esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Microalgae Characterization  
_Chloella vulgaris_ (Cv) and _Scenedesmus armatus_ (Sc) were used in the experiments. Algae were cultured in 250 mL flasks at the irradiance of 80 \(\pm\) 5 \(\upmu\)mol photons m\({}^{2}\)/s in a climatic chamber, with the light/dark period of 12/12 h and at temperature 29 \(\pm\) 0.5 \({}^{\circ}\)C during the day and 25 \(\pm\) 0.5 \({}^{\circ}\)C at night in order to increase the biomass production. Cells in the exponential growth phase were used.  
The strains of unicellular green microalgae _C. vulgaris_ and _S. armatus_ were obtained from the Culture Collection of Baltic Algae (CCBA), Institute of Oceanography, University of Gdansk, Poland. Microalgae were used in two forms: suspended in solution and immobilized on alginate beads. In the present work, batch and repeated fed-batch culture cultivation techniques of _C. vulgaris_ and _S. armatus_ during autotrophic nutrition were performed.  
The growth rate of microalgal biomass (\(\upmu\)) was determined from the Equation (1):  
\[\mu=\frac{dX}{dt}\,\frac{1}{X} \tag{1}\]  
where: \(\upmu\)--growth rate of algal biomass, d\({}^{-1}\), X--biomass concentration, mg/L, t--time, days.
# Similarity Estimation for Large-Scale Human Action Video Data on Spark
## 4 Experiments and Evaluation
### Experimental Results and Analysis  
In order to validate the performance of the proposed feature descriptor, we have computed the mean average precision (mAP) for the proposed method. Figure 5 demonstrates precision result.  
Figure 4: Sample frames of the UCF (University of Central Florida) Sports action dataset.  
Figure 5: Mean average precision by applying our proposed method.  
We observed that MAP (Mean average precision) of horse back riding is the highest (0.89). MAP of biking and tennis swinging are 0.85 and 0.84, respectively. MAP of basketball shooting is the lowest (0.72) due to dataset complexity. We also compared the proposed ELP with LBP [17], MBP [20], LTP [18] and MTP [21] feature descriptors. Experimental results show that the proposed ELP shows better performance. Figure 6 demonstrates the performance of the proposed ELP.  
The MAP of our proposed ELP is 0.806. The proposed ELP shows competitive results compared with other feature descriptors. The MAP of ELP is a little bit higher than the MTP. MAP of LBP is 0.69, which is the lowest. However, DRLTP (Discriminative Robust Local Ternary Pattern) [26] shows the best results among all of them, since they contain both edge and texture information along with discriminative power.  
Furthermore, in order to guarantee the scalability of our proposed approach, we test the scalability of our proposed approach by implementing on the stand-alone, two nodes and three nodes, respectively. The computation time is 82,562 s when we process the whole computation on the standalone. The computation time is 50,037 s when we implement on two nodes. The computation time is 33,024 s when we implement our proposed method on three nodes. Figure 7 illustrates the scalability of our proposed approach when using three different nodes.  
Figure 6: Comparison between proposed Edge based Local Pattern descriptor (ELP) and other feature extraction methods using mean average precision.  
Figure 7: Scalability of human action similarity measure in Spark using three different nodes.  
Therefore, we can notice that the computation time decreases when the number of nodes increase. The computation time reduces dramatically when we run it on two nodes. The time reduction of applying three nodes is less than applying two nodes. We observe that both the size of datasets and the number of nodes determine computation time of processing in a distributed environment.
