esa-sceva/satcom-chunk-collection huggingface dataset:
# Lightweight Semantic Segmentation Network for Real-Time Weed Mapping Using Unmanned Aerial Vehicles
## 4 Results
### Model Design  
As described in Section 3.2., a lightweight classification architecture (AlexNet) was selected as a baseline network. To reduce the number of network parameters, the first two fully-connected layers were removed, the number of neurons of the final fully-connected layer was set to three, and this layer was eventually transformed into a convolutional layer. A deconvolutional layer was appended to restore the feature map to the original image resolution. The training data were augmented using data enhancement (translation, rotation, and tailoring).  
During the training process, the Adam optimizer was used for adaptive momentum estimation. The Adam gradient descent method is an adaptive learning rate method with different parameters that yields a fast convergence speed. The entire training process was limited to 30 epochs, where each iteration traversed all training samples. After forward computation, the sigmoid cross-entropy loss function was used as a cost function. The initial learning rate was set to 0.00001. Since the learning rate can be adjusted adaptively according to the rate of gradient descent, the convergence speed of the model was improved, as shown in Figure 8. After 30 epochs, testing on the validation dataset resulted in an overall accuracy of 91.2%. As shown in Table 1, the model operates on a GTX1060 GPU at 0.9 fps,  
Figure 7: Example of image labeling: (**a**) Input images, and (**b**) labels.  
while implementing the same network on the Jetson TX2 module results in a rate of only 0.11 fps, which is insufficient for real-time processing applications.  
Our modified AlexNet-FCN was compared with the classic VGGNet, GoogLeNet, and ResNet. For the VGGNet-FCN, the skip architecture was omitted to increase speed. For the GoogLeNet, ReLU was used as the activation function, and the dropout strategy was used to overcome the problem of overfitting. For the ResNet-FCN, the ResNet-101 model was selected as a baseline architecture because it provides the best balance of accuracy and efficiency [20]. The performances of all models are summarized in Table 1. One can see that the accuracy of AlexNet-FCN is slightly lower than that of VGGNet-FCN and GoogLeNet-FCN. However, it is almost four times faster than the VGGNet-FCN and two times faster than the GoogLeNet-FCN, meaning it can meet the requirements of real-time scenarios. The accuracy of the ResNet-FCN is the highest among the three models based on its deep structure and residual architecture. However, this model is so large that the GTX 1060 GPU could not even perform the forward computation. In this case, we used the GTX 1080 TI GPU for network training and evaluation. However, the GPU resources of the GTX 1060 are greater than those of embedded devices. Therefore, it is impossible to copy the ResNet-FCN from the GTX 1080 TI GPU to the embedded Jetson TX2 module.
# Object-Wise Video Editing
## 2 Object Removal and Background Inpainting
### Temporal Error Correction  
After calculating the color \(C_{rgb}(p)\) from Equation (1), temporal consistency between neighbour frames is investigated to correct noisy inpainting result. To simplify this step, we consider two consecutive frames. To correct the temporal error in the result, we calculate the global displacement between every consecutive frame. We believe that consecutive frames in a video have very Small displacement in the background pixel. We minimize the sum of squared distance between patches taken from both inpainted (background) regions from our proposed method. We get very small displacement between the frames in the subpixel level (1/4 pixel distance) because the movement between consecutive frames is very small. The image patch and the range of the search space are automatically selected based on the input mask of the foreground. After finding the global displacement, we estimate the noisy pixel based on the absolute difference between the inpainted pixel. Bigger differences between the pixel indicate temporal inconsistency. We replace the wrong pixel color values sequentially over the neighbor frame when the difference is big. However, it is very difficult to decide which alternate pixel color is correct in the inpainted result frame. We only trust the pixel outside of the inpainted region and the pixels that are replaced by non-inpainted pixel. This method sequentially corrects inconsistent error point passing over the correct value from outside of the inpainted region frame by frame. We employ this method to avoid any complex and computationally expensive global optimization with all inpainted points from all frames. If inconsistency happens suddenly in the middle of the inpainted background, the corresponding point at neighbor frames also could be non-corrected inpainted point as well (so we cannot trust any). In such a case, we trace back through further neighbor frames until we find either non-inpainted point or inpainted but corrected point. The effects of Temporal Error Correction in our background inpainting results are shown in Figure 11.
