esa-sceva/satcom-chunk-collection huggingface dataset:
# From Brown-Field to Future Industrial Networks, a Case Study
## 2 Network Analysis and Modeling
### Objectives and Measurement  
Network traffic monitoring and analysis are generally categorized into four types of problems depending on the context of the network management objectives [6]. The state of resources and the relationship between them is the main focus in configuration management. The process follows a detailed view analysis, where communication patterns are studied for each pair of communicating components, i.e., source and destination. The purpose is to estimate the traffic volume for resource provisioning and capacity planning. Traffic matrix measurement with the general approach of network tomography and direct management is the analysis approach for this objective. This approach, with a detailed view of the system, can even assist in understanding the impact of fault on network capacity.  
A more abstract approach measures traffic volume to determine the total traffic of a network flow. This measurement is of use in network performance and security management to understand changes in traffic and identify abnormal flows. This type of measurement monitors traffic generated by any of the sources, using IP addresses and independent of possible destinations, and analysis packet counts and sizes for heavy usage.  
Networks are dynamic systems where the temporal variation of traffic represents their various operational states. Traffic dynamic measurement aims to monitor the dynamics and extract knowledge that can be used in configuration management for link capacity estimation and allocation. This type of measurement is also used in performance measurement to test the stability of the network. The monitored parameters usually include packet delay, packet loss and available bandwidth [6].  
A more recent approach in network monitoring and analysis aims to measure traffic mixture, i.e., aggregation of the detailed view of source and destination with the abstract view of traffic and temporal dynamics. The emphasis here is on the importance of extracted features in aggregated traffic data over time for performance and security managements. The extracted features with respect to performance are flow attributes such as delay, throughput or packet drop rate. In security management, frequent patterns in inter-arrival time and correlation attributes are extracted to model the flows and to detect anomalies [7; 9; 10; 13]. A comprehensive survey of research works for measurement and characterization of traffic network based on the objectives of analysis is presented in [6].
## 4 Results
### Impact of Obstacles  
Regarding the analysis of the measurement results for different obstacle materials (glass, concrete, and wood) at different distances (0.5 m and 2 m), several conclusions can be drawn. The average distances measured for glass and concrete are significantly greater than the actual distance, suggesting that waves undergo scattering or reflections when passing through these materials (Figures 9 and 10). The minimum and maximum distances differ from the actual distance, confirming that waves are scattered, reflected, or absorbed to varying degrees by different materials.  
Concrete appears to be a more effective barrier than wood, which is consistent with expectations given its higher density. Glass, despite its relatively small thickness, also demonstrates properties similar to those of concrete in absorption.  
At both distances of 0.5 and 2 m, there are significant outliers in the case of obstacles such as glass or walls. In both cases, wood shows values closest to the actual measurement (Figure 11).  
Figure 10: Box plots of obstacles made of different materials for measurement at a distance of 2 m.  
Figure 9: Box plots of obstacles made of different materials for measurement at a distance of 0.5 m.
## 5 Simulation
This algorithm seeks the best available server or node based on load and geographical proximity, helping to reduce latency and improve user experience. * Fewer servers and fewer fog nodes: In this scenario, the number of servers and fog nodes is reduced, testing the system's ability to manage load with less infrastructure while maintaining stability. * More servers and fewer fog nodes: This scenario increases the number of servers to support more clients while reducing the number of fog nodes, testing load distribution and latency behavior under these conditions. * Factors such as server load behavior, latency experienced by users (response time between user request and server), and system responsiveness under load were monitored. - In the load balancing scenario, fog nodes were invoked to handle load peaks, allowing for a more equitable distribution of users and a reduction in overall latency.
# Dynamic Analysis of Fuzzy Systems
## 3 Dynamic Analysis of Non-Linear Fuzzy Systems: First-Order Model
### First-Order Model  
A fuzzy dynamic model equivalent to the linear first-order dynamic model can be obtained by using the following logical operators in the fuzzy inference process; the product for conjunction, the product for implication, the sum for aggregation and the centroid for defuzzification [6,37].  
