esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Potential of Enhancing Delay-Tolerant Routing Protocols via Age of Information
## 4 Protocol Evaluation
### Simulation Parameterization  
In this section, some initial results from the simulations are presented, which serve the purpose of properly dimensioning the parameters of the system. More specifically, starting from very small networks and increasing their size progressively, various parameters of the problem are modified accordingly, such as the number of DTN nodes, the proximity radius (at the link layer), the size of the square grid, the number of time slots of simulation time and the Upper Bound for the AoI for the three versions of the modified protocol. For all cases, the metrics mainly considered are throughput (i.e., the percentage (%) of the successfully transmitted packets in the whole simulation), average age of packets and maximum age observed, while also keeping the total number of packets produced, the total number of successfully transmitted packets, the number of dropped packets (due to exceeding the AoI bound in the three versions of the modified protocol), the number of remaining packets in the network and finally the total execution time of each simulation. The results are presented in Tables 2-7 (it is emphasized that in the first column--Number of Nodes--the star superscript denotes that the Grid of the topology has size 100 \(\times\) 100, otherwise the size is 1000 \(\times\) 1000): \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \begin{tabular}{c} **Number** \\ **Nodes** \\ \end{tabular} & \begin{tabular}{c} **Time** \\ **Slots** \\ \end{tabular} & \begin{tabular}{c} **Radius of** \\ **Proximity** \\ \end{tabular} & \begin{tabular}{c} **Bound** \\ **of AoI** \\ \end{tabular} & \begin{tabular}{c} **Produced** \\ **Packets** \\ \end{tabular} & \begin{tabular}{c} **Dropped** \\ **Packets** \\ \end{tabular} & \begin{tabular}{c} **Successful** \\ **Packets** \\ \end{tabular} & \begin{tabular}{c} **Remaining** \\ **Packets** \\ \end{tabular} & \begin{tabular}{c} **Execution** \\ **Time (s)** \\ \end{tabular} & \
\begin{tabular}{c} **Throughput** \\ **Time (s)** \\ \end{tabular} &
\begin{tabular}{c} **Mean** \\ **AoI** \\ **Time (Time** \\ \end{tabular} \\ \hline
50 * & 500 & 20 & 30 & 998 & 681 & 267 & 50 & 3.32 & 0.267 & 25.51 & 30 \\ \hline
50 * & 1000 & 15 & 70 & 1993 & 349 & 1573 & 71 & 9.36 & 0.789 & 42.07 & 70 \\ \hline
100 * & 1000 & 15 & 70 & 1992 & 975 & 895 & 122 & 23.12 & 0.449 & 54.26 & 70 \\ \hline
100 & 1000 & 149 & 70 & 1987 & 1069 & 798 & 120 & 30.06 & 0.4 & 54.29 & 70 \\ \hline
100 & 2000 & 149 & 140 & 3947 & 2052 & 1680 & 215 & 84.69 & 0.42 & 111.77 & 140 \\ \hline
100 & 5000 & 149 & 200 & 10,041 & 5514 & 4203 & 324 & 407.18 & 0.418 & 163.45 & 200 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Initial results for Modified-near-v2.
## 1 Introduction
The principal research objective centers on integrating the SDCS method into a simulation environment via the Simulink of MATLAB 2021a. This integration is highlighted for its capability in generating controllers that are synthesizable and  
Figure 1: Classification of the MH algorithms. adhere to formal correctness standards. Autonomous UAV systems struggle with external parameters such as gravity and wind, demanding robust control approaches, whereas collaborative UAVs face complex coordination challenges. Current research investigates various control methodologies within simulation environments, with the principal objective of achieving seamless integration of the SDCS method into Simulink. This integration is intended to enhance the deployment of collaborative UAVs, thereby addressing critical safety and optimization challenges. The contributions of this study--including modeling and implementation within the proposed SDCS technique--aim to address a significant research gap in the existing literature. This paper aims to utilize the advantages of the SDCS method to enhance and integrate simulation platforms. In contrast to previous studies, it addresses the novel challenge of incorporating a symbolic approach for managing infinite systems within these simulation environments. This effort significantly contributes to the existing literature by introducing an innovative method that effectively circumvents the state explosion problem commonly associated with such systems. The contribution of this study to the literature is that the SDCS method optimizes the dynamic behavior and path planning of quadcopters together. Existing studies in the literature include low-level and high-level control targets separately [25; 26]. However, for a quadcopter or quadcopter swarm to perform a task while consuming the least amount of energy and time, optimizing only its dynamic behavior or path planning is not enough. Additionally, comparing the SDCS and MH methods developed in recent years has increased the academic and industrial contribution of this study. The results obtained in this study will contribute to the preferability of the SDCS method in the control and path planning of the quadcopters and other aircraft in current applications such as observation activities and cargo transportation. The remainder of this paper is organized as follows: Section 2 presents the background of the system modeling and FOPID controller. Section 3 gives the optimal controller design procedure and details of SDCS. In Section 4, the experimental evaluation of the proposed approaches was given, and the obtained results were presented. Finally, Section 5 concludes the paper with the main findings and highlights future research directions.