## 3 Results and Discussions
### Velocity Autocorrelation Function (VACF)  
The velocity autocorrelation function (VACF) allows us to characterize the extent to which the velocity of a particle at time \(t\) remains correlated (on average) with its initial velocity. In this regard, velocity correlations decay with time due to interparticle collisions.  
Figure 7 shows the VACF for dense beryllium calculated by the QMD method and the corresponding results for the Yukawa system at different temperatures. It can be seen that the characteristic correlation time decreases with increasing temperature. At low temperatures (\(T=4000\) K), the velocity correlations decay more slowly, which is due to the pronounced near-order in the structure of ions. At high temperatures (\(T=32\),000 K), the velocity-velocity correlations decay much faster as thermal energy dominates the interatomic interactions, strengthening the chaotic motion of particles.  
The comparison of the QMD and Yukawa-MD results, as shown in Figure 7, demonstrates a good agreement between the two methods. This confirms that the particle velocity dynamics are equivalent in both the QMD and Yukawa-MD simulations. Consequently, Figure 7 indicates that the Yukawa model accurately describes the dynamics of particles in dense beryllium at the temperatures considered.
# Stochastic Modeling of Smartphones GNSS Observations Using LS-VCE and Application to Samsung S20
## 4 Experimental Results
### Noise Assessment of Samsung S20

Table 5: The mean value of the estimated coefficients (\(a_{type}^{sys}\); _sys_: GPS and GLONASS and _type_: code and phase) on L1 frequency as well as the correlation among code and phase observations. E1, E5a, and B1) and constellations. In the case of the Samsung S20 with short-baseline internal antennas, the corresponding STD values for code and carrier phase observations were 0.003 m and 2.327 m, respectively. In the current research, we utilized the LS-VCE method, which allowed us to estimate frequency/observation type/system-dependent precision (i.e., code vs. carrier phase, L1 vs. L5, and GPS vs. other constellations) as well as any covariances between different observation types (e.g., between code and carrier phase). However, it is important to note some limitations associated with the proposed method. First, the proposed method relies on constructing the DD observations between two smartphones, which necessitates a sufficient number of common satellites between the devices. However, due to the low-cost linearly polarized antennas used in smartphones, they may not be able to track all the same satellites. Second, implementing the LS-VCE method can be time-consuming for two reasons: (1) it is an iterative method and (2) creating matrices with large sizes may surpass the maximum array size allowed. Third, the dataset used in the contribution did not include the measurements on the L5 frequency, thereby limiting the ability to evaluate the noise of observations on the L5 frequency.
## Abbreviations  
The following abbreviations are used in this manuscript:  
BfG Bundesanstalt fur Gewasserkunde  
BSH Bundesamt fur Seeschiffahrt und Hydrographie  
DWD Deutscher Wetterdienst  
IMU Inertial measurement unit  
NWM Numerical wave model  
RMS Root mean square  
SNR Signal-to-noise ratio  
STW Speed through water  
SWH Significant wave height  
UTC Under keel clearance  
WSV Wasserstein- und Schifffahrtsverwaltung des Bundes
# A Survey on Swarm Robotics for Area Coverage Problem
## 3 Algorithms and Methods for Swarm Robotics
### Classical Algorithms
#### 3.2.2 The Decentralised Space-Based Potential Field (D-SBPF) Algorithm  
D-SBPF is a simple decentralised method for dispersing a robot team to quickly explore and cover an area. The algorithm is considered a potential control method that supports knowledge of the area bounds to be explored. The basic idea is to solve the problem of deploying many robots in an unknown environment (buildings) to examine and collect information about the environment, using an effective method. The experiments were carried out using three maps, each 10 \(\times\) 10 m\({}^{2}\), and a grid representation of 30 \(\times\) 30. The approach uses an extended occupancy grid to represent the space where each cell can be attractive (if undiscovered) or repulsive (if discovered). A non-monotonic field scale factor proportional to coverage is also used to improve the searching of corners and niches and to assist in moving robots out of potential minima. The main characteristic of this method is that the robot, at any stage, can leave/join/rejoin the team. The simulation results show that using more robots at the beginning of exploration leads to more area coverage [61].
