esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 RPC Model and Generative Adversarial Network
### Generative Adversarial Network  
In this paper, the GAN model is used to improve the quality of the input satellite images, where \(I^{\text{HQ}}\) is used to represent high-quality satellite images and \(I^{\text{LQ}}\) is used to represent low-quality satellite images. The GAN model consists of a generator \(G_{\theta}(\cdot)\) and a discriminator \(D_{\rho}(\cdot)\), whose goal is to train a generator network that the generated satellite images are judged to be true by the discriminator. Here \(\theta\) and \(\rho\) are the tuning parametersto be calculated for the GAN model. These two sets of parameters contain the weights and biases of the generator network and the discriminator network. The \(\theta\) and \(\rho\) are obtained by optimizing the loss function in Equation (4). Assuming that there are \(N\) low-quality satellite images and \(N\) high-quality satellite images to be trained, Equation (4) needs to be solved for the GAN image enhancement.  
\[\underset{\theta}{\min}\frac{1}{N}\sum_{n=1}^{N}l^{IE}\Big{(}G_{\theta}\Big{(} I_{n}^{LO}\Big{)},I_{n}^{HQ}\Big{)}, \tag{4}\]  
where \(l^{IE}\) indicates the loss function to be optimized. In addition to optimizing the generator network, the GAN model also needs a discriminator network, which is obtained by optimizing and solving Equation (5).  
\[\underset{\theta}{\min}\underset{\rho}{\max}\ \Big{\{}\mathbb{E}\Big{[}\log D_{\rho} \Big{(}I^{HQ}\Big{)}\Big{]}+\mathbb{E}\Big{[}\log\Big{(}1-D_{\rho}\Big{(}G_{ \theta}\Big{(}I^{LQ}\Big{)}\Big{)}\Big{)}\Big{]}\Big{\}}, \tag{5}\]  
here we input the low-quality satellite images \(I^{LQ}\) into the generator and hope that the generator will output the high-quality satellite images \(I^{HQ}\) that can fool the discriminator. The discriminator is trained to distinguish between the real high-quality satellite images and the generated high-quality satellite images. When we solve Equation (5), we need to alternatively optimize the generating function and the discriminating function.
# Data-Driven Deployment of Cargo Drones: A U.S. Case Study Identifying Key Markets and Routes
## 3 Methodology  
The methodology to answer the research questions included a three-stage data mining and GIS workflow. Each stage applied data filters to (1) identify truck and air commodity demand clusters and their intersections, (2) recognize the regional demand distribution for the selected commodity clusters, and (3) determine a truncated distribution of geodesic distance bands suitable for cargo EAAs among the selected regions. Figure 1 illustrates the three stages as \"What\", \"Where\", and \"How\" in terms of what commodities to target,  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Paper** & **Key Contributions** & **Identified Gaps** \\ \hline
[31] & Examined safety, finding it to be a main hindrance to innovative technology adoption. & Did not provide a data-driven approach to optimize cargo shipping with EAA. \\  & Found that the most influential factors in EAA adoption are technological advancements and government regulations. & Did not specifically focus on the safety and reliability of EAA. \\  & Focused on how EAA deployments could affect carbon dioxide emissions. & Did not analyze the potential of EAA for cargo shipping. \\  & Explored how weather might affect EAA delivery services. & Did not consider the potential of EAA in optimizing cargo shipping. \\  & Suggested some urban airspace designs to address concerns of noise, safety, capacity, and privacy. & Did not provide a practical data-mining workflow for logistical planners and investors. \\  & Employed text mining in the study of social impacts and acceptance in EAA deployments. & Did not focus on the logistics sector and the potential of EAA for cargo shipping. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Key contributions and gaps in the literature.  
where to initiate service, and how to select the origin-destination pairs from the subset based on distance constraints, respectively.  
The data-mining methodology depends on the availability of a robust dataset of commodity flows by air and truck. The author selected the Freight Analysis Framework (FAF) dataset as it is the most comprehensive source of multimodal freight movements available within the United States. This dataset combines data from various sectors, including agriculture, extraction, utility, construction, and service [49]. Each row of the FAF version 5.5 dataset lists a commodity category moved between pre-defined zones by mode, weight in thousand-ton units, and value in million U.S. dollar (USD) units. The next three subsections describe each phase of the analytical workflow. The dataset had 2.4 million records.
