esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling Trust in IoT Systems for Drinking-Water Management
## 4 Results and Discussion
### Limitations and Future Works  
Despite the successes of our approach, certain limitations must be acknowledged. First, the modeling of trust relationships relies on dynamic weights determined by the (AHP). While this method is robust, the accuracy of the results depends on the relevance of the comparisons made when defining the weight matrices. Second, our model assumes a non-transitive trust relationship among all involved parties. In some situations, transitive trust relationships might be observed, requiring adaptation of our model to reflect this variability.  
Future work could be considered to address these limitations. Improving the accuracy of the model might involve exploring alternative methods for determining weights while maintaining the objectivity of the approach. Machine learning techniques could be integrated to dynamically adjust weights based on system evolutions over time. Furthermore, exploring more complex scenarios and validating the model on real-world data would contribute to consolidating its robustness and practical applicability. Finally, a potential extension of this research would be the integration of additional security mechanisms, such as real-time anomaly detection and secure communication protocols, to enhance the system's resilience against potential threats.  
\begin{table}
\begin{tabular}{p{71.1 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Aspect** & **Existing Models** & **Proposed Approach** \\ \hline Flexibility & Limited flexibility in adjusting trust factors and weights dynamically. & Remarkable flexibility through dynamic weights, allowing adaptation to diverse water-treatment plant conditions. \\ Temporal Dynamics & Often lacks consideration for temporal variations in trust relationships. & Integrates temporal dynamics, capturing changes in trust levels over time, enhancing adaptability. \\ ISO-Objective Approach & May not explicitly align with ISO standards, leading to potential subjectivity. & Objectivity is ensured by integrating ISO standards for trust factors, providing a transparent and standardized evaluation. \\ Model Robustness & Some models lack robustness when subjected to abnormal scenarios or sudden changes. & Demonstrates robustness under various conditions, including abnormal scenarios, ensuring reliability in real-world applications. \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison with Existing Models.
## 1 Holistic, Cross-Layer Threat Modeling and Experimental Evaluation  
The following papers analyze O-RAN security from multiple layers (system, interfaces, ML), propose coordinated threat models, and both discuss and experimentally validate mitigations [27; 46; 48]:  
* [PERSON] et al. [46] offers a holistic, experiment-driven analysis of O-RAN security covering interfaces, xApp/rApp ML, and platform; it presents cross-layer threats (attack chains involving open interfaces and ML) and proposes experimentally validated defenses such as robust aggregation, enclaves, code signing, and network policies.
* [PERSON] et al. [48] presents a system-level study focusing on adversarial attacks against ML within xApps in near-RT RIC. It details attack chains via database manipulation affecting ML outcomes and introduces defense via robust model distillation, as tested on a real O-RAN testbed.
* [PERSON] et al. [27] develops threat modeling for a DRL-based xApp using Graph Neural Networks; it demonstrates multi-vector evasion attacks from the wireless medium to the Application Layer, comparing ML and non-ML systems, and experimentally validates robust training-based defenses.
## 2 Research Methodology
### Literature Review
#### 2.1.4 Online Medical Services  
Online medical services are a form of telemedicine that provides healthcare services via the Internet. Consultations and treatments are provided through online consultations for individuals who are unable or unwilling to visit medical facilities due to time or location constraints. These services can help treat various conditions and improve access to high-quality medical care in remote areas [46; 47].  
Medical websites are considered convenient and efficient platforms [48], similar to primary care providers [49]. This is because they enable patients to access required medical services from anywhere at any time. Moreover, medical websites can reduce costs, enhance patient engagement, and facilitate easier access to information resources [50].  
However, as noted in a study by [PERSON] and [PERSON] [51], there are also downsides to Internet-based services, such as the risk of information leakage, financial risks, performance risks, and ineffective information. [PERSON] et al. believe that the credibility of medical websites determines patient choices [52]. Similarly, studies by [PERSON], [PERSON], [PERSON], and [PERSON] highlight consumer concerns about the quality of online health information [53; 54]. In summary, we considered convenience, efficiency, privacy protection, property protection, effectiveness, credibility, and user engagement as the usability benchmarks for online medical services.