## 5 Data Analysis and Results
### Moderation Effects  
Hypotheses H9, H10, H11, and H12 argued that there is a significant difference in the respondents' information adoption (IA) due to age, gender, education level, and previous experience. An independent samples \(t\)-test was utilized in order to explore if there were any significant differences in the respondents' information adoption (IA) that can be attributed to gender. Additionally, the ANOVA test was employed to examine if there were any significant differences in the respondents' information adoption that could be attributed to age, education level, and previous experience. The results of the \(t\)-test, displayed in Table 8, indicate that there is a significant difference in the information adoption that can be attributed to gender, that goes for males more than females. In addition, the outcomes of the ANOVA test, presented in Table 9, indicate that there is a significant difference in the respondents' information adoption (IA) supportive of age, education level, and previous experience. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multicolumn{3}{c}{**Male**} & \multicolumn{3}{c}{**Female**} & \multirow{2}{*}{\(T\)} & \multirow{2}{*}{**df**} & \multirow{2}{*}{**Sig.**} \\ \cline{2-2} \cline{5-8}  & **N** & & & & & & & & \\ \hline Information & \multirow{2}{*}{293} & \multirow{2}{*}{4.8498} & \multirow{2}{*}{0.39219} & \multirow{2}{*}{411} & \multirow{2}{*}{3.6740} & \multirow{2}{*}{1.13601} & \multirow{2}{*}{19.423} & \multirow{2}{*}{537.458} & \multirow{2}{*}{0.000} \\ adoption & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The \(t\)-test of the respondents’ information adoption (IA) attributed to gender. Figure 2: Proposed model with results’ coefficient value, mean, and Std. Dev. reflected. However, Table 10 provides the statistical significance of the differences between each pair of groups for age. As observed in Table 10, the five groupings (i.e., 18 to less than 34, 34 to less than 44, 44 to less than 54, 54 to less than 64, and 64 and over) were statistically different from one another.
# A GNSS-Cellular Network Hybridization Strategy for Robust Positioning
## 4 Discussion
This direction aims to further enhance the positioning reliability and accuracy, especially in dynamic and GNSS-challenged environments. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15116300/s1](https://www.mdpi.com/article/10.3390/app15116300/s1)]([https://www.mdpi.com/article/10.3390/app15116300/s1](https://www.mdpi.com/article/10.3390/app15116300/s1)). Conceptualization, M.J.J.-M. and M.Z.H. ; methodology, M.J.J.-M. and M.Z.H. ; software, M.J.J.-M.; validation, M.J.J.-M., M.Z.H., A.M.F. and A.A.J. ; formal analysis, M.J.J.-M., M.Z.H., A.M.F. and A.A.J. ; investigation, M.J.J.-M., M.Z.H., A.M.F. and A.A.J. ; resources, M.J.J.-M. and M.Z.H. ; data curation, M.J.J.-M. and M.Z.H. ; writing--original draft preparation, M.J.J.-M.; \begin{table}
\begin{tabular}{l l l l l} \hline  & **Method** & \(\sigma_{\mathbf{E}}\) **(m)** & \(\sigma_{\mathbf{N}}\) **(m)** & \(\sigma_{\mathbf{U}}\) **(m)** \\ \hline Outdoor & Measurement level & 1.27 (GNSS) & 3.70 (GNSS) & 4.44 (GNSS) \\ Position level & 0.04 (GNSS) & 0.17 (GNSS) & 0.11(Hybrid) \\ Indoor & Measurement level & 4.81 (4G) & 0.51 (4G) & 22.80 (4G) \\ Position level & 1.19 (Hybrid) & 1.22 (Hybrid) & 0.73 (4G) \\ Urban canyon & Measurement level & 5.95 (GNSS) & 4.62 (GNSS) & 12.48 (GNSS) \\ Position level & 0.58 (GNSS) & 0.90 (GNSS) & 0.12 (4G) \\ \hline \end{tabular}
\end{table} 
Table 16: Overall performance of hybrid, GNSS, and 4G positioning methods in outdoor, urban canyon, and indoor scenarios. writing--review and editing, M.J.J.-M., M.Z.H., A.M.F. and A.A.J. ; visualization, M.J.J.-M., A.M.F. and A.A.J. ; supervision, M.J.J.-M., M.Z.H., A.M.F. and A.A.J. ; project administration, A.M.F. and A.A.J. ; funding acquisition, M.Z.H., A.M.F. and A.A.J. All authors have read and agreed to the published version of the manuscript. This research received no external funding. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The raw data used in this study were submitted to Applied Sciences as Supplementary Materials and are available from the corresponding author upon reasonable request. **Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Underwater Acoustic Sensor Network Models and Query Models
### Relational Underwater Sensor Network Database Model  
For better sensor network data management, many attempts have sought to apply database models to sensor networks. Relational databases serve as useful tools for systematized sensor network data management because of their simple data structure, suitability for queries, and simple manipulation languages [20]. In our approach we follow a similar concept.  
