esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Developed Toolkoxes for Automated Processing  
This work was conducted within the in-house developed ARTMO GUI framework. Automated Radiative Transfer Models Operator (ARTMO) [53] is a Matlab scientific software package that provides tools and toolboxes for running a suite of leaf, canopy and atmosphere RTMs and for post-processing applications such as retrieval. The toolboxes used in this work are briefly explained below.  
* Atmospheric Look-up table Generator (ALG) [19] is an independent software tool that can be plugged into ARTMO and allows generating and analyzing LUTs based on a suite of atmospheric RTM, i.e., MODTRAN, 6 SV, libRadtran.
* A new so-called \"TOC2 TOA\" toolbox has been developed to enable coupling surface reflectance simulations with atmospheric simulations, i.e., to reach TOA radiance data. The TOC2 TOA toolbox couples the atmospheric transfer functions with canopy reflectance simulations or observations to enable TOA radiance data, thereby ensuring that consistent geometry at canopy and atmosphere is preserved. Either canopy LUTs, surface reflectance data, e.g., from a field spectroradiometer, or a BOA reflectance image can be coupled with atmospheric transfer functions to enable upscaling to TOA radiance data. In this version (1.0), the coupling assumes a Lambertian and homogeneous surface according to the formulation proposed in [54].
* The Global Sensitivity Analysis (GSA) toolbox [55] calculates a global sensitivity analysis on RTMs. The GSA toolbox enables to identify key driving input variables as well as non-influential input  
Figure 2: Flowchart of the pursued work-flow, divided into the Global Sensitivity Analysis (GSA) study (**right**) and retrieval performance assessment (**left**).  
variables across the spectral range of spectral outputs. The main limitation of GSA is that it requires many simulations, and is thus limited by the processing speed of the model under study [32].
* To speed up GSA run-time, the GSA toolbox can be coupled with the Emulator toolbox [31; 32]. This toolbox enables the evaluation of machine learning regression algorithms on their capability to approximate RTM outputs as a function of input variables.
* The machine learning regression algorithms (MLRA) toolbox [56] is one of ARTMO's retrieval toolboxes. The MLRA toolbox contains over 20 MLRAs that can be trained and validated with either experimental or RTM data. Afterwards, a selected model can be applied to an image for mapping applications.
## 3 Results and Discussion
### Evaluation of Volatile Aroma Compounds
#### 3.1.4 Fatty Acids  
Significant differences in fatty acid concentrations were observed in young wines, with TM7-Y and TM10-Y having higher levels than TM21-Y and TPHT-Y wines, with the exception of octanoic acid found in lower concentration in TM10-Y than in other wines (Table 1). During maceration and fermentation, yeasts utilize longer-chain fatty acids as a carbon source, potentially reducing their concentrations. Previous research observed that longer macerations decreased fatty acid concentrations, possibly due to ester formation [46]. It was also noted that grape solids inhibit the biosynthesis of volatile fatty acids, leading to lower concentrations in wine [19; 47]. However, other studies found a positive correlation between grape solids and volatile fatty acid concentrations in wine [48]. In this study, reduced hexanoic, octanoic, and decanoic acid concentrations were observed in TM21-Y and TPHT-Y treatments with some exceptions, which was consistent with previous findings showing that macerations lasting 10 to 15 days decreased the level of these acids [41]. Reduced medium-chain acids are generally considered beneficial as higher concentrations can lead to off-flavors like fats, cheese, and rancidity [33]. In this study, TPHT-Y treatment led to the most significant decrease in most fatty acids compared to the control, aligning with research noting a decrease with heating treatments, though not statistically significant compared to the control [19]. Fatty acid concentrations were found to increase with aging, as seen for butanoic acid [49], which was confirmed by this study for hexanoic and octanoicacid in wines of particular treatments, although decreases were also observed, e.g., for decanoic acid. It was assumed that the changes after a 6-month aging period were partly a result of two contrasting phenomena, ethyl ester hydrolysis and adsorption, by the wood of the barrel [37; 50].
