esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Validation of the Damping Estimation Methodology
### Validation of the Application of the Prony Method
#### 3.3.2 Dynamic Analyses with Train-Track-Bridge Interaction Methodology  
The numerical analyses were carried out though a train-track-bridge interaction methodology developed and validated in [49], in which the Alfa Pendular train crossed the Casalheira bridge under different speed and track condition scenarios. Details from the Alfa Pendular model can be found in [PERSON] et al. [28]. The scenarios consisted of two speeds, 135 km/h and 165 km/h, and four different types of vertical irregularities measured by the inspection vehicle EM120 from the Portuguese infrastructure manager corresponding to measurements carried out between 2018 and 2020, as depicted in Figure 8 (location of the bridge highlighted by the grey area).  
Figure 7: Casalheira bridge: (**a**) numerical model, (**b**) first vibration mode, (**c**) second vibration mode, and (**d**) Rayleigh damping curve applied for this bridge.
## 2 Materials and Methods
### Species Distribution Models (SDMs)  
Maxent (version 3.3.3k; [[http://www.cs.princeton.edu/~schapire/maxent/](http://www.cs.princeton.edu/~schapire/maxent/)]([http://www.cs.princeton.edu/~schapire/maxent/](http://www.cs.princeton.edu/~schapire/maxent/)) (accessed on 10 July 2022)) was used to model the current and future potential distributions of the nine IAPS based on the native and invasive ranges around the world at four spatial scales (2.5, 5.0, 10.0, and 30.0 arcminute resolutions). Maxent was used to estimate the latent distribution functions for nine IAPS based on the maximum entropy and then modeled their geographic locations based on environmental variables. In maps predicted using Maxent, cells with a value of 1 have the highest distribution probability, and those with a value of 0 have the lowest. Furthermore, potential distribution areas were determined in relation to the areas where climate conditions of the study region were similar to the sites where occurrence localities were already recorded [8; 26]. In this way, the computed result reflected the possibility of potential distributions used to evaluate the risk of IAPS for protected areas [18; 27].  
Four-arcminute-resolution current and future climate variables were used as environmental input layers for current and future data in Maxent. To improve the accuracy of Maxent, a 10-fold cross-validation method was used to divide the present dataset into 10 roughly equal partitions, nine of which were used to train the model, and the 10 th of which was used to generate SDM estimates [8]. The regularization multiplier (beta) was set to 2.0 to produce a smooth and general response [28]. The maximum number of background points was 10,000 using automatic features; other values remained defaults. We used the jackknife method to test the importance of bioclimatic variables [1; 26].  
The receiver operating characteristic (ROC) curve uses each value of the predicted outcome as a possible judgment threshold. We used the area under the ROC curve (AUC) to evaluate the performance of the Maxent model. When the randomly selected background points were removed from the dataset, this statistic treats each estimate as a possible threshold based on the corresponding sensitivity and specificity. The SDMs predicted the possibility of potential distribution of invasive species with high accuracy. A higher AUC indicates a better performance by the SDM [1; 19; 26].
# A Comprehensive Review on Music Transcription
## 4 Challenges for Music Transcription
### Pitch Ambiguity  
Some audio segments may have multiple possible interpretations, leading to ambiguity in choosing the correct pitch or note. This phenomenon can occur due to various reasons, such as harmonics, overtones, noise, or the limitations of pitch estimation algorithms [90; 91]. Pitch ambiguity poses a significant challenge for accurate music transcription because it can lead to incorrect or uncertain note identifications. Musical sounds often consist of a fundamental frequency and its harmonics. The presence of strong harmonics can lead to ambiguity in pitch estimation, as the algorithm may detect multiple potential fundamental frequencies that align with different harmonics.
## 3 Materials and Methods
### Synthetic Data Generation  
Synthetic data generation is the creation of artificial datasets that mimic the characteristics of real-world data in structure, statistical properties, and complexity, without containing actual sensitive or personally identifiable information. This technique is commonly used to protect privacy, enhance data availability, and facilitate model training and validation, without solely relying on original data [37].  