Any sensor network can be mapped to a relational database model. In our data model, each sensor node represents a relation, where each sensor and node ID is the relational attribute and the sensor value range is the attribute domain. Each node memory contains a number of ordered lists of sensor data at a certain instant. Each list is mapped to a tuple in a sensor node relation, and the collection of these relations is the sensor network database.  
In some of the relational sensor network database models, a sensor node keeps a partial history of recent sensor data. In these models old tuples are constantly updated and replaced by new ones, and queries extract data only from the remaining tuples. However, because underwater signal propagation is significantly vulnerable to water environments, there is an ample possibility that the requested data has already been deleted from the node relation before the query reaches the node. Also, difficulties in time synchronization make sensor node data timeline ineffective for underwater acoustic sensor network applications. Therefore, our network database model employs a single-tuple relation of current sensor data called \"current relation\". The current relation is updated whenever queries are assigned. For example, the sensor network in Figure 2(a) can be mapped to a corresponding relational database in Figure 2(b).  
Figure 1: Cluster-based underwater acoustic sensor network model.
# Improving Machine Learning Identification of Unsafe Driver Behavior by Means of Sensor Fusion
## 3 Method
### Feature Extraction  
Signals coming from in-vehicle and motion sensors have been divided into time windows and processed to extract descriptive features. For each signal, the following descriptors have been computed: average, maximum, median, standard deviation, skewness, and kurtosis. While average, median, skewness, and kurtosis can identify continuous misuse of a command or a tendency to drive constantly in a certain way, for instance, over the posted speed limits, the maximum value describes an instantaneous misbehavior. In addition, standard deviation can describe a high rate of change, which, on many occasions, implies aggressive behavior. For instance, a high standard deviation of the engine speed or of the car yaw reveals, respectively, fast and erratic engine control or rapid changes of the vehicle direction suggesting aggressive driving.  
Taking into consideration only gyroscopic and accelerometer signals from motion sensors, and considering each axis (\(x\), \(y\), and \(z\)) of these as a separate signal, we obtain a total amount of 24 features from in-vehicle sensors and 36 features from motion sensors. Each of these 60 features has been calculated over each 5 s time window with 50% overlap as proposed by [PERSON] et al. in 2010 [PERSON] et al. (2010) for a total amount of about 8500 records. Of these, about the 62% (corresponding to about 5300 records), have been labeled as \"safe\" and the remaining about 38% as \"unsafe\" (about 3200 records) as described in Section 3.4.
# Mean Reattachment Length of Roof Separation Bubbles Using Proper Orthogonal Decomposition
## 3 Methodology
### Analysis of the Pressure Eigenmode Using POD  
In this study, the pressure fluctuation component was subjected to POD to identify the dominant modes of pressure variability. For POD analysis, out of the 494 pressure taps shown in Figure 4, only 169 taps, evenly spaced at 60 mm intervals, were used. This selection was made to eliminate potential errors in the maximization of pressure projection, shown in Equation (2), caused by non-uniform pressure distributions.  
The pressure data were organized into a matrix, and a covariance matrix was computed to capture the relationships between pressures at different locations. The covariance matrix then underwent eigenvalue decomposition to obtain the eigenvalues and corresponding eigenmodes. The application of a covariance matrix in the POD analysis was essential for accurately representing the spatial interrelationships of fluctuating wind pressures on the roof. By utilizing the covariance matrix, correlations between pressure measurements at various locations were quantitatively analyzed, revealing how individual fluctuations contributed to the overall pressure pattern on the roof. This methodology played a key role in identifying the spatial dynamics of wind pressures, which are crucial for understanding roof separation bubbles and their reattachment.  
The POD eigenmodes identified distinctive pressure patterns associated with roof separation bubbles. The first three eigenmodes of the pressure under conditions of intermediate turbulence intensity (\(I_{u}\) = 12.7%) with two wind angles (\(\alpha\)) of \(0^{\circ}\) and \(5^{\circ}\) were given special attention, as they provided a comprehensive understanding of flow features over the roof, including roof surface separation and reattachment phenomena, vortex inside the separation bubble, and the roof corner's effects on flow.
