esa-sceva/satcom-chunk-collection huggingface dataset:
# Multifeature Fusion Neural Network for Oceanic Phenomena Detection in SAR Images
## 3 Establishment of the Sample Dataset of Oceanic Phenomena
### Sample Dataset Expansion  
For detection methods based on deep learning, the number of samples is related to the generalization ability. A sufficient and diverse training dataset is key to the excellent performance of deep learning methods in image detection. However, due to the wide coverage of the ocean, the oceanic phenomena are formed by complex mechanisms, which increases the difficulty of constructing the oceanic phenomena dataset of SAR images. Thus, the number of samples in the dataset is very limited, increasing the risk of network over-fitting.  
To satisfy the requirements of network training for data diversity and to achieve satisfactory automatic detection accuracy, we use image expansion methods to expand the training dataset. It increases the image diversity and improves the generalization ability and robustness of the network.  
Common image expansion methods include rotation transformation, flip transformation, scaling transformation, and translation transformation. To avoid the correlation between the detection result and the position of the oceanic phenomenon in the image, we use the translation transformation method to simulate the random distribution of phenomena within the images to improve the translation invariance of the MFNN. We rotate the images to change the attitude information, expand the diversity of the images, and make the MFNN more robust against angular features.  
Land and islands, which often appear in SAR oceanic images, can introduce problems in the detection of oceanic phenomena. Land and islands can be removed by image pre-processing, but this step takes additional time and significantly reduces the ability to automate system operation. Therefore, we do not use pre-processing but consider land, islands, and ocean background as negative samples during training so that these features are judged as a background class during testing to avoid incorrect detection results.  
By randomly cropping, rotating, and stitching the images, we obtained a dataset containing 2000 samples. Each oceanic phenomenon corresponded to 400 images. Furthermore, 200 land and island images and 200 images of oceanic phenomena in other categories were added, bringing the total size of the final dataset to 2400 samples. Finally, considering the retention of image information and the limitations of computing resources, we resized the images of the dataset to \(512\times 512\) pixels.
## 3 Materials and Methods
### Process Response Measurement and Uncertainty  
Process response metrics were measured as follows:  
* Operational KPIs: Cycle Time (CT) and Idle Time (IT) were calculated directly from the timestamped event log by measuring the duration between start and end events for each activity and the time between the end of one activity and the start of the next. Throughput (TP) was calculated as the number of completed cases per day.
* Environmental KPIs: Energy Consumption (EC) was measured by dedicated power meters on each machine, with data logged in kWh. Carbon Emissions (CE) were derived from EC using standard regional emission factors. Waste Generation (WG) was quantified based on the count of rejected products at inspection points, multiplied by the average weight per unit.  
Regarding uncertainty, the primary source lies in sensor precision and data logging granularity. To mitigate this, we worked with data from recently calibrated sensors as per the facility's maintenance schedule. A data cleaning and pre-processing pipeline was implemented to handle missing values and filter out obvious outliers before analysis. The results presented are, therefore, based on the highest quality data available, though we acknowledge that a degree of measurement uncertainty inherent to any industrial sensing system remains.
## 4 Experimental Results and Discussion
### Stiffness and Strength Deterioration  
The index of secant stiffness to measure the stiffness degradation is defined in Chinese Standard JGJ/T101 [43]. The peak point of the first cycle of each loading level was taken for the calculation of the secant stiffness, and the obtained results are shown in Figure 11. The stiffness of Test RW1-1 was significantly greater than that of the other two tests at a small drift level, indicating that the bond capacity of the grout between the wall panels and the foundation contributed significantly to the stiffness of the undamaged wall system. At the later stage of Test RW1-2, the MBSFs participated in the energy dissipation, slowing down the stiffness degradation of the system considerably. Due to the minor local crushing of the concrete before Test RW1-3, the stiffness of Test RW1-3 remained relatively lower than that of Test RW1-2. Furthermore, the concrete cracking on both sides of the wall panels also contributed to the reduction of the stiffness in Test RW1-3. Therefore, it was necessary for the system to enhance the sides of the wall panels and avoid the development of concrete cracks.  
