esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Drying Kinetics  
The most common and efficient method for the evaluation of the effect of various pretreatment techniques on the course of processing is the determination of the process kinetics. For processes based on mass transfer, e.g., drying, it would be tracking changes in mass that depicts the amount of water evaporating from the material tissue [36]. The drying kinetics shown in Figure 1 were calculated specifically to present the progression of water removal from the material tissue within the IR-HA time. The MR expresses the quantity of water in the material, starting from 1, which presents the total amount of water, which decreases until the equilibrium moisture content. The drying kinetics obtained in this study are divided into three groups. Figure 1a shows the impact of conventional (control) treatment methods on IR-HA drying. Figure 1b,c present how US pretreatment affected water removal from the beetroot slices compared to the untreated sample, respectively for the frequency of 21 kHz and 35 kHz. Drying times, expressing the time that the material needed to reach the equilibrium moisture content of 0.09 g H\({}_{2}\)O/g d.m. determined for each variant, are also included in the figure (next to the legends of the figures, marked with a gray color). As can be seen, although the statistical analysis did not recognize any significant differences between drying times, the tendency of blanching and soaking to prolong dehydration by up to 13% was observed. Regardless of the applied parameters, US pretreatment did not significantly affect either kinetics or drying time. For the majority of US-treated variants, the values varied from 129 to 132 min. Such results indicate that there was a reduction in drying time by 3-5%, however it cannot be considered impactful. The exception was noted for the sample subjected to US at 35 kHz for 20 min, for which the drying time was 141 min, which was 4% longer than it was for the untreated beetroot. [PERSON] et al. [37] reported that US pretreatment (20, 40, or 60 kHz) did not affect the catalytic IR drying time of sweet potato slices at 70 \({}^{\circ}\)C, but there was a correlation between drying time and US frequency in drying at 60 and 80 \({}^{\circ}\)C. The greatest reduction was observed after 30 min of pretreatment at 40 kHz. Nevertheless, IR-HA is more effective in terms of mass transfer and less harmful to the quality attributes of the dried material than traditional convective drying due to the heating mechanism that allows for quick water removal, thus a remarkably shorter exposition to elevated temperature [38; 39].
## 2 Background and Related Work  
### Spherical Autonomous Underwater Vehicles  
The spherical autonomous underwater vehicle (SAUV) is a particularly interesting architecture alternative for an underwater vehicle that must operate within entanglement-prone, complex environments such as confined spaces and littoral environments. Multiple SAUVs have been designed in recent years for such missions, including some that are highly advanced [5; 6; 7]. Two key benefits to a spherical hull profile are the reduction or elimination of external protuberances that can aggravate snagging and the fact that a spherical vehicle occupies the same volume of space as it rotates, making maneuvers easier in tight quarters. The WIEVLE was developed with these principles at the forefront while also seeking to minimize sediment disruption via a novel, quad-core, internal tunnel-thruster propulsion system that draws water from below the sphere and ejects the wash upward, at an angle, symmetrically, around the vertical axis. The original WIEVLE prototype, which presently lacks an anchoring mechanism, is depicted in Figure 1.  
Other highly advanced SAUVs, such as the ENDURANCE [5], DEPTHX [6], and UX-1 [7], lack anchoring mechanisms because they are designed to return to the point of entry upon completion of their missions and are not intended to loiter for extended periods on station. A solution to enable an SAUV to passively remain on station for extended periods, rather than simply allowing the vehicle to drift or roll on the seabed and risk damage, is therefore desired. Although the anchoring system proposed in this article may be used within any shape or style of AUV of appropriate size, the authors are particularly interested  
Figure 1: The first Wreck Interior Exploration Vehicle (WIEVLE) prototype: (**a**) WIEVLE on its test stand; (**b**) A top down view of the internal propulsion system. Note the central cavity where the RAATM may be installed; (**c**) WIEVLE’s spherical hull disassembled to reveal the internal propulsion core.  
in its application for spherical (or spheroid) hull profiles, as the operational capabilities offered by SAUVs may be particularly enhanced by the integration of such a mechanism. Furthermore, the authors contend that current UUV anchoring mechanisms are either unsuitable for implementation in an SAUV due to their negative impact upon the smooth profile of the hull exterior, or they are non-resetting, single-use mechanisms.
