esa-sceva/satcom-chunk-collection huggingface dataset:
# OPERA the Radar Project
## 3 Results
### Common Data Format  
During the first phase of OPERA, starting in 1999, the community adopted and promoted use of the BUFR format defined by World Meteorological Organization (WMO). Before that, each radar owner used the native formats of the software manufacturers, or nationally developed versions of these. Introducing a common format was a revolution, which allowed manufacturer-independent joint development, and made the international exchange of radar data much easier.  
The second phase (2004-2006) formulated a second-generation information model for use with weather radar data and the Hierarchical Data Format version 5 (HDF5) file format. It was implemented in 2009, and has been updated several times. Version 2.3 was published in 2019. During the 20 years of OPERA, all major weather radar software manufacturers have adopted the OPERA data format and provided conversion software for it. Additionally, the numerical weather prediction model consortia are currently developing tools to read the ODIM format.  
Development of a common data model started with a focus on Cartesian radar products (such as CAPPI or MAX); however, since 2013, bi-lateral and multi-lateral exchange between OPERA members has consisted of single-site data in the original polar coordinates.  
The information model was designed from the point of view of trying to harmonize all relevant information, independently of radar manufacturer and the organization from which the data originates. While the information model is intended to enable the representation of the data and products presently agreed upon within the framework of EUMETNET OPERA, we also look ahead to future needs and have tried to ensure that they will be appropriately met with this information model. This means that  
Figure 7: Monthly average daily precipitation intensity bias (OPERA–MESAN) over the OPERA composite domain.  
known products are supported, as are polarization diversity variables and virtually any quality-related information characterizing a given data set. It is also vital to recognize the importance of being able to represent polar (spherical coordinate space) data, and this is accommodated, as well, in a flexible manner.
###### Abstract
Moreover, most models focus on isolated components, lacking an integrated framework that combines probabilistic analysis, network flow optimization, and traffic simulation. This study aims to fill this gap by proposing a novel optimization framework that integrates Markov processes, max-flow problems, and traffic flow models. Applying this comprehensive approach to Dar es Salaam Port will provide a more holistic solution to improving hinterland connectivity, addressing the limitations of previous models. The primary objective of this study is to develop and apply a novel optimization framework that integrates Markov processes, max-flow problems, and traffic flow models to enhance hinterland connectivity. This framework aims to optimize the movement of goods from Dar es Salaam Port to inland destinations by addressing key challenges such as capacity constraints, traffic congestion, and the stochastic nature of transportation delays. By applying this model, the study seeks to improve the efficiency and reliability of port logistics, ultimately providing a more effective solution for managing port-hinterland connectivity. The remainder of this paper is structured as follows: The next section reviews the existing literature on hinterland connectivity and optimization models. The methodology section presents the framework in detail, describing the specific roles of Markov Processes, Max-Flow Problems, and Traffic Flow Models. Following that, the case study of Dar es Salaam Port is presented, where the proposed framework is applied, and results are discussed. Finally, the paper concludes with key findings, implications for the port's operations, and suggestions for future research. In this study, \"cargo flow\" refers to the movement of goods through the port and transport network, from arrival at the port to delivery at inland destinations [8; 9]. \"Traffic congestion\" is the condition where the volume of vehicles exceeds the capacity of the roads, leading to delays and inefficiencies in the transportation network [10; 11]. \"Capacity optimization\" involves adjusting the use of available infrastructure such as roadways, railways, and port facilities to maximize the throughput of goods while minimizing delays, congestion, and resource underutilization [12; 13]. These terms are central to understanding the dynamics of port operations and hinterland connectivity.
