esa-sceva/satcom-chunk-collection huggingface dataset:
# Binocular Vision-Based Pole-Shaped Obstacle Detection and Ranging Study
## 4 Analysis of Results
### Comparison of Different Attention Mechanisms  
To mitigate the interference caused by complex backgrounds and enhance the ability to focus on important features, this paper presents a hybrid attention mechanism that integrates channel attention and spatial attention. The spatial attention mechanism primarily focuses on the spatial dimension in the input data. It suppresses complex background features and enhances important features by assigning different weights. The channel attention mechanism primarily focuses on the channel dimension in the data. It learns which channels are more important for the task, emphasizes important feature information, and reduces the impact of background noise and redundant features.  
To further validate the effect of the channel attention mechanism and spatial attention mechanism on the model, in this paper, experiments are conducted on the self-made PSO 2023 dataset to test the current mainstream Global Attention Module (GAM), Efficient Channel Attention (ECA), Multidimensional Collaborative Attention (MCA), and CBAM attention mechanisms. The experimental results are shown in Table 5.  
From the experimental results in Table 5, it can be observed that, compared to YOLOv5s without any attention mechanisms, the use of the GAM attention mechanism increased the mAP value by 0.8%. The model parameters and floating-point operations increased by 1.73 M and 1.2 G, respectively, while the model detection rate increased by 113.7 f s\({}^{-1}\). Similarly, with the introduction of the ECA and MCA mechanisms, the mAP values increased by 0.7% and 1.3%, respectively. The model parameters and floating-point operations also increased, and the model detection rates were improved by 131.8 f s\({}^{-1}\) and 136.4 f s\({}^{-1}\), respectively. However, upon introducing the CBAM attention mechanism, the model parameters and floating-point operations only increased by 0.51 M and 0.6 G, while the model detection rate improved to 454.5 f s\({}^{-1}\), and the mAP value also increased by 1.9%. Hence, the CBAM hybrid attention mechanism demonstrates better performance in detecting pole-shaped obstacles in complex environments.
# AI-Driven Safety and Security for UAVs: From Machine Learning to Large Language Models
## 5 Challenges and Future Directions
### Challenges
#### 5.1.1 Real-Time Processing Latency  
The onboard memory of UAVs is typically limited. When high-resolution images and multi-layer Transformer models are executed locally, frequent data reads and writes can cause bandwidth bottlenecks, significantly increasing inference latency [207]. This latency is particularly significant in scenarios such as autonomous UAV obstacle avoidance, target tracking, or response to sudden weather changes, making it difficult to meet the real-time demands of navigation, tactical response, and decision-making [10]. Although current edge computing technologies can significantly reduce the inference latency of LLMs by means of model lightweight, computational offloading, and hardware acceleration, the performance of lightweight models is often insufficient for complex problems, leading to severe inference latency [208]. For example, a lightweight YOLOv8n model on the NVIDIA Jetson Orin Nano achieves only about 10 FPS after optimization, well below the 30 FPS required for real-time navigation [209]. Additionally, offloading inference tasks to the cloud can cause end-to-end latency exceeding 150 ms. This delay allows a UAV to travel several meters before a decision is made, missing the optimal window for obstacle avoidance [210]. Moreover, in a multi-UAV cooperative task, the difference in reasoning delays of different  
Figure 19: Challenges and future directions.  
nodes can cause coordination failure and increase the risk of collision, which seriously affects the safety and efficiency of the task.
## 3 Results
#### 3.1.3 \"Cross-Shaped\" Layout  
In Figure 6, each face utilized three transmitters or six receivers. This configuration aimed to reduce costs and accelerate computation speed by minimizing the number of transmitters and receivers. Additionally, the double-layer arrangement of receivers was implemented to achieve z-axis resolution capability.  
The experimental results aligned with expectations: the three-row transmitter or receiver arrangement along the x-axis provided resolution capability only in the x-direction. Similarly, the three-row arrangement along the y-axis yielded resolution capability only in the y-direction. When these two layout methods were combined, as shown in Figure 6e, the two elongated prediction areas overlapped at the hotspot. This combination resulted in resolution capability in both x and y directions.