# Analysis of Lightweight Structure Mesh Topology of Geodesic Domes
## 2 Shaping Geodesic Dome Mesh
### The Second Method of Dividing a Regular Octahedron

Table 4: Latitudinal coordinates \(\lambda^{k}_{w}\left[{}^{\circ}\right]\) for the first and seventh layers from group 1. Figure 4: Division of the triangular face of the initial regular octahedron according to the second method: (**a**) \(n=2\), (**b**) \(n=4\), (**c**) \(n=6\). \[\lambda^{\prime}_{w}=\frac{90}{\frac{n}{2}-\left(\frac{4}{6}+(a-1)\right)}\,[^{ \circ}], \tag{15}\]  
where:  
\(a\) is the consecutive layer number from the group;  
\(n\) is the number of the division points of the initial triangular octahedron face. \[\lambda^{k}_{w}=\lambda^{\prime}_{w}\cdot\frac{1}{3}+\lambda^{\prime}_{w}\cdot (k-1)[^{\circ}], \tag{16}\]  
where:  
\(k\) is the number of points in the layer, \(k\in\langle 1;\frac{n}{2}-(a-1)\rangle\). Table 5 shows the values of the angle \(\lambda^{k}_{w}\) for the exemplary second layer (\(a=1\)) and eighth layer (\(a=3\)) belonging to group 2. * group 3--for layers \(n_{w}=3,\;6,\;9,\;\ldots,\;(1.5n-5)\). In this case, the formula was also determined first for the latitudinal ordinate and then for the angular ordinate of the \(k\) element in a given layer. \[\lambda^{\prime}_{w}=\frac{90}{\frac{n}{2}-\left(\frac{4}{6}+(a-1)\right)}\,[^{ \circ}],\] (17) where: \(a\) is the consecutive layer number from the group; \(n\) is the number of the division points of the initial triangular octahedron face. \[\lambda^{k}_{w}=\lambda^{\prime}_{w}\cdot\frac{4}{6}+\lambda^{\prime}_{w} \cdot(k-1)[^{\circ}],\] (18) where: \(k\) is the number of points in the layer, \(k\in\langle 1;\frac{n}{2}-(a-1)\rangle\). Table 6 shows the values of the angle \(\lambda^{k}_{w}\) for the exemplary third (\(a=1\)) and ninth (\(a=3\)) layers belonging to group 3. \begin{table}
\begin{tabular}{c c c} \hline \hline  & \(\lambda^{k}_{\mathbf{2}}[^{\circ}]\) & \(\lambda^{k}_{\mathbf{g}}[^{\circ}]\) \\ \(k\) & **Layer 2** & **Layer 8** \\  & \(\lambda^{\prime}_{\mathbf{2}}=6.59[^{\circ}]\) & \(\lambda^{\prime}_{\mathbf{8}}=7.71[^{\circ}]\) \\ \hline
1 & 2.20 & 2.57 \\
2 & 8.78 & 10.29 \\
3 & 15.37 & 18.00 \\
4 & 21.95 & 25.71 \\
5 & 28.54 & 33.43 \\
6 & 35.12 & 41.14 \\
7 & 41.71 & 48.86 \\
8 & 48.29 & 56.57 \\
9 & 54.88 & 64.29 \\
10 & 61.46 & 72.00 \\
11 & 68.05 & 79.71 \\
12 & 74.63 & 87.43 \\
13 & 81.22 & – \\
14 & 87.80 & – \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Latitudinal coordinates \(\lambda^{k}_{w}\)\([^{\circ}]\) for the second and eighth layers from group 2. From the obtained results, a point cloud with Cartesian coordinates was created in a similar case for the dome generated according to the first division method, presented in Section 2.1.1 of the paper.
# THBase: A Coprocessor-Based Scheme for Big Trajectory Data Management
## 5 L-Index
### Update  
The update of L-index is performed by Observer coprocessor. After the client inserts trajectory data through Put API, Observer triggers the update of L-index by capturing the postPut event [16]. Given a trajectory segment \(TS_{i}\), its MBR is \(m_{i}\), and the rowkey is \(rk_{i}=\text{in}(moid_{i})+ti_{i}\). The update process of L-index is as follows.  
We first use \(ti_{i}\) to query T-index to obtain the matching TP element, and then query the first-level grid cells in the corresponding G-index through \(m_{i}\), there are two cases: (1) If \(m_{i}\) can be enclosed in a single first-level grid cell, the subgrid cells of this cell are queried level by level to select the deepest level grid cell that can enclose \(m_{i}\) to index \(TS_{i}\). (2) If \(m_{i}\) intersects with multiple first-level grid cells, all the matching first-level grid cells are used to index \(TS_{i}\).  
After \(TS_{i}\) is indexed, we count the number of SE in SE-List of each matching G-index grid cell. If the threshold is exceeded, the grid cell is split based on quad-tree, and the SE-List is divided into corresponding subsets according to the spatial location. If a SE cannot be enclosed in any of the subgrid cells, it is still indexed by the current grid cell.
## 5 Control Algorithms for UAV Networking
### Decentralized Control Algorithm  
Decentralized control algorithms are an important method in UAV swarm control. They allow each UAV to manage itself and make decisions based on local information without the need for central coordination. This method is usually based on three main mechanisms: inter-UAV communication, ambient sensing, and direct line of sight. The UAVs in the swarm coordinate with each other to maintain formation, avoid collisions, and complete common missions, ensuring high flexibility, scalability, and adaptability (as shown in Figure 28).
# Experimental Study on Dynamic Response Characteristics of RPC and RC Micro Piles in SAJBs
## 2 Experimental Program
### Loading Device and Scheme  
To be noted, previous studies [36; 44; 45] proposed that the sine wave was the most suitable wave to obtain the dynamic response characteristics of the pile-soil system. Therefore, the sinusoidal wave is the main load selected in this test to investigate the dynamic response characteristics of micro pile-soil system. In addition, the dynamic response characteristics of the micro pile-soil interaction system under the EI-Centro wave, the Kobe wave, and artificial wave loads were also discussed, respectively. The acceleration time history of the El-Centro wave, the Kobe wave, and the artificial wave load are plotted in Figure 5. According to the provisions of seismic fortification intensity, the design basic seismic acceleration is in the code for seismic design of GB50011-2010 (China) [46]. It is found that the seismic fortification intensity of the project was 7 degrees, and the design basic acceleration was 0.15 g. Therefore, the experimental study on dynamic response characteristics of RPC and RC micro pile-soil interaction were conducted in the structural laboratory based on a three-array system of the earthquake simulation shaking table.  
Figure 4: Layout of sensors for each micro pile. (**a**) Layout diagram of sensors (unit: cm). (**b**) Accelerometers and strain gauges. (**c**) Displacement meters.  
As mentioned previously, a total of eight loading cases were employed as the earthquake load to examine the dynamic response characteristics of RPC and RC micro piles in this paper, as summarized in Table 3. The sine wave loads in peak acceleration of 0.15 g have different frequencies such as 1 Hz, 2 Hz, 4 Hz, 8 Hz, and 16 Hz. Before each change of the sine wave frequencies, white noise scanning was adopted to obtain natural frequencies of the pile-soil system, as shown in Table 3 (from case 1 to case 5). Meanwhile, several typical seismic waves including the EI-Centro wave, the Kobe wave, and the artificial wave loads with peak acceleration of 0.15 g, respectively, are listed in Table 3 (from case 6 to case 8).
## 4 Conclusions  
The effect of sample presentation (number of larvae and aluminum foil cover) on the NIR spectra of BSFL was evaluated. A linear relationship between the number of larvae and the absorbance around 1200 nm, mainly associated with lipids, was observed in the NIR spectra of the samples. No effect of the aluminum foil cover on the NIR spectra of the BSFL was observed. Although the effect of the number of BSFL larvae influenced the NIR spectra, no evidence of this effect on the classification accuracy of the LDA models was observed where a classification rate higher than 93% was obtained. The limitations of this study are related to the number of samples and feed streams used to develop the classification models. In the future, the classification models will be validated by including other feed streams over time. Overall, the results obtained in this study provided information to better understand the effect of sample presentation on the classification models. The present study also demonstrated that a portable NIR instrument can be suitable for an initial and rapid classification or the determination of the origin of the waste stream used to feed the BSFL using a minimal number of larvae.  
C.M.S., formal analysis; D.C., conceptualization, methodology, data analysis, writing--original draft preparation; S.A., sampling, formal analysis, writing--review and editing; L.H., writing--review and editing, funding; O.Y. writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
The work has been supported by the End Food Waste Cooperative Research Centre whose activities are funded by the Australian Government's Cooperative Research Centre Program.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
Author [PERSON] was employed by the company Goterra. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of larvae** & & **Supermarket** & **Children** & **\%CC** \\ \hline
0.5 L & Supermarket & 38 & 2 & 93 \\  & Childcare & 3 & 37 & \\
1 L & Supermarket & 40 & 0 & 100 \\  & Childcare & 0 & 49 & \\
2 L & Supermarket & 40 & 0 & 100 \\  & Childcare & 0 & 40 & \\
3 L & Supermarket & 40 & 0 & 100 \\  & Childcare & 0 & 40 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Percent of correct classification of black soldier fly larvae samples sourced from two waste streams and analyzed using near-infrared spectroscopy.
## 4 Results and Discussion
### Gradual Microscopic Contact Behaviour of the Main Skeleton
#### 4.1.3 Contribution Rate of Coarse Aggregate Particle Size to Contact Points
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Contact Distribution of Coarse Aggregates with Different Particle Sizes (mm)**} \\ \cline{3-10}
**Type of** & **Loading Time** & **2.36** & **2.36** & **2.36** & **4.75** & **4.75** & **4.75** & **9.5** & **9.5** & **13.2** \\
**Mixture** & **(h)** & \(\updownarrow\) & \(\updownarrow\) & \(\updownarrow\) & \(\updownarrow\) & \(\updownarrow\) & \(\updownarrow\) & \(\updownarrow\) & \(\updownarrow\) & \(\updownarrow\) \\  & **2.36** & **4.75** & **9.5** & **13.2** & **4.75** & **9.5** & **13.2** & **9.5** & **13.2** & **13.2** \\ \hline \multirow{4}{*}{70\# asphalt} & Before loading & 40 & **90** & 8 & 2 & 52 & 14 & 1 & 0 & 0 & 0 \\  & After 1-h loading & 39 & **98** & 7 & 2 & 71 & 15 & 1 & 0 & 0 & 0 \\  & After 2-h loading & 41 & **110** & 8 & 1 & 82 & 14 & 1 & 0 & 0 & 0 \\  & After 3-h loading & 42 & **118** & 8 & 1 & 89 & 13 & 1 & 0 & 0 & 0 \\ \hline \multirow{4}{*}{Rock asphalt} & Before loading & 34 & **91** & 16 & 1 & 56 & 20 & 1 & 1 & 0 & 0 \\  & After 1-h loading & 39 & **100** & 18 & 2 & 59 & 23 & 1 & 1 & 0 & 0 \\  & After 2-h loading & 42 & **111** & 17 & 2 & 61 & 22 & 2 & 1 & 0 & 0 \\  & After 3-h loading & 41 & **115** & 19 & 2 & 63 & 24 & 2 & 1 & 0 & 0 \\ \hline \multirow{4}{*}{SMA-13} & Before loading & 6 & 30 & 11 & 2 & **63** & 37 & 5 & 7 & 1 & 0 \\  & After 1-h loading & 2 & 39 & 12 & 2 & **81** & 42 & 4 & 8 & 2 & 0 \\ \cline{1-1}  & After 2-h loading & 5 & 46 & 15 & 2 & 78 & 48 & 4 & 8 & 1 & 0 \\ \cline{1-1}  & After 3-h loading & 4 & 47 & 17 & 2 & **82** & 49 & 4 & 8 & 1 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Contact distribution of coarse aggregates at different loading stages. 9.5 mm increases rapidly under load, whereas the number of contact points involving coarse aggregates with particle sizes larger than 9.5 mm increases gradually. In coarse aggregates with particle sizes of 13.2-16.0 mm, the quantity of contact points tends to be steady and barely changes during loading.