###### Abstract  
For the dynamic collision avoidance problem of an unmanned surface vehicle (USV), a dynamic collision avoidance control method based on an improved cuckoo search algorithm is proposed. The collision avoidance model for a USV and obstacles is established on the basis of the principle of the velocity obstacle method. Simultaneously, the Convention on the International Regulations for Preventing Collisions at Sea (COLREGS) is incorporated in the collision avoidance process. For the improvement of the cuckoo algorithm, the adaptive variable step-size factor is designed to realize the adaptive adjustment of flight step-size, and a mutation and crossover strategy is introduced to enhance the population diversity and improve the global optimization ability. The improved cuckoo search algorithm is applied to the collision avoidance model to obtain an optimal collision avoidance strategy. According to the collision avoidance strategy, the desired evasion trajectory is obtained, and the tracking controller based on PID is used for the Lanxin USV. The experimental results show the feasibility and effectiveness of the proposed collision avoidance method, which provides a solution for the autonomous dynamic collision avoidance of USVs.  
unichannel surface vehicle; dynamic collision avoidance; improved cuckoo search; tracking controller
## 3 Calculation Methodology of the PVCS Carbon Impact
### Presentation of the Study Perimeter
#### 3.2.4 Carbon Impact of the PVCS Infrastructure  
The carbon impact of the PVCS infrastructure is presented by the carbon impact of the car parking shades.  
In order to obtain the desired quantities of materials used for construction of the car parking shades, the volume \(V_{RC}\) of necessary RC for these foundations in m\({}^{3}\) is required. This volume is expressed in the equation below:  
\[V_{RC}=L_{foundation}\cdot W_{foundation}\cdot H_{foundation}\cdot N_{poles} \tag{38}\]  
where \(N_{poles}\) is the number of poles, and \(L_{foundation}\), \(W_{foundation}\), and \(H_{foundation}\) are the length, width, and the height of the RC foundation in m.  
The carbon impact \(Imp_{RC,foundation}\) of the RC for the foundations of \(N_{pc}\) car parking shades is calculated according to the following equation:  
\[Imp_{RC,foundation}=CO_{2,RC}\cdot\rho_{c}\cdot V_{RC}\cdot N_{pc} \tag{39}\]  
where \(CO_{2,RC}\) is the carbon emission coefficient of RC in kgCO\({}_{2,eq}\)/m\({}^{3}\).  
The calculation of the carbon impact \(Imp_{Stel}\) of steel in the metal structure of a shade unit is expressed by Equation (40):  
\[Imp_{Stel}=m_{Stel}\cdot CO_{2,Stel} \tag{40}\]  
where \(m_{Stel}\) is the mass of steel of a shade unit in tons and \(CO_{2,Stel}\) is the steel carbon emission coefficient in kgCO\({}_{2,eq}\)/ton.  
Thus, the carbon impact \(Imp_{N_{pc}\_shades}\) (kgCO\({}_{2,eq}\)) for a shade with \(N_{pc}\) parking places is calculated by (41) as a function of the carbon impact of the RC used for the foundation of the shades units \(Imp_{RC,foundation}\) and the carbon impact of the steel contained in the shades units \(Imp_{Stel}\):  
\[Imp_{N_{pc}\_shades}=Imp_{RC,found}+Imp_{Stel} \tag{41}\]  
Finally, according to the obtained results, it is possible to calculate the carbon emissions coefficient for one parking place \(CO_{2,shade}\) and \(Imp_{infra}\):  
\[\left\{\begin{array}{l}CO_{2,shade}=\frac{Imp_{N_{pc}\_shades}}{m}\\ Imp_{infra}=CO_{2,shade}\cdot N_{pp}\end{array}\right. \tag{42}\]
# The Influence of Various Types of Functional Bread on Postprandial Glycemia in Healthy Adults
## 3 Results
### Nutrition Value and Glycemic Index of Bread According to Fermentation Type  
To verify the influence of fermentation type on postprandial glycemic response and, therefore, glycemic index value, we divided the group into three subgroups: with the addition of sourdough starter, with the addition of sourdough starter and yeast, and with the addition of yeast only. Figure 3 illustrates the average glycemic profile over two hours for each group. We observed a significant difference in GI depending on the type of fermentation. Significantly lower median GI was observed for sourdough bread compared to bread with the addition of yeast or sourdough and yeast. Table 7 presents characteristics of the glycemic index and average nutritional value for dedicated groups. No significant intergroup differences were observed in median energy, carbohydrate, and fat values. Products with added yeast were characterized by higher protein and fiber content. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Selected Relationships** & \