Figure 10: Skeleton curves.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Test No.** & **Yield Drift (\%)** & **Yield Strength (kN)** & **Limit Drift (\%)** & **Limit Strength (kN)** & **Ductility Factor** \\ \hline RW1-1 & 0.39 & 134.6 & 2.5 & 172.4 & 6.4 \\ RW1-2 & 0.57 & 132.6 & 2.5 & 229.2 & 4.4 \\ RW1-3 & 0.69 & 134.1 & 2.5 & 195.9 & 3.6 \\ \hline \end{tabular}
\end{table}
Table 3: Characteristic states of the specimen.  
The average values of strength in the positive and negative loading directions were selected for the evaluation of strength deterioration. The ratio of the peak lateral strength of the second or third loading cycle at each drift level to that of the first cycle was calculated separately as the strength degradation factor, which is shown in Figure 12. All strength degradation factors of the three tests were greater than 0.93, demonstrating that the lateral strength of the specimen was extremely steady during the reversal cyclic lateral loading tests. The unstable factors at the low drift level were caused by some gaps in the loading system.
## 4 Results and Discussion
### Homogeneous Isotropic Turbulence
#### 4.2.2 Effects of the Explicit SGS Models and the DB Model  
In this simulation, the total dissipation after \(t/\tau=1\) is dominated by the solenoidal dissipation \(\epsilon_{s}\) and the modeling dissipation \(\epsilon_{sgs}\) or \(\epsilon_{a}\) if any model is applied because the residual dissipation \(\epsilon_{res}\) is small and the pressure dilation dissipation \(\epsilon_{d}\) vanishes after \(t/\tau=1\). Figure 10 plots time evolution of the modeling dissipation. The Smagorinsky and the Similarity models produce lower level of dissipation, which seems not significantly affected by the solution order. The DB model is sensitive to the initial fluctuation and its behavior is excessively dissipative at the early stage of the simulation. The reason for this is that \(\epsilon_{a}\) is sensitive to magnitude of all components of the velocity gradient, shown in Equation (46). The \(p1\) result present higher \(\epsilon_{a}\) probably because the larger error of the velocity gradient. Though the duration of the initial fluctuation is short, the excessive  
Figure 12: Energy spectra at t/\(\tau=1\) of HIT simulation, \(Ma_{t0}=0.5\), \(120^{3}\) DOFs. (**a**) \(p1\); (**b**) \(p3\).  
dissipation of the DB model last until \(t/\tau=1\), resulting faster drop of the turbulent kinetic energy \(E_{k}\), shown in Figure 7. It can also observed that the excessive \(\epsilon_{a}\) is damped at higher solution polynomial, as shown in Figure 10. The DB model at \(p3\) also produces better resolution of \(\epsilon_{s}\) than \(p1\), though still underpredicted. Results of the decaying \(Re_{\lambda}\) shown in Figure 11 shows no evident difference between the simulation methodologies at \(p1\), while at \(p3\), the DB model produces slightly higher \(Re_{\lambda}\) than the other methodologies at the same order, which indicates that this model may lead to a slightly wider spectra of turbulent scales than the other models. The energy spectrum results at \(t/\tau=1\) shown in Figure 12 again exhibit the excessive dissipation of the DB model at p1 solution polynomial in this test case. It is also observed that at \(p3\) solution order the excessive dissipation of the DB model is damped. This test case indicates that the DB model may behave more dissipative as compressibility of the flow increases, while in this case the given initial conditions have a strong effect on the dissipation features.
