esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### WVTR Calculation Results  
For the WVTR analysis, three samples of each dose were weighed for six days. The WVTR was calculated using Equation (4), averaged, and plotted with the standard deviation shown in Figure 11. No significant trend was observed, as the standard deviation indicates high variability in the data. The thickness of the sample is higher than that of samples more commonly found in the literature while still meeting the requirements for the ASTM procedure [60; 61; 62].  
Figure 8: Diffractograms for unirradiated and 20 kGy irradiated PLA.  
Figure 9: Number average molar mass distributions for the unirradiated sample (black) and samples irradiated at doses of 5 kGy (green), 10 kGy (orange), 15 kGy (blue), and 20 kGy (red). The lines represent the number average molar mass M\({}_{\text{n}}\) value (center) of each trace at the peak of the distribution.  
Analysis of the data indicates that there is no notable effect of irradiation dosage on WVTR of PLA. Based on the EPR and GPC results, an increase in the WVTR was expected after irradiation of the PLA. It has been shown that chain scission increases the WVTR of the polymer as it reduces the pathway tortuosity, or the number of barriers that the water vapor molecules must overcome [63; 64]. However, in this study PLA was irradiated only up to a dose of 20 kGy and this resulted in low chain scission yields which did not have an impact in the WVTR.
## 3 Results and Discussion
### Mesopic Color Reproduction  
The mesopic color reproduction can be carried out by following the procedure, as shown in Figure 7. A test image is captured using a Canon EOS 7D DSLR camera under a light booth with a D65 light source under 318 cd/m\({}^{2}\) (\(\cong\)1000 lux). The image in CR2 format is converted to a 16-bit tiff format in linear RGB (gamma = 1) to minimize the impact of tone enhancement while ensuring wide color gamut. A third-order polynomial regression as Equation (15) is used to correlate the linear RGB value to \(X\), \(Y\), \(Z\), and \(V^{\prime}(\lambda)\) values as the colorimetric model. The simulated image of optimal SPDs under four luminance levels is shown in Figure 8.  
\[\left[\begin{array}{c}X\\ Y\\ Z\\ V^{\prime}\end{array}\right]=\text{M}_{4\times 14}\Big{[}\begin{array}{cccccccccccc}R ^{3}&G^{3}&B^{3}&R^{2}&G^{2}&B^{2}&R&G&B&RG&GB&RB&RGB&1\end{array}\Big{]}^{T} \tag{15}\]
## 4 Simulation Results
### Comparison of Prediction Models  
An open-loop test was used to compare the responses of three bicycle models. The structures of these models are listed in Table 2. The four-state bicycle model [34] has two inputs and two outputs that are the same as those of the proposed six-state bicycle model in (3). However, the actuator dynamics of EPS and EHB were not considered for the four-state bicycle. The five-state bicycle model that includes the EPS actuator dynamics has one steering angle control input and the lateral displacement output. The DYM input was not considered in the five-state bicycle model. Dynamic responses of these models are shown in Figure 4. The step steering angle input was applied at the beginning of simulation, and the DYM step input was applied at 1 s. CarSim denotes the dynamic responses of CarSim with two inputs of steering angle and DYM. Before 1 s, the main difference between the four-state model and other models was the transient response due the lack of actuator dynamics. The steady-state responses of three bicycle models were almost the same. The responses of the five-state and the six-state bicycle models were similar to those of CarSim. After the DYM was applied at 1 s, only the responses of the six-state bicycle model were close to those of CarSim. Since the six-state model with actuator dynamics can achieve both transient and steady-state responses closest to those of CarSim, it was used as the prediction model in the proposed algorithm.
