esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Adaptive Hybrid GA-SAA Algorithm
### Solving Algorithm Framework  
The adaptive hybrid GA-SAA algorithm is described in Algorithm 1, which is further illustrated in Figure 5.  
```
Input \(\text{HSR}\) network \(G=(V,E)\), train set \(L\), travel demand \(q_{rs}\), operation parameters in the model, objective weights \(w_{1},w_{2},w_{3}\), population size \(P\), elitist individual number \(m\), tournament size \(n\), crossover probabilities \(p_{c-\max}\), \(p_{c-\min}\), mutation probabilities \(p_{m-\max}\), \(p_{m-\min}\), neighborhood searching probabilities \(p_{skip}\), initial temperature \(T_{0}\), temperature drop ratio \(\theta\), and the maximum generation number \(g_{max}\).
```  
**Algorithm 1** The adaptive hybrid GA-SAA algorithm framework  
**Output** Optimized stop plan \(S^{*}=\{S_{i}|l\in L\}\).  
**Step 1 Initial source population generation and evaluation.**  
Generate the initial source population \(pop(0)\) by the technique in Section 4.3. Evaluate the individuals by the passenger flow method. Set the generation number \(\text{g}\gets 1\) Go to **Step 2**.  
**Step 2 Elitist preservation operation.**  
Select the best \(m\) individuals in the source population \(pop(g-1)\) to the successor population \(pop(g)\). Go to **Step 3**.  
**Step 3 Selection operation.**  
Use the Tournament Selection strategy in Section 4.4.1 to select 2 parent chromosomes. Go to **Step 4**.  
**Step 4 Recombination operation.**  
Use the crossover operator and mutation operator in Sections 4.4.2 and 4.4.3 to generate 2 child chromosomes, and adjust them according to the constraints. Collect the child chromosomes in the successor population \(pop(g)\).  
Go to **Step 5**.  
**Step 5 Examination of population size.**  
If the population size of \(pop(g)\) does not reach \(P\), then go to **Step 3**; otherwise, evaluate the individuals in \(pop(g)\) by the passenger assignment method and go to **Step 6**.  
**Step 6 Neighborhood searching based on SAA.**  
Update the current temperature \(T\)\(\leftarrow\)\(T_{0}\)\(\cdot\)\(\theta^{8-1}\). Randomly select an individual in \(pop(g)\) and use the neighborhood searching strategy in Section 4.5 to update \(pop(g)\). Go to **Step 7**.  
**Step 7 Examination of end condition.**  
If \(g<g_{max}\), then \(g\)\(\leftarrow\)\(g\) + 1 and go to **Step 2**; otherwise, go to **Step 8**.  
**Step 8 Solution decoding.**  
Output the individual with the highest fitness and decode it into the best stop plan \(S^{*}\).  
Figure 5: Flowchart of the solving algorithm framework.
## 4 Research Methods
### Inter-Agent Communication Framework  
The system implements an advanced communication framework between multiple specialized agents to deliver personalized, location-aware recommendations. Figure 3 illustrates the multi-agent interaction model, in which each agent performs specific functions while maintaining synchronized communication with other system components, while Figure 4 provides a detailed structural view of the agent framework through a UML class diagram.  
Figure 4: Multi-agent system interaction diagram.  
As shown in Figure 5, at the core of this framework, the user mobile agent serves as the primary interface between the customer and the system's specialized agents. This agent orchestrates a complex network of interactions.  
The product management agent processes product queries and manages inventory data, working in tandem with the mobile search agent to incorporate location-specific product availability. When a user initiates a search, the location agent provides real-time geographical coordinates that help contextualize the search parameters. This location data is then processed by the mapping agent (the GIS) to generate relevant spatial information, including store locations and delivery routes.  
Security and user data management are handled by the user management agent, which maintains secure authentication protocols and manages user preferences across sessions. This agent ensures that all communications between other agents are properly authenticated, and that user data remain protected throughout the recommendation process.  
The interaction flow is designed to minimize latency while maximizing data accuracy. When a user initiates a request, the system triggers a cascade of agent communications, with each agent contributing specific data or a functionality to generate comprehensive, location-aware recommendations. This coordinated approach ensures that users receive relevant suggestions based on their location, preferences, and real-time product availability.  
