esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Image Steganography  
The implementation of image steganography encompasses several variations, primarily based on the embedding techniques used. There are several embedding methods that can be used in the context of image steganography, including statistical, machine learning (ML), deep learning (DL), and quantum methods [7]. Among these, statistical methods in the form of Least Significant Bit (LSB) embedding is the simplest and most flexible [8].  
Despite advancements in other techniques, the fundamental LSB techniques still hold significant relevance. Recent studies, such as by [PERSON] and [PERSON] [9], demonstrated the continued importance and effectiveness of LSB-based methods in scenarios where simplicity and computational efficiency are crucial. [PERSON] et al. [10] proposed a novel approach by combining the \(k\)-LSB method with a pseudo-random number generator to select pixels for embedding. In this scheme, the variable \(k\) represents the number of bits from the cover image embedded in the secret image. This technique improves the level of security by randomizing the embedding positions. [PERSON]'s research [11] demonstrated the embedding of a grayscale secret image into an RGB cover image using LSB-1, LSB-2, and LSB-4 techniques. Their findings indicated that LSB-1 and LSB-2 exhibit small payload sizes, while LSB-4 result in poor Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) values.  
Previous studies have primarily focused on embedding text or grayscale images using various LSB schemes [11]. However, the use of basic logistic maps in LSB techniques has shown vulnerabilities to steganalysis due to their predictable patterns [12]. This highlights the need for increased randomness to ensure better security. In this study, we employ the Josephus permutation to significantly enhance the randomness of the embedding locations, thereby mitigating the risk of detection by steganalysis.  
Our inspiration for using the Josephus permutation comes from its successful application in cryptography to increase randomness. The Josephus permutation has demonstrated its effectiveness in improving scrambling effects and permutation efficiency, making it a valuable tool in cryptographic algorithms [13]. By leveraging this technique in steganography, we aim to enhance the robustness and security of our method, ensuring that the embedded data remain well protected against various types of attacks.  
Further, our method embeds RGB images as secret data, which presents a higher level of complexity compared to grayscale images used in previous works [11]. This not only extends the work of [11] by exploring the use of RGB images but also serves as a rigorous test of the effectiveness of the Josephus permutation in handling more complex data. The use of RGB images allows for a more detailed and nuanced representation of the secret data, which, when combined with the enhanced randomness from the Josephus permutation, provides a more secure and robust steganographic technique.
# Visual-Based Positioning of Aerial Maintenance Platforms on Overhead Transmission Lines
## 4 Results  
A set of experiments were conducted in laboratory, simulation and field environments. The proposed approach was simulated using the GAZEBO software [55] and linking this with ROS [50] and MATLAB [56]. Finally, field tests in a real transmission grid, located in the township of Casablanca, in the Valparaiso Region, Chile were carried out.
# Fault Detection Algorithms in Medium-Voltage Direct-Current (MVDC) Grids
## 2 Materials and Methods
### Overcurrent Algorithm  
The overcurrent (OC) algorithm is a widely used approach for detecting faults through current measurement. This method involves locally monitoring the direct current (DC) and comparing it against a predefined threshold. Once this threshold is exceeded, a fault detection event is triggered, as illustrated in Equation (1). By also considering the polarity of the current, it becomes feasible to ascertain the fault's direction. While this approach offers rapid operation, it lacks the capability to selectively pinpoint faults. Nonetheless, it serves as a dependable backup protection mechanism. This algorithm is most effective for short-circuit faults or overload situations where the current significantly exceeds normal levels. However, its effectiveness diminishes in the presence of high-resistance faults, as the fault current may not rise sufficiently above the normal operating current to trigger detection. Equation (1) demonstrates the operational mechanism of the algorithm.  
\[\text{Fault detection}=1\text{ if }\text{I}_{\text{DC}}>\text{OC}_{\text{THR}} \tag{1}\]  
where I\({}_{\text{DC}}\) is the measured direct current of the system and OC\({}_{\text{THR}}\) is the predefined threshold.  
In [11], the authors propose an overcurrent protection scheme in order to protect a multi-terminal DC system based on voltage source converters. It compares the measured current to a predefined threshold of 2.1 kA for that specific study case, which is below the limit of the maximum current of the circuit breaker. The OC method is used in [12] as backup protection for an islanded MVDC microgrid. Despite not being the main protection system, it is used to increase the robustness of the protected system. It only works in the case of failure of the main protection. On account of the low selectivity of the OC algorithm, the authors of [13] propose communication between the relays to improve the coordination. However, the advantage of coordination between the devices brings with it the disadvantage of the time delay due to communication.