## 4 Discussion
### AIGC Models
The combination of variational autoencoder and generative adversarial networks (VAE-GAN) gives the capacity to encode and decode data while creating different samples for image generation [122], image editing [124] and generating graphs [125]. GANs with dense convolutional neural network (DenseNet) or residual neural network (ResNet) designs have extensive feature learning capabilities and have demonstrated outstanding performance in image-to-image translation [54; 126]. Combining GANs with recurrent neural networks (RNNs) or convolutional neural networks (CNNs) delivers coherent and realistic outputs for sequence generating challenges [127]. GANs paired with denoising diffusion probabilistic models (DDPM) and transformers provide sample generation with both diversity and quality [53]. Transformers in conjunction with RNNs allow for successful sequential data modeling, but transformers in conjunction with graph convolutional networks (GCNs) capture both sequential and graph-based interactions [58; 60]. Vision transformers in conjunction with ResNet improve image comprehension [130], while diffusion probabilistic models in conjunction with contrastive language-image pretraining (CLIP) allow for controlled image editing [131]. In sequence-to-sequence challenges, convolutional sequence-to-sequence learning (ConvS2S) blends CNNs and local dependency capture [132]. These hybrid models address the constraints and problems of separate designs, but efficiency, computing resources, training stability, and optimization must all be taken into consideration.
# ICENET: An Information Centric Protocol for Big Data Wireless Sensor Networks
## 4 ICENET--Information Centric Protocol for sEnsor NETworks
If a node receives an interest message and possesses the requested data, it sends a data message to the parent node. Data caching is performed only at the gateway node. Since the network topology is based on a tree, interests are first received at the root (gateway) and data always converges to the root. Therefore, data caching performed by the gateway reduces the number of messages transmitted trough the network. In order to make ICENET robust and suitable to a dynamic environment, we propose a soft-state based design. Soft-state protocols use periodic refresh messages to keep the network state updated. State refers to the information stored in the network nodes. Soft-state protocols adapt faster and present great robustness to changes in network conditions [48]. For this reason, ICENET periodically  
Figure 4: Query example to extract information from an environmental wireless sensor network (EWSN). refreshes interests stored in Icaches and the information stored in FIBs. ICENET incorporates Trickle Algorithm [46] principles to determine the rate at which to refresh interests, which will be further detailed in Section 4.1. Trickle timers adapt dynamically to network conditions, so that the message rate increases during periods of instability, and decreases otherwise. Figure 5 shows interest processing and forwarding in ICENET. Interests are processed by nodes in-network, improving scalability. In addition, a single message can collect data from multiple nodes and for a certain time interval. Sensor nodes only process interests received from the parent node (denoted as Condition 1 in Figure 5). Then, the receiver node checks the Icache (Condition 2). A matching interest ID means that the interest was already received, so that the node only updates the entry if new information is provided. Otherwise, the node checks if it can provide the requested data (Condition 3) and schedules the data collection. Then, the node checks the FIB (Condition 4) in order to forward the interest to data sources. If data sources are found, the node creates an Icache entry, broadcasts the interest, and starts the execution of the Trickle Algorithm. Trickle allows interest messages to be delivered more efficiently and with less overhead, in situations where communication links are unstable or when the topology changes.
# FlowHaptics: Mid-Air Haptic Representation of Liquid Flow
## 6 Conclusions  
In this study, we proposed _FlowHaptics_, a midair haptic representation of flowing water using an air jet system. We developed the prototype system and focused on generating a pressure distribution on the fingers based on the velocity profile obtained from machine-learning-accelerated fluid simulation. We first evaluated the performance of _FlowHaptics_ using a psycho-physical discrimination experiment on the flow velocity. We found that the users were capable of perceiving the different flow velocities with the JND ranging from 8.42% to 13.05%. Furthermore, we conducted a subjective evaluation experiment based on the different flow velocity profiles. We found that using our estimated flow velocity profile from the fluid simulation with our proposed configuration of three air jets, the users felt that the sensation was more similar to flowing water when compared to the constant velocity profile.
