esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Box-Behlen Experimental Design and Statistical Analyses  
The experiment was performed on the basis of the Box-Behlen experimental design in the Design-Expert v13, Stat-Ease, Minneapolis, MI, USA). A three-level, three-factor BBD plan was used to determine the best combination of cannabis inflorescence extraction variables. Particle size (X\({}_{1}\)), extraction time (X\({}_{2}\)), and ultrasound intensity (X\({}_{3}\)) were independent variables, whereas the dependent variables were the total flavonoid content and the total anthocyanin content.  
The experiment comprised a total of 15 combinations, including 3 center points to estimate the pure error, and was carried out in randomized order. All experiments were performed in triplicate. The levels of the three factors evaluated in the design are listed in Table 1.  
A generalized, second-order polynomial model was used to explain the effect of the independent variables on each response of interest according to the following equation:  
\[\mathrm{Y}=\ \beta_{0}+\beta_{1}\ \mathrm{X}_{1}+\beta_{2}\mathrm{X}_{2}+ \beta_{3}\mathrm{X}_{3}+\beta_{11}\mathrm{X}_{1}^{2}+\beta_{22}\mathrm{X}_{2} ^{2}+\beta_{33}\mathrm{X}_{3}^{2}+\beta_{12}\mathrm{X}_{1}\mathrm{X}_{2}+ \beta_{13}\mathrm{X}_{1}\mathrm{X}_{3}+\beta_{23}\mathrm{X}_{2}\mathrm{X}_{3} \tag{3}\]  
where Y is the response variables (the total flavonoid content or the total anthocyanin content); X\({}_{1}\), X\({}_{2}\), and X\({}_{3}\) are the independent variables; \(\beta_{0}\) represents the constant; and \(\beta_{1,2,3}\); B\({}_{11,2,2,33}\), and \(\beta_{12,13,23}\) are the linear, quadratic, and interactive coefficients, respectively.  
The experimental data were assessed via analysis of variance (ANOVA). The statistical significances of the regression coefficients were checked with an F-test, and \(p\)-values less than 0.05 were considered significant.  
The optimal extraction conditions were estimated through [PERSON]'s desirability prediction tool, aiming at a maximum attainable response for each independent factor. The validity of the developed model was assessed by comparing the experimental values and the predicted values. Two additional independent experiments were conducted using theoptimal conditions estimated with the models for each dependent variable (separately), as well as one experiment for both variables.
## 5 IoT Platform Communication with IoT Sensors and Applications
### REST API and User Interface  
REST (the Representational State Transfer Application Programming Interface) is a system that can be integrated with any type of application, such as a mobile or web application, which is used as a connection point with the end-user or service client. The REST API is an interface that is used to exhibit various services that can generate, consume, and process data, which is required for the implemented definitions. It revolves around resources accessed by a common interface using standard methods and protocols. In the World Wide Web, the protocol used is the HTTP protocol, representing the security feature of REST API used to prevent unauthorized access via an authentication token platform to validate service requests. REST defines four major request or operation types, create, read, update, and delete, referred to as CRUD operations with the POST, GET, PUT, and DELETE request methods. Figure 11 shows the CRUD request and response flow between the client  
Figure 10: Optimized core IoT and NB-IoT data delivery network. The communication path through the EPC interfaces from the UE to the core IoT services.  
and the REST server. If a party fails to provide a valid token for the HTTP protocol request, the service will return an HTTP 403 error (forbidden) [45].  
CoAP is HTTP-like, so developers can work with any device on which CoAP is enabled, as they would with a traditional REST-based API. There are over 30 CoAP implementations in C, C++, Java, Python, JavaScript, and so on, all open-source. Figure 12 shows the client's interactions through the REST API and requests sent to the Oracle REST Data Service (ORDS).  
Simple HTTP interfaces can be refashioned using CoAP, which more importantly offers features for M2M connections. It is a one-to-one protocol that is used to transfer client and server states. **GET**, **PUT**, **POST**, and **DELETE** requests for the resources or services may be sent to the server from the client to be responded to.  