# Leveraging Blockchain and Consignment Contracts to Optimize Food Supply Chains Under Uncertainty
## 1 Introduction
### Novelty of the Study  
In recent years, transparency and traceability in supply chain management have become highly demanding issues. In the food industry, in particular, customers pay attention to food safety and contamination. The supply of fresh food items is more challenging than that of packaged food due to the chances of deterioration. Moreover, companies must achieve their targeted profit to run their business smoothly. Therefore, this study aims to consider some fundamental issues regarding food safety, customer satisfaction, and profitability. The originality of this manuscript relates to the impact analysis of adopting blockchain technology in the food industry. Several aspects are investigated, like how blockchain and its related costs impact customer demand, how the industry's profitability varies when a company switches traditional contracts with consignment contracts under a decentralized blockchain system, and how companies can deal with demand uncertainty due to perishable food products. The novelty of this research also includes consideration of constructive mathematical modeling and quantifying the profitability analysis of food industry supply chain management under the adoption of decentralized blockchain technology, which has not been investigated in previous studies. Moreover, this study investigates a thorough numerical analysis of profit and loss and the effect on demand for new technology adoption.
## 4 Numerical Analysis
### Sensitivity Analysis
#### 4.2.2 Sensitivity Analysis of Maritime Transportation Cost to Port Equilibrium Profit
Thus, conducting a sensitivity analysis of environmental factors to determine the impact of cross-border transportation corridor construction on the competition of neighboring countries' ports will be an important area for future empirical analysis. In addition, changes in the international political situation may lead to adjustments in port policies and regulations in relevant countries. These adjustments may involve tariffs, trade barriers, import and \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Shipping Route** & **Direction** & \begin{tabular}{c} **Maritime Transportation Cost \({}^{*}\)** \\ **(Ocean Freight)** \\ \end{tabular} & \begin{tabular}{c} **Maritime Transportation Cost Difference** \\ **(C\({}_{B}^{sta}-C_{A}^{sea}\))** \\ \end{tabular} \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Shipping routes to Japanese \\ and Korean ports \\ \end{tabular} } & Dalian–Buan & 2783.45 & \multirow{4}{*}{1109.45} \\  & Vladivostok–Buan & 2431.11 & & \(-\)352.34 \\  & Dalian–Tokoy & 2818.68 & & \\  & Vladivostok–Tokoy & 3382.42 & & 563.74 \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Shipping routes to Chinese \\ coastal ports \\ \end{tabular} } & Dalian–Shanghai & 1674.00 & & \\  & Vladivostok–Shanghai & 2783.45 & & 1109.45 \\  & Dalian–Xiamen & 1464.00 & & \\  & Vladivostok–Xiamen & 3840.45 & & 2376.45 \\  & Dalian–Nansha & 1335.00 & & \\  & Vladivostok–Nansha & 3840.45 & & 2505.45 \\ \hline \hline \end{tabular} \({}^{*}\)Transportation costs are calculated in RMB.  
\end{table} 
Table 3: Comparison of the cost of maritime transportation routes. export restrictions, etc., directly affecting the port's cargo throughput and international competitiveness. Therefore, in future research, it is necessary to further consider these factors and better simulate and analyze the impact of the new land-sea intermodal corridor on the oligarchic port competition of neighboring countries.
## 4 Results and Discussion
### Rotor Optimization Results
Actually, there are 19 individuals satisfying all the constraints, which are indicated in green color in Figure 13. The best trade-off solution is selected from Gen 30 (selected optimum). The optimal rotor weighs 0.82 kg (reduced by 14.2%), generates a maximum stress of 676 MPa (lower than the maximum allowable stress), achieves a total-to-static efficiency of 89.6% (increased by 1.2%) and changes the mass flow rate less than 0.3%. Figure 15 shows the performance map under a range of velocity ratios. It can be seen that T-100 re optimal design show better performance under all conditions. In the rotor parameterization, very small variation range is specified for the first three control points of the beta-angle distribution on the hub, mid, and tip sections in order to avoid performance deterioration under the off-design condition, and it is proved to be effective. Therefore, there is no need to conduct multipoint optimization for the rotor. Figure 14: Change of equivalent stress along with total-to-static efficiency (T-100 re). Figure 13: Change of rotor weight along with total-to-static efficiency (T-100 re). Figure 16 shows the comparison of Mach number contours between the original T-100 turbine and the optimal T-100 re turbine at mid-span section under design condition. At the outlet of the nozzle, the Mach number of T-100 turbine is lower than that of the T-100 re turbine, while it is adverse at the outlet of the rotor. It is indicated that the enthalpy drop of the T-100 re turbine in the nozzle and the rotor is well redistributed after 3-D optimization, and the range of lower Mach number in the rotor passage is reduced because of its higher rotational speed and reasonable rotor geometry. In addition, the absolute outlet velocity of the T-100 re turbine is 216.38 m/s, lower than that of T-100 turbine, 167.24 m/s, so the total-to-static efficiency of the turbine is improved. Figure 16: Comparison of Mach number contour at mid-span section. (**a**) T-100 turbine (original), (**b**) T-100 re turbine (optimal). Figure 15: Comparison of total-to-static efficiency.
# Spatial Analysis for Landscape Changes: A Bibliometric Review
## 3 Results
### Spatial Distribution of S.a.l.c.  
S.a.l.c. literature authorship is spatially distributed all around the world, even if a preliminary analysis of the results indicates a relevant prevalence of European-related papers coming from 129 different countries. Figure 3 shows the frequency of documents found for each country and classified in deciles. The first five countries interested in the research area are the United States of America (with 2765 documents, 32.9%), China (1205, 14.3%), Germany (888, 10.0%), the United Kingdom (836, 10.0%), and Canada (628, 7.5%). It is worth noting that the countries that seem not involved in the research topics and present zero articles or very few articles (1 or 2) are prevalently located in the African continent.  
It is also interesting to look at the co-authorship map between countries (Figure 4), and the four main geographical clusters found that are those with the following rank: (1 st) the USA, (2 nd) China, (3 rd) Germany, and (4 th) the Netherlands.
## 3 Methodology
### Proposed ML Models
#### 3.7.6 Logistic Regression  
Logistic regression is a statistical method used in binary classification problems. It predicts the probability of an event's occurrence by fitting the data to a logistic curve, which is an S-shaped function. This function outputs values between zero and one, representing the probability of an event. The model uses a linear combination of input features transformed by a logistic function to determine the probabilities. Logistic regression is valued for its interpretability, with coefficients indicating the influence of each feature. It works well with linearly separable data but may require regularization to prevent overfitting in complex scenarios [36].
## 4 Improved ACO for Global Path Planning
### Improved Thermone Update Mechanism  
In a traditional ACO, pheromones on each path are updated after all the ants finish searching. At the end of the algorithm, the optimal path has accumulated a lot of pheromones. The ants can choose the feasible path stably. If some unnecessary pheromones are still updated on all paths, it will reduce the stability of the optimal value and influence the convergence speed of the algorithm. The constant \(B\) is proposed. When iteration times \(n<B\), the average value \(L^{\prime}\) of the feasible path solutions is obtained from all ants in a cycle and the pheromones on the paths are updated. The solution is smaller than the average value \(L^{\prime}\). When \(n\geq B\), the pheromone is updated only on the optimal path. The improved mechanism mentioned above is shown in the Equations (10) and (11). In those equations, the value of \(B\) is a constant related to the total number of iterations \(N\). The \(m\) is the total number of ants. The \(L_{k}\) is the total length of the path that ant \(k\) walked through in a cycle. The \(L_{best}\) is the best path of the contemporary ant colony.  
\[L=\frac{\sum_{k=1}^{m}L_{k}}{m} \tag{10}\]  
Figure 6: A large value will lead to densely distributed initial pheromones.  
\[\Delta\tau_{ij}=\left\{\begin{array}{c}\sum\frac{Q}{L_{k}-\{1_{k}\leq L\}},n<B\\ \frac{Q}{L_{best}^{2}},n\geq B\end{array}\right\} \tag{11}\]  
It can be seen in Equation (11) that the new update mechanism updates the pheromone of ants. They obtain a short path at an early stage of the algorithm and only update the pheromone of the optimal path at the end of the algorithm. The improvements can accelerate the algorithm's convergence speed and searchability, while avoiding premature phenomenon.
# Comparative Study of Harmony Search Algorithm and its Applications in China, Japan and Korea
## 4 Harmony Search Applications in Japan
### Engineering
#### 4.1.2 Industrial  
To establish a fuel usage model in Indonesia, [PERSON] and [PERSON] [44] implemented the HS algorithm using general socioeconomic variables conveniently retrievable from public data. Their goals were to establish a reliable and specific model for the potential use of gasoline, and to include an appealing alternative model for forecasting gasoline usage. Inside the exponential model, the HS algorithm was used to decide the optimal weight variables. With respect to an absolute average error rate, the HS algorithm outperformed the traditional nonlinear regression method in the PSO-based model.  
