esa-sceva/satcom-chunk-collection huggingface dataset:
# Processing Fresh-Cut Potatoes Using Non-Thermal Technologies and Edible Coatings
## 5 Emerging Processing Technologies: Operating Principles and Examined Parameters
### Miscellaneous Technologies
#### 5.5.2 Irradiation  
The irradiation of food products with short-wave ultraviolet light (UV-C) is a promising non-thermal method already used to disinfect water, air, packaging, and solid surfaces [166]. The germicidal mechanism of UV-C is attributed to the disruption of nucleic acids (DNA, RNA) of pathogens at a wavelength of 254 nm [166]. One of the key advantages of UV-C irradiation is its effectiveness in reducing microbial populations on the surfaces of fresh-cut produce, which can enhance food safety without the use of chemicals. Additionally, UV-C treatment maintains the nutritional attributes of produce, as it is a non-thermal method that minimizes nutrient degradation [165]. In an industrial setting, UV-C irradiation can be employed to reduce surface microbial load on fresh-cut potato slices, helping to extend shelf life. This treatment is beneficial for potatoes stored in packaging where surface decontamination is critical. However, the disadvantages of UV-C irradiation include its limited penetration depth, as UV-C light only affects surface layers, making it less effective for products with complex shapes or denser structures [169]. Another potential downside is the formation of acrylamide in fried potato products treated with UV-C, a concern that has been observed in raw tubers [170] and potato slices [11]. However, the effect of UV-C treatment on fresh-cut potato products has been poorly studied (Table 6). While irradiation with UV-C has been effective in sterilizing other fresh-cut vegetables during storage by reducing bacterial populations, it did not significantly extend the shelf life of fresh-cut potato slices beyond 5 days [165]. In another study, the combination of dipping in an ascorbic acid and calcium chloride solution with UV-C exposure at 6.84 kJ/m\({}^{2}\) was observed to be effective in maintaining lower enzymatic activities (POD, PPO and PAL) and higher total phenolic content in potato slices during storage period compared to untreated or UV-C-treated samples [166]. This suggests that UV-C irradiation may be more effective when used as part of a combined treatment strategy rather than as a standalone method for fresh-cut potatoes.
# MFFNet: A Building Extraction Network for Multi-Source High-Resolution Remote Sensing Data
## 5 Discussion
Conversely, MFFNet can extract deep features that other models fail to capture, reducing misclassifications and omissions. It can be said that MFFNet achieves outstanding segmentation results on large-scale integrated building datasets. Figure 11: Comparison of evaluation metrics across multiple models on the Jilin-1 dataset. Our model encounters challenges in comprehensively segmenting small objects in complex scenes. When applied to the Massachusetts dataset, due to the dense and small-sized buildings, some small-scale structures are easily overlooked. Similarly, on the WHU building dataset, fine details of small building contours are often missed. However, MFFNet still demonstrates superior segmentation performance compared to other networks. Considering that networks represented by the spatial pyramid structure show significantly lower segmentation performance on the Massachusetts dataset compared to others, and also underperform on the other two datasets, we hypothesized that the spatial pyramid structure might not enhance MFFNet's segmentation capability. We conducted a new set of experiments, testing the MFFNet without the multi-scale spatial semantic extraction module on all three datasets. We found that the network without this module yielded significantly lower evaluation metrics on small-sample datasets (Table 4) compared to our MFFNet, but still performed exceptionally well. This indicates that our multi-scale spatial semantic extraction module is not an arbitrary addition. Rather, it enhances the feature  
Figure 12: Comparison of evaluation metrics across multiple models on the Massachusetts dataset. Figure 13: Comparison of evaluation metrics across multiple models on the WHU dataset. fusion capability of MFFNet, especially when applied to small-sample building datasets, thereby improving segmentation results. On the WHU large building dataset, MFFNet's segmentation capability is slightly weaker than the network without the multi-scale module, but the minor differences in evaluation metrics are acceptable. Considering that ViT consistently achieves good results on large datasets, we compared segmentation models with ViT as the backbone. On small-sample datasets, MFFNet outperformed ViT in several evaluation metrics. On the WHU large building dataset, ViT's MIoU was higher than that of MFFNet by 0.28%, and ViT also slightly exceeded MFFNet in precision, recall, and kappa, but MFFNet was superior in terms of accuracy and F1-score. Overall, segmentation models with ViT as the backbone perform better on large datasets compared to traditional convolutional networks such as MFFNet, but MFFNet excels in segmenting small datasets. In practical work, due to the time and effort required for data annotation, the use of extensive annotated samples is not the first choice of method and training on small-sample data, as MFFNet is more reasonable.