# Information-Aware Secure Routing in Wireless Sensor Networks
## 1 Introduction
The contributions of this work can be summarized as follows:  
* We propose a globally optimal secure routing method in the presence of malicious nodes. In this method, the security, energy consumption, and load balance of the entire path are comprehensively evaluated by sensing the status and trust of nodes in the network so that the optimal route is found. * On the basis of the sensed trust value and status, an information-aware secure routing evaluation method is proposed in this paper. A secure route is evaluated by a cost function, which is a comprehensive evaluation of the associated information of the nodes in the path, i.e., the trust value of the path from the source node to the sink node and the status of each node in the path, where the status of a node is a hybrid metric determined by the residual energy of the node and the distance to the sink. * In the same simulation environment, compared with the Reputation-Based Mechanism to Stimulate Cooperation (RBMSC) model [11], the proposed model can maintain a higher delivery ratio, which verifies the effectiveness of the proposed model on the basis of global optimization. Furthermore, compared with the traditional Dijkstra algorithm, the packet loss ratio in the improved Dijkstra algorithm is lower because it can more effectively avoid malicious nodes, thus verifying the effectiveness of the improved algorithm. The rest of this paper is organized as follows. In Section 2, we summarize the ideas and methods in the existing literature. Section 3 presents the secure routing model based on information-aware sensor nodes in WSNs. In Section 4, we show the implementation of the proposed secure routing model using an improved Dijkstra algorithm. In Section 5, the effectiveness of our model and algorithm is evaluated by five groups of simulations. Finally, the conclusion and future studies are provided in Section 7.
# Distributed Coordination of Space-Ground Multiresources for Remote Sensing Missions
## 4 Distributed Coordinated Resource Scheduling Algorithm Design Based on ADMM
### Augmented Lagrangian and ADMM Sequential Iterations
In the above iterative solving process, steps 1 and 3 can be divided into \(N+1\) independent parts to be executed in parallel by each satellite system, while step 2 can be executed by the DRS system. In other words, through exchanging the global variables and their local copies between RSS systems and the relay satellite system, the iterative steps can be implemented in a distributed manner. Upon the completion of the iterative steps, the relaxed problem P2 is solved. Note that P2 is obtained by relaxing binary variables \(\mathbf{y}\) and \(\mathbf{z}\) of the original problem P1. Therefore, the relaxed variables should be recovered by the RSS and DRS systems after the sequential iterative optimization steps, so that the solution of coordinated resource scheduling problem can be obtained. In the next subsection, a distributed coordinated resource scheduling algorithm based on the ADMM is developed for the RSS and DRS systems to solve P1 coordinately in a distributed manner, and two relaxed variable recovery algorithms are proposed for the DRS system and RSS systems to recover the global and local relaxed variables, respectively.