In Ref. [45], an approach for combining the Scatter Search Algorithm (SSA) and HS was proposed for the developing process of intelligence optimization. An incapacitated Single-Level Lot-Sizing (SLLS) problem checked performance and quality. The viability of the suggested system was proved by machine simulation tests. A new optimization method and technique were proposed to address the SLLS problem they experienced in their analysis.  
In Ref. [46], a method based on a HS algorithm was proposed for solving a Vehicle Routing and Cargo Allocation Problem (VRCAP), finding the minimum cost and CO\({}_{2}\) emissions. The experimental findings showed that the approach would produce lower CO\({}_{2}\) emissions. Experiments in a Japanese freight carrier were conducted while using both synthetic and real details. The findings confirmed the efficacy of the approach.  
[PERSON] and [PERSON] [47] optimized a workflow using handling subjective attributes by the HS. The solution increased job performance by using the HS method based on the individual qualities of the worker in such interruptions. A modification of HS requirements was proposed in order to prevent undue change by measuring the degree of negative disruption effect.  
In a study by [PERSON] and [PERSON] [48], a Green Vehicle Routing Problem (GVRP) was investigated and a hybrid algorithm based on the HS was developed for optimally solving GVRP. HS was hybridized into a geographic development procedure called the two-phase selection method.  
A supermarket firm was used to check the algorithm's adaptability and performance. The goal was to reduce the total fuel usage and at the same time meet all consumer service requirements. The study concluded the application of the alternative procedure was successful.
## 3 Materials and Methods
### PlaceNet-Based SBD Method
#### 3.1.3 Shot Boundary Verification  
Only using the pre-trained PlaceNet for SBD is insufficient for common conditions. The classification places inevitably have some errors compared to the ground-truth data. In Figure 5, the classified categories are listed and they are actually in a shot.  
Considering the consistency results, both Places365 and the Object-centric GoogleNet show better performance than PlaceNet, even though they have classified the wrong category. PlaceNet shows the right category in frame 2071, frame 2176, and frame 2206; however, as the middle frame, frame 2191 shows a different category. In this situation, the shot boundary should be verified.  
At this stage, we use SIFT matching for verification to reduce false detections of shot boundaries. This process is done after using the pre-trained PlaceNet to extract the places category of the candidate segment boundaries. Here, the threshold of the shot boundary decision is transformed to the threshold of the matched numbers. If the matched number values are less than the threshold, the adjacent two candidate boundaries, which are assigned to different place categories, are truly different. If the matched number values are larger than the threshold, PlaceNet has wrongly classified the places and the current candidate shot boundary will change to the next candidate boundary.  
Usually, matches for image content pairs that have little relation to each other are rare no matter how high the match threshold. As shown in Figure 6, the number of matches between them is greater than the threshold value. So, PlaceNet has made a false detection at the second stage, and SIFT matching can eliminate this falsity by feature matching.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Validation Set of Places Data** & \multicolumn{2}{c}{**Test Set of Places Data**} \\ \cline{2-5}  & **Top-1 acc** & **Top-5 acc** & **Top-1 acc** & **Top-5 acc** \\ \hline Places-GoogleNet & 37.25\% & 65.81\% & 37.04\% & 65.63\% \\ Places-ResNet-50 & 45.32\% & 74.28\% & 45.18\% & 74.21\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: The classification accuracy on our validation and test datasets of pre-trained PlaceNet.  
Figure 5: The classification results of PlaceNet, GoogleNet, and Places365 for the images in a shot.