_Appl. Sci._**2015**, \(5\), 1547-1559; doi:10.3390/app5041547  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Article_  
**Synthesis and Physical and Chemical Properties of Hyperbolic Chemicals such as \(N\),\(N\),\(N\)-Trimethylhydrazinium and 1-Ethyl-4-Methyl-1,2,4-Trirazolium Salts**  
**[PERSON], [PERSON], [PERSON] and [PERSON] ***  
Department of Chemistry, Hannam University, Daejeon 305-811, Korea;  
E-Mails: [EMAIL_ADDRESS] (Y.-S.K.); [EMAIL_ADDRESS] (G.-H.S.);  
[EMAIL_ADDRESS] (T.-K.N.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +82-42-629-8824; Fax: +82-42-629-8811.  
Academic Editor: [PERSON]  
_Received: 9 October 2015 / Accepted: 18 November 2015 / Published: 2 December 2015_  
**Abstract:** Hypergolic chemicals \(N\),\(N\),\(N\)-trimethylhydrazinium iodide, \([\)TMH\(]^{+}[\)I\(]^{-}\), and 1-ethyl-4-methyl-1,2,4-triazolium iodide, \([\)EMT\(]^{+}[\)I\(]^{-}\) were firstly synthesized by nucleophilic substitution (Sn2). The successful synthesis of hypergolic chemicals \([\)TMH\(]^{+}[\)I\(]^{-}\) and \([\)EMT\(]^{+}[\)I\(]^{-}\) was confirmed by IR and \({}^{1}\)H-NMR spectroscopy and, GC-mass spectrometry. Subsequently the hypergolic chemicals \([\)TMH\(]^{+}[\)X\(]^{-}\)(X = CN\({}^{-}\), N\({}^{3-}\), NO\({}^{3-}\), NO\({}^{2-}\), ClO\({}^{4-}\), AlCl\({}^{4-}\)) were prepared via an ion exchange reaction from \([\)TMH\(]^{+}[\)I\(]^{-}\) and \([\)EMT\(]^{+}[\)I\(]^{-}\), respectively. After that, a mixture of hypergolic chemicals was prepared by dissolving the synthesized hypergolic chemicals in 2-hydroxyethylhydrazine (HOCH\({}_{2}\)CH\({}_{2}\)NHNH\({}_{2}\)). The physical and chemical properties of the mixture such as decomposition temperature (T\({}_{\rm d}\)), density (d), viscosity (\(\eta\)), and decomposition energy (\(\Delta\)H\({}_{\rm d}\)) was then evaluated to determine suitability for use as liquid rocket fuels. The ignition delay (ID) time of the mixture of hypergolic chemicals with \([\)TMH\(]^{+}[\)N\({}_{3}]^{-}\) and \([\)TMH\(]^{+}[\)CN\(]^{-}\) using H\({}_{2}\)O\({}_{2}\) as an oxidizer was determined as 55.6 ms and 97.4 ms; respectively. The ID time of the mixture of hypergolic chemicals with \([\)EMT\(]^{+}[\)N\({}_{3}]^{-}\); \([\)EMT\(]^{+}[\)CN\(]^{-}\); \([\)EMT\(]^{+}[\)AlCl\({}_{4}]^{-}\); and \([\)EMT\(]^{+}[\)I\(]^{-}\) using H\({}_{2}\)O\({}_{2}\) as an oxidizer was also determined as 18.0 ms; 32.6 ms; 27.6 ms; and 7.96 ms; respectively. The synthesized mixture of hypergolic chemicals could thus be used as a rocket propellant liquid fuel.  
**Keywords:** hypergolic chemicals; rocket propellant fuel; \(N\),\(N\),\(N\)-trimethylhydrazinium salts; ethyl-methyl-triazolium salts
# A Deep Attention Model for Action Recognition from Skeleton Data
## 4 Experimental Results
### Results on NTU RGB + D Dataset
#### 4.1.1 Evaluation of the Deep Attention Network  
It is known that the skeleton data can be noisy. Therefore, in addition to the hidden state of the previous time step, the current input information is also used as input to obtain the attention, as shown in Section 3.1. Moreover, we show that it is important to explore relatively deep features to provide robust performance. Figure 3 shows comparisons among different configurations of the feature extractor and attention estimator for the attention network. Each figure shows the performance over 10 training processes of the corresponding configuration of the attention network. It can be clearly seen that with only a one-FC-layer attention estimator and without a feature extractor, the training process is likely to be trapped to a local optimum with poor performance, as shown in Figure 3a. As the number of layers in the attention estimator and the feature extractor increases, the training process becomes stable and less likely to be trapped to a local optimum. For a network with a four-FC-layer attention estimator and a five-FC-layer feature extractor, the training converges almost monotonically with improving performance.  
