearing roller surface inspection system. We obtained 1800 bearing rollers from the production line by manual screening, 600 for each size, including 300 qualified products (positive) and 300 unqualified products (negative). Then we used our surface inspection system to test these bearing rollers, and checked the precision and recall rate to calculate the F1 score. The F1 score was introduced in Section 4.1.2.  
To evaluate the actual performance of our surface inspection system, we compared our approach to the traditional method currently being used in the production line. The traditional method captured the images and adjusted the resolution to 500 \(\times\) 500, then it performed median filtering and divided the ROIs on the image, and then it performed threshold segmentation [39] and morphological processing in the ROIs to segment the defects. After the segmentation, defects were separated from the background. Finally, the traditional method determined whether the bearing roller was qualified by calculating whether the area of the defect exceeded the set threshold. The results of the comparison experiment are shown in Table 12 below.  
It can be seen from the results that the accuracy and recall rate of the traditional method, which was currently being used in the manufacturing, were lower than our method; the recall rate especially was very low. The main reason for this is that traditional methods can easily misjudge some non-defects (e.g., textures, oil stains, marks, etc.) as defects, so that some qualified products will be misjudged as unqualified, resulting in a low precision and a low recall rate. The recall rate and accuracy of our method were relatively higher because our method classifies defects well.  
\begin{table}
\begin{tabular}{c c} \hline
**Method** & **Micro F1 Score** \\ \hline GLCM + MLP & 75.53 \\ GLCM + SVM & 70.83 \\ HOG + MLP & 72.29 \\ HOG + SVM & 69.44 \\ Our method & 90.97 \\ \hline \end{tabular}
\end{table}
Table 11: Performance of classification using different methods.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Method** & \multicolumn{2}{c}{**Traditional Method**} & \multicolumn{3}{c}{**Our Method**} \\ \hline Diameter (mm) & 10 & 12 & 15 & 10 & 12 & 15 \\ Precision (\%) & 86.70 & 86.94 & 86.40 & 92.81 & 92.67 & 92.59 \\ Recall (\%) & 81.38 & 81.56 & 80.85 & 90.64 & 90.21 & 90.08 \\ F1 score & 83.96 & 84.16 & 83.53 & 91.71 & 91.42 & 91.32 \\ Detection time & 1.98s & 2.07s & 2.16s & 2.00s & 2.08s & 2.16s \\ \hline \end{tabular}
\end{table}
Table 12: Comparison between the traditional method and our method.
## 2 Broadband System
### Mobile Network Evolution
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Features** & **4G** & **5G** & **6G** & **Reference** \\ \hline \multirow{6}{*}{Technologies} & \(\bullet\) D2D communications & \(\bullet\) LDPC \& polar codes & \(\bullet\) AI/machine learning & \\  & \(\bullet\) OFDM & \(\bullet\) Flexible frame structure & \(\bullet\) SM-MIMO & \\  & \(\bullet\) Turbo code & \(\bullet\) Massive MIMO & \(\bullet\) Laser \& \(\bullet\) VLC & \\  & \(\bullet\) MIMO & \(\bullet\) Ultradense networks & \(\bullet\) LS and HBF & [223,225,226,229,231] \\  & \(\bullet\) Carrier aggregation & \(\bullet\) NOMA & \(\bullet\) OAM multiplexing & \\  & \(\bullet\) ICIC & \(\bullet\) SDN/NFV/network slicing & \(\bullet\) Blockchain-based spectrum sharing & \\  & \(\bullet\) Unlicensed spectrum & \(\bullet\) Cloud/fog/edge computing & \(\bullet\) Quantum communications \& computing & \\ \hline \multirow{6}{*}{Frequency bands} & \multirow{6}{*}{Sub-6 GHz} & \(\bullet\) Sub-6 GHz & \(\bullet\) Sub-6 GHz & \(\bullet\) Amb-wave for mobile access & [223,224,226,227,230, 231, 232, 233] \\  & & \(\bullet\) mm-wave for fixed access & \(\bullet\) Exploration of higher frequency and THz bands & [223,224,226,227,230, 231,233] \\  & & \(\bullet\) (0.0-10 THz) & & \\  & & & \(\bullet\) Non-8E (e.g., VLC, optical, etc.) & \\ \hline Reliability (\%) & 99.99 & 99.999 & [224,229,232] \\ \hline \hline \end{tabular}  
* IoT: Internet-of-Things; D2D: Device to device; ELDC: extremely low-power communications; AR: augmented reality; ERLLC: extremely reliable and low-latency communications; EDuRLLC:Event-defined uRLLC; uRLLC: ultra-high-speed-with-low-latency communications; MBRLLC: mobile broadband reliable low latency communication; mURLLC: massive ultra-reliable, low latency communications; uMUB: ubiquitous mobile ultrabroadband; dMBB: enhanced mobile broadband; uHD: ultrahigh data density; LDHMC: long-distance and high-mobility communications; FeMBB: further-enhanced mobile broadband; CAeC: Contextually agile dMBB communications; NFV: network function virtualization; mMTC: massive machine-type communications; UHD: ultrahigh definition; SDN: software-defined networking URLLC: ultra-reliable and low-latency communications; umMTC: ultra-massive machine-type communications; V2X: vehicle to everything VR: virtual reality; AI: Artificial intelligence; LDPC: low-density parity-check codes; KPI: key performance indicator; ICIC: Inter-cell interference coordination; HetNet: Heterogeneous network; mm-wave: Millimeter-wave; ILS: Large intelligent surface; HBF: Holographic beamforming; OAM: Orbital angular momentum; VLC: Visible-light communications; OFDM: Orthogonal frequency-division multiplexing; NOMA: Non-orthogonal multiple access; MIMO: Multiple-input multiple-output; SM-MIMO: Supermassive MIMO.  
\end{table} 
Table 7: _Cont._  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline