esa-sceva/satcom-chunk-collection huggingface dataset:
# Cellular and Virtualization Technologies for UAVs:
## 3 Testbed Components Description  
Until now, UAVs that have been deployed forming an aerial network are typically controlled using an RLoS link. Therefore, a GCS (in charge of UAV management) should be geographically adjacent to the UAVs in order to properly establish this link for its management. In consequence, as each GCS manages its own set of UAVs, the coexistence of several UAS in the same airspace is not feasible (i.e., if a UAS is operating in relative proximity to other UASs without considering the operations performed by the other UAS, the risk of having an accident is logically high).  
For the correct establishment of next-future UAV applications, UASs must be connected to the UTM regardless of the technology chosen for this connection, which can be separated into three different alternatives: direct connectivity (e.g., packet network), 3 GPP cellular connectivity, or satellite communications. This UTM manages the airspace, ensures safe operations, and computes the aircraft flying trajectories to avoid collision and accidents. The interconnection of all the elements encompassing a UAS with the UTM opens the BRLoS UAS management paradigm, producing a more automatic and agile deployment in comparison with legacy UAS scenarios and possibly allowing for an integrated airspace.  
As can be appreciated in Figure 2, this article presents a testbed to experiment with new UAV services and applications managed by both BRLoS and RLoS models. However, all the experiments and efforts have been designed to properly establish the BRLoS management (RLoS models have been studied in our previous work [10; 42]). To this purpose, both the GCS and UAVs are connected to a UAV-controller (in this case, also acting as UTM) using the regular 4G/LTE cellular network. The following subsections describe the communication architecture, the automated UAV control system, and the virtualization platform, including the devices and technologies chosen for its design.
## 3 Results  
### Supplementation of Ex-Situ Biofloc in Shrimp Grow-Out Culture at Low Salinity Conditions  
#### 3.1.1 Water Quality Parameters  
All water quality parameters, including temperature, DO, pH, salinity and alkalinity were maintained at the optimum range for shrimp growth (Table 1), while the biofloc concentration was kept at 0.3% (_p_/_v_) for 95%C+BF and 90%C+BF treatment groups.  
Ammonia, nitrite, nitrate and phosphate levels in the treatments supplemented with biofloc were lower than control (100%C) (Figure 2a-d). The levels of ammonia, nitrite, nitrate and phosphate in 100%C were dramatically increased along the culture period while those in 95%C+BF and 90%C+BF were slightly up until week 4 and were then stable. Ammonium concentrations in biofloc-supplemented groups were significantly lower than the control group (_p_ < 0.05) at week 4 (Figure 2a). Nitrite and nitrate concentrations in biofloc-supplemented groups were significantly lower than the control group (_p_ < 0.05) at week 5 (Figure 2b,c). Phosphate concentration in biofloc-supplemented groups were significantly lower than the control group (_p_ < 0.05) at week 2 (Figure 2d). The results  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Parameter** & **Optimum Range** & **100\%C** & **95\%C+BF** & **90\%C+BF** \\ \hline Temperature (\({}^{\circ}\)C) & 28–32 & 27.4–30.3 & 27.8–30.8 & 27.8–31.0 \\ Salinity (ppt) & 0.5–35 & 11.0–15.7 & 10.7–16.0 & 11.7–16.7 \\ DO (mg/L) & 5.0–9.0 & 5.8–6.4 & 6.0–6.4 & 5.9–5.5 \\ pH & 7.0–8.3 & 7.2–7.5 & 7.3–7.5 & 7.3–7.5 \\ Alkalinity (CaCO\({}_{3}\)/L) & 50–150 & 50.0–70.7 & 50.0–83.3 & 53.3–80.0 \\ Biofloc volume (mL/L) & 2.0–15.0 & 0.6–0.7 & 3.8–4.4 & 3.4–3.8 \\ \hline \end{tabular}
\end{table}
Table 1: Water quality parameters for six weeks shrimp grow-out culture.  
showed that microbial community in the ex-situ biofloc may play a role in managing and stabilizing inorganic nitrogen and phosphate molecules in the shrimp culture.