## 2 Materials and Methods
### System Description
#### 2.3.5 Experimental Setup and Verification  
In light of the identified gaps in SHM applications, this article presents a comprehensive effort to bridge these gaps through the experimental exploration of a novel framework for synchronous wireless sensor networks. The research encompasses two primary experiments to validate the proposed framework: (i) synchronization validation and (ii) the application of Frequency FDD in Operational Modal Analysis (OMA) of a Three-Degree-Of-Freedom (3 DOF) system. The vibration signals are resampled based on the optimal warping path obtained through DTW, leading to an improved spectrum of the resampled vibration signal [69]. This enhancement contributes to the effectiveness of the proposed framework in SHM applications.  
The experimental setup for the proposed TCP/IP socket programming-based mimic broadcasting was evaluated through the experiment depicted in Figure 3. The main objectives of this experiment was twofold: first, to assess the system's capability in achieving initial time synchronization among multiple end nodes, and second, to evaluate its wireless control functionality using SSH. Furthermore, the application of this framework in SHM scenarios, specifically in the context of DTW adaptive resampling [70,71] for FDD [72,73], is thoroughly examined.
## 2 Materials and Methods  
### Processing of the Rapeseed Meal By-Product and Experimental Design  
In this study, all experimental procedures were conducted using three batches of RM as a by-product from the industrial production of a Bulgarian local company. The production of these RM residuals from rapeseed seeds via thermal treatment followed by oil extraction with hexane has already been described [35]. In order to promote a selective extraction of antioxidant bioactive compounds (e.g., phenolic compounds), the RM samples were processed into ethanol-wash solutes (EWS), and then doubly optimised (OEWS) by an ultrasound-assisted (UA) extraction performed following the steps reported in previous studies [36]. Briefly, the rapeseed meal was treated four times with an aqueous ethanol solution (75%), then the spent ethanol-washed liquids were collected, mixed, vacuum-concentrated (RV3 V Rotary Evaporator, IKA(r), Staufen, Germany), and lyophilised (Lyovac GT2, Leybold-Heraeus, Hanau, Germany) to recover powdery EWS. Distilled water was used as the extraction solvent of EWS powder at a ratio of \(1/50\) (\(w/v\)), at room temperature for 20 min, under constant stirring (220 rpm). After that, the ultrasound-assisted extraction (UAE) of this solution was performed with a SONOPULS (HD 2200.2, Bandelin, Berlin, Germany) ultrasonic homogeniser at 20 \(\pm\) 0.5 kHz frequency, with a KE 76 tip (Bandelin electronic GmbH & Co. KG, Berlin, Germany). The independent variables studied were amplitude (10-35%) and time (1-8 min). After the first ultrasound treatment, the samples were centrifuged at 9500\(\times\)_g_, at 4 \({}^{\circ}\)C for 5 min, and a second UAE was carried out under the same conditions.
# Stable Throughput Analysis of Heterogeneous Hybrid FSO/RF Networks with Cognitive Radio Capability
###### Abstract
Additionally, it defines a physical-layer signal representation to be used in the subsequent analysis and introduces the concept of queue stability. Section 3 analyzes the stable throughput for the reference system in SAS and SMT modes, respectively. Section 4 presents the stable throughput of the cooperative system, addressing both SAS and SMT modes. Additionally, this section investigates a hybrid SAS and SMT application and provides an analytical expression for stable throughput. In Section 5, we obtain the stable throughput of the economic system. Section 6 includes numerical evaluations alongside the analytical results for stable throughputs, offering comparisons between the proposed systems. Finally, Section 7 summarizes the main findings of the study. Appendix A provides the expressions for the rate of successful packet transmissions used in the stability analysis of the cooperative system.