# A Dynamic Distributed Deterministic Load-Balancer for Decentralized Hierarchical Infrastructures
## 2 Related Work
However, the higher _fanout_ leads to larger update and load-balancing cost of \(O(m\cdot\log_{m}N)\). SPIS [11] is the decentralized version of Interpolation Search Tree (IST), the most recent version of which has been presented recently in [12], and its concurrent version in [13]. It exploits the Apache Spark engine, the state-of-the-art solution for big data management and analytics. In particular, it is a Spark-based indexing scheme that supports range queries in such large-scale decentralized environments and scales well w.r.t. the number of nodesand the data items stored. Towards this goal, there have been solutions in the last few years, which, however, turn out to be inadequate at the envisaged scale since the classic linear or even the logarithmic complexity (for point queries) is still too expensive. In contrast, range query processing is even more demanding. Therefore, SPIS goes one step further and supports range query processing with sub-logarithmic complexity. Also, the weak point of this structure is the load balancing and fault tolerance after massive failures. For more details, see [14]. In the last decade, a deterministic decentralized tree structure, called \(D^{2}\)-Tree [15], was presented that overcomes many of the weaknesses mentioned above of tree-based systems. In particular, \(D^{2}\)-Tree achieves \(O(\log N)\) searching cost, amortized \(O(\log N)\) update cost both for element updates and for node joins and departures, and deterministic amortized \(O(\log N)\) bound for load-balancing. Its practicality, however, has not been tested so far. As far as network's fault tolerance is concerned, P-Ring [3] is considered highly fault-tolerant, using [PERSON]'s Fault-Tolerant Algorithms [16]. BATON [9] maintains vertical and horizontal routing information not only for efficient search but to offer a large number of alternative paths between two nodes. In its successor BATON\({}^{*}\)[10], fault tolerance is greatly improved due to higher _fanout_. When \(fanout=2\), approximately 25% of nodes must fail before the structure becomes partitioned while increasing the fanout up to 10 leads to increasing fault tolerance (60% of failed nodes partition the structure). \(D^{2}\)-Tree can tolerate the failure of a few nodes, but cannot afford a massive number of \(O(N)\) node failures.
## 3 Technique Realizations
### Wireless System  
In this work, the transceiver, composed of transmitter and receiver modules, was developed by using stepped frequency modulation radar technology [26]. The former generated 400-450 MHz stepped frequency modulation signal, and the SAW sensor was awoken by receiving the electromagnetic wave (EW) signal. While the returned sensing signal was received by the latter, and corresponding sensing signal could be decoupled, and recorded in the computer. The built wireless system for sensing temperature was described in Figure 3, which was composed of the RF transceiver, SAW chip packaged as the transponder, RF antennas, the temperature chamber, and the computer for signal processing.  
\begin{table}
\begin{tabular}{c c} \hline Piezoelectric substrate crystal & YZ LiNbO\({}_{3}\) \\ Operation frequency & 433 MHz \\ IDT type & Bidirectional structure \\ Electrodes number in IDT & 20 \\ Aperture & 100 times of the acoustic wavelength \\ Electrodes number in reflectors & 8 \\ Electrode material and thickness & Aluminum (AL), 300 nm \\ Position of two reflectors & 1 \(\upmu\)s, 2.6 \(\upmu\)s \\ \hline \end{tabular}
\end{table}
Table 1: SAW temperature sensor design parameters.  