Four types of messages are defined by CoAP: **confirmable**, **non-confirmable**, **acknowledgement**, and **reset**. When a message is marked as confirmable (CON), then its reliability is assured. A message is confirmable if a default timeout is used until the recipient sends a message of acknowledgement. Some messages do not require reliable transmission, and as such are transmitted as non-confirmable or NON. Although not acknowledged, these messages have a mechanism for the detection of duplicate IDs. If the recipient cannot process a non-confirmable message, a reset message or RST reply may be sent [46].
## 4 Discussions
In order to determine the reliability and consistency of the algorithm furtherly, disparate starting nodes and end nodes cases are simulated, and several parameters, such as the path length, the number of expanded nodes, and the risk level of the path, are compared simultaneously, in which the depth risk level is calculated by Equations (11)-(13). All of the results are shown in Table 7. From Table 7 combined with Figures 12-14, it can be seen that the depth risk level of the path can be reduced, and the path is relatively short, especially in the area with complicated underwater topography, such as Figure 13 with WDLRA* algorithm, but in the cost of decreasing computational efficiency with redundant expanded nodes (see Figures 12c and 13c). This method can ensure the safety of small USV navigating in complicated sea areas. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Test**} & \multirow{2}{*}{**Start Point**} & \multirow{2}{*}{**Goal Point**} & \multicolumn{2}{c}{**Total Distance (m)**} & \multicolumn{2}{c}{**Risk Level**} & \multicolumn{2}{c}{**Nodes of Expanded**} \\ \cline{3-8}  & & **A*** & **WDLRA* & **A*** & **WDLRA* & **A*** & **WDLRA* \\ \hline
1 & (710, 902) & (990, 1408) & 15,667.5 & 17,319.5 & 38.86142 & 23.46830 & 37,682 & 102,283 \\
2 & (343, 975) & (417, 1066) & 3063 & 3063.0 & 5.68736 & 5.47936 & 1363 & 4469 \\
3 & (408, 560) & (520, 680) & 4457.75 & 4472.5 & 9.95472 & 7.03769 & 5851 & 7679 \\
4 & (200, 250) & (410, 530) & 9270.5 & 9255.75 & 18.68029 & 17.92211 & 21,407 & 41,840 \\
5 & (500, 889) & (650, 1039) & 5641.5 & 5405.5 & 14.43746 & 9.53375 & 2971 & 19,301 \\
6 & (500, 500) & (650, 650) & 5567.75 & 5567.75 & 7.39605 & 7.31804 & 7761 & 15,533 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of different starting and goal points. Figure 13: Comparison of three kinds path. **(a)** SDP that ignores the depth risk; **(b)** SSP that considers the depth risk level and distance. **(c)** SFP that considers only the depth risk. The colorful grids in figure **(a–c)** are extended nodes. The space of white grids represents the feasible space, and the black grids are obstacles. **(d)** Three paths, shown on the depth distribution map. **(e)** Three paths, shown on the depth risk level distribution map. **(f)** Three paths, shown on S57 ENC. The black line is the SDP, the blue one is SSP, and the purple one is SFP.
# A Distributed RF Threat Sensing Architecture
## 4 Test Environment and Experiment Setup  
### Test Environment  
The experimentation setup included the sensors developed for the purpose of this research, based on the logarithmic amplifiers and RF chain, as described in Section 3.1 and Figure 1, and an RF jamming system able to provide 48 and 45 dBm of EIRP RF jamming power at 2.4 and 5.8 dBm; the antennas of the system used were the directional antennas described in Section 3.3 and Figure 4. A DJI mini pro 3 drone, a real time spectrum analyzer, SMB200, from signal hound, and a security operation platform (SOC) based on OpenSearch were also used for collecting and visualizing the alarms. The SOC is designed around OpenSearch, which incorporates built-in machine learning capabilities, while visualization is achieved through an OpenSearch dashboard system. RF sensor alerts and raw data are securely sent through a VPN tunnel to the SOC, and over an MQTTs connection, where the SOC filters select fields from the alerts and raw data for the purpose of visualization. Sensor position and ID, timestamps, environment RF levels and RF jamming levels, alerts, and other information are sent over to the SOC.  