## 2 Materials and Methods
### QA/QC (Quality Assurance/Quality Control) Procedure  
Diazinon was extracted using 1 mL C18 solid-phase extraction (SPE) columns. The conditioning of columns was performed in the sequence of 5 mL each of acetonitrile, methanol, and reagent water, maintaining a flow rate of 3 mL/min. A total of 100 mL of water sample was used for the extraction process. Air suction was applied to the column for 20 min, and diazinon was eluted with 20 mL methanol [43]. Diazinon was detected using gas chromatography-mass spectrometry (GC-MS) (Agilent Technologies, Santa Clara, CA, USA; model 1909 IS-433) with an HP-5 MS capillary column (25 m \(\times\) 0.25 mm diameter \(\times\) 0.25 mm thickness). The operating parameters used during the analysis were: MS detector interface (290 \({}^{\circ}\)C), injector temperature (260 \({}^{\circ}\)C), oven program, 80 \({}^{\circ}\)C (2.0 min), 1.0 \(\mu\)L automatic splitless injection, and ultrahigh-purity helium (grade 5) carrier gas previously programmed at a flow rate of 1 mL/min. The recovery percentage of diazinon was then estimated [43]. The nominal concentrations were used throughout the experiment as the difference between the nominal and measured concentrations was lower than 5%.
# Comparison of Algorithms to Compute Relaxation Time Maps in Magnetic Resonance Imaging
## 3 Results
Figure 3 illustrates an example of the fitting results for a specific pixel from Test 2, which involves using the Inversion Recovery technique to analyze mouse data. The plot in the figure represents the fitting error as a function of Trel after the linear parameters have been fitted. The plot shows two different fits, labeled as fit a and fit b, obtained using two different methods. Fit a, obtained using the RD-NLS algorithm, corresponds to the global minimum, meaning it has the lowest fitting error among all the possible fits. On the other hand, fit b, obtained using Matlab, reached a local minimum, meaning that the fitting error is greater than that of the global minimum. This indicates that the Matlab algorithm did not perform as well as the RD-NLS algorithm in fitting the data to the model. The local minimum could be caused by the algorithm getting stuck in a suboptimal solution rather than finding the global solution.
## 4 Power Control Techniques  
### Power Control Technique Assisted by FUE  
In a real scenario, the HeNB instructs all the FUE to measure the received signal power from the neighboring interferer BS and send a feedback report [35]. The HeNB utilize the received information from the FUE and make necessary adjustments in downlink transmission power. The FUE-assisted power control technique (FUEAPCT) can be expressed as:  
\[P_{FUE_{Assisted}}=max(P_{min},min(P_{o},P_{max})) \tag{4}\]  
In (4), \(P_{min}\) and \(P_{max}\) is the minimum and maximum transmission power of the femtocell.  
\[P_{o}=P_{FUE_{i}}+PL_{FBS\to FUE_{i}}~{}+~{}\varepsilon_{o} \tag{5}\]  
Here, \(P_{FUE_{i}}\) is the received power at \(FUE_{i}\), \(PL_{FBS\to FUE_{i}}\) is the pathloss from the FBS to the \(FUE_{i}\). \(\varepsilon_{o}\) is set to maintain the necessary QoS of femto users in (5).
# Methods Improving Energy Efficiency of Photovoltaic Systems Operating under Partial Shading
## 3 Efficiency Estimation of PV Installation
### PV Installation with Differentiated Maximum Power Take-Off Systems  
The voltage matching of parallel arrays of modules also allows for the use of differentiated maximum power take-off systems, the block diagram of which is shown in Figure 9 [41]. The principle of operation of such systems is that parallel arrays of modules transmit electrical energy through active voltage stabilizers, which set their output voltage to the same nominal value. At the same time, the converters search for the maximum power point (MPP) and optimize it. This allows for the maximum extraction of electrical energy from each array at their different illumination, temperature, or the use of PV panels in arrays with different characteristics.  
The main disadvantages of using such systems are:  
* high cost and material consumption due to the fact that the stabilizers used are designed for power with a value not lower than the nominal power of the array of modules in which they are installed;
* decrease in energy efficiency due to the fact that, for voltage matching, all the energy generated by the array is converted. Thus, the output power is reduced by the amount of efficiency of the stabilizers.  