# Research on Dynamic Path Planning of Multi-AGVs Based on Reinforcement Learning
## 3 Global Path Planning Algorithm Based on the Improved Genetic Algorithm
### Optimization of the Fitness Function
#### 3.3.2 Node Busy Factor  
The node busy factor avoids congestion of multiple AGVs. We first design a node usage table, as shown in Table 3, which indicates the number of times the current node will be passed by AGVs and the number of times AGVs are working or will work at the current node in a given time period (\(T\)). Nodes can be further penalized with high hotness values to the extent that the paths that are about to be planned can be constrained by the paths that have been planned to achieve the goal of avoiding congestion of multiple AGVs. In addition to the number of tasks and the number of AGVs that affect the node congestion coefficient, AGVs starting the K-L algorithm to avoid dynamic obstacles affects the congestion coefficient of the nearest nodes to the AGVs through parameter \(\rho\) so that the nodes can sense the presence of dynamic obstacles and improve the real-time performance of the algorithm.  
Figure 4: Raster diagram with workstation nodes.  
The formula for calculating the node busy factor is as follows:  
\[P_{i}=\frac{M_{i}}{\sum_{j=1}^{m}M_{j}}+\frac{A_{i}}{MAX}+\rho*k_{i}\,\ i=1,2,\ldots,m;j=1,2,\ldots,m \tag{11}\]  
where \(i\) is the node number,\(j\) is the used node number, set \(M\) indicates the number of times the current node has been worked or will be worked by the AGVs, set \(A\) indicates the number of times the current node will be passed by the AGVs, \(MAX\) is the total number of AGVs, \(P_{i}\) denotes the busy factor of node \(i\), \(\rho\) is the penalty factor of dynamic obstacles, and \(k_{i}\) is the number of dynamic obstacles that appear.
# NBTI and Power Reduction Using an Input Vector Control and Supply Voltage Assignment Method
## 3 Methodology  
In this paper, we propose a combination method of input vector control and supply voltage assignment to reduce delay degradation as well as power dissipation. First, a co-optimization ILP formulation is constructed that considers both the NBTI and leakage reduction requirement. The result of this ILP formulation is the global optimal input vector that can provide a balanced tradeoff between performance and power. Then, the input vector is used when the circuit is in standby mode. Afterwards, the SVA method is applied to further mitigate the NBTI effect while minimizing the power dissipation simultaneously. The flow of the proposed method can be described as Figure 1.  
Figure 1: The flow of the proposed input vector control (IVC) and supply voltage assignment (SVA) combination method. NBTI, negative bias temperature instability.
## 5 Experiments and Results  
We evaluated SC-M* in simulation on a grid public mass transit network with an Intel Core i7-6700 CPU at 3.4 GHz with 16 GB RAM. As shown in Figure 4, the grid transit environment has 20 \(\times\) 20 stops. There are 20 bidirectional horizontal lines. Likewise, 20 bidirectional vertical lines are deployed in the environment. At each stop, agents can switch lines. The yellow areas are covered by some resources, such as the on-vehicle free Wi-Fi in our experiments. Agents traversing those areas can enjoy high-quality on-vehicle Wi-Fi connections. A fully covered edge has a Wi-Fi resource value of 100, and the Wi-Fi value of an edge is proportional to the length of coverage. Each agent wants to move from its source (square) to its destination (circle) with the lowest cost (i.e., a linear combination of distance cost and Wi-Fi cost) as well as bounded collision score. The second resource is the space on the edge, which is fixed at 5. The satisfying values are \(\varepsilon_{1}=20\) and \(\varepsilon_{2}=1\) for Wi-Fi and space resources, respectively.  