## 3 Results
### Threshold Determination of RTI  
Figure 6 shows the histograms and their statistical properties of non-red tide and red tide pixels determined by RTI calculated from the wavelength bands of a PlanetScope image acquired on 29 July 2018 at 01:42 GMT, where 10,000 pixels were selected randomly from the non-red tide and red tide pixel groups. As shown in Figure 6a, the RTI histograms of both groups showed a symmetrical bell-shaped distribution. Figure 6b shows the RTI values for both groups. The RTIs in both groups were distributed in a similar range. The minimum RTI of the non-red tide group was larger than that of the red tide group, whereas the opposite was true for the maximum RTI. Both groups showed similar mean and median RTI values. From the overlapped histograms and their similar statistics for both groups, we concluded that RTI was not sufficient to distinguish between red tide and non-red tide pixels via a threshold. Figure 7 shows the RTI thresholds determined by the median RTI values of the red tide group for 36 PlanetScope images. We recognized the red tide areas when the RTI value was above the threshold determined for each image.  
Figure 6: (**a**) Histograms of red tide index (RTI) values in relation to 10,000 non-red tide pixels and 10,000 red tide pixels in a PlanetScope image acquired on 29 July 2018 01:42 GMT near the coast of Goheung. (**b**) The statistics calculated from RTI distribution of randomly-selected pixels of each group. Min. and Max. are minimum and maximum values, respectively.  
Figure 7: RTI thresholds of 36 PlanetScope images. The threshold was determined by the median RTI value of the red tide pixel group in each image.
## 3 Results
### Eye-Tracking Metrics  
To assess the influence of SAT and scenario urgency on the participants' visual attention, the percentage of the duration of eye gaze on the driving scenario and the central  
Figure 6: Box plots showing the correctness of NDRT under SAT conditions (including outliers). Error bars are 95% confidence intervals.  
control display were analyzed. First, the 3D eye movement data were mapped in 2D pictures, then the driving scenario and central control display were set as areas of interest (AOIs) (As shown in Figure 7, the yellow box vs. the red box), and the duration of eye gaze on the two AOIs was measured during the entire driving experiment.  
The eye gaze duration data were normally distributed (\(p>0.05\)). A three-way ANOVA revealed that the main effect of the SAT on the percentage of the duration of eye gaze on the central control display was significant, F(2, 124) = 5.797, \(p<0.05\), \(\eta^{2}\)P = 0.086. Post hoc analysis revealed that the percentage of the duration of eye gaze on the central control display under the SAT level 1 + 3 condition and the SAT level 1 + 2 + 3 condition was significantly higher than that under the SAT level 1 condition (\(p<0.05\)), but the difference between the SAT level 1 + 3 condition and the SAT level 1 + 2 + 3 condition was not significant (\(p>0.05\)), as shown in Figure 8a. Correspondingly, the main effect of the SAT on the percentage of eye gaze duration in the driving scenario was also significant, F(2, 124) = 5.831, \(p<0.05\), \(\eta^{2}\)P = 0.086, and post hoc analysis yielded similar results (Figure 8b). However, there were no statistically significant effects of scenario urgency and driving stage on the two percentages of eye gaze duration (\(p>0.05\)), and none of the interaction effects were statistically significant (\(p>0.05\)).  
The descriptive statistics revealed that the percentage of eye gaze duration on the central control display was higher under the low-urgency condition (M = 80.75, SD = 12.436) than under the high-urgency condition (M = 79.29, SD = 12.751), and it was higher in stage  
Figure 7: Heat maps showing eye gaze duration under SAT conditions. From top to bottom are level 1, level 1 + 3, and level 1 + 2 + 3. Red represents areas of high attention and yellow and green represent areas of low attention.  
2 (M = 81.08, SD = 12.596) than in stage 1 (M = 78.96, SD = 12.546). The opposite was true for the attention on driving scenarios.