For testing the performance of our system, we selected a small airfield, as shown in Figure 7, within a fenced area of 500 m \(\times\) 500 m, which lies at the urban boundaries of a city. Such a scenario resembles a typical critical infrastructure area accommodating a power station, a data center, a telecommunications hub, or other critical infrastructures. It is not rare that these categories of infrastructures may lie at the proximity of an urban or semi-urban environment and they are typically fenced at a distance of several hundreds of meters to physically protect the critical assets.  
Figure 6: Visualized sensor data in a SOC.  
The sensor was deployed at the north barrier of the runway, with the Vivaldi directional antenna facing south towards the other end of the runway (azimuth 180\({}^{\circ}\), tilt 0\({}^{\circ}\), and vertical polarization).  
During the environment RF spectrum profiling procedure, the RF sensor recorded the \(min,max,average\) noise levels within a time slot of one hour before the execution of the experiment, as explained in Section 3.2.2. The values are presented in Table 3.  
The above RF noise measurements for this specific environment are much higher than the recordings performed within the lab, as per Table 4, where all indoor wireless networks were deactivated. This inevitably will result in degraded RF sensing capabilities, especially in the 2.4 GHz band.
# A Novel Machine-Learning Approach to Predict Stress-Responsive Genes in Arabidopsis
## 2 Materials and Methods
### Feature Selection Methods
#### 2.3.1 Least Absolute Shrinkage and Selection Operator  
LASSO is a regression-based feature selection method [22] commonly used in gene expression analysis. In this algorithm, a set of informative genes can be selected by shrinking the regression coefficient to zero in the linear regression model [23]. LASSO can be defined as follows:  
\[\min_{\beta_{0}},\ \frac{1}{\beta\mathrm{N}}\sum_{i=1}^{\mathrm{N}}\omega_{i} \mathrm{l}(\mathrm{y}_{i},\ \beta_{0}+\ \beta^{\mathrm{T}}\mathrm{x}_{i})+\ \lambda\left[\frac{(1-\alpha)||\beta||_{2}^{2}}{2}+\ \alpha||\beta||_{1}\right] \tag{1}\]  
where \(\mathrm{l}(\mathrm{y}_{i},\mathrm{n}_{i})\) is the negative log-likelihood contribution for observation i. \(\omega_{i}\) represents the weight for observation i and \(\mathrm{y}_{i}\) is the observed response for observation i, while the predicted response is given by \(\beta_{0}+\ \beta^{\mathrm{T}}\mathrm{x}_{i}\). The elastic net penalty in LASSO regression is controlled by \(\alpha=1\) (the default). The parameter \(\lambda\) is the tuning parameter that controls the overall penalty strength. It is known that LASSO tends to pick one of the coefficients of correlated predictors and discard the others. Further details on the principle and operation of LASSO analysis method can be found elsewhere [22]. In the present study, a 10-fold cross-validation was performed on the gene expression profile using the glmnet package in R (version 4.1.2), with alpha set to 1 and \(\lambda\) adjusted to select the optimal number of genes.
## 4 Results and Discussion
### Influence on and Absolute Error Analysis \(\theta(\eta)\)  
The results of the LMS-BPNN are examined using MATLAB software to explore the effects of changing the nanoparticles concentration (\(\phi_{1}=\phi_{2}=\phi_{3}\)) and thermal radiation (\(Rd\)) on \(\theta(\eta)\) with absolute errors, as shown in Figure 6. Figure 6 depicts the effects of changing the nanoparticles concentration (\(\phi_{1}=\phi_{2}=\phi_{3}\)) on temperature profiles (\(\theta(\eta)\)) with an absolute error \(10^{-12}\to 10^{-2}\) as shown in Figure 6. While Figure 6 shows the impacts of \(Rd\) on \(\theta(\eta)\) with absolute error \(10^{-8}\to 10^{-4}\) as depicted in Figure 6. The following impacts on temperature profiles are examined.  