Figure 2: (**a**) Developed SAW sensing device, (**b**) measured and simulated \(S_{11}\) of proposed SAW device.  
The sensing signal returned to the transceiver at wireless distance of 0.5 m and room temperature (20 \({}^{\circ}\)C), which was depicted in Figure 3, and corresponding time positions of two reflection peaks were the same as that in Figure 2b.
# A Scalable Framework for Sensor Data Ingestion and Real-Time Processing in Cloud Manufacturing
## 4 Materials and Methods
### Case Study
#### 4.2.1 Data Ingestion Systems  
To assess the data ingestion layer, Apache Kafka and RabbitMQ, which are widely used message queue systems, were deployed in three-node clusters and configured with a replication factor of three for enhanced scalability and fault tolerance. Varying message loads (100, 1000, and 10,000 messages per second) were simulated to reflect different sensor densities in the CM environment. Apache Kafka was set up with temperature and humidity sensors as data producers and data processing systems as consumers. Its distributed fault-tolerant design supports continuous high-volume data streams, which are essential for the CM. The performance metrics for each component were analyzed under various load conditions to confirm the robustness and efficiency of the proposed framework. Conversely, RabbitMQ was configured with sensor data as publishers and consumer applications subscribing to queues using a direct exchange routing configuration to benchmark performance alongside Kafka. Metrics such as the throughput, task duration, and scalability were evaluated across the tested message rates.
# Sensitivity Analysis of P-Wave Polarization Direction and Velocity Gradient Inversion
## 5 Tests and Discussion  
In order to validate the correctness of our analysis on the uneven sensitivity distribution of P-wave polarization direction data to relative velocity gradients, as well as to demonstrate the effectiveness of our proposed inversion method, we conducted trial calculations and analyzed the inversion results on both flat-layer and relief models. First, we used a homogeneous velocity model and a velocity model with a single velocity interface to validate the conclusion that the sensitivity of P-wave polarization data to relative velocity gradients decreases with increasing distance from the receiver. Then, based on a flat-layer model, we compared the relative velocity gradient maps obtained using the ART method with those obtained by our proposed method, demonstrating the effectiveness of our approach. Finally, we used a relief model to demonstrate the applicability of our proposed method in complex media.
# Carbon Nanocomposites in Aerospace Technology: A Way to Protect Low-Orbit Satellites
## 3 Importance of Carbon Nanomaterials for the Corrosion Mitigation
### Graphene Oxide and Reduced Graphene Oxide  
Graphene oxide and reduced graphene oxide are two other important examples of carbonous materials that are currently under investigation for space applications, including for the protection of low-orbit space assets from atomic oxygen, ultra-violet and space radiation [93]. The exceptional strength of graphene-based materials, along with the exceptional specific strength (i.e., the strength related to the specific density of the material) make graphene-based materials an attractive candidate for space applications. An example of an investigation of reduced graphene oxide (rGO), i.e., the processed graphene oxide with the reduced content of oxygen, is illustrated in Figure 13, top panel. Here, reduced graphene oxide was chemically functionalized and investigated for space application in the form of an additive to composites. The authors reported a decrease in electrical conductivity after the treatment; yet, rGO nanoparticles could be used to enhance the properties of composites. Specifically, the authors demonstrated an enhancement in the tensile strength of a high-density polyethylene matrix, thus evidencing the applicability of rGO-enhanced polymer coatings as protection solutions [93].  
The successful application of graphene oxide (GO) for the protection of space assets from \(AO\) was also reported [94; 95]. In one example, both silane and GO were grafted onto the poly(\(p\)-phenylene benzobis-oxazole) fibres to enhance their resistance to atomic oxygen. The grafting of graphene oxide was made using 3-aminopropyltrimethoxysilane. The produced hybrid fibres (Figure 13, bottom panel) featured a significant enhancement in resistance against the deleterious effects of atomic oxygen exposure.  