# Multidimensional Assessment of Lake Water Ecosystem Services Using Remote Sensing
## 3 Results
### Spatio-Temporal Development of Water Provisioning
#### 3.1.2 Spatial Analysis Results
Cold spots show a counterclockwise shift of \"South Central \(\longrightarrow\) East Central \(\longrightarrow\) North Central \(\longrightarrow\) West Central\". The spatial changes of hot spots and cold spots are very active with long-distance movements, indicating no stable spatial clustering pattern in the water provisioning service trend.
## 3 Results
### Mineral Phase Analysis  
In the XRD analysis, samples C8F120 and C8F280 cured in 28 days were selected for several reasons. Firstly, the cement-to-sand ratio of 1:8 in PCPB samples demonstrated improved strength during the later stages of hydration, which was the primary focus of this analysis. Additionally, the leaching results for PCPBs with an ash-sand ratio of 1:6 showed significant variability, making the selected samples more suitable for studying phase changes and fluoride immobilization during hydration. The use of PPG ensured that the XRD results were not influenced by other impurities. Both C8F120 and C8F280 primarily consisted of gypsum, with a composition exceeding 70%. The amorphous content was 16 wt% in C8F120 and 13 wt% in C8F280. In Figure 9, the characteristic peaks of C\({}_{3}\)A are more pronounced in C8F280; whereas, C4 AF peaks are more prominent in C8F120. Quantitative analysis revealed that C8F120 had lower amounts of C\({}_{2}\)S and C\({}_{3}\)S, while C8F280 contained less C\({}_{4}\)AF and C\({}_{3}\)A.  
C\({}_{3}\)S is the primary source of C-S-H and calcium hydroxide (Ca(OH)\({}_{2}\)). It is hypothesized that increased fluorine during early hydration accelerates C\({}_{3}\)A consumption, with C\({}_{4}\)AF becoming more depleted in later hydration stages. The presence of fluorine inhibits the C\({}_{3}\)S reaction during early hydration and subsequently affects C\({}_{2}\)S in later stages, negatively impacting PCPB strength. Gaps in amorphous term content support the hypothesis that fluorine inhibits hydration and affects the formation of cement hydration products. Specifically, fluoride ions may influence the generation rate of ettringite and C-S-H gel, as Ca(OH)2 is gradually converted. These substances are major contributors to the physical and chemical durability of cementitious materials in the later stages of hydration [58; 59].  
In addition, the delayed effect of fluoride ions in the early stage of cement hydration may come from the barrier of CaF2 precipitation to the anhydrate part. This barrier affects the hydration efficiency of PCPB and increases with the increase in IFC. Since the strength of PCPB in the late stage of hydration is mainly affected by the production of C-S-H gel, this indicates that additional fluoride will lead to a decrease in the strength of PCPB and  
Figure 8: Response surface curve and contour graph of the leaching results after (**a**) 14 days of curing and (**b**) 28 days of curing.  
Figure 9: XRD analysis of (**a**) C8120 after 28 days of curing and (**b**) C8280 after 28 days of curing, e.g. E—ettrinigite, P—portlandite, G—gypsum, Z—ZnO.  
is proved by the gap in the content of amorphous terms in the XRD results. The specific ways in which some fluorides are immobilized during hydration will be further explained in Section 3.6.
# Path Planning of Mobile Robots with an Improved Grey Wolf Optimizer and Dynamic Window Approach
## 1 Introduction
Furthermore, some improved algorithms have enhanced performance but introduced excessive tuning parameters, which may increase algorithmic complexity, reduce generalization ability, and hinder adaptation to diverse application scenarios. Based on previous research, this paper conducts an in-depth study of GWO and applies comprehensive improvements to overcome its performance limitations. It proposes the PAGWO algorithm and combines it with an enhanced version of the DWA, forming the PAGWO-IDWA approach for mobile robot path planning and dynamic obstacle avoidance. The structure of the paper is outlined as follows: Section 2 provides an overview of the standard GWO algorithm; Section 3 improves the GWO algorithm with various strategies to address its shortcomings; Section 4 describes the improvements to the Dynamic Window Approach (DWA); Section 5 integrates the PAGWO algorithm with the Improved Dynamic Window Approach (IDWA) to address the limitations in dynamic path planning; Section 6 simulates and analyzes the proposed algorithm to validate the performance of PAGWO-IDWA in path planning; and finally, Section 7 provides a summary and concludes the paper.