###### Abstract  
The students' performance prediction (SPP) problem is a challenging problem that managers face at any institution. Collecting educational quantitative and qualitative data from many resources such as exam centers, virtual courses, e-learning educational systems, and other resources is not a simple task. Even after collecting data, we might face imbalanced data, missing data, biased data, and different data types such as strings, numbers, and letters. One of the most common challenges in this area is the large number of attributes (features). Determining the highly valuable features is needed to improve the overall students' performance. This paper proposes an evolutionary-based SPP model utilizing an enhanced form of the Whale Optimization Algorithm (EWOA) as a wrapper feature selection to keep the most informative features and enhance the prediction quality. The proposed EWOA combines the Whale Optimization Algorithm (WOA) with Sine Cosine Algorithm (SCA) and Logistic Chaotic Map (LCM) to improve the overall performance of WOA. The SCA will empower the exploitation process inside WOA and minimize the probability of being stuck in local optima. The main idea is to enhance the worst half of the population in WOA using SCA. Besides, LCM strategy is employed to control the population diversity and improve the exploration process. As such, we handled the imbalanced data using the Adaptive Synthetic (ADASYN) sampling technique and converting WOA to binary variant employing transfer functions (TFs) that belong to different families (S-shaped and V-shaped). Two real educational datasets are used, and five different classifiers are employed: the Decision Trees (DT), k-Nearest Neighbors (k-NN), Naive Bayes (NB), Linear Discriminant Analysis (LDA), and LogitBoost (LB). The obtained results show that the LDA classifier is the most reliable classifier with both datasets. In addition, the proposed EWOA outperforms other methods in the literature as wrapper feature selection with selected transfer functions.  
educational data mining (EDM); student performance; Whale Optimization Algorithm (WOA); feature selection; Sine Cosine Algorithm (SCA); ADASYN
# 5G-AKA-FS: A 5G Authentication and Key Agreement Protocol for Forward Secrecy
## 5 Formal Verification
### Formal Verification via ProVerif  
#### 5.2.1 Implementations and designs  
In this section, we formally verify the 5G-AKA-FS protocol using a well-known formal verification tool, ProVerif [23]. We designed the processes of User Equipment (UE), Serving Network (SN), and Home Network (HN) respectively at **UE**, **SN** and **HN** in ProVerif to verify its security properties. Our implementation also contains the process protocol that concludes the proof.  
We implemented the processes under [PERSON]'s attacker model, where the attacker can access the encrypted data only if it has the correct key to decrypt them. We largely adopt the settings that the implementation of [PERSON] et al. [29] is based on. The differences between ours and [PERSON] et al. are as follows:  
1. First, we newly define the symbolic process DHkey for the perfect forward secrecy, that is the Diffie-Hellman Key exchange protocol, in the implementation.  
Although our settings are similar to [PERSON] et al. [29], the actual internal processes are different as the internal processes of UE, SN, and HN are fairly different from those of [PERSON] et al.'s protocol. We implement those differences in our implementation.  
Under [PERSON]'s attacker model, we simplify some cryptographic primitives as symbolic functions. Particularly, the Diffie-Hellman key exchange protocol can be implemented as a symbolic function as follows:  
```
type pubKey. type secKey.... fun pk(secKey):pubKey. fun DHkey(secKey,pubKey):bitstring. equation forall sk1:secKey, sk2:secKey; DHKey(sk2,pk(sk1))=DHKey(sk1,pk(sk2)).
```  
For the symmetric key encryption/decryption, we utilized a symbolic function defined in [29] to implement the protocols in our 5G-AKA-FS. Symmetric key encryption/decryption means that a message \(m\) is encrypted by senc using a secret key \(n\). Then, the encrypted message senc(\(m\),\(n\)) only can be decrypted using sdec with the same key. It is defined as follows:  
```
fun senc(bitstring,bitstring):bitstring. reducforall m:bitstring,n:bitstring; sdec(senc(m,n),n)=m.
```
# Networking of Multi-Robot Systems: Architectures and Requirements
## 4 Communication Issues and Requirements for MRS Systems
### Types of Traffic Generated by MRS Systems and Associated Requirements  
Depending on the application involved, the QoS requirements of the data traffic generated and communicated by MRS systems can vary in significant ways. Such requirements include bandwidth, delay, and delay jitter in addition to other parameters. The protocols used by the R2R and R2I links must support these requirements to ensure timely and effective communication leading to proper operation and completion of the tasks that are performed by such systems.  
For example, MRS teams working on a common task usually require real-time communication with low delay tolerance. On the other hand, MRS teams that are responsible for data collection from wireless sensor networks (WSNs) or taking audio/video information for later analysis need networking protocols with high data rates while tolerating longer end-to-end delays.