Figure 13: Graphene oxide and reduced graphene for space applications. Top panel: Schematics of the process used for composite fabrication, consisting of premixing (**a**), melt compounding (**b**), melt pressing (**c**,**d**) and, finally, forming of the samples (**e**). Reprinted with permission from [PERSON] et al., 2021 [93]. © 2019 Society of Plastics Engineers. Bottom panel: hierarchical reinforcement by grafting graphene oxide onto poly(\(p\)-phenylene benzobis-oxazole fibres for resistance to \(AO\)-induced degradation in space. Reprinted with permission from [PERSON] et al., 2015 [95]. © 2015 Elsevier.
## 4 System Software Realization
### Impact Monitoring  
The impact monitoring function is based on the sampling of time-domain signals. After sampling, quantification of the sampling data is launched to check whether an impact over the threshold has occurred. If so, then the system will save the history of this period for further evaluation while sending  
Figure 8: An application example of a scheduling module (on software).  
Figure 7: The scheme of the designed integrated interface.  
a warning message for the overload. If not, then the sampling will continue as before, until the user stops it. This quantification flow chart is illustrated in Figure 9.  
The initialization in Figure 9 means that before performing the damage detection function formally, the system will first sample several ADC points of each PZT sensor in a steady state for calibrating. This can improve the robustness of the system because it can adapt to different initial environments.  
In this paper, the method based on average voltage deviation is implemented to discover the sensor that is closest to the impact. This can be expressed as  
\[\text{Location}\ =\ \arg\max_{i}\hat{\
abla}_{\text{i}} \tag{1}\]  
\[\hat{\
abla}_{\text{i}}\ =\ \frac{\left[\sum_{j\ =\ 1}^{n}\left(V_{i}^{j}-\overline{V _{i}}\right)^{2}\right]^{1/2}}{n} \tag{2}\]  
where \(\hat{\
abla}_{\text{i}}\) is the average voltage deviation of the \(i\)-th sensor during a certain time period, \(V_{j}^{i}\) is the voltage of the ith sensor at time \(j\), and \(\overline{V_{i}}\) is the average voltage for the period. Note that only discrete sampling points can be obtained; thus, \(n\) represents the total sampling points during a sampling period. The right part of Equation (1) will return the sensor number that has the largest signal magnitude during sampling period. Here, location means the place where the impact happened. The \(i\)-th sensor must have the maximum magnitude among all the sensors attached to the same structure.
## 1 Introduction  
Peat moss (PM) has been widely used as a horticultural substrate due to its ideal physical and chemical properties, such as low bulk density (BD), high water holding capacity, high aeration ratio, and high cation exchange capacity [1; 2; 3]. Domestic PM sales in the US were 0.25 M m\({}^{3}\) in 2016 and almost 91% PM was sold to the horticultural industry [4]. The marketable PM estimated value in the US was $13.0 million in 2018 [4]. Peat moss mining, however, has been questioned due to the peatland ecosystem disturbance and/or loss, and its environmental consequences. Hence, alternative materials such as pretreated manure composts and processed timber by-products have been introduced as PM replacements [5].  
Biochar, a carbon-rich by-product from biomass pyrolysis, has potential for substituting PM as greenhouse growing media [6]. Pyrolysis biochar is generated from biomass thermo-chemical decomposition in oxygen-depleted or oxygen-limited atmosphere [7; 8; 9]. Biochar has been considered as a sustainable material because it can be derived from various sources, such as pinewood [3; 10; 11],green waste [12], wood, sugarcane bagasse [13], straw [14; 15; 16; 17; 18], bark [19], rice hull [20], and wheat straw [16; 21]. For the same reason, biochar properties can vary widely [22]. Most greenhouse trials have used biochar derived from lignin-based materials, which has appropriate properties for plant growth [12]. [PERSON] [23] reported that citrus wood biochar has potential to improve pepper and tomato plant growth in a systematic way, increasing the leaf area, canopy and yield. [PERSON] [6] found that incorporating pinewood biochar with PM-based commercial substrate increased poinsetia growth. [PERSON]'s [24] study showed that mixing hardwood biochar with two different composts could lead to similar or better plant growth in basil and tomato plants in comparison to those in PM-based commercial substrates. [PERSON] [12] confirmed that the total biomass could be significantly increased (by 22%) by mixing green waste biochar with a PM-based substrate. When adding biochar in composted green waste, the shoot fresh weight, shoot dry weight, root fresh weight, and root dry weight of _Calathea insignis_ were increased by 57.3%, 79.7%, 64.5%, and 82.0%, respectively [25]. Similar works had also been reported on Easter lily [6; 26; 27]. The biochar from red oak feedstock mixed with vermiculite also increased hybrid poplar total biomass and shoot biomass [28].  
