esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### The Definitional Question
Their study illustrates that the terminology used influences attitudes and actions, but there is not a direct correlation between beliefs and behaviors; for instance, concerns about animal welfare may not lead to vegetarianism. They frame this through Social Representation Theory, where participants reinterpret the unfamiliar concept of cultured meat into a more acceptable context. [PERSON] et al. [11] conducted a study with 625 U.S. respondents over eighteen, revealing that preferences and marginal willingness to pay (WTP) for chicken vary based on the \begin{table}
\begin{tabular}{l c c c} \hline \multicolumn{2}{c}{**Variable**} & **Freq.** & **\%** \\ \hline \multirow{3}{*}{Gender} & Male & 115 & 39.0\% \\  & Female & 167 & 56.6\% \\  & Nonbinary/Other & 13 & 4.4\% \\  & Boomers & 17 & 5.8\% \\  & Gen X & 38 & 12.9\% \\ Gen & Gen Y & 82 & 27.8\% \\  & Gen Z & 157 & 53.2\% \\  & Gen Alpha & 1 & 0.3\% \\ \multirow{3}{*}{Eating style} & Omnivore & 243 & 82.4\% \\  & Vegetarian/Vegan & 49 & 16.6\% \\  & No choice & 3 & 1.0\% \\  & Apolitical & 78 & 26.4\% \\  & Center-right & 32 & 10.8\% \\ Political & Center-left & 53 & 18.0\% \\ orientation & Extreme-left & 102 & 34.6\% \\  & No choice & 30 & 10.2\% \\  & Primary School & 1 & 0.3\% \\  & Junior High School & 31 & 10.5\% \\ Educational & High School & 115 & 39.0\% \\ background & Bachelor’s Degree & 56 & 19.0\% \\  & Master’s Degree & 68 & 23.1\% \\  & Ph.D. & 24 & 8.1\% \\ \hline Total & & 295 & 100\% \\ \hline \end{tabular}
\end{table} 
Table 1: Sample characteristics (n = 295). terminology used for in vitro meat. They found that in vitro chicken was valued less than conventional chicken, with \"cultured\" having the least negative impact compared to \"artificial\" or \"lab-grown\". Additionally, language barriers can affect terminology; direct translations may change meaning and influence acceptance levels.
## 4 Test Results and Discussion
### Action for Receiving Emergency Dispatch Command  
In this section, to test the control capability of the E-dReg controller when receiving an emergency dispatch capacity command from the Taiwan Power Company during the service period, a simulator will be used to send the emergency dispatch command signal. The signal will be initially received by the energy management system (EMS), and then, through MODBUS TCP, the dispatch command will be transmitted to the E-dReg controller. Upon receiving the emergency dispatch command, the E-dReg controller will perform emergency dispatch control actions according to the specified capacity and designated time.  
This test involved continuously testing the E-dReg controller for 48 h over two days, referencing the frequency measurements taken throughout the day on 29 th and 30 th April 2023. During the execution of service control actions, random emergency dispatch commands were sent for testing. Through the random dispatch commands, the test aimed to observe the control performance and execution quality of the E-dReg controller.  
The actual control test results for the E-dReg controller receiving emergency dispatch commands are depicted in Figures 16-19. Figures 16 and 18 illustrate the human-machine interface (HMI) of the E-dReg controller in the EMS. From these two figures, it is evident that when the E-dReg controller receives the emergency dispatch command simulated by the emulator, the interface allows monitoring of the capacity to be executed and the commencement time of the execution. In Figures 17 and 19, the curve charts of output power corresponding to frequency show a sudden change in the horizontal part highlighted by red circles. This change indicates that the controller is executing the emergency dispatch command. Similarly, the SBSPM execution rate consistently remains at the 100% level. This confirms that the E-dReg controller can receive emergency dispatch commands while in operation and successfully complete the control actions without affecting the execution quality.
## 6 Conclusions and Future Work  
In this paper, the use of multiple input single output (MISO) transmit beamforming system using dimmable light emitting arrays (LEAs) in the form of a uniform circular array (UCA) as transmitters was proposed. With this technique, visible light communication between a transmitter and a receiver (LED reader) is achieved with excellent performance and with good estimation of the receiver's positions. A hexagonal lattice alignment of LED transmitters was deployed to reduce the coverage holes and the areas of overlapping radiation. As a result, the accuracy of the position estimation is better than the one with a typical rectangular grid alignment. The dimming control was done using pulse width modulation (PWM) in order to obtain the optimal closed-loop beamforming and minimum energy consumption with good lighting satisfaction.  