# Automatic Measurement of Inclination Angle of Utility Poles Using 2D Image and 3D Point Cloud
## 3 Experiment and Analysis
### Angle Verification and Analysis
#### 3.4.3 Analysis of Experimental Results  
The error range of the inclination angle of the pole is 0-1.35\({}^{\circ}\), the average error is 0.66\({}^{\circ}\), and the variance is 0.12\({}^{\circ}\) (Table 2). Under the same inclination angle and different observation directions, the average error range of detection is 0.61\({}^{\circ}\)-0.72\({}^{\circ}\). Among them, there is a maximum error of 1.35\({}^{\circ}\) when the inclination is 2\({}^{\circ}\) and the observation direction is 270\({}^{\circ}\). One possible reason is that the inclination angle error is affected by the measurement error of the angle sensor and remote observation error of the depth camera. The variance range is 0.11\({}^{\circ}\)-0.2\({}^{\circ}\) under different observation directions at the same inclination angle, the reason for which is that under different observation angles, the distribution of pole objects and background interference points in the frustum generated by the mask fusion depth map are different, so the segmented pole point cloud is naturally different, which eventually leads to the difference in the inclination angle calculated by the central axis fitting.
# Quantification and Analysis of Carrier-to-Interference Ratio in High-Throughput Satellite Systems
###### Abstract  
Recently, the demand for high data traffic transmission is experiencing unstoppable growth, and multi-beam high-throughput satellite (HTS) systems have proven to be effective solutions. However, how to manage and eliminate the co-channel interference caused by frequency reuse in multi-beam HTS systems is still a challenging issue. Motivated by this background, this paper begins with discussing the interference mechanism in the uplink and downlink of HTS systems and then uses the quantification method of carrier-to-interference ratio (CIR), whereby the influencing factors of the CIR are accordingly analyzed. In addition, the common CIR selection strategies are summarized and compared with each other in terms of their advantages and disadvantages. Based on the proposed CIR quantification and statistical selection methods, two frequency plan cases are simulated for the scenario of 19 beams with 4-color reuse. The results show that the proposed quantification and selection methods are rather practical and offer a feasible approach for frequency plan adjustment based on CIR optimization. Finally, a simulation analysis of the CIR under different power spectral density (PSD) values is conducted, and the results show that the PSD has a substantial impact on the CIR.  
multi-beam; high throughput satellite; frequency reuse; co-channel interference; carrier-to-interference ratio; frequency plan +
Footnote †: journal: Electronics Letters  
**2023**, _12_, 3443. [[https://doi.org/10.3390/electronics12163443](https://doi.org/10.3390/electronics12163443)]([https://doi.org/10.3390/electronics12163443](https://doi.org/10.3390/electronics12163443))
## 4 Proposed mmWave Assisted CR-IoV
### Network Model  
The proposed mmWave-assisted CR-IoV network is utilized to resolve the three major issues: insufficient spectrum availability, lower data rates and involvement of attackers. The proposed network consists of a number of intelligent vehicles. The entities involved in the CR-IoV environment are Primary Users (PUs) (receivers), Secondary Users (SUs), Cognitive Base Station (CBS) or Fusion Center (FC), Trusted Authority (TA), and primary user transmitter. The PUs are the licensed users that are static in the environment and act as transmitters and receivers. On the contrary, SUs are vehicles that can exploit the licensed spectrum of the PUs if the PU spectrum gets idled. The FC or CBS is deployed to enable beamforming for the secondary users based on the sensing report, which is equipped with Massive MIMO antennas. The mmWave communication is used to communicate among the entities for high data rates. Further, a Trusted Authority (TA) is deployed in the network to validate the authenticity of SUs. The TA is responsible for issuing certificates to the SUs, which are further used for validation and security constraints. The overall network model is shown in Figure 2.  