The system employs asynchronous communication protocols to maintain responsiveness, allowing agents to process requests and update information independently while maintaining system coherence. This architecture enables the system to scale efficiently while providing consistent performance across varying load conditions.
## 4 Data Analysis and Findings
### Hypotheses Testing Measurements
#### 4.3.2 Coefficient of Determination and Predictive Relevance
Figure 3 presents a snapshot of the R\({}^{2}\) and Q\({}^{2}\) results from the SmartPLS 3.0 PLS-SEM application, which considers that the same results are depicted by the application to each of the dependent variables.
###### Abstract  
Safe and efficient operation of port cranes is vital for maintaining the efficiency of global maritime logistics. However, traditional non-destructive testing methods face significant limitations in harsh port environments, such as periodic inspection intervals, restricted access to structural components, and a lack of real-time monitoring. This review explores the emerging paradigm of remote non-destructive testing through the integration of vibration and acoustic emission sensors with Internet of Things platforms. By enabling continuous, real-time monitoring, these sensor systems can detect early indicators of mechanical degradation, structural fatigue, and corrosion. This study synthesizes findings from over 100 peer-reviewed sources and identifies a significant gap in the application of these technologies to port cranes. Although vibration and acoustic emission sensors have been widely studied in various fields, their application to port cranes remains underexplored, presenting a novel and promising avenue for future research and practical applications. The unique operational demands and structural complexities of port cranes, coupled with their critical role in global trade logistics, make them ideal for leveraging these sensors in tandem with Internet of Things solutions. This integration not only overcomes the limitations of traditional non-destructive testing methods, but also offers substantial benefits, including enhanced safety, reduced inspection costs, and improved operational efficiency. This review concludes by proposing future research directions to enhance sensor performance, data analytics, and Internet of Things integration, paving the way for predictive maintenance strategies that increase operational uptime and improve safety in port crane operations.  
acoustic emission testing; vibration sensor; non-destructive testing; Internet of Things; real-time monitoring 2025
## 1 Introduction  
Studies on sound localization mainly focus on the directional aspect and auditory distance perception receives substantially less scientific attention. However, a recent review of localization studies could show that when listeners are asked to describe the location of perceived auditory objects, the most commonly attribute used is _distance_[1]. The several distance cues assessed by the auditory system vary in their effective ranges and can be divided into two group, cf. [2,3]. While the first group of cues yields an _absolute_ distance perception based on internal references, distance perception obtained from cues of the second group is a _relative_ judgment. The most-studied indicator from the relative cues is amplitude. In the free field, where only the direct sound is present, the amplitude decreases with distance. As the auditory system is exquisitely sensitive to small changes in amplitude, it permits fine relative distance discrimination. For simple implementation electro-acoustic applications can use gain modifications to shape auditory scenes with regard to distance. The so-called D/R ratio (direct-to-reverberant energy ratio) is a cue providing coarse but absolute distance information, as shown in several studies [4, 5, 6, 7]. In a room, the D/R ratio is inversely related to the distance of the sound source and characterizes the energy ratio of direct and reflected sound.  
[PERSON] [8] proposed an elegant solution to control the D/R ratio from a single point in the room. He employed a sound source controlled to approximate an omni-directional directivity and a second-order cardioid pattern steering away from or towards the listener. This variation of the directivity achieved control of the D/R ratio in a relatively dry and small room.  
Our contribution extends [PERSON]'s approach by an auralization-based listening experiment of (i) a directional source in various ideal higher-order beampattern designs/beam constellations, (ii) two different rooms, (iii) two different source-listener distances, (iv) including single-channel reverberation. In addition to [PERSON]'s work, our paper establishes models of the hereby achieved auditory distance using simple acoustical measures. In extension to our previous study presented at DAFx [9], this article also includes the results of a second experiment using (v) the icosahedral loudspeaker (IKO), with a more elaborate third-order beampattern control [10] to synthesize the various beampatterns in a room.  
The paper is arranged as follows: It outlines the first experiment based on auralized rooms and directivities and presents detailed results with discussions of the influence of room, signal, and reverberation. The second section presents models of the experimental results. The last section presents the second experiment that verifies the practical applicability of the beampattern designs to the directivity synthesis by the IKO in a real room.