\begin{table}
\begin{tabular}{c c c} \hline
**Method** & **CS** & **CV** \\ \hline Deep learning on Lie Group [45] & 61.37\% & 66.95\% \\ \hline JTM + CNN [22] & 73.40\% & 75.20\% \\ \hline Res-TCN [24] & 74.30\% & 83.10\% \\ \hline SkeletonNet (CNN) [23] & 75.94\% & 81.16\% \\ \hline JDM + CNN [32] & 76.20\% & 82.30\% \\ \hline Clips + CNN + MTLN [25] & 79.57\% & 84.83\% \\ \hline Enhanced Visualization + CNN [4] & 80.03\% & 87.21\% \\ \hline ST-GCNN [46] & 81.5\% & 88.3\% \\ \hline
1 Layer RNN [29] & 56.02\% & 60.24\% \\ \hline
2 Layer RNN [29] & 56.29\% & 64.09\% \\ \hline
1 Layer LSTM [29] & 59.14\% & 66.81\% \\ \hline
2 Layer LSTM [29] & 60.09\% & 67.29\% \\ \hline
1 Layer PLSTM [29] & 62.05\% & 69.40\% \\ \hline
2 Layer PLSTM [29] & 62.93\% & 70.27\% \\ \hline JL\_d + RNN [31] & 70.26\% & 82.39\% \\ \hline ST-LSTM + Trust Gate [30] & 69.20\% & 77.70\% \\ \hline GCA-LSTM [20] & 74.4\% & 82.8\% \\ \hline STA-LSTM [19] & 73.40\% & 81.20\% \\ \hline IndRNNN [14] & 81.80\% & 87.97\% \\ \hline Proposed DA-IndRNN & 83.24\% & 88.70\% \\ \hline \end{tabular}
\end{table}
Table 1: Results of the skeleton-based action recognition on NTU RGB + D dataset in comparison with the existing methods.
# Effect of Formation Size on Flocking Formation Performance for the Goal Reach Problem
## 3 Results
### Simulation Environment
In our simulation, each time step varied between 0.095 and 0.973 s, based on the step length taken by the simulation code. Therefore, we suppose that the time step interval is 1 s. Each agent initially has a random speed. This random speed is assumed to be a floating number between 0 and 0.5. Hence, the movement of the agent could not exceed 0.5 patches ahead. In contrast to [14], where various perception zones were used, we conducted multiple tests to determine suitable common perception zones for all experimented flock sizes. Several zone sizes (\(\Delta r\), \(\Delta o\), and \(\Delta a\)) were tested on flock sizes of 30, 50, and 100 agents. Agent perception zone sizes were selected based on the results of these tests. The test results considered goal zone localization time, the average distance to the center of mass, collisions per member, and the applied times of flocking rules. According to [14], a smaller value of \(\Delta r\), and larger values of \(\Delta o\) and \(\Delta a\) led to better flocking behavior. However, there were no precise ratios or constraints on the total perception radii. Therefore, a number of zone sizes were tested (e.g., \(\Delta r=2\), \(\Delta o=6\), and \(\Delta a=7\) for 30 agents) where the flock did not show stable flocking behavior. To the contrary, the flock kept avoiding collision between its members and maintained cohesion simultaneously. Therefore, we eliminated the sizes that caused a similar situation from the next test on bigger flock sizes (detailed results provided in Appendix A). Experimentally, we found that smaller values of \(\Delta r\) and \(\Delta a\), and larger values of \(\Delta o\) led to better flocking behavior. The final selected zones were the minimum common sizes among all flock sizes that provided stable flocking behavior (\(\Delta r=1,\Delta o=13,\Delta a=1\)). According to the arena specifications set earlier, the agent's perception area covered 5.5% of the arena's size. The repulsion priority weights \(w_{1}\), \(w_{2}\), and \(w_{3}\) (Equation (1)) were set according to reference [14] to allow for improved repulsion behavior and to avoid having the desired position be too far from the agent, as follows: \(w_{1}=0.4\), \(w_{2}=0.075\), and \(w_{3}=0.025\).