Figure 1: Problem Statement.  
Figure 2: Proposed mmWave assisted CR-IoV Model.  
As shown in the figure, all vehicles can move in any road direction. The SUs are initially registered to the CA to ensure their legitimacy. For legitimate SUs, two types of attack detection processes are taken place named SSDF and PUEA. For the attack-free SUs, the optimal beams are provided by the FC or CBS for the sake of success spectrum sensing. Eventually, data transmission among the source and destination SUs is carried out using an optimization algorithm. Routing, beamforming, and security were the three main aspects of network performance that were improved symmetrically.
# Time-Sensitive Networking Technologies for Industrial Automation in Wireless Communication Systems
## 3 3 GPP 5G Technology for TSN  
5 GS is a representative example of wireless TSN, and standardization work has continuously been made to provide deterministic communication for transmitting TSN traffic by integrating with TSN network. In 3 GPP, a 5 GS was established to support large-capacity broadband services in Release 15; in Release 16, standardization work satisfying service requirements, such as massive IoT, reliability, and low latency, was completed in 2020. In particular, in Release 16, the three technologies listed in Table 1 were presented to support industrial IoT, and standard specifications were developed [24]. Among them, the interworking technology with IEEE 802.1 TSN, which requires time-sensitive communication, continued to develop standards in Release 17 [25]. This section describes the standard technology for 5 GS interworking with TSN based on 3 GPP Release 16 and introduces issues in TSN interworking technology developed in Release 17.
## 3 Anomaly Detection Process for Battery Cells  
Based on the results of data preprocessing in Section 2, the anomaly detection process of battery cells is shown in Figure 4. Initially, the RPCA technique is employed for data processing, followed by the application of the Average Deviation-3\(\sigma\) principle to filter out anomalous battery cells and output their serial numbers. Subsequently, a consistency assessment is conducted on them.
# Modeling Shearing and Bending Behavior of Kiwifruit Branches Using the Discrete Element Method
## 1 Introduction
However, most of these models are established with one single type of experiment, such as cutting force, bending force, or repose angle, which makes the model unreliable when it is utilized in a different process. For kiwifruit branches, only one DEM model with high moisture content was established based on a three-point bending test [12]. However, shearing simulation was not conducted and the influence of DEM parameters on the mechanical and dynamic behavior of kiwifruit branches was not revealed. Despite these advancements, there remains a lack of suitable modeling methods for the bending and shearing processes of kiwifruit branches. The overall goal of this study is to develop a DEM model establishment methodology based on more than one type of experiment and to investigate the failure characteristics of kiwifruit branches with the established model. Specific objectives of this study are as follows: (1) determine the shearing and bending properties of kiwifruit branches; (2) establish a kiwifruit branch model with the DEM and simulate its bending and shearing behavior; and (3) calibrate the key DEM parameters of the model with simulation and laboratory tests and verify the calibrated parameters.
# EPO Extension of Dispatching Rules to Minimize Effects of Time Uncertainty in Production Scheduling
## 3 EPO Extension  
Production scheduling can be very time consuming if there is a large number of jobs. Therefore, a range of heuristic algorithms is used in practice. Dispatching rules are a common method to schedule jobs in practice due to their simplicity and efficiency, even for large datasets. However, they have a weakness that is important from a practical point of view, that is, they cannot take into account the uncertainty of processing times. The EPO extension proposed in this paper extends the functionality of the dispatching rules to include the uncertainty of the job processing times, which makes the generated schedules robust while keeping the time complexity of the method the same.  