Figure 8: The operating principle of reconfigurable switching systems; (**a**)—solar modules before commutation; (**b**)—reconnected modules (1—normally lit module; 2—shaded module).  
The analysis shows that, at present, there are quite a large number of technical solutions that make it possible to partially neutralize the influence of uneven illumination on the power generation of PV modules. Moreover, each of them has its own characteristics that limit their widespread use in solar power plants, which have a wide variety of design solutions, including various options for support structures, solar cell switching, orientation systems, the nature of illumination, etc.  
It should also be noted that some of the methods initially do not seem promising for implementation at industrial power plants due to the practical complexity of their implementation, high cost, or the impossibility of incorporation into existing plants. Such methods include those that allow solving the problem of voltage matching of arrays of PV modules in parallel switching, reconfigurable switching systems, microinverters, and differentiated maximum power take-off systems. As a result, one of the urgent tasks is to create an economically viable method that allows the selection of electrical energy from parallel groups of PV modules (mixed switching) at the points of maximum power operating in shading conditions.
# Routing Path Assignment for Joint Load Balancing and Fast Failure Recovery in IP Network
## 5 Conclusions  
A network must be able to efficiently manage the traffic routing to improve utilization and to prevent congestion. IP networks are reliable and robust due to distributed link-state routing protocols, even though, relatively, routing is not flexible in IP networks. Fibbing is a new approach that achieves flexibility and robustness with centralized control over distributed routing. Even though a Fibbing approach offers flexible routing in IP networks, strategies for attaining fast recovery from failures have not been thoroughly examined. To speed up failovers, the IP fast reroute, Loop-Free Alternative (LFA) technology, is proposed by IETF. We found that LFA technology with Fibbing is not compatible. Traffic loops will result if LFA technology is used directly with the Fibbing network. We offer workable pathways that guarantee 100 percent survivability in the event of a single node or single link failure. We offer ILP (Integer Linear Programming) models. The output of the programs is used to calculate the placement of fake nodes in order to achieve the appropriate load-balanced routing paths and backup paths. We use a CPLEX optimization solver for LBO, LB&LFP, and LB&NFP models. The objective function of our optimization models is to minimize the utilization of the most congested link. We put our models to the test under both fixed and random traffic demands. Our optimization models give optimal solution for these three models. The results show that the system identifies single node and link failures and assigns a routing path for load balancing and fast failure recovery by decreasing link utilization on the most congested link and ensuring 100% network survivability.  
Conceptualization, S.S.W.L.; software, M.I. and T.A.; validation, T.A.; formal analysis, T.A.; writing, T.A. and M.I.; supervision, S.S.W.L. and J.-Y.P.; project administration, S.S.W.L. and J.-Y.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and Technology of Taiwan under Grant MOST 108-2221-E-194-021-MY3.  
The authors declare no conflict of interest.  
Figure 14: Survivability ratio (%) when LBO model has a link failure Event.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Degree** & **N:10** & **N:12** & **N:14** & **N:16** & **N:18** & **N:20** \\ \hline
**4** & 64 & 70 & 74 & 79 & 83 & 87 \\ \hline
**5** & 67 & 71 & 76 & 81 & 85 & 88 \\ \hline
**6** & 69 & 73 & 77 & 82 & 86 & 90 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Survivability ratio (%) when LBO model has link failure event.
# A Method for Ferulic Acid Production from Rice Bran Oil Soapstock Using a Homogenous System
## 3 Results and Discussion  
### HPLC Chromatography  
The HPLC chromatograms (UV 325 nm) of authentic \(\gamma\)-oryzanol and its hydrolysis products were shown in Figure 2. The peaks eluted in the range of 33-45 min were corresponding to the four ferulate components of \(\gamma\)-oryzanol. The peaks at retention time of 31.5, 36.0, 38.0, 44.5 min were assigned to cycloartenyl ferulate (Or\({}_{1}\)), 24-methylene cycloartanyl ferulate (Or\({}_{2}\)), campesteryl ferulate (Or\({}_{3}\)), and sitosteryl ferulate (Or\({}_{4}\)), respectively [28]. The peaks appeared at retention times of 9.1, 9.3 min were assigned to the _trans_- and _cis_-FA by comparison with their standards. As hydrolysis proceeded, ethyl ferulate appeared at retention time of 14.4 min as the reaction intermediate.  