# Topology Optimization Based on SA-BESO
###### Abstract  
Bidirectional asymptotic structure methods have long been used to solve topological optimization problems, but are prone to being stuck in local optimal solutions. To solve this problem, this paper proposed a topology optimization method based on the Bi-directional Evolutionary structure Structural Optimization and Simulated Annealing algorithm (SA-BESO). First, the structural elements of the structural partition are encoded by a dual encoding, where elements are assigned with density values and binary strings. Second, binary strings are crossed and mutated, while criteria for adding and removing structural units are formulated. Then, structures are updated randomly. Finally, the structural compliance of the current structure is evaluated. If the structural compliance of the original structure increases, it will be accepted with a certain probability, thus jumping out of the local optimal solution. Related examples show that the SA-BESO method improves the smoothness of the optimization process and can obtain optimized structures with lower structural compliance and computational cost.  
structural topology optimization; simulated annealing algorithm; Bi-directional evolutionary structural optimization; structural compliance +
Footnote †: journal: Applied
# On Neighborhood Structures and Repair Techniques for Blocking Job Shop Scheduling Problems +
## 5 Neighborhood Structures
### Introducing Interchange- and Shift-Based Neighborhoods
#### 5.1.3 Definition of the Neighborhoods  
In line with the transition schemes described in the previous section, the examined neighborhoods are defined as follows:  
**Definition 4**.: _The **API neighborhood** of a schedule \(s\) is defined as the set of schedules \(s^{\prime}\), where \(s^{\prime}\) is a feasible schedule involving a given API move implemented by a left shift or a right shift._  
**Definition 5**.: _The **TAPI neighborhood** of a schedule \(s\) is defined as the set of schedules \(s^{\prime}\), where \(s^{\prime}\) is a feasible schedule involving a given TAPI move implemented by left shift or right shift._  
**Definition 6**.: _The **TJ neighborhood** of a schedule \(s\) is defined as the set of schedules \(s^{\prime}\), where \(s^{\prime}\) is a feasible schedule resulting from a TJ move._  
In the following, all neighboring solutions constructible through a given API or TAPI are generally denoted as API-based neighbors of a schedule. Note that, due to the required repairing schemes, the actual distances of neighboring schedules are not precisely determined, cf. [15]. The minimum distance of a schedule and its API-based neighbor is given by 1, and the maximum distance is theoretically bounded by \(\sum_{M_{k}\in\mathcal{M}}\binom{|\Omega_{2}^{k}|}{2}\), where \(\Omega^{k}\) defines the set of operations requiring machine \(M_{k}\). As mentioned in the previous section, schedules in the TJ neighborhood might have a minimum distance of 0 to the initially given one, while the maximum distance is equivalently restricted by the structural upper bound. While the leftward shifting strategy applied by the ART in the adaptation of the permutation is required for the termination of the method, it is not guaranteed that the smallest number of necessary changes is implemented. To the best of the authors' knowledge, there does not yet exist a general neighborhood structure or repairing scheme for BJSP schedules capable of certainly constructing the closest possible neighbor for an initial solution and a given change. An empirical study on the distances resulting for the proposed neighborhoods together with the ART is reported in the next section.
# Analysis of Internal Gas Leaks in an MCFC System Package for an LNG-Fueled Ship
## 4 Conclusions  
The flow characteristics inside a fuel cell balance-of-plant (BOP) package were analyzed through computational fluid dynamics (CFD) techniques. The simulation was used to interpret the characteristics of gas, pressure, and temperature flows according to chemical species. The developed model indicated that the inside flow structure plays an important role not only in heating the internal units, but also in the distribution and emission of the leaked gas. In addition, the heat flow was confirmed to be controlled, excluding the entrance and exit of the stack module. However, the flow field through the stack module, the outer walls of the package, and the top of the module were not cooled to the ambient temperature. Moreover, temperature increases were observed at the rear side of the stack module, in the stagnant area at the top of the stack module, and within the leaked gas accumulated in these areas. Therefore, the density distribution can be decreased by lowering the temperature inside the package and quickly venting the leaked gas. The capacity to supply external air should thus be designed by generating a smooth flow between the stack module and the outer walls of the package.  