# An Adaptive Fast Incremental Smoothing Approach to INS/GPS/VO Factor Graph Inference
###### Abstract
The incremental smoothing (IF) technology is employed to efficiently and swiftly compute the complete navigation solution [24; 25; 26; 27]. In contrast to batch optimization and fixed-lag smoothing, the incremental smoothing algorithm updates only a subset of nodes altered in the factor graph during each iteration. However, the incremental smoothing algorithm incrementally computes the system's square root information matrix and optimizes all state variables at each iteration. Despite its faster calculation speed compared to batch optimization, the dimension of state variables expands with the progression of navigation time, inevitably amplifying the computational complexity for each global optimization. Given the above analysis, the processing of asynchronous and delayed measurements in traditional KF-based integrated navigation methods is relatively complex. Additionally, the current factor graph algorithms (including batch optimization, fixed-lag smoothing, and incremental smoothing) cannot meet the accuracy and real-time demands of integrated navigation systems. To address the mentioned problems, this paper proposes an improved INS/GPS/VO adaptive fast integrated navigation algorithm based on factor graph. The main contributions of this paper are as follows:  
1. A novel factor-graph-based INS/GPS/VO integrated navigation model is formulated, which exhibits commendable efficacy in managing dynamic alterations in sensor effectiveness, as well as accommodating asynchronous and delayed sensors;
2. The adaptive fast incremental smoothing algorithm is proposed, which demonstrates superior performance compared to the traditional incremental smoothing approach. This paper is organized as follows. The proposed factor graph model for the INS/GPS/VO integrated navigation system is presented in Section 2.1. The AFIS factor graph optimization algorithm is derived in Section 2.2. The simulation results and analysis are shown in Section 3. The discussion is shown in Section 4. The conclusions are drawn in Section 5.
## 5 Discussion  
### Flow of Model Consideration  
Factors that influence the fatigue behavior of metallic materials include the strain rate, microstructure, temperature, and strength. Therefore, we focus on material hardness, a factor whose influence is not considered in the axial-loading model in terms of rate process theory [23]. Material hardness is the ability of a material to resist loads from plastic deformation, and the strength values are determined by the chemical composition and structural properties. Cyclic softening and hardening are considered to be coupling factors that change the original strength state of the material and influence the tendency for loading frequency to have effects [13]. Therefore, material hardness was investigated as the primary factor. In this chapter, the flowchart shown in Figure 5 is used to validate and derive the equations.  
First, the relationship between the hardness, loading frequency, and fatigue limit was investigated, and a model using the hardness parameter was proposed based on the data for the axial-loading fatigue limit. Next, we checked whether the proposed model was applicable to the rotating bending fatigue, which is a different loading mode from axial-loading fatigue. Finally, the effects of different loading modes on the parameters were clarified, and a new model for estimating the loading-frequency-dependent fatigue limits for different loading modes was proposed.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Group**} & \multirow{2}{*}{**\%C**} & **Loading Frequency** & **Estimated Fatigue Limit, MPa** & **Fatigue Limit \(\sigma_{\text{w}}\), MPa** & **Error, \%** \\ \hline G1 & 0.08–0.01 & 70 & 159 & JIS S10C & 235 & 47.7 \\ G2 & 0.12–0.22 & 70 & 190 & JIS S25C & 265 & 39.5 \\ G3 & 0.33–0.46 & 7 & 226 & JIS S35C & 285 & 26.3 \\  & & & JIS S45C & 395 & 75.0 \\  & & & JIS S35C & 295 & 25.3 \\  & & & JIS S45C & 415 & 76.3 \\ \hline \end{tabular}
\end{table}
Table 4: Results comparing estimated fatigue limits and fatigue test results.  
Figure 5: Flowchart of model derivation in this study.
# A Real Model of a Micro-Grid to Improve Network Stability
## 3 Description of the DC Micro-Grid
### Energy Sources
#### 3.1.1 Photovoltaic Panels  
A major energy source utilized within our micro-grid consists in two solar panels with the total power of 360 Wp; these panels are connected into the 24 V\({}_{\mathrm{DC}}\) grid via a Solartec regulator. If the voltage provided by the solar panels is higher than that of the battery block, the device will charge the battery to its full capacity, using overvoltage protection to terminate the process at the desired moment.