## 6 Performance Evaluation
### Simulation Results
#### 6.2.1 Effect of the Number of Queries
We note that, however, GQRT performs better than BQRT because the server in GQRT assigns much larger resident domains to the moving objects than that in BQRT for the reason mentioned in the description of Figure 8. Under the default parameter settings, the average sizes of resident domains assigned to the moving objects in GQRT and BQRT for _Uniform_ are 58.7 km\({}^{2}\) and 37.5 km\({}^{2}\), respectively. On the other hand, those in GQRT and BQRT for _Skewed_ are 52.4 km\({}^{2}\) and 34.1 km\({}^{2}\), respectively. It is also observed from the figure that SR performs the worst for _Uniform_, whereas MQM performs the worst for _Skewed_ due to the limitations of the BP-tree used in MQM. The details of the limitations of the BP-tree are described in our previous paper [1]. In all  
Figure 9: Total number of messages _vs._ cardinalities of _Uniform_ and _Skewed_. (**a**) _Uniform_; (**b**) _Skewed_. the cases, GQRT performs the best in all the cases. As compared to SR, MQM, QRT, and BQRT, GQRT incurs \(7.2\%\), \(9.7\%\), \(17.6\%\), and \(71.4\%\) respectively, of the communication cost for _Uniform_. On the other hand, GQRT incurs \(12.7\%\), \(10.9\%\), \(16.9\%\), and \(67.6\%\) of the communication cost as compared to SR, MQM, QRT, and BQRT, respectively, for _Skewed_.
## 4 Implementation and Deployment
Binary variable constraints, i.e., \(x_{ij}^{k}\in\{0,1\}\quad\forall i\in N,\forall j\in N,\forall k\in K\). _Goal:_ Minimization of the function \(\sum_{k\in K}\sum_{i\in N,j\in N,j\
eq i}c_{ij}x_{ij}^{k}\). Figure 12: Formalization of pickup and delivery multi-vehicle problem with time windows. Figure 13: An example of a graphical representation of an input to the pickup and delivery problem with time windows, with 17 locations, identified by integer ids from 0 to 16, and their associated time windows, shown within square brackets. Now, given the formalization, once an approximation algorithm is identified, the decision/optimization of the routes to assign to vehicles to satisfy requests is performed at the beginning of any working day by the EGC component of the system. After this initial phase, vehicles are loaded and the system is in a regular operational scenario, i.e., vehicles proceed to follow assigned routes and to visit specified locations. During such a scenario, the EMERGE system (the EGC component) starts collecting (i) data on the status of vehicles and the network via V2X (terrestrial and/or satellite) from the vehicles themselves; (ii) traffic data provided by third-party services, if available. The collected data are processed at EGC via suitable estimation/AI algorithms in order to detect/predict the possibility of congestion events occurring on road segments of routes assigned to vehicles (we refer the interested reader to [53; 54; 55] and references therein for some results in this area concerning the estimation of dynamical models with control purposes). If EGC detects or predicts with sufficiently high probability that a change in the status of the network will affect portions of the network itself that do not overlap with routes being followed by vehicles or that overlap but with observed or predicted delays that are not large enough to induce violations in the time windows of locations to be visited, then vehicles are not \"warmed\", and the system proceeds in a regular operational scenario: i.e., each vehicle keeps operating on the originally assigned route. Conversely, if observed or predicted delay/disrupting events happening in the network (or affecting vehicles) make it impossible to continue planned operations without violations of time windows (i.e., at least one scheduled visit cannot be performed correctly), then EGC implements the following attempts to reoptimize the plan for visits and corresponding routes:  
1. EGC computes a set of alternative routes, one per vehicle, trying to preserve the satisfiability of the time-window constraints;
2. If the computation is successful, EGC sends updated routes to affected vehicles, bringing the system back to a condition of regular operation;
3. If the computation fails, i.e., there exists at least one location that cannot be visited by any of the vehicles in the prescribed time window, then EGC marks such location(s) as discarded, removes it/them from the set of users' requests, storing a trace of the events that caused such removal(s), and tries to compute a new, alternative plan of routes for remaining users' requests.
## 4 Performance Validation
### Performance Metrics of Scheduling Algorithm  
Scheduling algorithms optimize the use of resources by managing their allocation and are widely employed in various systems. One of the critical aspects of scheduling is the ability to measure the algorithm's performance. This section will discuss three standard performance measures used in scheduling: turnaround time, waiting time, and context switching [10; 15].