# Evaluating Retrieval-Augmented Generation Models for Financial Report Question and Answering
## Appendix A  
\begin{table}
\begin{tabular}{p{14.2 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline
**No.** & **Question/Prompt** & **Period** & **Type** \\ \hline \multirow{4}{*}{1.} & What was the total revenue for [Bank Name] in quarter Q1 2022? Retrieve the total revenue for [Bank Name] from the report of the quarter Q1 2022. Also, consider alternative terms such as ‘total income’ or ‘gross earnings’ if they are used in the document. & \multirow{4}{*}{Quarter} & \multirow{4}{*}{Quantitative} \\ \cline{1-1}  & What was the percentage change in total revenue for [Bank Name] in Q1 2023 compared to the same quarter of the previous year? Find or calculate the percentage change in total revenue for [Bank Name] of Q1 2023 compared to the to the same quarter of the previous year. Include terms like ‘percentage growth’ or ‘percentage decline’ if mentioned. & \multirow{4}{*}{Quarter} & \multirow{4}{*}{Quantitative} \\ \cline{1-1}  & What is the net profit reported by [Bank Name] for the half-year period H1 2022? Identify the net profit for [Bank Name] for the half-year period H1 2022, including any references to ‘net income’ or ‘net earnings’. Ensure the figure reflects income after all expenses. & \multirow{4}{*}{Quarter} & \multirow{4}{*}{Quantitative} \\ \cline{1-1}  & What are the total assets held by [Bank Name] at the end of the half-yearly period H1 2023? Extract the total assets held by [Bank Name] at the end of the half-yearly period H1 2023. Include all categories such as cash, investments, loans, and other financial instruments. & \multirow{4}{*}{Half-yearly} & \multirow{4}{*}{Quantitative} \\ \cline{1-1}  & What strategic initiatives or projects did [Bank Name] undertake during the reporting period Q1 2022? Summarize the strategic initiatives or projects undertaken by [Bank Name] during the reporting period, including new product launches, market expansions, and digital transformation efforts. There might be also similar strategic initiatives or projects. & \multirow{4}{*}{Quarter} & \multirow{4}{*}{Qualitative} \\ \cline{1-1}  & What trends or market conditions affecting the banking sector were highlighted by [Bank Name] in their report of the half-yearly period H1 2022? & \multirow{4}{*}{Half-yearly} & \multirow{4}{*}{Qualitative} \\ \cline{1-1}  & Highlight the trends or market conditions affecting the banking sector as discussed by [Bank Name], such as interest rate changes, regulatory impacts, and technological advancements. There might be also similar trends and market conditions. & \multirow{4}{*}{Half-yearly} & \multirow{4}{*}{Qualitative} \\ \cline{1-1}  & What key risks and challenges for the upcoming period did [Bank Name] identify in their report of the half-yearly period H1 2023?
# Nutritional and Medicinal Properties of Microbial Oil
## 2 History and Development of Microbial Oil Manufacture
The researchers genetically modified _Yarrowia lipolytica_ to produce increased amounts of C20 fatty acids, mostly eicosapentaneoic acid (EPA, C20:5) [17]. The lipids were produced by CP Kelco (Atlanta, CA, USA) and sold in the USA as NewHarvest(tm) EPA oil for human consumption and Verlasso(tm) for animal feed. The oil gained GRAS (generally recognized as safe) status by the FDA, but the consumers criticized the product for hexane traces and being produced by GMOs (genetically modified organism) [2; 18]. The company J & E Sturge (North Yorks, UK) was one of the first to extensively test the oil from _Mucor circinelloides_ for any possible toxicity. The UK Advisory Committee on Novel Foods and Processes evaluated those results and approved the oil for sale in the UK in the late 1980s. Additional safety issues involved causing allergic reactions. Still, when assessing the safety of microbial oil, it is important to evaluate the production organism itself. Animal and plant pathogens are not equally considered. It should be noted that oils from _M. circinelloides_, _C. cohini_, _Schizochytrium_ sp., and _Mortierella alpina_ were subjected by food authorities to acute oral studies, sub-chronic feeding, and their exposure to pregnant animals and carcinogenicity. The safety of SCOs was evaluated by regulatory authorities like the FDA in the USA, the EU \"Regulations for novel foods and novel food ingredients,\" the FDR (Food and Drug Regulations) in Canada, and the FSC (Food Safety Code) in Australia [19]. In the 21 st century, the increased interest in alternative energy sources and natural resources has contributed to the growing popularity of microbial oil as a biofuel feedstock. Microorganisms such as yeast and algae have become important candidates for biofuel production, because they can accumulate large amounts of fats that can be converted into liquid biofuels, such as biodiesel [20]. Research on microbial oil also focuses on its potential therapeutic applications, especially in the context of dietary supplementation of omega-3 fatty acids such as docosahexaenoic acid (DHA) and eicosapentaneoic acid (EPA), and their effects on heart, brain, and nervous system health [21]. In the field of functional nutrition, microbial oil is used as a nutritionally enhanced food ingredient, especially in products for people who require special diets, such as infants, children, and the elderly [22]. Some studies have shown the beneficial effects of these fatty acids on heart health, lowering triglyceride levels, and improving cognitive function [23]. Microbial oil,which is rich in fatty acids with anti-inflammatory properties, has revealed potential in the treatment of autoimmune diseases such as rheumatoid arthritis (RA) and psoriasis. Clinical studies have suggested that supplementation with omega-3 fatty acids may reduce the severity of inflammatory symptoms and improve the quality of life of patients with these conditions [23]. More medicinal and nutritional properties of SCOs are presented in the next chapters.