For future work, various circular array configurations can be considered in which each configuration consists of \(M\) LED bulbs, for instance, uniform circular centered array, planar uniform circular array, and planar uniform hexagonal array [30]. We can expect better position estimations from these array configurations, since they can provide beam patterns with smaller side lobes [29].  
Especially, among these array configuration, a planar uniform hexagonal array can provide the narrowest main beam and the smallest side lobes. This is suitable for adaptive array applications which require high spatial resolution [30]. Multiple LED readers can be used and a physical experiment should be carried out to compare the results to the simulation. Ray-tracing based simulations and actual experiments are future plans to compare with the results from the simulations.
# Future Internet Architectures on an Emerging Scale--A Systematic Review
## 3 Evolutionary and Clean Slate Approaches
### ONOS  
Open Network Operating System (ONOS) [27] is an open-source SDN network operating system that aims to build next-generation SDN and NFV (network function virtualization) solutions. One of the main advantages of using an NFV is the separation of communication services from the dedicated hardware, such as routers and firewalls, which allows network operations to offer new services dynamically, without installing new hardware. This flexibility can reduce the capital expenditure and operational costs while increasing network agility and innovation. ONOS also offers advanced features such as real-time network analytics, programability, and scalability, making it a powerful platform for building and operating large-scale, carrier-grade networks. The reasons to use NFV include [28]:  
* Pay-as-you-go: Businesses can pay only for the services that they utilize using NFV models, resulting in cost savings.
* Fewer physical appliances: The NFV model works on virtual machines (VMs) and requires fewer physical appliances, reducing operational costs and simplifying the network management.
* Scalability: It allows for the faster scaling of a network architecture with virtual machines, without the need for extra hardware, making it easier to handle increasing network traffic demands.  
ONOS is designed to meet the requirements of mission critical networks by focusing on three specific areas: the distributed core, abstractions and models, and application platform. A distributed core is necessary for high scalability, availability, and performance. Abstractions and models are needed to allow for network configuration and control, without relying on device specifics. An application platform is necessary for developers to dynamically extend the device's capabilities. ONOS was designed to leverage white box merchant silicon hardware and provide flexibility for creating and deploying new network services with simplified interfaces. By adding intelligence to the ONOS cloud controller, new network applications can be created without altering the data plane systems, reinforcing the configuration and real-time control while avoiding the need to run switching and routing control protocols. The ONOS includes:  
* A platform and a set of applications that act as an extensible, modular, distributed SDN controller.
* The simplified management, configuration, and deployment of new software, hardware, and services.
* A scale-out architecture to provide the resiliency and scalability that are required to meet the rigors of production carrier environments.
## 4 Experiment and Results
### Evaluation Indicators Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**IGD**} & \multicolumn{2}{c}{**HV**} & \multicolumn{2}{c}{**Spacing**} \\ \cline{2-7}
**Algorithm** & \multirow{2}{*}{**Mean Value**} & \multirow{2}{*}{**Standard**} & \multirow{2}{*}{**Mean Value**} & \multirow{2}{*}{**Standard**} & \multirow{2}{*}{**Mean Value**} & \multirow{2}{*}{**Standard**} \\  & & & & & & **Deviation** \\ \hline MOPSO & 0.024 & 0.0018 & 1.05 & 1.60 & 0.012 & 0.002 \\ MOGWO & 0.007 & 0.0016 & 3.39 & 1.10 & 0.088 & 0.018 \\ MOBWO & 0.022 & 0.0035 & 8.92 & 0.50 & 0.211 & 0.005 \\ MOWOA & 0.012 & 0.0011 & 3.23 & 0.77 & 0.039 & 0.032 \\ MOCOA & 0.015 & 0.0053 & 5.13 & 2.44 & 0.071 & 0.033 \\ IMOWOA & 0.009 & 0.0025 & 7.80 & 1.18 & 0.033 & 0.019 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Each algorithm’s evaluation metrics. Therefore, the IMOWOA evidences a remarkable optimization efficacy for this specific category of dephosphorylation process parameter optimization problem.
# Resilience Assessment for the Northern Sea Route Based on a Fuzzy Bayesian Network
## 2 Proposed Resilience Measurement Methodology  
The resilience level of a specified system is widely regarded as the measurement of the interaction between system resilience and disruptions exerted on the system. Thereinafter, disruption identification and resilience capacity decomposition are essential for resilience measurement, based on which some quantification technology can be applied to calculate the resilience level and investigate the resilience characteristic. In the present study, a methodology that includes five steps based on a fuzzy Bayesian network is proposed to evaluate system resilience, as illustrated in Figure 2.  