* When concentrations of nanoparticles (\(\phi_{1}=\phi_{2}=\phi_{3}\)) are enhanced then the temperature of fluid raises these phenomena as shown in Figure 6.
* Thermal radiation has increasing effects on the temperature profile of ternary hybrid nanofluid.
## 3 Materials and Methods
### Processing Strategy  
GLONASS-only PPP time transfer performance in a static model were investigated in our work. Note that the solutions of GPS-only PPP, which use IGS final precise products, are used as the external reference values for comparison and validation. The GPS and GLONASS orbit and clock products with sample intervals of 5 min and 30 s, respectively, were provided by IGS, the Center for Orbit Determination in Europe (CODE) ([[ftp://cddis.gsfc.nasa.gov/gps/products/mgex/](ftp://cddis.gsfc.nasa.gov/gps/products/mgex/)]([ftp://cddis.gsfc.nasa.gov/gps/products/mgex/](ftp://cddis.gsfc.nasa.gov/gps/products/mgex/))), ESA (European Space Agency), and GFZ (German Research Center for Geosciences) ([[ftp://cddis.gsfc.nasa.gov/gps/products/](ftp://cddis.gsfc.nasa.gov/gps/products/)]([ftp://cddis.gsfc.nasa.gov/gps/products/](ftp://cddis.gsfc.nasa.gov/gps/products/))). The detailed processing strategies for GPS- and GLONASS-only PPP are summarized in Table 4. To verify the uncertainty of GLONASS-only PPP and the impact of IFCBs on GLONASS-only PPP-based time transfer, Bernese 5.2 software [27] is used for data processing. In addition, the standard deviation (STD) of the clock differences between GLONASS- and GPS-only PPP link solutions are used to assess the performance of GLONASS PPP-based time transfer due to the different reference clock of precise clock products [28].
## 2 Literature Review and Hypothesis Development
### Cultural Differences in the TAM and Privacy
In collectivist societies, social influence, conformity, and group-oriented motivations may override individual perceptions of utility when making technology decisions. Without accounting for such differences, cross-cultural applications of the TAM risk oversimplifying the complex interplay between technology adoption and culture. Building on this foundation, the present study addresses a relatively underexplored issue: how privacy concerns intersect with cultural values to influence ARSGs' adoption. While prior research has often focused on how privacy concerns affect personal risk perceptions--such as concerns about personal data security or invasions of one's own privacy [34]--this study extended the inquiry to examine privacy concerns regarding others' privacy (i.e., bystanders or third parties unknowingly recorded by ARSGs). This distinction is particularly important in collectivist contexts, where the protection of group harmony and respect for others' dignity may elevate sensitivity to third-party privacy violations. Privacy concerns may influence multiple pathways within the TAM. They may diminish perceptions of usefulness by making the technology seem socially problematic or ethically questionable. Privacy concerns may also undermine positive attitudes toward ARSGs' adoption by generating discomfort, fear of social disapproval, or ethical conflict. As a result, behavioral intention to adopt ARSGs may decrease even when individuals recognize the potential benefits. In collectivist cultures, concerns about violating others' privacy may amplify these effects. Understanding how privacy concerns interact with cultural dimensions in shaping TAM pathways is critical for developing more culturally sensitive models and interventions that promote responsible technology adoption across global markets. Therefore, this study investigated the following research questions:  
RQ1: How do privacy concerns impact U.S. and South Korean participants' perceptions of ease of use, perceived usefulness, attitudes, and intention to use ARSGs? RQ2: To what extent do privacy concerns and perceptions of ease of use, perceived usefulness, and positive attitudes about ARSGs impact behavioral intentions to use ARSGs?