## 3 Results and Discussion
### Oxidation Tests  
Lipid oxidation is very sensitive to temperature [36; 37]; in different oxidation experiments in emulsions, especially those rich in unsaturated lipids, this process could be accelerated by increasing temperatures. It is known that C11-BODIPY 581/591 is oxidized by lipid radicals as part of the propagation reaction. Free-radical-induced oxidation results in the modification of fluorescent properties, manifesting an increase in fluorescence emission observed at approximately 520 nm [38]. Thereby, this fluorescence probe has commonly been used to determine lipid peroxidation in liposomes and mammalian cells [26; 37; 39; 40; 41]. It is also widely recognized that \"chain-breaking\" antioxidants (such as Toc or phenolic compounds) decelerate the process of oxidation by effectively scavenging free radicals [42]. In our study, the oxidation in LA emulsion and the influence of bird cherry extracts on this process was measured with C11-BODIPY581/591 probe used as a fluorescence indicator.
###### Abstract  
When the CNC machining of continuous small line segments is performed, the direction of the machine tool movement will change abruptly at the corner of adjacent line segments. Therefore, a reasonable constraint on the feedrate at the corner is the prerequisite for achieving high-speed and high-precision machining. To achieve this goal, a feedrate-constraint method based on the nominal acceleration was proposed. The proposed method obtains the predicted value of acceleration during the machining process by the machining trajectory prediction and acceleration filtering. Then, the feedrate at the corner is constrained, according to the predicted acceleration. Specifically, for any corner of adjacent line segments, the proposed method assumes that the CNC machining of a short path centered on the corner is carried out at a constant feedrate. First, the actual machining trajectory is predicted according to the transfer function of the servo system. Then, the nominal acceleration, when the CNC machining is carried out to the corner, is calculated and processed by a low-pass FIR filter. Last, the feedrate-constraint value at the corner is obtained according to the nominal acceleration and the preset normal acceleration. The advantage of the proposed method is that it can be used for different machining paths consisting of long segments or continuous small segments and it has no special requirement for the accuracy of the machining path. As a result, the feedrate-constraint value obtained is reasonable and the smooth machining process can be ensured. The simulation results in both 2D and 3D machining paths show that the proposed method is insensitive to the length of the line segment and the angle of the corner, and the calculated feedrate-constraint value is close to the theoretical value, which has good stability and versatility. In contrast, the feedrate-constraint values obtained by conventional methods change abruptly along the machining path, especially in the 3D simulation, which will damage the machining quality. The experiment was performed on a three-axis CNC machine tool controlled by a self-developed controller, and a free-form surface workpiece was machined by a conventional feedrate-constraint method and the proposed method, respectively. The experimental results showed that the proposed method can make the feedrate of the machining process higher and more stable. Then, machining defects such as overcutting and undercutting can be avoided and the machining quality can be improved. Therefore, the article proposes a new method to constrain the feedrate at the corner of continuous small line segments, which can improve the machining efficiency and quality of the CNC machining.  
continuous small line segments; CNC machining; corner; feedrate; acceleration +
Footnote †: journal: Applied Sciences
# Stochastic Model Driven Performance and Availability Planning for a Mobile Edge Computing System
###### Abstract
The use of SPNs has already been successfully applied in the context of MCC in previous works [17; 18; 19]. However, SPNs representing MEC architectures are scarce until the present moment. This paper presents SPN models to evaluate MEC architecture with significant difference to the literature, including sensitivity analysis and unique metrics, such as discard probability of requests and resource utilization level. The proposed models allow evaluating the trade-off between MRTand resource utilization, besides the availability and downtime of the system. In summary, the main contributions of this paper are:  
* SPN models, which are useful tools for system administrators to evaluate the performance and availability of MEC architectures, even before they are deployed. Other types of models could be used, for example the Markov Chains, however, SPNs are equivalent to these models with higher representativeness;
* Sensitivity analysis under the SPN models parameters to identify the most important components;
* Case studies that provide a practical guide to performance and availability analysis in MEC architectures. The remainder of this paper is divided as follows: Section 3 presents the MEC architecture, considered to design the SPN models. Section 4 presents performance SPN models, with respective metrics, case studies, and validation. Section 5 presents SPN availability models (including redundancy) case studies and a sensitivity analysis under the model components. Section 2 discusses the main related works; and Section 6 traces some conclusions and future works.