Step 1--Identify the disruptions exerted on the system. The first step is to identify the potential disruptions that impacts the performance of the NSR resilience. A potential disruption may be caused by a severe nautical environment, the failure of equipment or facilities, and navigational service outages; these issues will be analyzed in detail based on expert elicitation and a literature review.  
Step 2--System resilience decomposition. This step will focus on the design of the resilience capacity structure that fully relates to the different stages of disruption identified in Step 1. In this step, the system resilience capacities are primarily decomposed into three capacities: absorptive capacity, adaptive capacity, and restorative capacity.  
Step 3--Quantification of resilient variables. It is critical for the resilience measurement to quantify the identified disruption and relevant capacities. Therefore, in this step, a methodology associated with the combination of fuzzy theory and expert elicitation is applied based on the present data concerning the NSR resilience. Additionally, the noisy-OR function is used, and it can be regarded as the input for the subsequent Bayesian network.  
Step 4--System resilience measurement. The resilience level of the Arctic NSR is calculated through the establishment of a Bayesian network in which the prior probabilities of the nodes without parent nodes and the conditional probability table (CPT) for the child nodes with multiple parent nodes are obtained according to Step 3.  
Step 5--Result analysis. Different techniques are employed in this step to gain insights based on the developed Bayesian network in Step 4. The techniques include forward propagation, backward propagation, sensitivity analysis, and information uncertainty analysis.  
Step 6--Proposed recommendation. Some potential recommendations are proposed according to the abovementioned steps to improve the resilience level of the Arctic NSR to defend against disruptions from the environment.
## 4 Results of Experiments
### Kacem Dataset  
In every experiment described in this work, the selected methods were tested with the same datasets as those exposed in their references, making the presented analysis reliable. For each instance, 30 independent runs were executed and the smallest makespan obtained was taken. The best values reported in their respective papers were taken for the other algorithms.  
For the Kacem dataset, the HBSO-LAHC does not report results, and only the GLNSA and IJA report complete results. Table 4 presents the outcomes of the GA-RRHC, where \(n\) indicates the number of jobs, \(m\) the number of machines, and \(\beta\) the flexibility rate of each instance. These problems have total flexibility and start with fewer jobs and machines until they reach a high dimensionality.  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Instance** & \(\mathbf{n}\times\mathbf{m}\) & \(\mathbf{\beta}\) &
\begin{tabular}{c} **GA-** \\ **RRHC** \\ \end{tabular} & **GLNSA** & **GRASP** & **HA** & **IJA** & **TIPSO** \\ \hline K1 & \(4\times 5\) & \(1\). & \(11\) & \(11\) & \(-\) & \(-\) & \(11\) & \(11\) \\ K2 & \(8\times 8\) & \(0.81\) & \(14\) & \(14\) & \(14\) & \(14\) & \(14\) \\ K3 & \(10\times 7\) & \(1\) & \(11\) & \(11\) & \(-\) & \(-\) & \(11\) & \(-\) \\ K4 & \(10\times 10\) & \(1\) & \(7\) & \(7\) & \(7\) & \(7\) & \(7\) & \(7\) \\ K5 & \(15\times 10\) & \(1\) & \(11\) & \(11\) & \(11\) & \(11\) & \(-\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Kacem dataset results.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Method** & **Complexity** & **Rank** \\ \hline TIPSO & \(\mathcal{O}(o(G_{n}(2X+G_{n}2X)))\) & \(1\) \\ GA-RRHC & \(\mathcal{O}(o(G_{n}(S_{n}+X+S_{n}H_{n}m)))\) & \(2\) \\ GLNSA & \(\mathcal{O}(o(G_{n}(S_{n}+X+S_{n}H_{n}m)))\) & \(2\) \\ IJA & \(\mathcal{O}(o(G_{n}(S_{n}X+XH_{n}m))\) & \(3\) \\ HA & \(\mathcal{O}(o(G_{n}(4X+XH_{n}m)))\) & \(4\) \\ HBSO-LAHC & \(\mathcal{O}(o(G_{n}(4X+XH_{n}m)))\) & \(4\) \\ GRASP & \(\mathcal{O}(o^{2}(G_{n}*H_{n}))\) & \(5\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Computational complexity of the methods used in the experiments.  
Figure 13 presents the number of best results obtained for each algorithm in this dataset. Table 4 exposes that the GA-RRHC calculates the best makespan values besides the GLNSA and IJA, confirming the satisfactory operation of the GA-RRHC for instances with high flexibility.