Given the limited number of patient records in both datasets, we chose to bolster their size and diversity by incorporating synthetic data generation. To achieve this, we utilized the Gaussian Copula Synthesizer (GCS), a versatile open-source tool renowned for its efficacy in generating synthetic datasets [38]. Prior to synthesizing the additional data, we created tailored metadata tables for each dataset. Employing the SDV single-table metadata method, we emulated the structure and attributes of the original datasets. Subsequently, a validation process was undertaken to verify the fidelity and compatibilityof the created metadata with the actual data frames. With the metadata curated, we sampled synthetic records in GCS to construct dataset-specific models. These models, informed by the corresponding metadata and a normal distribution, were trained on the original datasets. The data augmentation process was carried out on the training data for both datasets, meaning on 70% of the original size. In the case of the training set of the AF dataset, which initially comprised 58 records, a GCS model was fitted to generate 1000 synthetic records. For the training set of the CE dataset, which originally contained 23 real records, a GCS model was trained to produce 100 synthetic patient records.  
The choice of synthetic data ranges was motivated by two key considerations:  
* Minimum number of records for generalization: To ensure the deep learning model was able to generalize well, we needed a dataset size sufficient to capture the variability in the underlying data distribution. Based on previous empirical experiments, a minimum of 100 records was found to provide enough variability to train a model that avoids overfitting while maintaining predictive accuracy. This ensures the model can generalize effectively to unseen data.
* Maximum number of records to optimize model size: Given that the trained model was intended for deployment across multiple devices, we aimed to limit the number of records, to keep the model size manageable. Generating excessively large datasets, such as 10,000 records, can unnecessarily increase model complexity, resulting in longer training times and larger model sizes. This can hinder deployment efficiency, particularly on devices with limited computational and storage capacities.  
For the AF model, 1000 records were needed to better generalize on the underlying HRV features; however, we only needed 100 synthetic records for the CE model, as general knowledge was already captured in the larger model of AF then transferred to the CE model.
###### Abstract  
The integration of the Internet of Things (IoT) with Wireless Sensor Networks (WSNs) typically involves multihop relaying combined with sophisticated signal processing to serve as an information provider for several applications such as smart grids, industrial, and search-and-rescue operations. These applications entail deploying many sensors in environments that are often random which motivated the study of beamforming using random geometric topologies. This paper introduces a new algorithm for the synthesis of several geometries of Collaborative Beamforming (CB) of virtual sensor antenna arrays with maximum mainlobe and minimum sidelobe levels (SLL) as well as null control using Canonical Swarm Optimization (CPSO) algorithm. The optimal beampattern is achieved by optimizing the current excitation weights for uniform and non-uniform interelement spacings based on the network connectivity of the virtual antenna arrays using a node selection scheme. As compared to conventional beamforming, convex optimization, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), the proposed CPSO achieves significant reduction in SLL, control of nulls, and increased gain in mainlobe directed towards the desired base station when the node selection technique is implemented with CB.  
robust optimization; internet of things; collaborative sensor; beamforming; antenna array
# An Efficient Approach for LBS Privacy Preservation in Mobile Social Networks
## 2 Related Work
### Continuity of LBS Privacy Preservation  
Sporadic privacy protection [8] and continuous privacy protection [26,27] are two important research respects. Sporadic privacy protection is often used for the case that users do not frequently use LBS services for a period of time. The schemes of sporadic privacy protection mainly focus on users' single process of privacy protection with LBSP, and apply to the situations when the degree of association between the two requests is low. Such as paper [28], a scheme named precision-based LPPM was proposed to reduce the precision of users' locations sent to LBSP. Continuous privacy protection is represented by trajectory privacy protection, which treats the problem of privacy protection from the view of users' trajectory [29]. The schemes of continuous privacy protection usually take a user's locations of a certain period as the object of protection, thus an adversary could use the spatial and temporal correlations on users' locations to infer their private information.
# PUE Attack Detection by Using DNN and Entropy in Cooperative Mobile Cognitive Radio Networks
## 5 Results
### Discussion  
The designed algorithms are an efficient combination of energy, entropy, DNN, and CSS models, which allow the detection system to obtain a PD of above 90% at \(-21\) dB of SNR in an actual SDR environment. Compared to our previous works, such as [9], our proposal demonstrates improved outcomes regarding noise, SNR, and PD while maintaining a fast method to release the channel and prevent interference with the PU.  
In the implemented model, there is no need to know previous information such as the modulation schemes, the threshold, or some earlier data similar to other solutions. The DNN is a high-speed method that, after a learning process, can detect PUE attacks. In combination with the CSS schemes, it increases the probability of detection by using the SUs as sensors and transmitting the individual detections to the FC. The use of entropy allows us to work with lower SNR values.  