## 2 Materials and Methods
### Gas Chromatography-Olfactometry  
Analysis of each sample extract by GC-O was made in triplicate, each performed by a different trained assessor. The GC-O system comprised a Trace GC Ultra (Thermo Fisher Scientific GmbH, Dreieich, Germany) using two different capillary columns, namely a DB-FFAP and a DB-5 (both 30 m \(\times\) 0.32 mm, film thickness 0.25 \(\upmu\)m; J & W Scientific, Agilent Technologies GmbH, Waldbronn, Germany). The system further incorporated a pre-column (uncoated, deactivated fused silica capillary, 2-3 m \(\times\) 0.32 mm) and a Y-splitter after the main column, splitting the effluent in a 1:1 volume ratio between a flame ionization detector (FID) and an olfactory detection port (ODP) via two uncoated, deactivated fused silica capillaries (0.5 m \(\times\) mm). The FID signal was recorded by a SE 120 chart recorder printer (BBC Georz Metrawatt, Nuremberg, Germany). Aliquots (2 \(\upmu\)L) of the undiluted distillate and the solutions were injected manually using the cold on-column technique (40 \({}^{\circ}\)C). After 2 min, the temperature was raised at 7 \({}^{\circ}\)C/min to 230 \({}^{\circ}\)C (DB-FFAP) and 250 \({}^{\circ}\)C (DB-5), and held for 5 min. The flow rate of the helium carrier gas was 2.5 mL/min. The temperatures of the FID and the ODPwere set to 250 \({}^{\circ}\)C and 235 \({}^{\circ}\)C, respectively. Linear retention indices (RIs) of the odor-active (aroma) compounds were calculated as described in the literature [28].
## 4 Discussion  
The LP1 weighing lysimeter has been designed for horticultural crops, so that its dimensions (1000 \(\times\) 600 mm and 350 mm depth) were adapted to the crop planting pattern, reducing as much as possible the surface and depth of the lysimeter without altering the root growth of the crop. This prototype allows the ease of components carriage and assembly.  
Figure 8: Evolution of the ETc by LP1 and ITAP lysimeters.  
Other small weighing lysimeters from the literature, such as the Smart Field Lysimeter and the Ready-To-Go lysimeter have a cylindrical shape. The Smart Field Lysimeter have models with 30 cm diameter and different depths of 30, 60 and 90 cm of the cultivation tank. The Ready-To-Go lysimeter have models with 30 or 80 cm diameter and different depths of 30, 60 and 90 cm of the cultivation tank. The lysimeters mentioned are cylindrical and their dimensions are not sufficiently adapted to horticultural crops.  
LP1 weighing lysimeter is able to register with precision the increase and the decrease of the water content provided by the irrigation, precipitation, dew, Etc and drainage. Etc is reflected in the slow decrease of weight of the cultivation tank. The validation of LP1 with ITAP lysimeter shows a good performance during the experiment. Thus, it can be used to get very accurate parameters measurements for the irrigation management and to validate other lysimeters, Etc methods or crop development functions.  
Considering the decrease of water content is related to the evolution of drained tank weight, the drainage water amount and field capacity of the soil can be determined. It is also possible to know other relevant parameters as infiltration rate using the weight variations between cultivation tank and drainage tank.  
During the evaluated period the ITAP lysimeter was flooded because of heavy rains occurred from March 13 to March 17. Before this date, the ITAP lysimeter was working properly and the values were very similar. After the rains, the ITAP lysimeter did not function well providing erroneous data. In spite of the heavy rains our prototype continued working properly.
## 2 Materials and Methods
### Methodology  
The methodology used in this study comprised six major components: data acquisition, pre-processing, land use/cover classification, accuracy assessment, land use/cover change analysis, and estimation of maize yield. In this study, pre-processing and post-processing of images were performed in ENVI 4.7 [28] and ArcGIS 10.2.2 [29] software applications.
# An Advanced SAR Image Despeckling Method by Bernoulli-Sampling-Based Self-Supervised Deep Learning
## 1 Introduction
In addition, the dropout-based ensemble in the testing process is proposed, to avoid the pixel loss problem caused by the Bernoulli sampling and to boost the despeckling performance;
* We conducted qualitative and quantitative comparison experiments on synthetic speckled and real SAR image data. The results showed that our proposed method significantly suppressed the speckle noise while reliably preserving image features over the state-of-the-art despeckling methods. The rest of this paper is organized as follows. Section 2 introduces our proposed method in detail. Section 3 describes the compared methods, experimental settings, and experimental results on synthetic speckled and real SAR image data. Section 4 discusses the impacts of the several components of our proposed method. Section 5 summarizes the paper.