The interpretation of the 300-kW MCFC BOP package model shows that the flow of air generated inside the package and the surface temperature of the heating apparatus influenced the flow of heat and air in the package. In other words, if the heat generated is higher than that of the exothermic device, the temperature of the surface of the heated ventilation system would need to be kept at less than the ambient temperature. In this case, the design of the airflow inside the device is important for preventing an increase in the temperature of the internal device.  
The simulation of gas leakage and fuel gas density in the BOP package has indicated that the natural gas line should be ventilated to the outside of the chamber because methane diffused in all directions throughout the whole chamber, whereas the hydrogen gas flowed directly upwards. Therefore, the safety ventilation outlet should be located with the gas alarm on the ceiling near the hydrogen pipeline. For maximum safety, the ventilation and alarm systems should be designed according to the simulated flow field.  
[PERSON] wrote the manuscript, conceptualized, and analyzed; [PERSON] and [PERSON] analyzed and validated results; and H.K. supervised.  
This material is based upon work supported by the Ministry of Trade, Industry & Energy (MOTIE, KOREA) under industrial Core technology Development Program. NO. 10070159, 'Development of CCS technologies (design and verification) to apply LH\({}_{2}\) Carrier.'  
The authors declare no conflict of interest.  
Figure 10: Pressure distribution at vertical section inside the MCFC stack package at 25 \({}^{\circ}\)C.
## 3 Results and Discussion
### Stage III
#### 3.3.2 Moisture Content  
The quadratic model facilitated the significant prediction (_p_-value < 0.05) of MC of tested model solutions. A very well-fitting model to the data obtained from the experiment was observed, which was confirmed by the high value of the determination coefficient (R\({}^{2}\) = 0.8754) and the insignificant result of the lack of fit test (_p_-value > 0.05) (Table A2). On the basis of the estimated parameter values (Equation (4)) and response surface plots (Figure 8), it can be noted that increasing the IAT and FC had a negative effect on MC, while increasing IAH had a positive influence on MC. The effect of IAT was larger than IAH and FC. However, ANOVA showed IAT significantly influenced the MC of MD powders (both linear and quadratic effects), while IAH and FC terms were not significant (_p_-values < 0.05).  
As aforementioned, powders with MC below 5% can generally be considered as stable [23]. MC produced MD powders varied from 4.6 \(\pm\) 0.1% to 8.3 \(\pm\) 0.6% (Table A1). A higher IAT in combination with drying air of low humidity, as mentioned before, increase the heat transfer rate, thus the driving force of water evaporation is greater, which leads to the production of powders of lower MC. Taking into account the effect of solely IAT, which was the term that significantly influenced the MC of model powders, [PERSON] et al. (2012) and [PERSON] et al. (2017) observed the same relationship between IAT and MC of black mulberry juice and stingless bee honey powders [34; 35]. As for the IAH, [PERSON] et al. (2008), who spray dried skim milk powder, noted that when the relative humidity of drying air increased, the MC of powders increased as well [24]. Moreover, the authors underlined that MC is strongly dependent on the _a\({}_{w}\)_ of the material, which was confirmed as well by PCA in this research (Figure A4). FC influences the MC of the final product, as with the increased FC, there is less total moisture to evaporate. Secondly, increased FC usually leads to formation of particles of a larger diameter, thus the surface from which the moisture must evaporate is larger, which consequently increases the MC of the powders.  
Figure 8: Moisture content (MC) response surface plots for paired factors: (**A**) inlet air temperature (IAT) and inlet air humidity (IAH); (**B**) inlet air temperature and feed solution concentration (FC); (**C**) feed solution concentration (FC) and inlet air humidity (IAH), when the third factor was adjusted to its midpoint.  