## 4 System Application Example
### Generation of Assembly Interference and Matching Schemes and Incomplete Interchange Schemes  
We investigated and obtained the production process statistics of workshops I and II (Table 10), updated the mesh solid model layer of the virtual prototype for the updated data statistics (Table 11), and counted the interference data and interchangeability matching data after virtual assembly (Table 12).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Workshop I**} & \multicolumn{2}{c}{**Workshop II**} \\ \hline \multirow{2}{*}{**Dimensional Tolerance (mm)**} & \multirow{2}{*}{**Accuracy Class**} & \multirow{2}{*}{**IT6**} & \multirow{2}{*}{**IT7**} & \multirow{2}{*}{**IT6**} & \multirow{2}{*}{**IT7**} \\ \cline{1-1} \cline{5-5}  & & & & \\ \cline{1-1} \cline{5-5}  & & & & \\ \cline{1-1} \cline{5-5}  & & & & \\ \cline{1-1} \cline{5-5}  & & & & \\ \cline{1-1} \cline{5-5}  & & & & \\ \cline{1-1} \cline{5-5}  & & & & \\ \cline{1-1} \cline{5-5}  & & & & \\ \cline{1-1} \cline{5-5}  & & & & \\ \cline{1-1} \cline{5-5}  & & & & \\ \cline{1-1} \cline{5-5}  & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 10: Production process statistics table in the knowledge base.  
\begin{table}
\begin{tabular}{l c c} \hline \hline \multicolumn{1}{c}{**Part Features Name**} & \multicolumn{1}{c}{**Tolerance Zone**} & \multicolumn{1}{c}{**Basis Fit Systems**} & \multicolumn{1}{c}{**Limit Size (mm)**} \\ \hline \(\varphi\)110 Case input shaft & \multirow{2}{*}{_J7_} & shaft-basis system of & D min = 109.9825 \\ locating bearing hole & & fits & D max = 110.0175 \\ \hline \(\varphi\)50 Input axle box body & \multirow{2}{*}{h6} & hole-basis system of & d min = 49.984 \\ positioning bearing & & fits & d max = 50.000 \\ journal & & & \\ \hline \(\varphi\)110 Locating bearing & \multirow{2}{*}{_J7_} & shaft-basis system of & D min = 109.9825 \\ hole of secondary & & fits & D max = 110.0175 \\ transmission axle box & & & \\ \hline \(\varphi\)70 Positioning bearing & \multirow{2}{*}{h6} & hole-basis system of & d min = 69.981 \\ journal of secondary & & fits & d max = 70.000 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Fit tolerance data of key parts.  
Figure 21: Solid model of the sub\(-\)assembly part mesh.  
According to the data in Table 12, the product size of workshop 1 is small and that of workshop 2 is large. The system suggests that the bearing with a larger mean limit size should be matched with the product of workshop 1, while the bearing with a smaller mean value should be matched with the product of workshop 2. Then, according to the given data, we select three types of bearings in the table for virtual assembly. Finally, the interference data of the system statistics show that it meets the proposed fit clearance requirements.
## 3 Proposed Current Detection Schemes with Reduced Sensors
### Current Control without Sampling Demagnetization Current  
#### 3.4.1 Soft Current Chopping Control (SCCC)  
When applying SCCC, only one switch is controlled and the other one is left turned on during the ECR. By only controlling the upper switch for chopping, SCCC can be implemented without sampling demagnetization current. The principle of SCCC with the upper switch controlled is illustrated in Figure 9, where \(i_{\text{A}}^{*}\) and \(i_{\text{e,A}}\) are the reference and error currents of phase A, respectively; \(h\) is the hysteresis-band width; \(\mathbf{b}\) is the rotor angle; \(\theta_{\text{on,A}}\), and \(\theta_{\text{off,A}}\) denote the turn-on and turn-off angles of phase A, respectively.
# Closing Connectivity Gap: An Overview of Mobile Coverage Solutions for Not-Spots in Rural Zones
## 3 Potential Solutions
### UAV-Assisted Networks
#### 3.1.2 Regulation
Release 17 approaches the use cases and requirements for UAS identification and tracking beneath the application layer. It also gathers the 5G connectivity needs of drones in new KPIs into a 3 GPP subscription.
