esa-sceva/satcom-chunk-collection huggingface dataset:
# Co-Operatively Increasing Smoothing and Mapping Based on Switching Function
###### Abstract  
Collaborative localization is a technique that utilizes the exchange of information between multiple sensors or devices to improve localization accuracy and robustness. It has a wide range of applications in autonomous driving and unmanned aerial vehicles (UAVs). In the field of UAVs, collaborative localization can help UAVs perform autonomous navigation and mission execution in complex environments. However, when GNSS is not available, it becomes challenging to position the UAV swarm relative to each other. This is because the swarm loses its perception and constraint of the position relationship between each member. Consequently, the swarm faces the problem of the serious drift of relative accuracy for an extended period. Furthermore, when the environment is faced with complex obstruction challenges or a camera with low texture scenes, noise can make it more difficult to solve the relative position relationship between drones, and a single UAV may lose positioning capability. To solve these specific problems, this paper studies a swarm co-operative localization method in a GNSS-denied environment with loud noise interference. In this paper, we proposed a method that utilizes a distributed scheme based on an incremental smoothing and mapping (iSAM) algorithm for state estimation. It incorporates new anchor-free topological constraints to prevent positioning failures and significantly improve the system's robustness. Additionally, a new switching function is applied in front of each factor of the loss function, which adjusts the switches in real time in response to the input information to improve observably the accuracy of the system. A novel co-operative incremental smoothing and mapping (CI-SAM) method is proposed and the method does not require a complete relative position measurement, which reduces the need for vehicle measurement equipment configuration. The effectiveness of the method is verified by simulation.  
collaborative positioning; sensor fusion; anchor-free; switching function +
Footnote †: journal: Applied Sciences  
18 January 2024  
Revised: 13 February 2024  
Accepted: 13 February 2024  
Published: 14 February 2024
# Comparison of Feedforward Perceptron Network with LSTM for Solar Cell Radiation Prediction
## 2 Materials and Methods
### Deep Neural Network Approaches
#### 2.2.3 Long-Short-Term Memory (LSTM)  
LSTM is based on the RNN architecture. It is a model designed to expand the RNN memory [45; 46]. This memory has the ability to store information over an arbitrary length of time. There are three gates, which are the input, output, and forget gate, to control the information flow into and out of the neuron's memory [48; 49; 50; 51]. Those three gates get the same input as the input neuron. Furthermore, each gate possesses an activation function [41; 48; 52].  
Figure 4 shows the figuration of LSTM at time \(t\). Mathematically, LSTM can be described using the following functions [50; 51; 52; 53; 54; 55; 56; 57; 58].  
\[f_{t}=g\;\Big{(}W_{f}\;x_{t}+U_{f}\;h_{t-1}+b_{f}\Big{)}, \tag{5}\]  
\[i_{t}=g\;(W_{i}\;x_{t}+U_{i}\;h_{t-1}+b_{i}), \tag{6}\]  
\[k_{t}=tanh\;(W_{k}\;x_{t}+U_{k}\;h_{t-1}+b_{k}), \tag{7}\]  
\[c_{t}=f_{t}\;c_{t-1}+i_{t}\;k_{t}, \tag{8}\]  
\[o_{t}=g\;(W_{o}\;x_{t}+U_{o}\;h_{t-1}+b_{0}), \tag{9}\]  
\[h_{t}=o_{t}\;tanh\;(c_{t}), \tag{10}\]  
where \(x_{t}\) is the input vector at time \(t\) and \(g\) is an activation function (sigmoid, tanh, or ReLU). \(W\) and \(U\) are weight matrices, and \(b\) is the bias vector. \(h_{t}\) and \(c_{t}\) are output and cell state vector at time \(t\). \(f_{t}\) has been used for remembering old information and it has been used for getting new information [38; 49; 50; 52].