We randomly generated a source-destination pair for each agent. Each trial was given a 1000-s run-time limitation to find a solution. For each configuration (including the number of agents, collision threshold \(T\), and offset parameter \(\delta\)), we ran 20 random trials to calculate the average metrics (i.e., the success rate and run time). The success rate is the number of trials ending with a solution divided by the number of trials. The run time is the average over trials ending with a solution or a no-solution declaration. If all trials under a certain configuration exceeded 1000 s, we used \"\(>\)1000\" to represent the run time of the corresponding configuration. We used the standard A* as the coupled planner and policy generator in the SC-M* framework and compared our results to the baselines.  
Figure 3: Counterexample of the inclusion property of soft-collision M* (SC-M*) in the soft-collision context. Agents \(r1,r2\), and \(r3\) have the planning O-D demands \((a,e),(f,g)\), and \((h,i)\), respectively. Vertexes in the system are labeled as \(a,b,c\), etc.
# A Review on Reconfigurable Liquid Dielectric Antennas
## 3 Liquid-Assisted Antennas  
In the previous Section 2, liquid was the essential element of an antenna, acting as a radiator or even reflector. Moreover, all of the discussed antenna designs were three dimensional and often required significant amount of liquid to change the radiation behavior. In this section, other types of antenna design will be considered that have a dielectric liquid in the immediate vicinity of metal radiating structures. In these designs, small amounts of liquid modify local currents of an antenna to alter its radiation performance. In most of the designs, microfluidic channels are introduced in the substrate between the ground and the top layers. By pumping a liquid dielectric through strategically placed channels, a controlled frequency tuning can be achieved.
# Assessment of Different Stochastic Models for Inter-System Bias between GPS and BDS
## 4 Results and Discussion
### Comparison of Estimated Inter-System Bias  
The estimated ISB is the most direct factor to reflect which the stochastic model is the optimal one for the PPP performance and how to select an ideal stochastic model for different precise products. Thus, the comparisons of the estimated ISBs from the schemes ISB-PW, ISB-RW, ISB-AD, and ISB-WN using GBM and WUM precise products are made. Figure 6 shows the estimated ISBs using the GBM products in static and kinematic modes. The first 3-h ISB results on DOY 251, 2017 in the station STR1 are taken as an example.  
From Figure 6, we can see that the four estimated ISBs from PPP performance with different stochastic models using GBM products have completely different results. The ISBs in ISB-AD have the largest values. The ISBs in ISB-PW are coincident with those in ISB-AD in the first 30 min, but they show different values in the following periods because ISB in ISB-PW was initialized every 30 min. The ISBs in ISB-RW and ISB-WN demonstrate the consistent trend but with a systematic difference, which will be absorbed by the ambiguity in the PPP solution. To deeply show the feature ISBs with different precise products, PPP performance using the WUM products is made. The estimated ISBs with the WUM product is shown in Figure 7.  
Figure 6: The estimated ISBs derived from the four different schemes with considering ISB using the _GBM_ products. Different ISBs are shown in different colors. The top subfigure displays the results for the static mode, while the bottom on is the results for the kinematic mode.  
It can be noticed from Figure 7 that entirely different from the results in Figure 6, the ISBs in ISB-PW, ISB-RW, and ISB-AD have the same trend and very close values. A consistent trend is displayed between the ISB in ISB-WN and the other three ISBs, but the ISB in ISB-WN is rougher than others. The impacts of the different features of ISBs on PPP performance will be analyzed and assessed in the next step.
# Monitoring of the Variation in Pore Sizes of Woven Geotextiles with Uniaxial Tensile Strain
###### Abstract  
Characteristic pore-opening size O\({}_{95}\) or O\({}_{90}\) has been widely used in the filter design of woven geotextiles. These manufactured products have different pore size proportions of large pore diameters, medium pore diameters, and small pore diameters, respectively. Therefore, uncertainties still exist regarding the prediction of geotextile pore diameter variations under the uniaxial tensile strain. This paper investigates the variations in five characteristic pore-opening sizes O\({}_{95}\), O\({}_{80}\), O\({}_{50}\), O\({}_{30}\), and O\({}_{10}\), with uniaxial tensile strain by using the image analysis method. The large pore diameters, medium pore diameters, and small pore diameters show different variation behaviors as the uniaxial tensile strain increases. Fifteen specific pores are selected and then their pore diameter variations are monitored under each tensile strain of 1%. The colorful pore size distribution diagram is a visual way to identify the variation of pores arranged in the tension direction (warp direction) and the direction perpendicular to tensile loads (wet direction). The various pore diameters are proved to agree well with the bell-shaped Gaussian distribution. The results exhibit an accurate prediction of the variation in large pore sizes, medium pore sizes, and small pore sizes, respectively, for all tested woven geotextiles with uniaxial tensile strain.  