## 3 Effect of Cryogenic Technologies on Physicochemical and Aroma Compounds of Grape Must and Wine
### Effect of Cryogenic Technologies on Varietal Thiols
Pre-fermatative cold maceration has gained popularity in white wine production as it was shown to enhance the varietal characteristic of the wines [30; 34; 54]. Two volatile precursors of 3-SH (3-S-cysteineylhexan-1-ol (Cys-3-SH) and 3-S-glutathionylhexan-1-ol (Glut-3-SH) were significantly higher in frozen thawed berries than in the juice of fresh berries as well as in the frozen juice of fresh berries [30; 34]. Glut-3-SH was fourfold higher in frozen grapes stored at \(-20\)\({}^{\circ}\)C for two months compared to that found in frozen or fresh juices [30; 34]. [PERSON] et al. [71; 72] made similar observations where a fivefold difference was found in precursor concentrations between freezing whole grapes and freezing juice, especially for the Glut-3-SH precursor, whilst no significant difference was found in the concentrations of the Cys-3-SH precursor. These results revealed that berry damage was the primary cause of the differences, and the major contributor was the glutathione conjugate formation rather than the extraction process resulting from the freezing and thawing processes [71; 72]. The results suggested that the Cys-3-SH precursor was already present in the grape berry whereas the Glut-3-SH precursor was formed because of berry damage [71; 72]. Although numerous studies have been conducted using cryogenic pre-treatment techniques on whole grapes and grape juice, their focus was on the overall aroma compounds and precursor formation in the grape juice. However, the effect of such treatments shows no direct relationship between the levels of precursors in the grape juice and the levels of varietal thiols in the wine. Therefore, this warrants further investigation and understanding [30; 34; 53; 71; 72; 73].
# Comparison of the Observation Errors of Augmented and Spatial Reality Systems
###### Abstract  
Using 3D technologies such as virtual reality (VR) and augmented reality (AR), has intensified nowadays. The mainstream AR devices in use today are head-mounted displays (HMDs), which, due to specification limitations, may not perform to their full potential within a distance of 1.0 m. The spatial reality display (SRD) is another system that facilitates stereoscopic vision by the naked eye. The recommended working distance is 30.0-75.0 cm. It is crucial to evaluate the observation accuracy within 1.0 m for each device in the medical context. Here, 3D-CG models were created from dental models, and the observation errors of 3D-CG models displayed within 1.0 m by HMD and SRD were verified. The measurement error results showed that the HMD model yielded more significant results than the control model (Model) under some conditions, while the SRD model had the same measurement accuracy as the Model. The measured errors were 0.29-1.92 mm for HMD and 0.02-0.59 mm for SRD. The visual analog scale scores for distinctness were significantly higher for SRD than for HMD. Three-dimensionality did not show any relationship with measurement error. In conclusion, there is a specification limitation for using HMDs within 1.0 m, as shown by the measured values. In the future, it will be essential to consider the characteristics of each device in selecting the use of AR devices. Here, we evaluated the accuracies of 3D-CG models displayed in space using two different systems of AR devices.  
virtual reality; augmented reality; spatial reality; dental arch; 3D display; head-mounted display; stereoscopic display; 3D-CG; dentistry; orthodontics +
Footnote †: journal: Applied Sciences