# Overlap Avoidance of Mobility Models for Multi-UAVs Reconnaissance
## 2 Related Work & Research Motivation
### Related Work
A UAV reconnoiters the scanning area with repulsive pheromone when there is no target. On the other hand, the attractive pheromone is employed for target tracking. If a UAV discovers a target, the UAV traces that target by attractive pheromone. Random Destination with Partitioned Zone is another model which reconnoiters by communicating with each UAV [15]. This model divides the scanning area to be reconnoitered into \(n\times n\) zones and manages the number of visited destinations in each zone by communicating with the nearby UAVs. When a UAV flies to the destination, it stochastically selects the zone with fewer destinations.
# Optimization of the AODV-Based Packet Forwarding Mechanism for BLE Mesh Networks
## 4 Optimized-AODV Protocol Topologies and Testbed Design
### Experimental Result Findings
Nonetheless, the improved efficiency of O-AODV in terms of the average one-way delay and overheads compared to the flooding-based mesh protocol had a tradeoff, in terms of the achieved Packet Delivery Ratio (PDR). For example, in Scenario 3, although O-AODV showed a higher PDR (82%) than AODV (69%), it was lower than the 92% achieved by the flooding-based mesh protocol. Nonetheless, that tradeoff may well be worthwhile given that the higher PDR of the flooding-based mesh was achieved only with much higher average one-way delays, whereas message delivery reliability can be improved via selective message retransmission in the application layer, which was expected to incur lower overheads and a lower increase in the end-to-end delays compared to the flooding-based mesh protocol. In addition, for this research, we optimized the proposed protocol for use in scenarios involving static nodes since AODV was originally designed for mobile nodes.
## 1 Introduction  
Biochar is a kind of solid carbonaceous residue produced from low-temperature pyrolysis of biomass under oxygen-free or low oxygen conditions. Biochar has distinct physico-chemical properties (i.e., highly condensed aromatic structures, hydraulic conductivity and cation exchange capacity), which can vary depending on the feedstock [1]. Recently, biochar soil amendment has received a substantial amount of attention because biochar could increase crop yield and enhance soil carbon sequestration [2]. Several factors have been proposed to impact crop production, such as soluble nutrients contained in the fresh biochar, the sorption of growth-inhibiting substances by biochar, the retention of dissolved organic matter in soil, and the ameliorated soil properties [3]. However,a growing body of evidence suggests that the enhancement of crop production is correlated with biochar-induced changes in soil biota, a component of the terrestrial ecosystem that plays a key role in nutrient cycling [4].  
Biochar-induced stimulation of microbial activities, along with changes in microbial community composition and structure, has also been reported [5]. It seems that fresh biochar supplies labile organic carbon fractions for soil biota, whereas the direct evidence for the transfer of available carbon and nutrients from biochar to microorganisms is still lacking [6]. Moreover, chemical compounds that inhibit microbial activities have also been found in biochar [7]. Other studies have indicated that biochar supplies a habitat for soil microbes [8], but so far only a few microscopic studies have directly observed microbial colonization in biochar and the reason for this process remained uncertain [6]. Actually, it is unlikely that these biochar-induced changes spread equally across different phylotypes or functional groups [9].  
Biochar has been reported to positively or negatively affect soil microbial abundance [10]. The Illumina sequencing of soil bacterial 16S rRNA genes suggested that the relative abundance of Acidobacteria (phylum), Chloroflexi (phylum), and Gemmatimonadetes (phylum) decreased but that of Proteobacteria (phylum), Bacteroidetes (phylum), and Actinobacteria (phylum) increased after biochar soil amendment [11]. A pot experiment showed that biochar had a positive influence on the abundance of Braydyrhizobiaceae (family), Hyphomicrobiaceae (family), Streptococcusporangineae (family), and Thermomonosporaceae (family), but had a negative effect on the abundance of Streptomycesetaceae (family) and Micromonosporaceae (family) [12]. So far, almost all the studies have focused on the changes in microbial community above the family level, while there has been little information about the effect of biochar on bacteria at genus or species levels. Furthermore, very little is known about how individual bacteria are affected by biochar. In this study, influences of biochar and its water-soluble compounds on the growth of individual bacteria were examined in the artificial culture, and the results suggested that biochar together with its water-soluble compounds had a strain-specific impact on bacteria growth.