\begin{tabular}{c} **Bread Without Oilseeds** \\ **(\(n=9\))** \\ \end{tabular} &
\begin{tabular}{c} **Bread with Oilseeds (\(n=14\))** \\ \end{tabular} \\ \hline IAUC vs. carbohydrate content & \(p=0.0062\); r = 0.273, R\({}^{2}=6.51\) & \(p=0.0004\); r = 0.287, R\({}^{2}=7.60\) \\ \hline IAUC vs. protein content & NS & NS \\ \hline IAUC vs. fat content & \(p=0.0207\); & \(p=0.0001\); \\  & r = \(-0.232\), R\({}^{2}=4.49\) & r = \(-0.307\), R\({}^{2}=8.84\) \\ \hline IAUC vs. fiber content & NS & NS \\ \hline IAUC vs. fiber/carbohydrates ratio & \(p=0.0015\); r = \(-0.315\), R\({}^{2}=9.02\) & NS \\ \hline Delta IAUC vs. carbohydrate content & \(p=0.0139\); r = 0.246, R\({}^{2}=5.10\) & NS \\ \hline Delta IAUC vs. protein content & \(p=0.0114\); r = \(-0.253\), R\({}^{2}=5.45\) & \(p=0.00001\); r = 0.328, R\({}^{2}=10.15\) \\ \hline Delta IAUC vs. fat content & \(p=0.0002\); r = \(-0.371\), R\({}^{2}=12.84\) & \(p=0.0069\); r = 0.219, R\({}^{2}=4.16\) \\ \hline Delta IAUC vs. fiber content & NS & NS \\ \hline GI vs. carbohydrate content & NS & \(p=0.0020\); r = 0.250, R\({}^{2}=5.61\) \\ \hline GI vs. protein content & NS & NS \\ \hline GI vs. fat content & NS & \(p=0.00001\); r = \(-0.338\), R\({}^{2}=10.83\) \\ \hline GI vs. fiber/carbohydrate ratio & NS & \(p=0.0214\); r = \(-0.187\), R\({}^{2}=2.85\) \\ \hline NS—not significant. & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Selected associations between IAUC, glycemic index, and nutritional value of groups divided according to dominant oilseed addition. Figure 3: The profile of the mean glycemic values for each group within two hours after the test product consumption; for variables with other letters, the difference is statistically significant between groups (ANOVA, test LSD, \(p\geq 0.05\)); mean \(\pm\) SD.
## 2 Standard Methods
### Field Trial
* Remove CD traps and collect remaining SHBs when t = 19 d (t = 14 d since inserting traps). Note: We left the traps in the hives for 14 d. This time can vary based on the specific research question being asked. * Work one hive at a time, beginning with the negative and/or solvent controls, followed by the experimental group, and finally the positive control. This will avoid contamination and confusion of samples. * Collect dead SHBs by removing traps. * Place each trap in a sealable plastic bag. You can collect other dead SHBs in the hive. Do not include them in the count of SHBs that died due to exposure to the test compound. Instead, they can be saved for residue analysis or other research purposes. * Record the number of live SHBs in each trap. * Collect live SHBs from the hive via aspiration. * Remove all live SHBs from each piece of equipment and frame using the \"bump method\" described in Section 3.2.3.1 of [PERSON] et al. [17]. * Use an electrically powered aspirator rather than a mouth aspirator and avoid inhaling compounds throughout the field trial. Wear appropriate PPE. * Use a new collection vial for each hive. Clearly label each vial with the corresponding hive number. * Do not aspirate any dead SHBs, since the cause of death is unknown. Collect dead SHBs outside of traps separately. * Place traps and sealed vials of live SHBs into a freezer (\(-20\pm 1\)\({}^{\circ}\)C) to euthanize all remaining live SHBs and preserve the samples until processing. * Process SHB samples. * Open each trap and count the number of dead SHBs found inside the trap. Note: The trap may contain SHB fragments. When this occurs, only count one body segment (for example, only SHB abdomens) to estimate the number of dead SHBs. Subtract the number of live SHBs recorded, if any, inside traps on the date of data collection to create an accurate number of dead SHBs inside each trap due to compound exposure. * Count and record the number of live SHBs aspirated from each hive. * For each hive calculate: Live SHBs recovered = # of live SHBs aspirated (include from multiple aspirators, if applicable) + # of live SHBs recorded inside traps during data collection (include from multiple traps, if applicable). Dead SHBs inside traps = # of frozen SHBs in traps (include from multiple traps, if applicable)--live SHBs recorded inside traps during data collection (include from multiple traps, if applicable). Dead SHBs outside traps = # of dead SHBs found in the hive, outside the traps. Average live SHBs recovered per treatment = sum of the number of live SHBs recovered for all hives/total number of hives. Average dead SHBs inside traps per treatment = sum of the number of dead SHBs inside traps for all hives/the total number of hives.