# Dynamic Relief Items Distribution Model with Sliding Time Window in the Post-Disaster Environment
###### Abstract  
In smart cities, relief items distribution is a complex task due to the factors such as incomplete information, unpredictable exact demand, lack of resources, and causality levels, to name a few. With the development of Internet of Things (IoT) technologies, dynamic data update provides the scope of distribution schedule to adopt changes with updates. Therefore, the dynamic relief items distribution schedule becomes a need to generate humanitarian supply chain schedules as a smart city application. To address the disaster data updates in different time periods, a dynamic optimised model with a sliding time window is proposed that defines the distribution schedule of relief items from multiple supply points to different disaster regions. The proposed model not only considers the details of available resources dynamically but also introduces disaster region priority along with transportation routes information updates for each scheduling time slot. Such an integrated optimised model delivers an effective distribution schedule to start with and updates it for each time slot. A set of numerical case studies is formulated to evaluate the performance of the optimised scheduling. The dynamic updates on the relief item demands' travel path, causality level and available resources parameters have been included as performance measures for optimising the distributing schedule. The models have been evaluated based on performance measures to reflect disaster scenarios. Evaluation of the proposed models in comparison to the other perspective static and dynamic relief items distribution models shows that adopting dynamic updates in the distribution model cover most of the major aspects of the relief items distribution task in a more realistic way for post-disaster relief management. The analysis has also shown that the proposed model has the adaptability to address the changing demand and resources availability along with disaster conditions. In addition, this model will also help the decision-makers to plan the post-disaster relief operations in more effective ways by covering the updates on disaster data in each time period.  
dynamic scheduling; relief items distribution; disaster; humanitarian supply chain; optimisation; sliding time window; smart cities +
Footnote †: journal: Applied Sciences  
1
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
###### Abstract  
Existing methods for restoring color-distorted images in specific environments typically focus on a singular type of distortion, making it challenging to generalize their application across various types of color-distorted images. If it were possible to leverage the intrinsic connections between different types of color-distorted images and coordinate their interactions during model training, it would simultaneously enhance generalization, address potential overfitting and underfitting issues during data fitting, and consequently lead to a positive performance boost. In this paper, our approach primarily addresses three distinct types of color-distorted images, namely dust-laden images, hazy images, and underwater images. By thoroughly exploiting the unique characteristics and interrelationships of these types, we achieve the objective of multitask processing. Within this endeavor, identifying appropriate correlations is pivotal. To this end, we propose a knowledge selection and allocation strategy that optimally distributes the features and correlations acquired by the network from the images to different tasks, enabling a more refined task differentiation. Moreover, given the challenge of difficult dataset pairing, we employ unsupervised learning techniques and introduce novel Transformer blocks, feedforward networks, and hybrid modules to enhance context relevance. Through extensive experimentation, we demonstrate that our proposed method significantly enhances the performance of color-distorted image restoration.  
image enhancement; multi-task knowledge allocation; transformer; unsupervised learning; image denoising; color correction +
Footnote †: journal: Applied
# HBIM: Background, Current Trends, and Future Prospects
## 4 Data Collection and Modeling
### Data Collection Techniques
#### 4.2.1 Laser Scanning  
Three-dimensional scanners use lasers to generate millions of points that create a point cloud representing the geometrical composition of the scanned element [54]. This point cloud can subsequently be processed and optimized to reduce the number of points without losing relevant information. Various 3D laser scanners are currently available, and several factors influence their selection [53]. Depending on the angle of capture, there are aerial and terrestrial lasers. The scale of the element to capture (since CH can range from small decorative elements to monumental complexes or urban settlements) determines whether a long-range, medium-range, or short-range laser scanner should be used [60]. Other 3D laser scanners may vary based on whether they can be used with or without contact, whether they consist of passive or active systems, or whether they employ static or dynamic methods. The availability of these scanners is one of the main challenges for both public and private institutions, as the prices of the scanners, particularly those designed for longer ranges, are often prohibitive. This makes it difficult to ensure the homogenization of data acquisition among HBIM models [61]. Moreover, software that facilitates working with the acquired data to simplify the model without loss of accuracy can also be considerably expensive and is rarely open access [62].
# Speech Act Theory as an Evaluation Tool for Human-Agent Communication
## 5 Evaluating Human-Agent Communication Using SAT
### Data Collection and Data Processing  
Collecting data for the events that take place in a VE may require different methods according to the goals of the VE. [PERSON] et al. (2018) provided a taxonomy of data collection forms and when they are appropriate. For the purposes of this study, data was collected by two means as follows:  
1. For objective data analysis: automatic data logging to track the human's and the agent's behaviours, messages, and selections and to register any problems experienced by the participants.
2. For subjective data analysis: surveys, one before the scenario to collect biographical data and another one after the scenario to evaluate users' experience.  
The biographical survey included questions about participants' linguistic skills, levels of computing skills, and experience in using computer games and other 3D applications. A second section of the survey (10 Likert scale items) aimed to acquire the users' opinions about the agent's verbal and non-verbal communication and whether it was relevant to the collaborative situation.  
Figure 1: Screenshot from the scenario in Omosa Virtual World.  