## 2 Data
### RAP  
National Oceanic and Atmospheric Administration's (NOAA's) Rapid Refresh (RAP, [41]) is a numerical forecast model that generates weather data on a 13-km horizontal grid resolution. RAP covers the entire North American continent including the contiguous United States and its coastal regions, generating forecasts every hour with forecast lengths going out 18 h. Data are available from the National Centers for Environmental Information (NCEI). The u and v components of the wind at 10-m height were obtained for 28 May 2016 at 03:00 UTC and 04:00 UTC.
## 2 System Overview and Research Directions in WOBAN
### Challenges Addressed  
The key challenge addressed in this work is to design network protocols for achieving adequate quality of service, in terms of throughput and delay, in the wireless domain of the WOBAN. The system model considers that the wireless access network is a mesh network comprising a fixed set of wireless mesh nodes or routers, which employ multihop routing to convey wireless data packets between user stations to ONUs. Since communication is assumed to be between end users and an external destination that can be reached through any ONU using the optical backbone, this involves _quality aware anycast_ routing between each user and any ONU. As interference is a key factor that affects data transmissions in multi-hop wireless networks, our primary objective is to take routing decisions based on _interference considerations in addition to the path length_, which is often the primary factor considered for routing in dynamic multi-hop wireless networks. We propose an approach that tries to optimize the end-to-end _probability of success (POS)_ and delay in all active routes in the network by using a novel quality based routing metric summarized in Section 3.  
In addition, to improve the throughput performance, wireless mesh networks have been considered to use multi-channel radios, usually employing multiple network interface cards (NICs). Having multiple wireless interfaces greatly reduces wireless interference and channel contention, which results in higher network throughput. Optimizing the performance of multi-channel mesh networks involves optimum channel selection at the mesh nodes, which is coupled with the routing problem. This necessitates the need for developing a quality aware routing in multi-gateway, multi-channel WOBAN, which is described in Section 4. It is assumed that all communication requests are directed towards the gateway, which serves as the centralized manager for all routing decisions based on global knowledge of node locations and activities. Channel assignment at the radio interfaces of the wireless routers must be solved _together instead of solving them in isolation_. At the same time, the collaboration among multiple gateways also fits well in the WOBAN context, where the gateways are connected through optical fibers, which is absent in a pure wireless mesh network.
# The Effect of Carob Extract on Antioxidant, Antimicrobial and Sensory Properties of Bread
## 2 Materials and Methods
### Antioxidant Activity  
The ability to neutralize DPPH (1,1-diphenyl-1-2-picrylhydrazyl) radicals was determined by a slightly modified method described by [PERSON] et al. [27]. A methanol solution of DPPH reagent (65 \(\upmu\)M) was freshly prepared, and the absorbance was adjusted to 0.70 (\(\pm\)0.02). The DPPH reagent was added to appropriate dilutions of the extracts (2.9 mL + 0.1 mL), and the reaction mixture was incubated for 60 min. Absorbance was measured at 517 nm using a spectrophotometer (6300 Spectrophotometer, Jenway, UK). The calibration curve was obtained using a freshly prepared aqueous solution of Trolox. The results were expressed as \(\upmu\)M of Trolox equivalents (TE) per gram of bread.  
The ability to neutralize ABTS radicals was determined by a spectrophotometric method [28]. For the preparation of the ABTS reagent, a mixture of 7 mM aqueous solution of 2,2\({}^{\prime}\)-azino-bis (3-ethylbenzothazoline-6-sulfonic acid) diammonium salt (ABTS) and 2.45 mM potassium persulfate in a 1:1 (\(v/v\)) ratio was prepared, and the resulting solution was incubated for 16 h in the dark at room temperature. The obtained ABTS reagent was diluted with acetate buffer (pH 3.3) to adjust the absorbance to 0.70 \(\pm\) 0.02 at 734 nm. Appropriate dilutions of the extracts (0.1 mL) were mixed with 2.9 mL of the ABTS reagent, and the reaction mixture was incubated at room temperature in the dark for 300 min. Absorbance was measured at 734 nm. A calibration curve was generated using a water solution of Trolox. The results were expressed as \(\upmu\)M of Trolox equivalents (TE) per gram of bread.  