characteristic pore size; woven geotextile; uniaxial tensile strain; image analysis +
Footnote †: journal: Applied Sciences
## 1 Introduction
### Related Works
#### 1.1.6 Comparisons  
Regarding comparison studies, an early work that accurately compared monocular visual odometry systems was the study of [38], comparing the state-of-the-art methods of that time, DSO, ORB-SLAM, and SVO, on the TUM-Mono benchmark. The authors found that the DSO system, even being a visual odometry system, outperformed the SLAM method and the popular SVO. In that study, the authors also tested the photometric calibration, the motion bias, and the rolling shutter effect, with the available information provided in the TUM-Mono dataset, finding that the photometric calibration improved the performance of the direct methods considerably, and the motion bias effect was more prominent in the indirect method. In contrast, we compared ten methods following a taxonomy, where the three methods tested in [38] were addressed, exploring the same photometric calibration, motion bias, and rolling shutter effects by applying the TUM-Mono dataset. Then in 2020, [PERSON] et al. published two comparisons [39; 40] testing first the DSO, LDSO, and ORB-SLAM2 and then the ROS-based methods, DSO, LDSO, DynaSLAM, and ORB-SLAM2, on the TUM-Mono and EuRoC benchmarks, where the authors verified the performance of the algorithms implementing an open-source code in their hardware to determine whether there were improvements in the LDSO and DynaSLAM--updates of the original DSO and ORB-SLAM2. The authors found that the updates achieved slight error reductions over their predecessors on both benchmarks, reported as medians of 10 executions of each algorithm in each sequence.  
Comparing those studies, we tested ten methods following a taxonomy to test whether the newer versions improved their previous performance and to identify the advantages and disadvantages in the entire taxonomy. We also provided a complete inferential statistical analysis of each method's performance, not only their median values. In addition, we included machine-learning-based versions of the classic methods in our comparison. One of the most recent related works was the study of [41], which explored the state-of-the-art classification and tested visual and visual-inertial algorithms in the ERoC benchmark. In that work, the authors briefly overviewed the existing methods and reviewed the classic classification of direct, feature-based, and RGB-D methods, adding DSO, ORB-SLAM2, and Vins-Mono methods to their comparison. In contrast, this comparison is focused only on monocular RGB methods; so, we followed an appropriate taxonomy for monocular RGB SLAM and VO systems. In addition, we used the TUM-Mono benchmark and its metrics, which is a broader and more complete benchmark.
# Purification of Produced Water by Solvents to Enhance Oil Recovery and Reuse Separated Droplets
## 2 Materials and Methods
### Preparation of Produced Water and Purification Procedure  
Laboratory-prepared produced water under conditions simulating real-world scenarios is used. The procedure involves adding sodium chloride to water until it reaches a concentration of 80,000 ppm. Under magnetic stirring (at 300 rpm) and in a water bath at 45 \({}^{\circ}\)C, 150 ppm of crude oil is added until it emulsifies. The oil comes from oil wells in Iraq. Exactly 100 cm\({}^{3}\) of the prepared produced water is placed into a beaker. Solvent volumes of 5, 10, 15, 20, and 25 cm\({}^{3}\), respectively, are added for each of the five experiments conducted with each solvent. The contents of the beaker are stirred at 300 rpm to ensure thorough mixing and the coalescence of emulsion droplets. Subsequently, it is allowed to stand for 10 min to allow the oil with solvents less dense than water to float to the surface. In this case, 10 cm\({}^{3}\) of the purified produced water is extracted from the bottom of the beaker. Conversely, when the solvents are denser than water, they settle at the bottom, and the extraction of the purified produced water is performed halfway up.