# Potential Use of Paper Mill Sludge in Improving Soil Quality for Plant Growth
###### Abstract  
This study evaluated the impact of soil modifiers on seed germination and soil quality. Mixtures of paper mill sludge (PMS) with camelia oilseed cake (CO) and peat moss (P), as well as with oilseed cake of toothache tree (TO) and peat moss (P), showed promising results as biostimulants for seed germination. The addition of PMS increased the germination index, indicating its beneficial effects on seed performance. The pH levels remained optimal, and the electrical conductivity values stayed within acceptable ranges, suggesting minimal hindrance to plant growth. The cation exchange capacity increased with PMS, enhancing nutrient availability. Composed mixtures exhibited increased phosphorus levels, contributing to soil fertility. While the organic matter content decreased after composting, the carbon-to-nitrogen ratio remained balanced. The bulk density increased after composting, potentially improving soil drainage. Overall, the TO-containing mixture demonstrated superior growth rates compared to the P. These findings support the use of PMS-based soil modifiers for enhancing seed germination and improving soil quality.  
soil modifiers; paper mill sludge; camelia oilseed cake; peat moss; oilseed cake of toothache tree
## 3 Target Detection and Tracking Algorithm
### Hough Transform-Based Algorithm for Concentric Circular Pattern Targets
Figure 7 shows an example target detection result from the first distortion-corrected plan-view video frame in MID 15. With the feature points tracking-based algorithm for sqaure checkerboard patterns and the Hough Transform-based algorithm for concentric circular patterns, 10 stationary targets and 28 moving targets are successfully tracked for all test videos. The range of pixel resolution is 14-16 mm/pixel for stationary targets on the ground, 9.5-11 mm/pixel for stationary targets on the safety tower, and 8-10.5 mm/pixel for moving targets on the roof panel. Figure 6: Detected circles with center points by Hough Transform and the final target center for the concentric circular pattern (Type 4 pattern). Figure 7: Target detection results in the first video frame (distortion corrected) from plan-view video of MID 15. Note that each cross represents the geometric center of the reference target.
# False Information Detection via Multimodal Feature Fusion and Multi-Classifier Hybrid Prediction
## 3 False Information Detection Method  
Problem Definition: Suppose \(P=\{p_{1},p_{2}\ldots\ldots,\ p_{m}\}\) is a dataset of multimodal posts in social networks where \(p_{i}\) is the ith post. \(T=\{t_{1},t_{2},\ldots\ldots,t_{m}\}\) is the text set, \(t_{i}\) is the text content in the ith post. \(V=\{v_{1},v_{2},\ldots\ldots,v_{m}\}\) is an image set, \(v_{i}\) is the image included in the ith post. \(L=\{l_{1},l_{2}\ldots\ldots,l_{m}\}\) is the tag set, \(l_{i}\) is the tag of the \(i\)th post. \(p_{i}=\{t_{i},v_{i},l_{i}\}\). The task of false information detection can be described as learning a function \(f(T,V)=Y\), \(Y=\{y_{1},y_{2},\ldots,y_{m}\}\), \(y_{i}\) is the predicted label value of the \(i\)th post, \(y\in\{0,1\}\), 0 represents true information, and 1 represents false information.  
The model in this paper is mainly composed of four parts: text feature extractor, image feature extractor, early fusion module and late fusion module. Figure 1 shows our proposed false information detection method based on multimodal feature fusion and multi-classifier hybrid prediction.  