The FA produced by hydrolysis was the _trans_-form as shown in Figure 2a. The result indicated that the four ferulate components of \(\gamma\)-oryzanol are configured in the _trans_-form.  
The Dry-extracts were prepared from the Vietnamese RBO soapstock samples collected in March, May, and June of 2016. The HPLC chromatograms of the Dry-extracts are shown in Figure 2b. The four peaks of ferulates appeared in the range of 31.5-44.5 min were as same as those of an authentic \(\gamma\)-oryzanol as shown in Figure 2a. However, no peaks of FA were detected. The results indicated that no FA exists in free form, but in the ester form such as \(\gamma\)-oryzanol and/or conjugated form with bio matrix in RBO soapstock. The content of \(\gamma\)-oryzanol in the Vietnamese RBO soapstock was 3.1 \(\pm\) 0.3% (\(wt/wt\)). It was reported that the soap stock obtained by alkali refining of the rice bran oil contained 1.3-3.1% \(\gamma\)-oryzanol [29]. After three extractions with the EtOAc solvent, Dry-extract could effectively be prepared for the following experiments.  
Figure 2: The High performance liquid chromatography (HPLC) chromatograms of (**a**) authentic \(\gamma\)-oryzanol before (lower line) and after hydrolysis (upper line); and (**b**) the Dry-extracts from the rice bran oil (RBO) soapstock samples obtained in March, May, and June of 2016.
# Research Status of Pathogenesis of Pneumoconiosis and Dust Control Technology in Mine--A Review
## 2 Research Progress of Mine Pneumoconiosis  
Mine pneumoconiosis is a disease caused by mine dust particles entering the lungs. Its occurrence process is very complex, involving a variety of cells and bioactive substances, including immune response, inflammatory response, structural damage and repair of cells and tissues, collagen proliferation and fibrosis. [PERSON] [10] reviewed the pathogenesis of coal workers' pneumoconiosis. He pointed out that coal worker pneumoconiosis is the result of the interaction and restriction of many factors, and the development of pneumoconiosis is mostly gradual. The general process of coal workers' pneumoconiosis is that the particles deposited in the lung cannot be removed, and the dust interacts with the components of the lung fluid and lung cells; this reaction causes the release of a variety of chemicals and cytokines to form a cytokine network, and finally, this phenomenon acts on a variety of target cells to form corresponding pathophysiological changes [10,11].  
Pulmonary fibrosis is the common result of many lung diseases in the late stage, and it is also a prominent lung disease in the late stage of pneumoconiosis. However, due to the different pathogenic factors of pneumoconiosis and the different time and degree of pulmonary fibrosis, the pathogenesis of pulmonary fibrosis becomes complex and the cure is more difficult. A large number of epidemiological investigations of coal workers' pneumoconiosis in China and abroad show that the prevalence and progress of coal workers' pneumoconiosis are related to coal dust particle size, carbon content and free silica concentration [11; 12; 13]. When the content of free silica in lung dust exceeds 18%, the lesion is silicosis; below 18%, it is coal workers' pneumoconiosis, including pneumoconiosis caused by coal dust and coal silicosis caused by mixed dust (rock dust and coal dust) [11; 12].
# Low-Light Mine Image Enhancement Algorithm Based on Improved Retinex
## 3 Proposed Method
### Improved Guided Filtering
To prove that the improved guided filtering algorithm (WWGIF) has a more effective edge-preserving result, the filtering results are compared with the other three filtering algorithms GIF, WGIF, and gradient domain guided image filtering (GDGIF) [18], using the same regularization parameters and filtering radius, \(\varepsilon=0.2^{2},r=16\). The original image in Figure 4 is from a publicly available image on the Internet, from which we can see that the algorithm presented in this paper exhibits superior edge-preserving effects compared to GDGIF and effectively avoids the local halo blurring phenomenon of GIF, which can maintain the intricate edges of the image and retain the weak details of the relatively flat areas. Figure 4: _Cont._  
According to the data provided in Table 1, it is apparent that PSNR and SSIM indexes of the improved filtering algorithm are significantly improved compared to GIF, WGIF, and GDGIF. This means that WWGIF improves the quality of the image and shows better performance in these filters.