When considering the effect of FC combined with IAT or IAH, which is similar to the effect of solely FC, the increase in both terms at the same time, decreased the MC of the model MD powders (Figure 8, Equation (4)).  
\[\begin{split} MC=8.09-0.9063\cdot IAT+0.2437\cdot IAH-0.21833\cdot FC+0.2 063\cdot IAT\cdot IAH-0.0062\cdot IAT\cdot FC-\\ 0.3562\cdot IAH\cdot FC-1.63\cdot IAT^{2}\end{split} \tag{4}\]
# New Approaches in Dynamic Metrics for Lighting Control Systems: A Critical Review
## 5 Discussion of Results  
### DA\({}_{300}\) and DA\({}_{500}\) and Non-Spatial Metrics' General Patterns  
The results obtained from DA highlight notable differences between thresholds. The variation between DA calculated with 300 lx (DA\({}_{300}\)) and at 500 lx (DA\({}_{500}\)) thresholds is particularly pronounced, with the latter yielding values up to 46% higher in certain configurations, such as in the SEV40N_500 case (Figure 5).  
All dynamic metrics evaluated--including DA, DAo, DAcon, DAo.con, and Circadian Stimulus Autonomy (CSA)--exhibit a consistent spatial pattern: values tend to decrease with an increasing distance from the window. However, the rate of decline and the final values reached within the test space vary across metrics, reflecting their distinct sensitivities to daylight distribution.  
The key aspects to highlight from this section are as follows:  
* Significant differences observed between DA\({}_{300}\) and DA\({}_{500}\), with DA\({}_{500}\) showing up to 46% higher values in some cases.
* All dynamic metrics show a spatial decrease with distance from the window.
* The rate of decrease and final values differ between metrics, indicating a varying sensitivity to daylight distribution.
# Design and Control of a Battery Charger/Discharger Based on the Flyback Topology
## 6 Simulation Results  
This section presents circuital simulations of the proposed battery charger/discharger, which validate the design procedure developed in the previous sections. Figure 10 shows the circuital implementation of the battery charger/discharger in the power electronics simulator PSIM, where the flyback converter follows the same circuital structure described in the theoretical circuit of Figure 2, including the voltage sensors for \(v_{dc}\) and \(v_{b}\), and the current sensors for \(i_{p}\), \(i_{s}\) and \(i_{dc}\).  
The circuital implementation also includes the implementation of the adaptive sliding-mode controller described in Figure 3, where can be observed the dynamic calculation of \(K_{i}\). Such SMC implementation is defined by the hysteresis band limit \(\delta\Psi\) using two voltage sources, but such a limit could be also imposed using zener diodes, requiring a single power source for polarization. The addition/subtraction and gain functions can be implemented using operational amplifiers, while the multiplication and division functions can be implemented with integrated circuits. Moreover, the S-R flip-flop produces the main \(u\) and complementary \(\bar{u}\) control signals, which are observed in the circuital implementation of Figure 10. Finally, the simulation uses a current source to produce the dc current profile in the bus, which could produce three possible states: discharge the battery (\(i_{dc}>0\)), charge the battery (\(i_{dc}<0\)), and battery in stand-by (\(i_{dc}=0\)); thus, the SMC must be able to regulate the charger/discharger in those operating conditions.