# Multi-Scale Flame Situation Detection Based on Pixel-Level Segmentation of Visual Images
## 4 Experiments
### Experimental Setup  
The experiments in this paper are deployed on a computer equipped with an NVIDIA GeForce RTX 3090 GPU (Nvidia Corporation, Santa Clara, CA, USA).The Pytorch deep learning framework is employed for model training, leveraging the CUDA, Cudnn, and OpenCV libraries to facilitate the training and testing of the flame segmentation model. The network is optimized using the Adam optimizer. The initial learning rate is 0.0002 and the weight decay is 0.01. In the two datasets, a batch size of 6 is employed during the training process. The training images are randomly scaled and then cropped to the fixed size of 512 \(\times\) 512.
###### Abstract
The association between nodes is measured by the information transmission efficiency of the whole network G and the information center degree of the corresponding edge from the node. The data transmission probability is built into the Hidden Markov Model(HMM) decision. It is designed as a two-phase algorithm, which ensures that the data delivery can adapt to the vehicle density. The rest of paper is organized as follows. In Section 2, we introduce some different routing protocols in vehicular ad hoc networks; OVMA is shown in Section 3; a simulation experiment and results analysis are introduced in Section 4; finally, Section 5 concludes the paper.
## 4 TinyOS
### Communication Protocol Support  
Earlier versions of TinyOS provide two multi-hop protocols: dissemination and TYMO [9; 10]. The dissemination protocol reliably delivers data to every node in the network. This protocol enables administrators to reconfigure queries and to reprogram a network. The dissemination protocol provides two interfaces: _DisseminationValue_ and _DisseminationUpdate_. A producer calls _DisseminationUpdate_. The command _DisseminationUpdate_.\(change()\) should be called each time the producers wants to disseminate a new value. On the other hand, the _DisseminationValue_ interface is provided for the consumer. The event _DisseminationValue_.\(changed()\) is signaled each time the dissemination value is changed. TYMO is the implementation of the DYMO protocol, a routing protocol for mobile _ad hoc_ networks. In TYMO, packet formats have changed and it has been implemented on top of the active messaging stack.  
[PERSON] _et al._[11] have presented DIP, a new dissemination protocol for sensor networks. DIP is a data discovery and dissemination protocol that scales to hundreds of values. TinyOS version 2.1.1 now also provides support for 6 lowpan [12], an IPv6 networking layer within a TinyOS network.  
At the MAC layer, TinyOS provides an implementation of the following protocols: a single hop TDMA protocol, a TDMA/CSMA hybrid protocol which implements Z-MAC's slot stealing optimization, B-MAC, and an optional implementation of an IEEE 802.15.4 complaint MAC.
## 3 Result and Discussion
### Yields  
Measured average methane production for the four cases investigated and two blank cases are presented in Figure 5. Blank adjusted total methane productions after 40 d were 403 \(\pm\) 73 mL for RF, 621 \(\pm\) 54 mL for RF-CEL, 331 \(\pm\) 61 mL for CF and 462 \(\pm\) 57 mL for CF-CEL. The highest volumes of methane were produced by cellulase containing samples RF-CEL and CF-CEL. Cellulase enhanced the COD conversions from 45% to 69% and 62% to 87% for RF and CF samples, respectively (Table 5). As expected, centrifuged samples resulted in lower ultimate methane production but higher specific methane yield than their non-centrifuged counterparts. From RF to RF-CEL specific yield increased from 390 to 600 L CH4/kg VS and from CF to CF-CEL it increased from 742 to 1037 L CH4/kg VS (Table 6).
# Generative Adversarial Networks for Zero-Shot Remote Sensing Scene Classification
## 2 Methods
### Training and Testing  
In the training phase, the generator was trained using class semantic features and visual features from the seen categories. We used classification loss, semantic regression module, and class-prototype loss to constrain the generator. We used classification loss to allow the generator to learn to generate discriminative image features. We used the semantic regression module to ensure that the generated image features represent semantic features. Class-prototype loss ensures that the synthetic image features are more consistent with the distribution of real remote sensing image features. The model is trained with Equation (11).  
After the training is complete, we repeated the generator to generate an arbitrary number of image features for each unseen category. We obtained a training set of synthetic images. In the ZSL task, we can classify the unseen images by the classifier trained on this dataset. In the GZSL task, we added the synthetic images dataset to the seen class dataset and trained a classifier to recognize both seen and unseen class images. We can choose a softmax classifier or KNN, etc. In this study, we used the softmax classifier.