## 3 Proposed Optimal Orchestration Process
### Automation of Algorithm Selection  
To facilitate the orchestration process, the integration of LazyPredict, Auto Reinforcement Learning (AutoRL), and Ray Tune is introduced. These tools automate the selection of machine learning algorithms based on the changing environment, optimising the orchestration process.  
LazyPredict module automates the process of algorithm selection based on performance metrics. It is utilised by the Network Slice Requirements Analyser and Network Sub-Slice Mapper components of the Slice Orchestrator to automatically evaluate and select the best supervised learning algorithms for their respective tasks. This approach significantly reduces the time and effort required for manual experimentation, analyses about 30 machine learning algorithms against the monitored environment, and allows for rapid identification of the most effective algorithms tailored to the current specific resource allocation challenges. The LazyPredict Python library used in the proposed framework can be modified to include future algorithms as new methods are developed and integrated into the Python ecosystem. This ensures that our framework remains relevant and adaptable to advancements in ML techniques.
# A Novel Effective Multipath Routing Technique Providing High Availability in Wireless Networks
## 2 Related Works
### Mp-Aodv
The main contributions of the proposed NDMR approach are as follows:  
* NDMR discovers and establishes two node-joint paths instead of many paths between a source and a destination in a wireless network to provide redundancy communications, resulting in reducing the number of network traffic frames sent between the source and the destination in the network compared with the existing multipath routing techniques. Figure 1: The route discovery process. * NDMR reduces duplicates of the control messages used for discovering and establishing paths between the source and destination nodes in wireless networks compared with the existing multipath routing techniques. * NDMR uses a two-way or three-way handshake route discovery process instead of four-way process, resulting in decreasing the route discovery period compared with the existing multipath routing techniques.
# Early Neighbor Rejection-Aided Tabu Search Detection for Large MIMO Systems
## 3 Proposed Low Complexity TS Algorithms
### Complexity Analysis  
In this section, we analyse the overall computational complexity of the proposed ENR-QRT-TS algorithms and the conventional TS.  
_(1) Initialization:_ To obtain the \(\mathbf{Q}\) and \(\mathbf{R}\) matrix, we used the QR Householder method, which costs \(\frac{2}{3}N^{3}\) additions and \(\frac{2}{3}N^{3}\) multiplications [17]. The computation of the initial solution \(\mathbf{x}_{ZF}=\mathbf{H}^{\dagger}y=\mathbf{R}^{-1}\mathbf{Q}^{T}\mathbf{y}\) requires \(N^{2}+\frac{1}{2}N^{2}+\frac{1}{2}N\) multiplications and \(N^{2}-N+\frac{1}{2}N^{2}-\frac{1}{2}N\) additions. Computing the ML metric of the initial solution \(\phi(\mathbf{x}^{(c)})=\|\mathbf{Q}^{T}\mathbf{y}-\mathbf{R}\mathbf{x}^{(c)} \|^{2}\) required \(\frac{1}{2}N^{2}+\frac{1}{2}N\) additions and \(\frac{1}{2}N^{2}+\frac{3}{2}N\) multiplications. Therefore, the total computation cost of the initial solution is \(\frac{2}{3}N^{3}+2N^{2}-N\) additions and \(\frac{2}{3}N^{3}+2N^{2}+2N\) multiplications.  
_(2) Neighbor search:_ The computation of \(\mathbf{z}\) in (9) is only computed once in each iteration because \(\mathbf{z}\) is the same for all neighbors. Therefore, the computational complexity of computing \(\mathbf{z}\) is negligible and ignored in the following analysis. To find the best neighbor in (10) requires \(\sum_{i=1}^{L}(2N-1)L\) additions and \(2 NL\) multiplications an average for a conventional TS. In addition, the ENR algorithm requires an average of \(L(N+1)\) comparisons in each iteration. The fairness of our complexity assessment used the same contribution in the TS iteration for all variants of TS. It is worth noting that the computational load of the iterative searching process is significantly higher than that of the initial computation and dominates the overall complexity.  