Figure 14: Probability of detection in comparison with SVM and entropy.  
Figure 13: Example of the FC-or rule.  
As a future work, the attack and detection methods could be implemented in a 5G scenario using, for example, a 5G testbed, as mentioned in [33]. There can be experiments with some mobile technologies, CRN, attacks, and detection methods that can be compared with the results of our proposal. Another detection method could be to use the Boltzmann concept in a game strategy, as demonstrated in [34].
## 4 Discussion
### Developing and Detailing a Protocol for Isolation and Characterization of Gingipains
#### 4.1.5 Gingipain activity assay  
1. Prepare the gingipains sample of 20 \(\mu\)L and 50 \(\mu\)L for Rgp and Kgp activity assay, respectively, and dilute the samples with deionized water, making the total volume of 90 \(\mu\)L.
2. Add 100 \(\mu\)L of 2\(\times\) gingipain assay buffer to the 96-wells flat bottom microtiter plates.
3. Add 90 \(\mu\)L of prepared gingipains sample as test group; add 90 \(\mu\)L of deionized water as positive control; add 100 \(\mu\)L of deionized water as blank.
4. Mix the samples well by pipetting them up and down gently.
5. Incubate the samples for 10 min at 37 \({}^{\circ}\)C.
6. Add 10 \(\mu\)L colorimetric substrate, Bz-L-arginine-pNA (L-BAPNA), and Ac-Lys-pNA for Rgp and Kgp activity assay respectively.  
[Note: For positive control and sample group only]
7. Measure the absorbance at 405 nm.
## 3 Case Analysis
### Analysis of Cooling Load Characteristics and Influencing Factors
Regarding indoor parameters, the supply and return air temperature difference of the air handling units in the four zones shows the highest correlation with the supply air temperature, approximately 0.8. Considering that the supply and return air temperature difference is derived from the difference between the supply air temperature and the return air temperature, it serves as a more representative feature. Therefore, by comprehensively considering the influence of indoor and outdoor parameters, the final input features include \begin{table}
\begin{tabular}{c c c} \hline \hline
**Influencing Factor** & **Specific Content** \\ \hline Building structure & The physical structure of the building, such as the material properties of the enclosure and the insulation performance of wall materials, etc. \\ \hline Internal heat source & The internal heat sources of the workshop (such as the operation of production equipment, lighting systems, etc.), the number of employees, and their activity intensity. \\ \hline Outdoor meteorological parameters & Environmental factors such as outdoor temperature, humidity, ground air pressure, wind speed, and solar radiation intensity. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Influencing Factors of the Cooling Load in the Workshop. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature Name** & **Correlation Coefficient** & **Feature Name** & **Correlation Coefficient** \\ \hline Day of the Week & 0.110 & Average Supply Air Temperature in Zone C & \(-\)0.846 \\ \hline Temperature Differential Between Supply and Return Air of AHU in Zone D & 0.880 & Average Supply Air Temperature in Zone B & \(-\)0.789 \\ \hline Temperature Differential Between Supply and Return Air of AHU in Zone C & 0.921 & Average Supply Air Temperature in Zone A & \(-\)0.788 \\ \hline Temperature Differential Between Supply and Return Air of AHU in Zone B & 0.894 & Average Temperature in Zone D & \(-\)0.307 \\ \hline Temperature Differential Between Supply and Return Air of AHU in Zone B & 0.886 & Average Temperature in Zone C & \(-\)0.574 \\ \hline Average Return Air Temperature of AHU in Zone D & \(-\)0.141 & Average Temperature in Zone B & \(-\)0.361 \\ \hline Average Return Air Temperature of AHU in Zone C & \(-\)0.044 & Average Temperature in Zone A & \(-\)0.379 \\ \hline Average Return Air Temperature of AHU in Zone B & 0.203 & Outdoor Temperature & 0.463 \\ \hline Average Return Air Temperature of AHU in Zone A & 0.149 & Wet Bulb Temperature & 0.114 \\ \hline Average Supply Air Temperature in Zone D & \(-\)0.797 & Outdoor Humidity & \(-\)0.336 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Influence of Various Factors on the Cooling Load. the historical data of outdoor temperature, humidity, the supply and return air temperature difference of air handling units in the four zones, and the cooling load, totaling seven features, to predict future cooling load.