## 5 Experimental Comparison with HS Variants and Well-Known EAs
### Overall Statistical Comparison among HS Variants  
Table 2 presents the overall statistical comparison results for the aHSDE and its competitors based on the Wilcoxon rank-sum test with the significance level \(\alpha\) of 0.05 for each dimension case on all the benchmark functions. The symbols \"+\", \"\(-\)\" and \"\(-\)\" mean that the aHSDE performs significantly better, significantly worse, or not significantly different compared with its competitors. Overall, it demonstrated that the performance of the aHSDE is quite competitive compared with four HS variants on the CEC2014 benchmark.  
The following facts can be observed from Table 2. For three unimodal functions, the aHSDE outperforms all its competitors for 10, 50 and 100 dimensions. For thirteen multimodal functions, the aHSDE performs a little better than HS on 10 dimensions and performs much better than HS with the increase of dimension size for all competitions and all functions. For six hybrid functions, the aHSDE clearly outperforms HS, IHS, SGHS and IGHS for all the cases. These results illustrate that the aHSDE has a superior advantage to the state-of-the-art HS variants when solving various optimization problems, whose varieties may have no features. For the eight composition functions, the aHSDE significantly outperforms HS, IHS, SGHS and IGHS for 10, 50, 100 dimensions, except that the aHSDE performs comparably to SGHS for the 50-dimensional case and IGHS for the 10-dimensional case, respectively. The advantages are more obvious on higher dimensional functions. As a whole, the aHSDE performs much better than the canonical HS algorithm and HS variants in total on 10, 50, 100 dimensions on 30 benchmark functions for all dimensional cases.
## 3 Results and Discussion
### Parametric Study
#### 3.3.3 Effect of Vehicle Wheel Loads  
Figure 9 shows the effects of different wheel loads on the total settlements, vertical stresses and axial forces of solidified soil embankments. As shown in Figure 9a, all the total settlements under different wheel loads were upside-down italicized '\(\gamma\)'-shaped. As  
Figure 7: The effects of the cohesion on solidified soil embankment: (**a**) total settlement along the depth of embankment, (**b**) vertical stress, (**c**) total settlement along AC, (**d**) total settlement along AB, (**e**) axial force along the depth of pile.  
the vehicle wheel load increased, the total settlements at point A and C also increased. However, the total settlements of the subsoil (at point C) under different loads were still small. Furthermore, differential settlements were observed on the top surface of the embankment, and these differentials rose as the vehicle wheel loads increased. These differential settlements may cause tensile stress in the pavement. In Figure 9a, the largest total settlement was 17.5 mm, which was only about one eighth of the total settlement in gravel embankment. The total settlements of solidified soil embankment remained small, even under heavy loads. This indicates that solidified soil embankments possess the ability to withstand heavy wheel loads with minimal settlement. Figure 9b shows that increasing vehicle loads led to a remarkable increase of the vertical stress on the upper part of embankment. The maximum value of the vertical stresses also increased with increasing vehicle loads. There was little change in the vertical stresses at point C, which remained around 0 kPa. However, the axial force significantly increased, as the vehicle loads increased in Figure 9e. Furthermore, the height of soil arch increased with increasing vehicle loads shown in Table 6. When the vehicle loads increased in solidified soil embankment, more loads were transferred to pile due to increasing soil arching height. It is indicated that the pile undertook the majority of embankment and dynamic loads, while the soft soil surrounding the pile contributes less to the load-bearing capacity. Figure 9c,d shows that all total settlements along AB and AC increased with increasing vehicle loads. The differential settlements between point C and point A increased as well. For instance, when the wheel loads increased from 25 kN to 137.5 kN, the total settlements of subsoil (point C) increased from 6 mm to 15 mm. This slight increase means that only a small portion of the load transferred to the soft soil. The total settlements in solidified soil embankment were within acceptable range compared to the gravel embankment.