_(3) Layer ordering:_ The layer ordering scheme is performed in steps 1-6. In layer ordering, computing \(|r_{i,d}|^{2},i=1,2,\ldots,d\) requires \(\frac{1}{2}d\log_{2}d\) comparisons and \(d\) multiplications on average using the quick-sort algorithm in [18]. Therefore, the total complexity of the layer ordering is \(\sum_{d=1}^{N}(d+\frac{1}{2}dlog_{2}d)=\frac{1}{2}(N^{2}+N+log_{2}\mathbb{H}( N))\) flops, where \(\mathbb{H}(\cdot)\) denotes the hyperfactorial function of \(\mathbb{H}\)[19]. The complexity cost of ENR-QR-TS is increases by employing the layer ordering method; however, the complexity reduction achieved by layer ordering is significant, as shown in Figures 4 and 5. The overall complexity of the conventional TS and QR-TS comes by only adding the initialization phase and iterative searching phase. In contrast, to compute the total complexity cost of the proposed ENR-TS algorithms are considered all three phases.
## 4 Experimental Setup  
To validate the proposed methodology, a road trajectory was conducted in Cairo, Egypt, within a suburban environment. The smartphone used for the experiment was securely mounted inside the testing land vehicle, as depicted in Figure 6, aligning with the vehicle's axes. The starting point of the trajectory was carefully selected, considering the initialization of the employed sensors, and the entire trajectory lasted approximately 8 min. The data collection process was carried out using the REALME RMX2030 smartphone, which operates on Android 11 and is equipped with a Qualcomm Snapdragon 665 SM6125 processor. The road test was performed using the fused location provider (FLP) trajectory data as a reference. The FLP system integrates multiple location technologies, including GNSS (multiple constellations), Wi-Fi, and network-based positioning, to provide accurate location information [55; 56].  
Figure 6: Mounting of the smartphone within the vehicle.
# A MILP Model for a Byzantine Fault Tolerant Blockchain Consensus
## 3 Formulation for the dBFT Consensus
### Goals to Be Optimized  
**Objective function values are calculated in each of the following variables:**  
1. \(B^{\prime}\)**:**: Number of** _blocks_ **relyaved considering all views and total simulation time.**
2. \(V^{\prime}\)**:**: Number of** _views/rounds_ **effectively used, which is the number of agreed view changes.**
3. \(C^{\prime}\)**:**: Number of** _communications_**, i.e., sent** \((PrepReg^{-}_{t,i,v}\)**,** \(PrepResp^{-}_{t,i,v}\)**,** \(Commit^{-}_{t,i,v}\) **and** \(ChView^{-}_{t,i,v}\)**) and received** \((PrepReg^{+}_{t,i,j,v}\)**,** \(PrepResp^{+}_{t,i,j,v}\)**,** \(Commit^{+}_{t,i,j,v}\) **and** \(ChView^{+}_{t,i,j,v}\)**) messages.**  
**The goal of Objective** 1 **is to guide if the model should maximize or minimize the numbers of produced blocks. When maximizing, if we want to ensure no sports, it should be limited to one. When minimizing, the desired value is also to be kept to one, which would result in a conclusion that says that liveness of the model is preserved.**  
**The parcel of Objective** 2 **guides the model to finish faster or slower, which would enforce more or fewer rounds, and it need** \(M\) **nodes to agree throughout** \(ChangeView\) **payloads.**  
**Finally, Objective** 3 **brings more chaos to the system by maximizing the number of received message, as well as optionally minimizing it.**  
**Weights** \(\omega_{1},\omega_{2},\omega_{3}\) **are, respectively, associated with these objectives functions and varying these weights can results in different optimal solution, as explored in** **[**27, 28**]****.**