The model first uses the text feature extractor and the image feature extractor to extract the text and image features, and then input the two obtained features into the early fusion module to obtain joint features, and the text features, image features and joint features are concatenated to strengthen the joint features. Finally, the text features, the image features and the enhanced joint features are used to obtain the prediction results through the later fusion module. The early fusion module realizes early fusion between different modal features through the deep autoencoder, and the late fusion module uses the method of multi-classifier mixed prediction to fuse the prediction probabilities of different modalities, The pseudocode is shown in Table 1.
# Modeling and Control of an Articulated Multibody Aircraft
## 2 Development of the Multibody Equations of Motion
### Reference Frames and Coordinate Systems  
The reference frames and associated coordinate systems used for the development of equations of motion are as follows and shown in Figure 2b.  
1. The inertial reference frame \(I\{X_{I},Y_{I},Z_{I}\}\): The Earth frame is assumed to be the inertial frame with its origin, \(I\) fixed at an arbitrary point relative to the Earth's surface. The orientation of the inertial frame is such that the \(X_{I}\) axis is positive facing North, \(Y_{I}\) axis is positive facing East and \(Z_{I}\) axis is positive downwards towards Earth's center of gravity
# Gearbox Fault Diagnosis Based on Multi-Sensor and Multi-Channel Decision-Level Fusion Based on SDP
## 3 Fitting Fault Identification Based on SDP Technology
### Single-Channel Fusion of Multiple Sensors  
In order to verify the effectiveness of the already-trained and stable VGG16 fault diagnosis model, 100 sets of data were randomly generated for each fault state of the gear for detection. In this single-channel experiment, three three-phase sensors were selected, and the experimental data in the direction of more obvious state changes were selected in each of the three sensors. Therefore, in the DS evidence theory, the evidence body was \(E=\{E1,E2,E3\}\), where \(E1\), \(E2\), and \(E3\) denote the data that were collected in the Y-direction, the X-direction, and the Y-direction of sensors one, two, and three, respectively. In the identification frame, \(\Theta=\{A,B,C\}\), where \(A\), \(B\), and \(C\) denote the normal state, single pitting, and double pitting of the gear teeth, respectively. The results were obtained by inputting the 100 randomly generated sets of data into the VGG16 fault diagnosis model, which was used as the evidence body of DS evidence theory, and then the decision-level fusion was performed. The accuracy of each sensor before and after the fusion is shown in Figure 13 and Table 3.  
Figure 13: Before and after single-channel sensor fusion. (**a**) sensor 1 and sensor 2 fusion; (**b**) sensor 1 and sensor 3 fusion; (**c**) sensor 2 and sensor 3 fusion; and (**d**) 3 sensors fusion.  
According to Table 3, it can be concluded that before fusion the accuracy of the single-channel of all three of the sensors was not high, at 88.32%, 82.39%, and 88.36%, respectively. When the decision-level theory was applied to the diagnostic results that were obtained from each sensor, the accuracy improved greatly, reaching 99.77% when sensor one and sensor two were fused, which was 11.48% and 17.42% higher than that of single sensors one and two, respectively. When sensor one and sensor three were fused, the accuracy reached 99.98%, which was an improvement of 11.66% and 11.62% over single sensors one and three, respectively. The accuracy reached up to 99.46% when sensor two and sensor three were fused, which represented an improvement of 17.16% and 11.16% over single sensors two and three, respectively. When the three sensors were fused simultaneously, the accuracy reached 99.99%, which was 11.67%, 17.60%, and 11.63% better than single sensors one, two, and three, respectively. Obviously, the diagnostic accuracy of a single channel before fusion was not high, and the accuracy after using the DS fusion theory was higher than that of a single sensor before fusion. Nevertheless, as the two sensors were fused together, the fusion accuracy was not as high as that of three sensors fused simultaneously. Hence, it is required to collect more comprehensive data signals for gear teeth, as much as possible, so as to immensely enhance the gears' quality. In addition, the fault diagnosis accuracy of the gear teeth can be improved to a large extent.