The ability to reduce Fe\({}^{3+}\) ions (Ferric-ion-reducing antioxidant power) is determined by the FRAP (Ferric-Reducing Antioxidant Power) assay [29]. The FRAP reagent was prepared by mixing 10 mmol/L 2,4,6-tripyrid-s-triazine (TPTZ) in 40 mmol/L HCl, 0.02 mol/L FeCl\({}_{3}\) and acetate buffer (pH 3.6) in a 1:1:10 (\(v/v/v\)) ratio. Broad extracts (0.1 mL) were mixed with 2.9 mL of FRAP reagent and incubated at 37 \({}^{\circ}\)C for 10 min in the dark. The absorbance of the samples was then measured at 593 nm. The series of freshly prepared Fe\({}^{2+}\) (Fe\({}_{2}\)SO\({}_{4}\)) aqueous solutions was used for obtaining the calibration curve. Results were expressed as \(\upmu\)M of Fe\({}^{2+}\) equivalents per gram of bread.
# Phase Risk Analysis of Overhead Lines Under Complex Icing Conditions
## 4 Analysis of Discharge Risk Coefficients of Overhead Conductors Under Different Icing Conditions
### Effect of Uneven Ice Cover on the Safety Distance of Conductor-Ground Wires  
In practice, ice coverage is often uneven along the line, leading to potential variation in the arc droop, which may not occur at the mid-point if there is no height difference. To simulate uneven ice cover, finite element analysis is used with different point loads applied to the line. For a 40 mm ice thickness in specific areas and 30 mm elsewhere, the overhead line is divided into 400 grids with measurements taken every 50 grids totaling eight points. Adjustments to the ice-covered area are illustrated in Figure 8 for the single-file line ice-covering model.  
Due to the transmission line's symmetry, only the midpoint of one side is analyzed for varying ice cover scenarios. The analysis sequentially examines ice concentrations at locations 12, 23, 34, and 45, focusing on changes in the safety distance between the conductor and ground line and variation discharge intervals. The results are presented in Tables 3 and 4.  
Figure 8: Ising model of the single-gear line.  
Figure 7: Risk range of wire de-icing and bounce under different icing thicknesses.  
Uneven ice cover has a notable impact on transmission lines. When heavy ice is near the conductor's midpoint, the maximum sag of the conductor and earth wire decreases, and the sag shifts accordingly. With heavy ice at the symmetrical midpoint, the sag is maximized, and the conductor can bounce up to 22.187 m. The minimum conductor-ground distance remains at 2 m, resulting in a phase-to-ground discharge risk coefficient of 1, indicating high risk. When heavy ice is near the insulator, the risk zone increases to about 55%, with the area near the midpoint rising to approximately 58%, concentrated between 83 and 315 m. Compared with uniform ice cover, this results in an expanded risk zone. Therefore, to mitigate risks, measures should be taken to reduce ice accumulation in the conductor's central area during operation.
## 7 Simulation Results
### Dynamic Control and Simulation of Marine Vehicles
Considering the limitation of velocity sensor, we differentiate depth with respect to time to get the velocity along k, which is shown in Figure 8(b). The glider pitch angle is approximately \(20^{\circ}\), as shown in Figure 8(c). Figure 8(d-f) give glider control inputs of \(\mathrm{m_{b}}\), \(\mathrm{r}_{\mathrm{m}rr}\) and \(\gamma\). \(\mathrm{\parallel\!\Delta}\)\(\mathrm{r}_{\mathrm{m}rr}\)\(\mathrm{\parallel\!\!\parallel}\) which are 10 mm approximately and the net buoyancy is 0.2 kg, and rotational angle \(\gamma\) is \(48^{\circ}\). Figures 8(g,h) show roll angle and yaw angle with respect to the variation of \(\gamma\). Figure 8(h) shows that the turning direction is the same withopposite input of \(\gamma\) during turning up and down. Both the input \(\gamma\) and direction of glider lift force change oppositely during up and down. The centripetal force is a projection of lift force with a sine function of \(\gamma\). As a result, the turning direction is the same. The simulation results are agreement with the experiment in most cases, with a little difference caused by the flow, the glider mass distribution, and other disturbances. The comparison shows that the hydrodynamic coefficients and dynamic model predict the overall glider dynamic accurately.