Using the Mamdani model:  
\[R^{(11)}:\text{IF $u$ is $N$ AND $x$ is $N$ THEN $\dot{x}=0$}\] \[R^{(12)}:\text{IF $u$ is $N$ AND $x$ is $P$ THEN $\dot{x}=-\frac{2}{\tau}$}\] \[R^{(21)}:\text{IF $u$ is $P$ AND $x$ is $N$ THEN $\dot{x}=\frac{2}{\tau  
The output of the system can also be computed by applying (3)  
\[\sum_{i_{1}=1}^{2}\sum_{i_{2}=1}^{2}\mathrm{w}^{(i_{1}i_{2})}(u,x) \dot{x}^{(i_{1}i_{2})} =\mu_{N}(u)\mu_{N}(x)\dot{x}^{(11)}+\mu_{N}(u)\mu_{P}(x)\dot{x}^{(12)}\] \[\quad+\mu_{P}(u)\mu_{N}(x)\dot{x}^{(21)}+\mu_{P}(u)\mu_{P}(x)\dot{ x}^{(22)}\] \[=\bigg{(}\frac{1-Ku}{2}\bigg{)}\bigg{(}\frac{1-x}{2}\bigg{)}(0)+ \bigg{(}\frac{1-Ku}{2}\bigg{)}\bigg{(}\frac{x+1}{2}\bigg{)}\bigg{(}-\frac{2}{ \tau}\bigg{)} \tag{12}\] \[\quad+\bigg{(}\frac{Ku+1}{2}\bigg{)}\bigg{(}\frac{1-x}{2}\bigg{)} \bigg{(}\frac{2}{\tau}\bigg{)}+\bigg{(}\frac{Ku+1}{2}\bigg{)}\bigg{(}\frac{x+1 }{2}\bigg{)}(0)\] \[=-\frac{x}{\tau}+\frac{Ku}{\tau}\]  
where:  
\[\sum_{i_{1}=1}^{2}\sum_{i_{2}=1}^{2}\mathrm{w}^{(i_{1}i_{2})}(u,x) =\mu_{N}(u)\mu_{N}(x)+\mu_{N}(u)\mu_{P}(x)\] \[\quad+\mu_{P}(u)\mu_{N}(x)+\mu_{P}(u)\mu_{P}(x) \tag{13}\] \[=\bigg{(}\frac{1-Ku}{2}\bigg{)}\bigg{(}\frac{1-x}{2}\bigg{)}+ \bigg{(}\frac{1-Ku}{2}\bigg{)}\bigg{(}\frac{x+1}{2}\bigg{)}\] \[\quad+\bigg{(}\frac{Ku+1}{2}\bigg{)}\bigg{(}\frac{1-x}{2}\bigg{)} +\bigg{(}\frac{Ku+1}{2}\bigg{)}\bigg{(}\frac{x+1}{2}\bigg{)}\] \[=1\]  
Which matches Equation (10).  
\[\dot{x}=-\frac{x}{\tau}+\frac{Ku}{\tau} \tag{14}\]  
Input and output scaling factors [38] will facilitate model fitting by modifying \(K,\tau\). Thus, the universe of discourse will be normalised within the interval \([-1,1]\), as shown in Figure 3.  
Figure 3: Fuzzy sets; (**a**) input set \(u\), (**b**) input set \(x\), (**c**) output set \(\dot{x}\) centre of gravity and (**d**) output surface between \(\dot{x},\ x\) and \(u\).  
The number of sets used in the input can be modified by applying scaling factors. For example, from 2 to 3 fuzzy sets, as shown in the output inference map in Figure 4 below.  
The scaling factors will be as follows for the input \(u=K\), at the output \(\hat{x}=\frac{2}{\tau}\) (see Figure 5).  
After obtaining an equivalent first-order fuzzy model, it is possible to modify the triangular function with linguistic hedges and thus adjust the obtained model. Usually, the model is adjusted by replacing the triangular membership functions with existing membership functions.
## 2 Materials and Methods  
### Optimization of VOC Collection from Simulatively Infested Spruce Logs in a Laboratory  
The experiments were conducted under laboratory conditions (25 \(\pm\) 1 \({}^{\circ}\)C, humidity 60% \(\pm\) 10%) using three fresh-cut logs (height of 40, diameter of 25 cm) of _Picea abies_ previously stored in a cold room (\(-\)5 \({}^{\circ}\)C). The experiment was repeated three times at one-week intervals. Prior of all repetitions, logs were acclimated for 24 h before volatile collection in the conditions described above. Logs were arranged vertically in piles from the floor: The lowest log represented level 1--L1 (20 cm), the middle log level 2--L2 (60 cm), and the upper log level 3--L3 (100 cm) (Figure 1a). To prevent unwanted VOC emissions from fresh-cut logs, the upper and lower exposed surfaces were covered with PE plastic film.  
The bark beetle attack was simulated by drilling holes (\(\circledapprox\) 0.3 cm deep) into the bark and phloem on the surface of the logs (100 holes per log, spaced in 5 cm spin) to mimic the production of VOC emissions during bark beetle infestation.  