## 3 Results and Discussion
### Physicochemical Properties of the Raw Materials
We should remind that these values should be used for guidance, since EN197-1 is applied for coal FA. Significant amounts of alkali, earth-alkali, and Cl were detected in BFA samples. Alkaline earth (calcium, magnesium) and alkaline (sodium, potassium) sulphates can chemically react with the components of the cement, reducing mechanical properties of hardened bodies and causing volumetric changes. The SO\({}_{3}\) content, in both used BFA is close than that of OPC, so it will not constitute a problem. The presence of chlorides might also reduce the mechanical strength of the hardened bodies by causing the appearance of efflorescence and, consequently, decreasing the durability, or by reaction with tricalcium aluminate (C3A) and tetracalcium aluminoiferrite (C4 AF) forming chloroaluminates linked to the increase of expansion and porosity [36]. According to the EN 998-2:2016 [30], the Cl\({}^{-}\) content of a mortar/concrete formulation should be lower than 0.1 wt.%. The formulations developed with 17 and 50 wt.% of OPC replaced by BFA1 have Cl amounts below this limit. In the case of BFA1_S just the one with 17 wt.% does not overcome that limit. All the samples prepared with BFA2 have Cl amounts higher than 0.1 wt.%. Commonly, despite the high processing temperature (825 \({}^{\circ}\)C), due to kinetic and mass transfer limitations [4], BFA has some organic matter. The organic matter contents of the BFA under study varies between 2.8 (BFA1_S) and 1.4 wt.% (BFA2_S). BFA1_GS and BFA2_GS was assumed to be equal to their parent powders BFA1 and BFA2, respectively. The particle size distribution of the materials under study are presented in Table 2 and in Figure 1. OPC particles (Dmean = 15.89 \(\upmu\)m) are finer than BFA2 (Dmean = 30.91 \(\upmu\)m) ones, but when comparing to BFA1 (Dmean = 306.40 \(\upmu\)m) OPC presents a mean particle diameter one order of magnitude lower. The pre-treated (sieved at 63 \(\upmu\)m) BFA has a mean particle diameter closer to OPC. BFA2 pre-treated fractions present a smaller mean particle diameter than BFA1: BFA2_S 17.71 \(\upmu\)m, BFA2_GS 9.30 \(\upmu\)m, BFA1_S 21.95 \(\upmu\)m, and BFA1_GS 14.64 \(\upmu\)m. The sieving narrowed the particle size distribution of BFA, becoming closer to that of OPC. The milling and sieving of BFA narrowed this distribution even further. The BFA particle size distribution plays an important role in their reactivity with the OPC constituents, as well in the compactness of the samples. It will also change the required amount of kneading water. Finer materials tend to be more reactive, and the mechanical strength might improve [36].
# Method for Detecting Underwater Microbubbles Using Dual-Mode Fusion of Laser Polarization
## 4 Conclusions  
This research proposes a dual-mode fusion of laser polarization image-based bubble detection techniques based on the polarization and imaging properties of underwater bubbles. Three components make up the scheme: dual-mode fusion bubble feature recognition, picture fusion and feature extraction, and ideal bubble state simulation. An image-polarization-weighted fusion approach is proposed to improve the polarization features of the bubble target using weighted coefficients of polarized images and unbiased images, with the threshold value being the outcomes of an ideal bubble simulation. Lastly, a probabilistic fusion approach based on laser polarization and dual-module image data is suggested, and the confidence fusing of bubble polarization features and picture gray level is executed from a statistical probability standpoint. A posterior distribution probability model of discriminative characteristics, including bubble size and number, is established, a fitting of bubble small-sample and multi-source data is realized, and continuous perception of bubble characteristics in complex underwater environments is achieved through the use of the gray wave value (GF) to correlate the characteristics of bubbles and impurities. These results are based on the target confidence adaptive fusion dual-mode detection results, which show that the impurities and bubble characteristics are accurately distinguished. Adaptive analysis at the bubble instance level is achieved. The results of the experiments indicate that the measurement accuracy of quantity distribution and bubble size change may approach 93%, and the detection rate of bubbles in the 50-1000 \(\upmu\)m and 100-2000 \(\mathrm{cm}^{-3}\) range can reach 95%. The method can detect that the minimum size of bubbles is 50 \(\upmu\)m, the minimum number of bubbles is 100 \(\mathrm{cm}^{-3}\), the change in bubble size is \(\pm\)5 \(\upmu\)m, and the change in bubble number is \(\pm\)50 \(\mathrm{cm}^{-3}\). Based on this mechanism, real-time identification and tracking of ship track and fish activity can be realized, which provides a potential application scheme for ocean detection and marine biological monitoring. However, this method has the disadvantage of a low speed being required by the moving platform, which is insufficient for anti-jitter and algorithm recovery of images captured in high-speed motion, and the recognition rate decreases after a high-speed platform is installed.  
Conceptualization, S.Z.; methodology, S.Y.; software, S.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by The National Defense Foundation of China (2019-JCJQ-JJ-056), The National Defense Pre-Research Foundation of China (2020-JCJQ-ZD-099-00-02).  
Figure 13: Recognition results in the field dynamic environment.