# Internet of Things Meets Computer Vision to Make an Intelligent Pest Monitoring Network
## 2 Material and Methods
### Internet of Things
#### 2.1.1 Computation Types  
As depicted in Figure 1, edge, fog, and cloud are interconnected computation types that should cooperate to fulfil the final service [14].  
Edge computing is the computing performed on devices used to collect the data, i.e., devices that are closer to data sources. The devices used at the edge usually have low computational power and low storage capacity, which makes it difficult to store or process large amounts of data at the edge. Furthermore, the edge has low latency, mobility of its devices (like the fog), and low bandwidth available [13; 15]. Examples of edge devices include low-cost cameras or humidity sensors.  
Fog computing is the computing used to bring data storage and processing closer to the edge, enhancing the edge ability. The fog can have several devices with different computational and storage resources, which allows it to store or process data locally and almost in real-time. Normally, the fog is a bridge between edge and cloud computing that allows the reduction of the resources used by the cloud, while enhancing edge capabilities [13]. Examples of fog devices include Raspberry Pi and Arduino.  
Cloud computing allows us to make more demanding calculations that are difficult at the edge or fog. This type of computation is efficient, scalable, and allows the tailoring of resources as needed [13]. It has disadvantages such as high latency, high bandwidth, and high energy consumption. This type of computing performs the data processing and analysis in the same place, usually in a centralised way. The types of cloud are public, community, hybrid, or private [13; 15].  
As the IoT is a network composed of several interconnected devices, it is necessary to choose the most appropriate communication protocols to communicate between nodes in each scenario.
# The Intelligent Layout of the Ship Piping System Based on the Optimization Algorithm
## 2 Optimization A*-Genetic Algorithm for Intelligent Arrangement of Single Pipeline
### Select Operation  
The selection operation mimics the elimination of winners and losers in Darwinian evolution by selecting some individuals of higher quality from the population and using them in the next generation. The commonly used selection methods are tournament selection, roulette wheel method, expected value method, best individual retention method, ranking selection method, competition method, and linear standardization method. Among them, the roulette method is the most widely used. In the traditional roulette method, the probability of an individual being selected is determined by the ratio of its fitness value to the sum of the fitness values of all individuals in the population. The disadvantage of this method is that most of the individuals in the final population are highly similar, and the diversity of the population is low. The intelligent arrangement of the ship pipeline will lead to the pipeline paths obtained from the search being concentrated in a specific region, and the optimal global solution cannot be obtained.  
Concentration value is introduced in this paper to solve the drawbacks mentioned above in the traditional roulette selection method, and the probability of individuals being selected is no longer determined by the fitness value only. Still, it is obtained by the combined judgment of concentration value and fitness value to enhance the population diversity and obtain the optimal global solution of pipeline paths while ensuring the quality of individuals in the population.  
The concentration value \(C_{i}\) indicates the proportion of individuals with similar fitness values to the individual \(i\). The concentration value \(C_{i}\) of individual \(i\) is calculated by the following formula:  
\[C_{i}=\frac{1}{n}\sum\limits_{j=1}^{n}\left|F_{j}-F_{i}\right|\ \left(\left|F_{j}-F_{i} \right|\leq\varepsilon\right) \tag{2}\]  
where \(\varepsilon\) is an actual number indicating the threshold of similarity between individuals within the population, obtained from multiple simulations; \(d\) indicates the population size in the genetic algorithm; \(F_{i}\) indicates the fitness value of individual \(i\); and \(F_{j}\) (\(j=1,2,\ldots,d\)) denotes the fitness value of individual \(j\).  
The selection probability operator \(P_{si}\) for individual \(i\) is calculated as  
\[P_{si}=\frac{F_{i}}{\sum\limits_{j=1}^{d}F_{j}}-\lambda\cdot C_{k} \tag{3}\]  
where \(\lambda\) denotes the influence coefficient of the concentration value.