###### Abstract
Predominantly, inductive coupling and magnetic resonant coupling are used for WPT technologies. However, such approaches have a restricted range for optimal electric charging of EV. WPT technology using lasers is a technology that uses high-intensity laser power beams (HILPB) and transmits power generated by solar power generation in space to the ground, transmitting power from the earth to the moon, or is for military use. However, charging through HILPB involves complex processes where the power transmitter (PTX) performs a series of tasks to position the target and power receiver (PRX) accurately [16]. The higher the positioning accuracy of the receiver is, the higher the PTE of HILPB will be, and vice versa. Additionally, the large distance between the PTX and PRX makes the positioning process challenging which affects the PTE of HILPB. To overcome these issues, this study proposes a scheme for selecting appropriate transmitters for HILPB charging and makes the following contributions:  
* A power reception model of the PRX using coded laser pilot is proposed and validated through extensive simulations. * A novel model is proposed for selecting appropriate transmitters from several PTXs, where the PRX uses various wireless power channels (WPC) and coded laser pilots. The proposed algorithm enables developing a simple system because it does not require the PTX to perform a series of processes for positioning the PRX. * The performance of three different schemes for WPC is analyzed with the proposed algorithm, including PTX selection using the highest PTE first, the least PTE first, and PTX selection based on the best PTE first. * The photovoltaic (PV) model is validated through simulations and experiments while the path configuration model through simulations using a multi-channel WTP system comprising several PRXs and several PTXs that employ a coded laser pilot and received channel power indicator (RCPI)-based selection system. The rest of this paper is composed as follows. Background on WPC, description of various technologies used for WPC, and related work are discussed in Section 2. Section 3 proposes a power reception model of the PRX using laser pilot and validates through extensive simulations. Section 4 describes the structure and operation of a multi-laser PTX-PRX system using the proposed method. Section 5 describes two scenarios for HILPB WPT, and the operation of the multiple PTXs and PRXs system is verified by simulation. In the end, the conclusion and future works are given in Section 6.
# Wideband Channel Characterization for 6G Networks in Industrial Environments
## 6 Measurement Results and Analysis
### Path Loss  
The CI and FI path loss models are used to investigate the measured path loss at the three locations and compared with the FSP,L as shown in Figure 8a-c. It can be seen from the figures that the CI and FI path loss models fit the measured path loss very well for all environments. It can be noticed that the measured path loss is less than the FSPL with all Tx-Rx separation distances at the NTNU MANULAB and Manufacturing hall at Dokka Fastners AS and for a 2-5 m Tx-Rx separation distance in the Test room at Dokka Fastners AS, implying that the MPCs added up, constructively contributing additional power to the LoS component. The highest measured path loss is 85 dB for all environments at a 4.5-5 m Tx-Rx separation distance. The parameters for the CI and FI path loss models are listed in Table 4. Using reference points (\(d_{0}\)) of 0.85 m, 1 m, and 1 m, the PLE values for the CI path loss model are 1.7, 1.6, and 1.9 with standard deviations of 0.7 dB, 0.4 dB, and 0.3 dB for the Manufacturing hall at Dokka Fastners AS, the Test room at Dokka Fastners AS, and NTNU MANULAB, respectively. For the FI path loss model, with a floating \(\alpha\) of 73.3 dB, 73.1 dB, and 71.5 dB, the slope lines \(\beta\) are 1.7, 1.6, and 2.0 with standard deviations of 0.4 dB, 0.3 dB, and 0.7 dB, for the Manufacturing hall at Dokka Fastners AS, the Test  
Figure 7: Total measured received power with Tx-Rx separation distance in **(a)** the Manufacturing hall at Dokka Fastners AS, **(b)** the Test room at Dokka Fastners AS, and **(c)** NTNU MANULAB.  
room at Dokka Fastners AS, and NTNU MANULAB, respectively. The values of these parameters of CI and FI along three indoor industrial settings indicate that the CI and FI path loss models have the same performance of accuracy and stability to perform the best fitting for the measured path loss in indoor industrial environments.
## 2 Classification of Propagation Models  
Propagation models design a radio interface to optimize performance and deploy systems in the field to determine radio coverage. The models will be used in engineering tools to predict various values that will benefit the deployment of radio telecommunications systems and radio coverage research (site selection, frequency allocation, power definition) and interference description. The models rely heavily on geographic datasets that include topography and land use types. This is because the way ultra-high frequency (UHF) radio waves propagate in a given space is intimately related to the obstacles (buildings, tree trunks, mountainsides, etc.) encountered along the propagation channel.  
Therefore, the modeling of geographical objects is essential in any UHF wave propagation model [3]. Propagation models are then used to provide a mathematical prediction of wave propagation between the origin and destination service area. This allows a system receiver to assess whether a planned radio system will sufficiently serve the desired service area. The following subsections present the fundamental models studied and their classification, data requirements, and coverage notes.