# Short Cyclic Algorithm for Turbo Block Construction
## 4 Turbo Block Construction--New Algorithm
As a result, the lane width between the red and blue spiral, in the sector enclosed between translation axes _Ta3_ and _Ta4_, starts at the axis _Ta3_, with the pavement width equaling _Wi_ and not _Wm_. Although, the nominal width of the middle segment of the pavement is _Wm_ (as labeled in Figure 3), it starts with a width equal to _Wi_. The width of the pavement enclosed between the red and blue spirals drops to _Wm_ at the translation axis _Ta4_. This is the location where the set of arcs with radii _Rm4_, _Rc4_, _Rc5_, and _Rm5_ is attached to the arcs with radii _Rm2_, _Rc2_, _Rc3_, and _Rm3_. Blue center _C45_ of these four new arcs is to be moved for _Rm4-Rm2_ along axis _Ta4_, from blue center _C23_. Consequently, the pavement width between red and blue spirals, at the _Ta4_ location, equals _Wm_. At the top of Figure 12, a detailed disposition (\"Detail a\") of roundabout centers is given. Detailed transitions from first to second and from second to third segments are marked with \"Detail b\" and \"Detail c\". These two details are given enlarged in Figure 10.0  
\"Detail b\" in Figure 13 shows how radii _Rm4_ and _Rm2_, as well as radii _Rm3_ and _Rm1_, are connected at the transition from the first to the second segment. The first segment ends with width _Wi_, which is constant along the entire first segment. To provide for pavement width _Wm_, the arc with radius _Rm4_ should have been moved (copied) for _Wm_ (the width of the middle segment) from the arc with radius _Rm3_ along axis _Ta3_. These preliminary positions of _Rm4_ and _Rm5_ (and _Rc4_ and _Rc5_) are shown in bright blue. But, to precisely connect to _Rm2_ and _Rm3_, the entire set of bright blue arcs had to be pushed additionally for _Wi-Wm_ outwards (amber vector). Thus, the final positions of _Rm4_ and _Rm5_ (and _Rc4_ and _Rc5_) are reached. These are the red arcs. Figure 10.1 “Detail b”—transition from first to second segment; “Detail c”—transition from second to third segment. Similarly (Figure 13 \"detail C\"), the arc with radius _Rm6_ (and _Rc6_) is to be moved (copied) from the arc with radius _Rm5_ for _Wo_ along axis _Ta4_. But, to connect to arcs with radii _Rm4_ and _Rc4_, they must be pushed further for _Wi-Wo_ along the same translation axis (dark green vector). Even after that, _Rm6_ and _Rc6_ would not precisely click with _Rm4_ and _Rc4_. As _Rm4_ and _Rc4_ had been moved for _Wi-Wm_ along the axis _Ta3_ in the previous instance (amber vector), _Rm6_ and _Rc6_ must travel along this vector as well (magenta vector is identical to amber vector). Figure 14 shows the dispositions of all the centers. All centers defining the first segments (centers _C1_ and _C23_) sit at the large square whose vertices coincide with the intersection points of translation axes. All the centers defining the third part of each spiral (green arcs) sit at the slightly rotated green square whose vertices still rest on the translation axes. Finally, the centers defining the finishing parts of each spiral form the amber square, the smallest of all squares.
# Review on Silver Nanoparticles as a Novel Class of Antibacterial Solutions
## 5 Challenges and Future Prospects  
The main challenge in predicting the antibacterial efficiency of a newly synthesized silver nanoparticle resides in the inhomogeneity of data in up-to-date literature. The large variety of sizes, shapes, synthesis methods, surface chemistry, the wide spectrum for choices of characterization techniques make data difficult to integrate and analyze. Moreover, the variety of bacterial strains used for testing result in a wide spectrum of identified bacterial cell targets and pathogenetic bacterial mechanisms. However, consensus exist on the benefit of usage of silver nanoparticles as antibacterial agent, and their potential to represent a promising tool against resistance [66]. The physical interaction with bacterial cell membrane, coupled with ionic silver elution, may serve as supplementary mechanisms in antibiotic therapy, resulting in enhancing standard antibiotic treatment effects [67]. Also, future directions of use may benefit from the already validated data on antimicrobial effects of silver nanoparticles. Medical devices with AgNPs films would represent a new step in using their antibacterial protection [68; 69]. Usage in dental amalgams and various dental treatment and hygiene products could also represent a future direction [70]. Use of silver nanoparticles in textiles and regenerative-intend films (wound healing, burn treatment) may open new doors for the development of the field [71; 72]. Osteo-synthesis products are also to be benefit from the antimicrobial effect of silver nanoparticles [73; 74]. Also, non-medical, environmental applications are increasingly appealing (water, soil treatment) [75].