# Prediction of Unconfined Compressive Strength in Cement-Treated Soils: A Machine Learning Approach
## 2 Materials and Methods  
### Characterization of the Materials Used  
#### 2.1.1 Soil Samples  
Soil samples were collected from a site in the Copou area of Iasi, Romania, at depths of 1 to 2 m to capture variations in subsurface soil characteristics. To preserve in situ moisture levels, the samples were carefully retrieved as both monoliths and bagged specimens, promptly sealed in airtight containers to minimize moisture loss, and transported to the laboratory for analysis.  
A comprehensive set of geotechnical tests was conducted to characterize the soil's physical properties. Grain size distribution, determined through hydrometer analysis in compliance with STAS 1913/5-85 [47], revealed a composition dominated by silt (64%), followed by clay (27%) and sand (9%). Afterberg limit testing yielded a liquid limit (LL) of 36%, plastic limit (PL) of 24%, and plasticity index (PI) of 12%.  
According to the current European standard EN ISO 14688-2:2018 [48], fine soils or fine fractions within coarse composite soils should be classified primarily based on their plasticity characteristics, using the Casagrande plasticity chart. In this case, the material would fall in the medium plasticity clay (CIM) region, albeit near the boundary with silt. However, the material exhibited characteristic silt behavior, including rapid disintegration in water and low cohesion when dry. These behavioral observations, combined with the predominant silt fraction (64%), led to the identification of the soil as clayey silt (clSi) according to EN ISO 14688-1:2018 [49]. Thus, while the plasticity-based classification places the soil within the clay category, its actual behavior and granulometric composition justify its identification as clayey silt.  
Further laboratory assessments quantified the soil's bulk unit weight (\(\gamma_{\text{bulk}}\)) as 16.87 kN/m\({}^{3}\) and moisture content (\(w\)) of 20.24%. The material had a void ratio (\(e\)) of 0.87 and a degree of saturation (\(S_{r}\)) of 0.62, indicating slightly moist conditions. Compaction testing identified an optimum moisture content (OMC) of 14.9% and a maximum dry unit weight (\(\gamma_{d,\text{max}}\)) as 15.37 kN/m\({}^{3}\), critical for evaluating its engineering behavior under densification.
# Electrical Monitoring under Transient Conditions:
## 3 Signal Processing Tools  
The application of the new transient-based diagnosis approaches implies the use of special signal processing tools that are able to provide a time-frequency representation of the analyzed transient signal (e.g., starting currents or fluxes). These are known as time-frequency transforms, and they enable tracking the time-frequency evolutions of the fault components during the corresponding transient as well as to compute the corresponding fault severity indicators based on these evolutions. There is a wide variety of available time-frequency transforms, each of them having its respective advantages and drawbacks. The research carried out in the last years has yielded two main groups of t-f transforms: discrete and continuous [40]. Table 2 shows the respective advantages and drawbacks and specifies some representative examples of each group (note: DWT = Discrete Wavelet Transform; UDWT = Undecimated Discrete Wavelet Transform; WP = Wavelet Packets; CWT = Continuous Wavelet Transform; HHT = Hilbert-Huang Transform; WVD = Wigner-Ville Distribution; CWD = Choi-Williams Distributions). Based on the comments specified there, the discrete transforms seem to be better suited for online diagnosis systems or portable condition monitoring devices where an instantaneous diagnosis is required, whereas the continuous transforms are more suitable for off-line and detailed diagnostics of the motor condition.
# A Comprehensive Survey of Recent Routing Protocols for Underwater Acoustic Sensor Networks
## CEETHCoR  
Reference [72] proposes a cross layer, 2-hop routing protocol called the channel aware energy efficient two-hop cooperative routing protocol (CEETHCoR). The design of the protocol includes physical layer, MAC layer, and network layer. In two-hop communication, the source node selects a next-hop node, a relay node, and a 2-hop forwarder. The data from the source is sent to the 2-hop forwarder through a forwarder and a relay for reliability. The protocol operates in 4 phases, which are: (1) network initialization, during which information about neighbors, such as SNR, number of hops from destination, residual energy levels of nodes, etc. are acquired. (2) RTS/CTS message exchange, which reduces contention, and helps nodes to update their tables and decide to offer themselves as relays. (3) Relay Selection: During relay selection, phase relays are selected based on link quality and symmetry in link quality. (4) Data transmission phase: During this phase, actual data is transmitted and acknowledged. CEETHCoR improves reliability by incorporating a cooperative transmission scheme. Moreover, redundant relay transmissions are controlled by using one cooperative relay transmission over two hops.  