VOCs were collected from each of the three levels (L1, L2, and L3) at two distances, 5 cm and 30 cm from the bark surface, using Solid-Phase Microextraction (SPME) fibers (PDMS/CAR/DVB, Supelco, PA, USA) as shown in Figure 1b. The SPME fibers were fixed in protective hard plastic cylindrical chambers hung in an open space (7.2 cm high, \(\circledapprox\) 5.2 cm, material PP, Merci, CZE), with an open bottom and a hole covered by septa in the upper lid to hold the fiber. To follow the distance and cardinal directions described above, the chambers in the experimental apparatus were fixed by metal wires.  
SPME collection was conducted for 15 min, beginning 2 h after drilling, under controlled conditions (25 \(\pm\) 1 \({}^{\circ}\)C, humidity 60% \(\pm\) 10%). A total of 24 samples were taken, with 8 samples per level (\(n\) = 24).
# A Hybrid Neural Network Model for Predicting Bottomhole Pressure in Managed Pressure Drilling
## 3 Dataset and Methodology
### Data Pre-Processing  
#### 3.1.1 Data Description  
In this paper, the basic data used to train and test the intelligent model are 130,000 sets of BHP measurements from surface monitoring, with a maximum measured depth  
Figure 5: CNN-LSTM parallel network structure.  
Figure 6: Process of BHP output forecasting.  
of 6705 m and the maximum vertical depth of 4942 m. Firstly, the missing key drilling parameters were removed. At the same time, considering that the rheological parameters of drilling fluid have a similar single distribution, which plays a pivotal role in the performance of the model and will amplify the influence of subtle noise or lead to the high linearity of the model and reduce the stability of the model, this paper selectively supplements the performance parameters of drilling fluid, including funnel viscosity, s, sand content, %, and drilling fluid density, g/cm\({}^{3}\). In addition, to check the validity of the data, empirical correlations and mechanistic models were used to estimate BHP, and the outliers were removed. Finally, 100,000 data points of pressure while drilling were used to train and test the model. The characteristics of BHP after cleaning are shown in Table 1.
## 4 Data Analysis and Results  
### Structural Model Test  
A structural equation model (SEM) was proposed. Lisrel 8.51 and SPPS 25.0 software were used for the data analysis. On the one hand, [PERSON]'s \(\alpha\) coefficient exceeded the minimum acceptable level of 0.70 [97], affirming the reliability of every construct (Table 2). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Dimension** & **Characteristic** & **Statistic (\%)** \\ \hline \multirow{2}{*}{Gender} & Male & 581 (49.11\%) \\  & Female & 602 (50.88\%) \\ \hline \multirow{6}{*}{Age} & Less than 20 years old & 321 (27.2\%) \\  & Between 21 and 25 & 394(33.38\%) \\  & Between 26 and 30 & 226 (19.15\%) \\  & Between 31 and 40 & 144 (12.20\%) \\  & Between 41 and 50 & 78 (6.61\%) \\  & More than 50 years old & 20 (1.44\%) \\ \hline \multirow{6}{*}{Pokemon Go use frequency} & Several times a day & 627 (53\%) \\  & Once a day & 311 (26.28\%) \\ \cline{1-1}  & Once a week & 101 (8.53\%) \\ \cline{1-1}  & Rarely & 144 (12.17\%) \\ \hline \multirow{6}{*}{Online Games use frequency} & \textless{}5 hours a week & 363 (34.12\%) \\ \cline{1-1}  & Between 5 y 10 hours & 345 (32.42\%) \\ \cline{1-1}  & Between 11 y 15 hours & 111 (10.43\%) \\ \cline{1-1}  & \textgreater{}20 hours a week & 247 (23.03\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Demographic Profile. On the other hand, the convergent validity of the items was evaluated using two indicators [98]: (1) composite reliability for each construct and (2) average variance extracted (AVE). In this manner, an exploratory factor analysis (EFA) for each construct was developed [99] in order to assess its unidimensionality (Table 2). The results of this analysis show that all constructs widely surpassed the threshold of 0.50 for AVE [100], affirming the convergent validity in all cases. In addition, the discriminant validity was evaluated. Table 3 shows that the square roots of the AVEs are higher than the absolute values of the off-diagonal values in the corresponding rows and columns of the correlation matrix. These findings indicate that each construct is more strongly correlated with its own indicators than with the other constructs in the model [101]. Finally, the model fit indices have been incorporated into Table 4 along with their recommended values. These indicate that the research model has a good fit considering the maximum thresholds proposed by [99].