In the VE scenario, the task was to trap a Yernt that was surrounded by eight regions. Both the human and the agent should take turns to select one region at a time and observe each other's action, at the same time exchanging verbal messages to convey their intention and request a recommended selection from the other party. We call the process of selecting each pair of regions out of the four pairs a \"cycle\". There are four cycles, and each cycle includes the human and the agent selecting a region (except for the first cycle), and they exchange requests and replies verbally. In the first cycle, i.e., the initial selection for the human and the agent, the human selects freely any region out of the eight regions, and they do not exchange verbal messages. Dividing the data collected from the log files during the task into cycles helps to understand the effect of the continuous communication on the achievement of the successive cycles.
# Real-Time Flood Monitoring with Computer Vision through Edge Computing-Based Internet of Things
## 4 Experimental Setup and Performance Evaluation
### Environment of System Testing Site  
The measurement site was a water gate in a rural setting located in Petrajaya, Sarawak, Malaysia. The gate itself acted as a barrier and as a controlled wall, functioning to regulate safe water levels between the sea water and the river stream. Figure 5 displays the water gate whereby the shaded platform houses the control system for the ultrasonic sensors, the RealSense camera and the gates themselves.
# Ex-Situ Evaluation of Commercial Polymer Membranes for Vanadium Redox Flow Batteries (VRFBs)
## 2 Materials and Methods
### Ex-Situ Characterizations
#### 2.2.3 Vanadium Crossover Measurement  
Vanadium crossover represents the diffusion of vanadium ions across the membrane, leading to coulombic efficiency (CE) drop, capacity decay and OCV decline in VRFB charge/discharge cycling. In order to determine the diffusion coefficients of the VO2+ ions penetrating through the selected membranes, a two-half diffusion cell (PermeGear, Inc., Hellertown, PA, USA) was employed [25]. The tested membrane was sandwiched between the two half diffusion cells and a pair of rubber gaskets. The membrane's exposed area to the two half cells was 1.76 cm2. A total of 5 mL of 1 M VOSO4 in 2 M H\({}_{2}\)SO4 was placed in the left half-cell and 5 mL of 1 M MgSO4 in 2 M H\({}_{2}\)SO4 in the right half-cell, and each side was magnetically stirred at 200 RPM to prevent the concentration polarization. The MgSO4 solution was used to equalize the ionic strengths of the two solutions and to minimize any osmotic pressure effects. A measurement of the concentration of VO2+ ions was determined by UV-Vis spectroscopy (at RT), whereby 2 mL of the right-side solution was taken at selected time intervals. The extracted sample solution was returned to the right half-cell immediately after UV-Vis measurement. Prior to the experiment, a standard curve of VO2+ solutions of different concentrations was prepared and is shown in Figure S2. The peak absorbance of VO2+ ion was established at 760 nm and acts as a reference.  
The crossovers of VO2+ ions across the selected membranes were compared and their diffusion coefficients of vanadium ions were calculated using [PERSON]'s approach [25; 37] by Equation (1):  
\[V_{B}\cdot\frac{dC_{B}(t)}{dt}=A\cdot\frac{D}{L}(C_{A}(t)-C_{B}(t)) \tag{1}\]  
where \(V_{B}\) is the volume of solution on the deficiency side (mL); \(C_{A}\) and \(C_{B}\) are the concentration of VOSO4 on the enrichment side and deficiency side (mol\(\cdot\)mL\({}^{-1}\)), respectively; \(A\) is the effective area of the membrane (cm2); \(D\) is the diffusion coefficients of VO2+ (cm2\({}^{2}\cdot\)s\({}^{-1}\)); \(L\) is the thickness of the membrane (cm); \(t\) is the testing time (s).  
The concentration of VO2+ ions on the deficiency side is lower than that at the enrichment side; therefore, the change of VO2+ ion concentration at the enrichment side is minor. Equation (1) was modified to Equation (2):  
\[C_{B}=A\cdot\frac{C_{A}(0)\cdot D}{V_{B}\cdot L}\cdot t \tag{2}\]  
The plot of \(C_{B}\) versus time is linear and has a slope (\(k\)) equal to \(A\cdot\frac{C_{A}(0)\cdot D}{V_{B}\cdot L}\), so the diffusion coefficient (\(D\)) can be calculated as Equation (3):  
\[D=\ \frac{k\cdot V_{B}\cdot L}{A\cdot C_{A}(0)} \tag{3}\]
## 5 WiFi 7: IEEE 802.11 be
### Coordination Schemes  
In legacy WiFi, multiple APs cannot be completely optimized and utilized effectively as a result of a lack of coordination between them. Moreover, such multi-AP situations heighten the negative effects of interference [46]. In order to overcome these multi-AP-related issues, IEEE 802.11 be introduces multitude coordination schemes, as follows:  
* Coordinated spatial reuse (Co-SR);
* Coordinated OFDMA (Co-OFDMA);
* Coordinated beamforming (Co-BF);
* Joint transmission (JT).  