# Application of Neural Networks in Distribution of the Load in Cluster-Based Web Systems
## 5 Design of Executor Model
### Additional Input  
Then, we proposed adding a fifth input, which would implement a state memory of the module \(E\). An additional input was used for each neural network in several variants (Equations (7)-(9)):  
(1) the last predicted request (\(tp_{i-1}\)) response time was given to the input;  
\[tp_{i}^{k}=f_{i}\cdot q1_{i}^{k}+g_{i}\cdot q2_{i}^{k}+d_{i}\cdot q3_{i}^{k}+(1 -d_{i})\cdot q4_{i}^{k}+tp_{i-1}\cdot q5_{i}^{k} \tag{7}\]  
(2) the last request response time (\(tr_{i-1}\)) was given to the input;  
\[tp_{i}^{k}=f_{i}\cdot q1_{i}^{k}+g_{i}\cdot q2_{i}^{k}+d_{i}\cdot q3_{i}^{k}+( 1-d_{i})\cdot q4_{i}^{k}+tr_{i-1}\cdot q5_{i}^{k} \tag{8}\]  
(3) the difference between the response time (\(tr_{i-1}\)) of the last request and its estimated value (\(tp_{i-1}\)) was given to the input.  
\[tp_{i}^{k}=f_{i}\cdot q1_{i}^{k}+g_{i}\cdot q2_{i}^{k}+d_{i}\cdot q3_{i}^{k}+ (1-d_{i})\cdot q4_{i}^{k}+(tr_{i-1}-tp_{i-1})\cdot q5_{i}^{k} \tag{9}\]  
The diagrams of proposed neural networks are shown in Figure 10, while the simulation results are presented in Figure 11.  
Figure 9: The division into groups with a balanced total time of requests processing.  
The lowest value of the average response time for the load of 2500 clients was obtained for the fifth input with the last estimated request response time, and it was 0.50 s (marked as \(NN\) in the chart). This structure of neural networks was used for further research.
## 5 Results
To observe the variation of the deformation in the soil domain and examine the behavior in the _free-field_ conditions compared to the one observed at the caisson foundation, the vertical displacements were computed at the caisson top and at about 30 m from the caisson, as shown in Figure 8. It can be observed that the presence of the foundation modifies the seismic response at the surface, since its inertia contribute to reduce the vertical displacements. Moreover, the smaller vertical displacement observed at the caisson are due to the design choice of embedding the foundation in the stiff Marly formation. Table 3 shows the summary of the results obtained for the relative and residual horizontal displacement at the pier top for the four seismic inputs. A relevant effect of the SSI is apparent for all the four cases considered, with amplification of the horizontal relative displacement varying between 2.36 and 3.21. This amplification is quite similar for the three stronger seismic inputs, and decreases slightly for the weakest seismic input (n. 4). The same trend is also evident in Table 4, summarizing the results obtained in terms of horizontal displacements at the top (U\({}_{\text{top,k}}\)) and base (U\({}_{\text{bas,k}}\)) of the pier, for the four seismic inputs. Figure 8: Comparison between the vertical displacement time-histories computed for the 3D model, at the caisson top, and in free-field conditions. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & \(\mathbf{U_{rel,1.5}}\) (\(\mathbf{m}\)) & \(\mathbf{U_{rel,1.0}}\) (\(\mathbf{m}\)) & \(\mathbf{U_{rel,0.75}}\) (\(\mathbf{m}\)) & \(\mathbf{U_{rel,0.4}}\) (\(\mathbf{m}\)) & \(\mathbf{U_{res,1.5}}\) (\(\mathbf{m}\)) & \(\mathbf{U_{res,1.0}}\) (\(\mathbf{m}\)) & \(\mathbf{U_{res,0.75}}\) (\(\mathbf{m}\)) \\ \hline SSI & 0.027 & 0.025 & 0.019 & 0.009 & 0.0024 & 0.0020 & 0.0012 & 0.0003 \\ \hline No-SSI & 0.0084 & 0.0068 & 0.0058 & 0.0038 & – & – & – & – \\ \hline Ratio & 3.21 & 3.67 & 3.27 & 2.36 & – & – & – & – \\ \hline \end{tabular}
\end{table} 
Table 3: Results obtained by the two models, considering and neglecting SSI, in terms of relative horizontal (U\({}_{\text{rel,k}}\)) and residual (U\({}_{\text{res,k}}\)) displacements of the pier, for the \(k\) seismic inputs. Figure 7: Comparison between the relative horizontal displacement time-histories obtained by the two models, for the seismic input n. 1 (F\({}_{\text{a}}=1.5\)).