# A Random Sampling-Based Method via Gaussian Process for Motion Planning in Dynamic Environments
## 3 Experiments  
### Simulations  
In order to evaluate the performance of the DGPL, we provide a 2-dimensional dynamic local path re-planning simulation. The simulation is run on the Matlab 2016b (Northeastern University, Shenyang, China) and a computer with a 3.7 GHz Intel Core i7-8700K CPU and 16 GB RAM. The DGPL parameters \(n_{p}\), \(n_{s}\), and \(v_{a}\) are 100, 10, and 0.9, respectively. As shown in Figure 5, the dynamic obstacle moves from left to right in the environment, and as the dynamic obstacle touches the given global path, the partial invalid paths are replaced by the local re-planning paths provided online by the DGPL method.  
Figure 4: The GRPS provides the local re-planning path based on the EPOC method. As the \(\varepsilon\to 0\), the \(x_{c1}\) and \(x_{c2}\) are the control nodes of the path end orientation constraint. (**a**) The partial path is invalid due to an environmental change. (**b**) The local re-planning path with the end orientation constraint replaces the invalid path and smoothly merges into the global path.  
Figure 3: The local paths sampled from the GRPS method, where the red points are waypoints, color curves represent random paths, and the shadow is the sampling range of 99.74% random paths. (**a**) The local paths without end orientation constraint. (**b**) The local paths based on the EPOC method.  
As shown in Figure 5b-e, the DGPL can re-plan the local paths online for the partial invalid paths as the environment changes and the average planning time is 0.002 s. Because the SE kernel function is employed in the Gaussian process model, the local re-planning paths are smooth naturally. The local re-planning paths are also merged into the global path smoothly since the EPOC method constrains the orientation of both ends of the local path. As shown in Figure 5d,\(e\), the 100 local re-planning paths from the DGPL are all smooth and their ends are added with orientation constraints, so as to merge into the global path smoothly. In addition, the length of the local re-planning paths from the DGPL is reasonable, which indicates that the DGPL performs well in local path planning quality.  
As shown in Figure 5d, if the obstacle is too close to the initial and goal nodes of local re-planning, most of the random path candidates will collide with it, which leads to reduction of local re-planning efficiency and success rate. To avoid the case and guarantee the planning effect, the initial and goal nodes of local re-planning should be located far away from the obstacle.
# A Secure Opportunistic Network with Efficient Routing for Enhanced Efficiency and Sustainability
## 5 Challenges and Opportunities  
The integration of cybersecurity and blockchain technology with OppNets brings forth both challenges and opportunities that require attention for the improvement of efficiency, reliability, and security in data exchange.  
_Scalability:_ Blockchain's inherent scalability challenges pose issues in resource-constrained OppNets. Research focuses on lightweight consensus algorithms and off-chain solutions to enhance scalability, while maintaining security [27].  
_Security and Privacy:_ Ensuring secure and private transactions, while preserving blockchain's transparency is a significant challenge. Advanced cryptographic techniques, such as zero-knowledge proofs and homomorphic encryption, are explored to enhance security and privacy in blockchain-integrated OppNets [28].  
_Incentive Mechanisms:_ Designing efficient incentive mechanisms using blockchain is crucial for encouraging active participation in routing. Smart-contract-based incentive models, combined with game-theoretic approaches, are proposed to encourage cooperation among nodes [29].  
In [30], the authors address the challenges faced in OppNets, provide insights into protocol implementation, and conduct evaluations. By examining these aspects, the paper offers a comprehensive understanding of the complexities involved in routing within OppNets, shedding light on practical implementations and their evaluations.  
The authors in [31] propose a novel OppNet routing method designed for campus environments. Their approach is grounded in an improved Markov model, a probabilistic system that enables a more efficient and context-aware routing strategy. By leveraging this model, the paper introduces an innovative routing method tailored for campus-based OppNets, enhancing the network's reliability and performance. In [32], a new routing algorithm is proposed for sparse OppNets. The algorithm's foundation lies in considering node intimacy, highlighting the importance of social aspects in opportunistic networking scenarios. By incorporating this element, the proposed algorithm optimizes routing decisions, addressing the challenges posed by sparse connectivity. The paper's findings contribute to enhancing the efficiency and effectiveness of OppNet communication in scenarios where nodes are sparsely distributed.  
As a result, the integration of blockchain technology with innovative routing protocols holds immense promise for enhancing the efficiency, reliability, and security of data exchange in OppNets. While challenges such as scalability, security, and incentive mechanisms persist, ongoing research efforts are focused on addressing these issues.