Biochar that affects greenhouse seedling production or subsequent seedling growth has seldom been reported. As biochar from different resources has varied properties, some may have adverse effects on plant growth due to possible phytotoxicity [29]. Phytotoxicity assessment is critical for successful soil/soilless amendment with bioenergy by-products such as biochar [30], and the germination test is a reliable procedure for different types of biochar phytotoxicity examinations [30]. We conducted this study to test the phytotoxicity of two biochars from different raw materials and to explore the use of the two biochars in subsequent container seedling production.
# Fault Detection in Wireless Sensor Networks through the Random Forest Classifier
## 4 Classifiers
### Rf  
The RF method or random decision forest is a collective learning method for classification, regression, and other tasks. It operates by creating a cluster of decision trees at training time and producing a class that is the mean prediction of individual trees. In [10] RF, there is a directly proportional relationship between the number of trees in the forest and the resultant accuracy. There are two steps in the RF algorithm, which are as follows:  
* The first step is to create an RF tree. It is further elaborated in the following five stages:
* \(K\) number of random features are selected from total features \(m\), where \(K\) is less than \(m\),
* within the selected features, node \(d\) is determined using the best split point,
* those nodes are further distributed into daughter nodes through best split,
* the first three steps are repeated until \(l\) number of nodes are obtained,
* all of the above steps are repeated for \(n\) times to achieve \(p\) number of trees, where \(n\) is not equal to \(p\).
* The next step is to classify the data based on an RF tree that has been created in the first step. It has the following stages:
* with the rules created for each randomly-formulated decision tree and test features, data are classified,
* the votes are calculated for each target value,
* the highest voted prediction target is considered to be the final result of the RF algorithm.
## 6 Conclusions and Future Work  
This paper presented SAUSA, which combines SDN and Blockchain technology to support efficiency, assurance, and resilience-oriented point cloud applications. The hierarchical SDN-enabled service network can provide efficient resource coordination and network configuration to satisfy the QoS of point cloud applications. A lightweight data authentication framework atop the Blockchain and DDS aims to secure 3D point cloud data access, usage, and storage in a decentralized manner. The experimental results based on a prototype implementation demonstrated the effectiveness and efficiency of our SAUSA. However, there are open questions that need to be addressed before applying SAUSA to real-world 3D point cloud scenarios. We leave these limitations to our future works:  
1. SAUSA uses Ethereum to build a Blockchain network, which ensures security and scalability in open-access networks. However, PoW mining brings unsustainable energy consumption, longer transaction committed latency, and lower throughput. Thus, it is not suitable for time-sensitive applications. Lightweight Blockchain designs, such as Microchain [32], are promising to optimize computational utilization and improve performance in terms of end-to-end latency and transaction throughput. Our on-going efforts include validating SAUSA in a real-world point cloud scenario and the investigation of the integration of Microchain to reduce data authorization latency.
2. This paper focused on the decentralized security scheme's implementation and validation; however, there are still unanswered questions and challenges about networking service intelligence in point cloud applications. In future work, we will investigate SDN controllers and virtual network functions (VNFs) to efficiently manage network and storage resources within each domain and evaluate the system performance and security properties according to various attack scenarios.  
Conceptualization, R.X., Y.C., G.C. and E.B.; methodology, R.X. and Y.C.; software, R.X.; validation, R.X. and Y.C.; formal analysis, R.X. and Y.C.; funding acquisition, Y.C.; investigation, R.X. and Y.C.; resources, R.X. and G.C.; data curation, R.X.; writing--original draft preparation, R.X. and Y.C.; writing--review and editing, R.X. and Y.C.; visualization, R.X.; supervision, Y.C.; project administration, Y.C. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by the United State National Science Foundation (NSF) under the grant CNS-2141468.  
Not applicable.  
The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the Air Force Research Laboratory or the U.S. government.  
The authors declare no conflict of interest.