The EPO extension of decision rules describes the uncertain processing time of a job by using the following three values:  
* \(E\)--expected processing time
* \(P\)--pessimistic processing time
* \(O\)--optimistic processing time  
which satisfy the following inequalities:  
\[O\leqslant E\leqslant P.\]  
Using the values \(E,P,O\) (EPO), we can take into account the uncertainty of processing time (\(PT\)) of jobs in production scheduling, and therefore we can minimize the makespan and downtime (stemming, for example, from the lack of components needed to continue the production process) of production lines and the need for frequent replanning.  
To better illustrate the advantages of the EPO extension, we will use an example. Suppose that in the exemplary scenario, the expected, optimistic, and pessimistic job processing times are 10, 9, and 13 units, respectively. In this scenario, the EPO extension allows for scheduling with implicit time buffers. Specifically, if the job takes 9 time units, the next job can start earlier, as it should be ready to start by the 9 th time unit. Conversely, if the job takes 13 time units, subsequent operations will not be delayed. Taking these dependencies into account enables us to achieve a robust schedule without the need to add extra time buffers explicitly.
## 4 Investigation of Proposed Method by Solving Test Functions
### Three-Objective Test Function Results  
Further, all the samples acquired by the two methods (the proposed multi-fidelity/multi-objective EGO and the single-fidelity/multi-objective EGO) are compared as shown in Figure 7. According to these results, the proposed multi-fidelity/multi-objective EGO and the single-fidelity/multi-objective EGO have similar non-dominated solutions. However, the results of the multi-fidelity/multi-objective EGO are better than those of the single-fidelity/multi-objective EGO because some of the results of additional sampling by the single-fidelity/multi-objective EGO stall at local optimum points. The hypervolumes of the two methods are compared in Figure 8. The hypervolume comparison of the proposed multi-fidelity/multi-objective EGO and single-fidelity/multi-objective EGO is shown in Figure 9. It suggests that the proposed multi-fidelity/multi-objective EGO gives better solutions than that of the single-fidelity/multi-objective EGO. These results indicate that the proposed multi-fidelity/multi-objective EGO has a higher convergence rate than the single-fidelity/multi-objective EGO. The solution of the proposed multi-fidelity/multi-objective EGO converges after 18 iterations with a larger hypervolume, whereas the solution of the single-fidelity/multi-objective EGO converges after 23 iterations.  
Figure 6: Cross-validation results of the two-objective test problem: (**a**) result of \(f_{1}\); (**b**) result of \(f_{2}\).  
Figure 7: Initial sampling data and additional sampling data of three-objective test problem: (**a**) multi-fidelity approach; (**b**) single-fidelity approach.  
Figure 8: Hypervolume comparison of the multi-fidelity approach and the single-fidelity approach of three-objective test problem.  
Figure 9: _Cont._  
The proposed approach  
The cross-validation results for \(f_{1}\), \(f_{2}\) and \(f_{3}\) are shown in Figure 9. The results indicate that the multi-fidelity surrogate model can achieve higher accuracy than the single-fidelity surrogate model for \(f_{1}\) and \(f_{2}\). Moreover, the results show that the non-dominated solution of the multi-fidelity/multi-objective EGO has a higher convergence rate because of the higher accuracy of the multi-fidelity surrogate model. With regard to \(f_{3}\), a comparison between the multi-fidelity and single-fidelity surrogate models shows that the two methods have similar accuracy because \(f_{3}\) has a simpler shape than \(f_{1}\) and \(f_{2}\).
# Coded-GFDM for Reliable Communication in Underwater Acoustic Channels
## 3 System Architecture
### Forward Error Correction Codes
Then the contents of the shift register are shifted to the right by \(k\) memory elements while the rightmost \(k\) bits of the register leave the encoder. During the phase when the \(k\) bits have entered the register, using modulo-2 addition, the \(n\) adders add the contents of the memory elements they are connected to. This results in the code sequence of length \(n\). Figure 1: A typical convolutional encoder.