# Rank and Wormhole Attack Detection Model for RPL-Based Internet of Things Using Machine Learning
## 5 Conclusions  
This paper proposed the machine-learning-based classification model called MC-MLGBM for the detection of RPL-specific RA and SN-inherited WHA in an RPL-based IoT network. For our study, we have generated the static and mobility-induced datasets gathered in a pool called LIoTN-RPL using the Cooja simulator for training and testing the models. This dataset was generated by simulating various network models depending on the number of nodes, and state of the nodes. The LIoTN-RPL dataset was used as a benchmark for our model to perform multiclass classification. Extensive analysis was performed to test the proposed model and compare with the benchmark research including binary light gradient boosting machines as well as other classifiers. The confusion matrix was used for analysis and the derived metrics such as accuracy, precision, and recall were used for model performance evaluation. The results were promising and better than our benchmarks in terms of multiclass classification of RA and WHA, achieving average accuracy, precision, and recall of 99.7%, 99%, and 99.7%, respectively, for the multiclass LIoTN-RPL dataset. We have further validated our model using metrics such as cross-entropy (0.116), Cohn's kappa (0.93), and MCC (0.927) for multiclass classification and imbalanced class in the dataset to avoid biased accuracy results. The model outperforms overall when compared with benchmark research, binary classifiers, and multiclass SVM.  
Although the proposed model shows promising results, there are some limitations to our work. Further developments are required in the LIoTN-RPL dataset. Currently, it contains benign traffic data, RA, and WHA attack data. More attacks from both RPL-specific and SN-inherited categories need to be simulated and collected in the LIoTN-RPL data pool for diversification, and further model evaluation. The future research direction is inspired by this limitation and the current promising results that are obtained for two attacks. In future, more attacks from both categories of RPL attacks will be considered by designing attack models, conducting simulation studies, dataset generation, and detection using the proposed model.  
All authors have an equal contribution. All authors have read and agreed to the published version of the manuscript.  
Taif University Researchers Supporting Project number (TURSP-2020/98), Taif University, Taif, Saudi Arabia.  
Not applicable.  
Data Availability StatementData will be available on request.  
The authors declare no conflict of interest.
# The Cybersecurity Focus Area Maturity (CYSFAM) Model
## 2 Related Work
### Information Security and Cybersecurity Maturity Models
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Features** & **CYSFAM** & **C2M2 [30]** & **SSE-CMM [31]** & **NICE [32]** & **O-ISM3 [33]** & **ISFAM [15]** \\ \hline Cybersecurity-focused & Yes & Yes & No & Yes & No & No \\ \hline FAM or CMM & FAM & CMM & CMM & CMM & CMM & FAM \\ \hline Target sector & Generic & Critical infrastructures & Security engineering & Workforce & Generic & Generic \\ \hline Incorporates standards & Mainly & Partly & No & No & Partly & Mainly \\ \hline Analysis of interdependencies & Yes & No & No & No & No & Yes \\ between the processes/capabilities & & & & & & \\ \hline \end{tabular}
\end{table} 
Table 2: Comparison of the features of information security and cybersecurity maturity models. Incorporates standards: This feature shows whether the maturity model incorporates processes or capabilities derived from standards. _Mainly_ means that most of the capabilities or the processes are derived from standards. _Partly_ means some of the capabilities or the processes are derived from standards. As the development phases of all of the maturity models in Table 2 are not clearly known to us, our decisions for this feature were based on available information on the maturity model. Analysis of interdependencies between the processes/capabilities: This feature shows whether the maturity model provides an analysis of interdependencies between the processes and capabilities. The FAMs entail this feature by design, whereas CMMs only state that lower-level processes are to be implemented before higher-level processes. The characteristics of CYSFAM in comparison to existing alternatives can be seen in Table 2. CYSFAM has the following advantages: focusing on cybersecurity of generic organisations, being mainly based on standards, and showing the interdependencies between capabilities to facilitate implementation planning.