# The Potential of Focusing Acoustic Retroreflectors for Architectural Surface Treatment
## 3 Results
### Large Ideal Reflector Array
#### 3.1.1 Retroreflected Energy Levels
The optimum patch size (in the 1.2 to 2.4 m range) was larger than for the non-focusing space-reversal cases. Focusing was ineffective for small patch sizes. The energetically averaged retroreflected energy levels over the 25 collocated source receiver positions are shown in Figure 6 for space-reversal and focusing space-reversal patches. The space-reversal patch reflectors in the range 0.8 m to 1.8 m have mean values consistently greater than would be produced by a simple energy focus (Equation (5)) for a source centered on the reflector. Space-reversal patch sizes in the 0.6 m to 1.2 m range yield the greatest mean retroreflected energy levels. For the focusing space-reversal cases, the single 7.2 m patch yields mean retroreflected energy levels substantially greater than the smaller patches, with a mean in the vicinity of 10 dB. Apart from the single 7.2 m patch, the focusing space-reversal patch reflectors in the 1.2 m to 1.8 m range yield the greatest mean retroreflected energy levels for the smaller surface distances, for which mean values are often above 5 dB. At the largest surface distance, larger patches (1.8 m and above) are the most effective. Figure 6 also shows the theoretical values for a simple flat surface (Equation (2)) and a pressure focus (Equation (4)) for a source-receiver centered on the reflector (or an equivalent solid angle spherical cap) at the given distance. All mean results are substantially less than the theoretical coherent pressure focus.
# Determination of Arsenic Species Distribution
## 4 Results of Geochemical and Geophysical Studies
### Inner Zonality of Pond Dumps  
The sections of electrical resistivity tomography demonstrate the alternation of low- and high-conductivity areas in the subsurface space (Figure 2). Zones with low resistivity indicate the presence of flooded lenses, which gradually expand downward from the surface to a depth of more than 40 m. It is likely that incoming seasonal precipitation gradually seeps down and, when interacting with the waste tailings, leaches easily soluble components from it, including arsenic.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline ComponentsSamples & **P1–1** & **P1–2** & **P3–1** & **P3–2** & **P4–1** & **P4–2** & **P5** \\ \hline pH & 8.47 & 8.24 & 7.63 & 8.88 & 8.49 & 7.78 & 8.55 \\ Eh, mV & 420 & 385 & 370 & 460 & 472 & 340 & 496 \\ SO42- & 140 & 56 & 114 & 115 & 23 & 81 & 74 \\ NO3- & 27 & 34 & 70 & 70 & 23 & 52 & 160 \\ HCO3- & 70 & 160 & 140 & 140 & 110 & 120 & 87 \\ NH4+ & 1.1 & 0.8 & 1.3 & 1.4 & 0.5 & 0.5 & 1.0 \\ Ca2+ & 38 & 17 & 12 & 12 & 7.7 & 23 & 9.3 \\ Mg2+ & 23 & 21 & 52 & 54 & 20 & 23 & 58 \\ Na+ & 0.50 & 0.46 & 3.6 & 3.8 & 0.91 & 0.83 & 1.4 \\ K+ & 2.2 & 2.0 & 2.9 & 3.1 & 0.68 & 0.78 & 1.4 \\ As & 12 & 9.3 & 4.6 & 4.8 & 13 & 12 & 7.2 \\ Fe & 52 & 23 & 15 & 20 & 11 & 39 & 46 \\ Mn & 0.67 & 2.6 & 12 & 12 & 2.5 & 12 & 14 \\ Co & 6.7 & 6.0 & 7.0 & 6.1 & 4.7 & 16 & 9.9 \\ Ni & 4.8 & 3.0 & 3.9 & 2.9 & 4.9 & 12 & 7.2 \\ Cu & 6.1 & 2.6 & 0.82 & 0.76 & 0.85 & 1.0 & 1.3 \\ Zn & 2.0 & 0.42 & 1.4 & 0.61 & 0.91 & 0.96 & 1.2 \\ Sb & 62 & 60 & 41 & 40 & 45 & 37 & 54 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Composition of water in the surface reservoirs SO42–As in mg/L and Fe–Sb in \(\upmu\)g/L.  
As can be seen from the sections, low-resistivity zones tend to expand towards the bottom of the valley in the direction of litho- and hydroflows. Thus, components from the tailings penetrate into groundwater and further: into the horizons of drinking waters. Given the results of stepwise leaching and determination of arsenic species (even on the surface of the tailings, the percentage of AsIII as the most toxic form is 32% of the total arsenic content), it can be argued that groundwater is affected by tailings. We emphasize that the existence of conditions for arsenic oxidation in underground horizons is unlikely. Under these conditions, it is possible to predict the stability of AsIII and its distribution with groundwater.