Using these schemes, the emerging technologies discussed earlier can experience better coordinated communication with reduced interference.
## 4 Computational Results
### Parameters  
In [36], the authors conducted a sensitivity analysis to determine the values of the parameters to be used for the ABC algorithm, as well as the entropy and robustness functions. In that study, the best results were obtained when using higher levels on parameters that increase the search space and using the lowest levels for those parameters that are linked directly to the solution's quality. Additionally, given that this is an extension of that methodology from the MRCPSP to the MRCPSP/max, the same parameter settings developed and tested in that previous study are adopted here. It is worth noting that even though the MRCPSP and the MRCPSP/max are initially different models, the parameter setting for the solution procedure introduced is not affected because for both cases, the use of the algorithm is only after a mode vector is selected and the activity list is defined, and by this point, the same objective functions are optimized. Table 4 shows the parameter values used in this study.  
With these parameters, the method is implemented as described in Section 3: in Stage 1, the discrete ABC algorithm is used to minimize the instances' makespan (MS\({}_{1}\)); this will serve as the lower bound of the execution time-frame. In Stage 2, the entropy Equation (1) determines the upper bound (MS\({}_{2}\)). Finally, in Stage 3, the third makespan is computed also using the discrete ABC algorithm, but in this stage, seeking to maximize robustness (Equation (9)), while keeping the makespan lower than the upper bound, though not necessarily higher than the lower bound.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Population size & 30 \\ Abandonment limit & 5 \\ MNC & 20 \\ \(\delta t\) (relevant time interval) & 1 \\ _frac_ & 0.25 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters used for the ABC algorithm. MNC, Maximum Number of Cycles.
# Development of Korean Indigenous Low-Temperature-Tolerant Yeast for Enhancing the Flavor of _Yakju_
## 3 Results and Discussion
### Characteristics of Yakju  
The characteristics of _yakju_ after fermentation are shown in Figure 1. In the case of the co-culture samples, the growth of each strain was confirmed by the viable cell count and is shown in Supplementary Figure S1 (Figure S1, the results of the single cultures are not shown). The co-culture with non-_Saccharomyces_ strains showed the lowest pH value of 3.32-3.35 (Figure 1a). The soluble solid content was at the maximum (10.37 Brix\({}^{\circ}\)) in _W. anomalous_ A159, and _H. vineae_ G818 showed the highest reducing sugar content (Figure 1b,c). _S. cerevisiae_ single/co-culture samples consumed all the reducing sugars, whereas the non-_Saccharomyces_ group did not, with the expectation of a decrease or death occurring as the ethanol content increased during fermentation [35]. Because most non-_Saccharomyces_ species had lower ethanol resistance and titers than those of _S. cerevisiae_[36], the final ethanol content was also lower than that of _S. cerevisiae_. Nevertheless, all fermented samples showed a high ethanol content of 8% or more, which was expected to be characteristic of low-temperature long-term fermentation and _yakju_[7; 37].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Strains** & **Species** & **Cell Growth** & \(\beta\)**-Glucosidase** & **Cerulenin** & **FPA** \\  & & **(OD\({}_{\text{60 nm}}\), 15 \({}^{\circ}\)C)** & **Activity (U/mL)** & **Resistance** & **Resistance** \\ \hline W153 & _Saccharomyces cerevisiae_ & 1.615 & 0.679 & + & – \\ N373 & _Pichia kudriavzevii_ & 1.285 & 1.408 & + & + \\ G818 & _Hanseniospra vitenee_ & 0.977 & 0.567 & – & + \\ A159 & _Wickerhamomyces anomalus_ & 1.161 & 0.922 & + & – \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of final selected strains.  
In the case of the co-culture group (W153 + N373, W153 + G818, and W153 + A159), an amount of ethanol similar to that of the single culture of _S. cerevisiae_ was produced (Figure 1d), which was consistent with the results of previous studies [36; 38; 39]. The _S. cerevisiae_ W153 strain exhibited fermentation characteristics similar to those of the control.
## 2 Related Works
### Path Planning Approaches  
In [29], a proposal is presented that integrates inertial SLAM with trajectory planning for UAVs, focusing on maximizing the accuracy of navigation estimates in environments where GPS is unreliable. The combination of information-based measures, such as entropy, with an observability analysis offers a robust methodology to control UAV actions and improve map quality and localization estimates.  