Figure 21: Cooperative routing.
## 3 Multiaccess Resource Allocation Algorithms and Power Control  
The equation presented above involves binary variables \(\{C_{i}^{p}\), \(C_{j}^{p}\}\) and a continuous variable. This formulation represents a challenging MINP issue, which poses difficulties in obtaining optimal solutions. Hence, this article undertakes the task of breaking down the MINP problem into two distinct sub-problems. The first issue to be addressed is the multiaccess channel matching problem involving both CUEs and VUEs. Furthermore, there are power control concerns associated with VUEs. A heuristic approach was developed to collectively assign subcarriers for CUEs and VUEs to address the issue of allocating multiaccess resources. Subsequently, the issue about power has been resolved. This work employs a continuous convex technique as a solution to the non-convex optimization problem it addresses. The SCA technique is employed to convert non-convex problems into convex problems, hence enabling their solution. The reason for dividing the initial _Problem 1_ into two sub-problems _Problem 2_ and _Problem 3_ is to simplify the optimization procedure and reduce the computational complexity. The splitting process yields several advantages. Firstly, it reduces the complexity and computational expense of solving the original _Problem 1_, which is inherently difficult. Secondly, it separates the binary and continuous variables, allowing them to be solved independently using distinct methods. Lastly, it achieves a solution that is nearly optimal, maximizing the sum rate of the VUEs while adhering to the constraints imposed by the CUEs and power limits.
## 2 Data Sources and Methods
### Network Architecture of Our Method
#### 2.2.2 Inner-Encoder and Edge-Encoder  
The inner-encoder and edge-encoder are used to encode the eigenvector extracted by the convolution layers on the pixel, ensuring that the classifier can establish the relationship between the eigenvector and pixel type. In the HSCNN model, the inner- and edge-encoders are both 2 \(\times\) n matrices, where n is the length of the eigenvector.  
Let X denote the eigenvector of the pixel, W denote the encoder matrix, and R the encoded vector result. The encoding calculation is displayed in Equation (1).  
\[\left[\begin{array}{c}\tau_{1}\\ \tau_{2}\end{array}\right]=\left[\begin{array}{cccc}\mathrm{w}_{11}&\mathrm{ w}_{12}&\cdots&\mathrm{w}_{1n}\\ \mathrm{w}_{21}&\mathrm{w}_{22}&\cdots&\mathrm{w}_{2n}\end{array}\right] \times\left[\begin{array}{cccc}\mathrm{x}_{1}&\mathrm{x}_{2}&\cdots&\mathrm{ x}_{n}\end{array}\right]^{\mathrm{T}}+\left[\begin{array}{c}\mathrm{b}_{1}\\ \mathrm{b}_{2}\end{array}\right] \tag{1}\]  
where each row of matrix w represents a fitting function for a particular type of pixel, b\({}_{1}\) and b\({}_{2}\) are the respective biases, and the corresponding component of r is the encoded value of eigenvector x on that pixel type. The inner- and edge-encoders are trained separately.
# Data Security-Based Routing in MANETs Using Key Management Mechanism
## 5 Experimental Results and Comparative Analysis
### Comparative Analysis  
Table 2 shows the comparative analysis among the proposed and the competitive routing protocols for MANETs. The selected competitive protocols are: ENACSC [13], ALARM [14], FACES [16], AMRP [17], STARS [18], and NSC [19]. The number of MANET nodes are set to 40. It is found that the proposed protocol achieves significantly better performance in terms of the average end-to-end delay (EED), packet-delivery ratio (PDR), throughput, and energy consumption. The major difference between the proposed and the competitive protocol is the use of the CK and DK nodes, which reduces the computation time of the MANET nodes significantly. Bold values indicate higher performance. The proposed protocol outperforms the competitive protocols in terms of EED, PDR, throughput, and energy consumption, by 2.4597%, 2.1578%, 2.6246%, and 3.6872%, respectively.