# Distributed Reputation for Accurate Vehicle Misbehavior Reporting (DRAMBR)
###### Abstract  
Vehicle-to-Vehicle (V2V) communications technology offers enhanced road safety, traffic efficiency, and connectivity. In V2V, vehicles cooperate by broadcasting safety messages to quickly detect and avoid dangerous situations on time or to avoid and reduce congestion. However, vehicles might misbehave, creating false information and sharing it with neighboring vehicles, such as, for example, failing to report an observed accident or falsely reporting one when none exists. If other vehicles detect such misbehavior, they can report it. However, false accusations also constitute misbehavior. In disconnected areas with limited infrastructure, the potential for misbehavior increases due to the scarcity of Roadside Units (RSUs) necessary for verifying the truthfulness of communications. In such a situation, identifying malicious behavior using a standard misbehaving management system is ineffective in areas with limited connectivity. This paper presents a novel mechanism, Distributed Reputation for Accurate Misbehavior Reporting (DRAMBR), offering a fully integrated reputation solution that utilizes reputation to enhance the accuracy of the reporting system by identifying misbehavior in rural networks. The system operates in two phases: offline, using the Local Misbehavior Detection Mechanism (LMDM), where vehicles detect misbehavior and store reports locally, and online, where these reports are sent to a central reputation server. DRAMBR aggregates the reports and integrates DBSCAN for clustering spatial and temporal misbehavior reports, Isolation Forest for anomaly detection, and Gaussian Mixture Models for probabilistic classification of reports. Additionally, Random Forest and XGBoost models are combined to improve decision accuracy. DRAMBR distinguishes between honest mistakes, intentional deception, and malicious reporting. Using an existing mechanism, the updated reputation is available even in an offline environment. Through simulations, we evaluate our proposed reputation system's performance, demonstrating its effectiveness in achieving a reporting accuracy of approximately 98%. The findings highlight the potential of reputation-based strategies to minimize misbehavior and improve the reliability and security of V2V communications, particularly in rural areas with limited infrastructure, ultimately contributing to safer and more reliable transportation systems.  
V2V; rural environments; reputation; trust; misbehavior; SCMS; security; reporting; CRL +
Footnote †: journal: Future Internet
## 3 Backfilling and Mining Methods  
### Principles of Paste Cut-and-Backfill Mining  
Slope stability in open-pit operations is largely controlled by backfill techniques, which include a variety of approaches such as hydraulic fill, paste fill, and cemented rock fill. By adding materials to mined-out areas, these techniques help to stabilize the surrounding rock mass and increase overall stability. Furthermore, by occupying the spaces left by the mining process, they can assist in mitigating environmental issues like water intrusion and sedimentation [10,11].  
But there are several drawbacks to the paste cut-and-backfill technique as well. In order to guarantee that the cement paste backfill has the required strength and rheological qualities, thorough design and management are required [12,13]. The process can also be expensive and time-consuming because it calls for the high wall to be excavated as well as the shipping and installation of the cement paste backfill [14].  
Overall, managing the high walls of open-pit coal mines can be accomplished with the paste cut-and-backfill approach, but success depends on careful planning and execution.  
In order to mitigate the increased stress on the soil layers, coal pillars will be left in place to accept pressure from the rock layer above throughout the excavation process. This will allow the cement paste backfill method to be employed in this phase of the research and examine the safety factor induced by this method [15]. See Figures 2 and 3 for the highwall mining layout.  
Figure 1: Location of the Vulcan open-pit coal mine.  
1. Preparing the raw materials (cement, fly ash, and waste rock): Cement and fly ash are delivered to silos for reserve, and waste rock is crushed using a crusher system.
2. Weighing of raw materials: Using weighing equipment, each raw material is weighed proportionately based on the intended content (waste rock, fly ash, and cement).
3. Water-based mixing: To ensure that the mixture of various ingredients is balanced, weighed raw materials are given to the mixer, and water is added to stir in line with the planned paste density.
4. Pumping and backfilling: The thoroughly blended slurry is put into the pumping bucket and pumped to the subsurface backfilling working face with the backfilling pump.
# Forensic Analysis of Blackhole Attack in Wireless Sensor Networks/Internet of Things
###### Abstract  
The internet of things (IoT) is prone to various types of denial of service (DoS) attacks due to their resource-constrained nature. Extensive research efforts have been dedicated to securing these systems, but various vulnerabilities remain. Notably, it is challenging to maintain the confidentiality, integrity, and availability of mobile ad hoc networks due to limited connectivity and dynamic topology. As critical infrastructure including smart grids, industrial control, and intelligent transportation systems is reliant on WSNs and IoT, research efforts that forensically investigate and analyze the cybercrimes in IoT and WSNs are imperative. When a security failure occurs, the causes, vulnerabilities, and facts behind the failure need to be revealed and examined to improve the security of these systems. This research forensically investigates the performance of the ad hoc IoT networks using the ad hoc on-demand distance vector (AODV) routing protocol under the blackhole attack, which is a type of denial of service attack detrimental to IoT networks. This work also examines the traffic patterns in the network and nodes to assess the attack damage and conducts vulnerability analysis of the protocol to carry out digital forensic (DF) investigations. It further reconstructs the networks under different modes and parameters to verify the analysis and provide suggestions to design robust routing protocols.  
digital forensics; computer forensics; blackhole attack; wireless sensor network; forensic analysis; internet of things; network simulator (NS3) Article