In [30], a solution for the path planning problem in the context of active SLAM is presented. The main technical contribution lies in the adaptation of the D* algorithm to handle negative edge weights, a significant challenge in the domain of pathfinding algorithms due to the presence of negative cycles. The algorithm stands out for its ability to efficiently recalculate paths in dynamic scenarios with moving obstacles, maintaining smooth robot motion and reducing localization uncertainty.  
In [31], an analysis of the applicability of SLAM algorithms and path planning in indoor rescue environments is proposed. The research evaluates three widely used SLAM algorithms: GMapping, Hector-SLAM, and Cartographer, combining them with the A* algorithms for global path planning and DWA for local planning. The goal is to determine the effectiveness of these combinations in simulated, emulated, and real rescue environments.
# Dual-Window Superpixel Data Augmentation for Hyperspectral Image Classification
## 3 Results
### Superpixel-Based Classification
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Oitaven**} & \multicolumn{3}{c}{**Ermidas**} \\ \hline
**Technique** & **OA** & **AA** & **\(\kappa\)** & **OA** & **AA** & **\(\kappa\)** \\ \hline None & 94.23 \(\pm\) 0.44 & 90.46 & 92.37 & 97.20 \(\pm\) 0.38 & 92.92 & 96.00 \\ Rotate 4\(\times\) & 95.18 \(\pm\) 0.46 & 92.40 & 93.62 & 97.83 \(\pm\) 0.27 & 94.81 & 96.91 \\ Inner-Rotate 4\(\times\) * & 95.39 \(\pm\) 0.55 & 92.22 & 93.90 & 98.08 \(\pm\) 0.29 & 95.61 & 97.25 \\ Dual-Rotate 4\(\times\) * & 94.96 \(\pm\) 0.33 & 91.45 & 93.33 & 97.70 \(\pm\) 0.16 & 94.11 & 96.71 \\ Dual-Rotate 16\(\times\) * & **96.20 \(\pm\) 0.14** & 93.76 & 94.97 & 98.32 \(\pm\) 0.11 & 96.36 & 97.61 \\ Flip 4\(\times\) & 95.21 \(\pm\) 0.39 & 92.78 & 93.67 & 98.01 \(\pm\) 0.26 & 95.49 & 97.16 \\ Inner-Flip 4\(\times\) * & 95.56 \(\pm\) 0.20 & 93.08 & 94.13 & 98.12 \(\pm\) 0.22 & 95.86 & 97.31 \\ Dual-Flip 4\(\times\) * & 95.17 \(\pm\) 0.30 & 91.78 & 93.62 & 97.91 \(\pm\) 0.26 & 95.58 & 97.02 \\ Dual-Flip 16\(\times\) * & 96.19 \(\pm\) 0.13 & 93.16 & 94.97 & **98.56 \(\pm\) 0.15** & 97.00 & 97.95 \\ PVSA(+/ -) 4\(\times\)[12] & 95.32 \(\pm\) 0.25 & 91.26 & 93.81 & 97.82 \(\pm\) 0.29 & 95.06 & 96.88 \\ PVSA(+/ -) 16\(\times\)[12] & 95.75 \(\pm\) 0.15 & 92.66 & 94.39 & 98.05 \(\pm\) 0.18 & 95.35 & 97.22 \\ Random Occlusion 4\(\times\)[13] & 93.81 \(\pm\) 0.49 & 89.18 & 91.81 & 97.19 \(\pm\) 0.30 & 93.67 & 95.99 \\ Random Occlusion 16\(\times\)[13] & 95.68 \(\pm\) 0.22 & 92.07 & 94.28 & 98.08 \(\pm\) 0.24 & 96.08 & 97.26 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Superpixel-based classification performance (in percent) for Oitaven, Ermidas, Eiras and Mestas scenes using the training and testing sets detailed in Table 3. The best result for each scene is displayed in bold. Figure 15: Classification maps for images from the Galicia dataset using superpixel-based classification with dual-flip 16\(\times\) augmentation: (**a**) Oitaven, (**b**) Eiras, (**c**) Ermidas and (**d**) Mestas. Color codes are the same as those introduced in Table 3.  
In order to summarize the results, Table 6 displays the performance differences between the baseline performance and each of the augmentation techniques. The results for the standard dataset show dual-rotate 16\(\times\) was the best performing method overall, followed by flip 4\(\times\). For the Galicia dataset, dual-flip 16\(\times\) and dual-rotate 16\(\times\) obtained the highest increase in OA, with 1.51% and 1.42%, respectively.