# Recognition of Manual Welding Positions from Depth Hole Image Remotely Sensed by RGB-D Camera
## 2 Previous Research
### Motion Capture System  
To recognize a worker's posture, it is first necessary to track the movement of the body. Recently, devices capable of tracking various types of body movements have been introduced. These motion capture systems are generally classified into a marker system, a markerless system, and a wearable system. The most accurate and reliable way is to track the body's motion using a marker system [14; 15; 16] that attaches a marker to each joint or a wearable sensor, such as an accelerometer or pressure sensor [17; 18; 19]. Despite their high accuracy, markers or wearable sensors are inconvenient for the subject because separate equipment must be attached to the body ahead of time. There are also limitations to the operational range and location of those systems. Although these devices can be used for simulation in a laboratory setting, they have limitations when it comes to collecting data in a real-world setting.  
Figure 2: Welding positions: (**a**) flat, (**b**) horizontal, (**c**) vertical, and (**d**) overhead.  
A widely used method among markerless systems is to use images including depth information. Many studies have been conducted on body tracking and motion analysis using depth images [20; 21; 22; 23]. There are two methods for creating depth images: extracting features from two-dimensional images and inferring depth through learning [24; 25; 26; 27] or shooting with a 3D depth camera [28; 29; 30]. The former method has disadvantages in that an additional process is required to extract and learn features of an image, it takes a lot of time, and the accuracy is low.  
A 3D depth sensor has been widely used to take a depth image. There are two types of 3D depth sensors: time of flight (ToF) and LiDAR. The ToF sensor calculates depth information by measuring the time it takes to emit infrared rays and return. This sensor is mainly used for motion capture because of its high accuracy [31; 32]. Similar to the ToF sensor, the LiDAR sensor generates a depth image by measuring the return time after firing a laser. Some studies track body motion using the LiDAR sensor, but its accuracy is reported to be low [33; 34]. Furthermore, since motion capture using a LiDAR sensor not only requires a separate RGB camera but has been slow to commercialize, its applicability is currently low [35].  
This study focuses on improving accessibility, portability, and accuracy to collect motion data for posture recognition in real-time. We selected an RGB-D camera based on ToF sensors with cost-effectiveness and excellent usability in the current market. The selected Azure Kinect provides clear resolution and high accuracy through the ToF method using an IR projector and depth sensor [36].
## 2 Related Works
Genetic Algorithms (GAs) are a global optimisation technique that can find near-optimal CH selection and routing solutions. They are adaptable to dynamic network conditions. This algorithm can optimise CH placement and routing paths to minimise energy consumption and maximise network lifetime. GAs may outperform KNN in large-scale networks with complex topologies, but could require more computational resources [32]. Ant Colony Optimisation (ACO) is well-suited for path optimisation and can adapt to dynamic network changes. Its distributed nature aligns with the architecture of WSNs. It can optimise routes to reduce energy consumption and improve data delivery reliability. ACO may outperform KNN in routing optimisation, but could have higher computational complexity [33]. Particle Swarm Optimisation (PSO) has a fast convergence rate and effectively solves continuous optimisation problems. It is simple to implement and computationally efficient. PSO can optimise CH selection and routing paths by considering energy levels, distance, and network density. It also may achieve better energy efficiency than KNN, but could be less interpretable [34]. In our previous work, we used the KNN algorithm to modify the TEEN protocol [18]. This article presents a new modification method by integrating the machine learning KNN algorithm with four WSN protocols: LEACH, SEP, TEEN, and DEC. These protocols have one important common factor: they all deal with energy usage. Therefore, this paper aims to improve the protocol's efficiency and reliability, making it more robust in handling various network scenarios and improving its performance in real-world applications. There will be no overlapping of CHs in the network, and all cluster regions in the network contain CH. Our work is mainly focused on static WSN configurations. While dynamic network configurations are essential for specific mobility applications, such as vehicular networks or mobile healthcare systems, static sensor networks remain highly relevant and practical for many real-world applications. Static sensor networks are widely used in security systems, such as surveillance systems with cameras and motion detectors, where stationary sensors continuously monitor specific areas. Similarly, in smart homes and gardens, static sensors monitor and control systems like lighting, temperature, and irrigation. Additionally, meteorological measurements rely on static sensor networks to collect long-term environmental data for weather forecasting and research, such as temperature, humidity, and precipitation. Other applications include industrial monitoring, where static sensors detect equipment faults, and infrastructure monitoring, where they assess the structural health of bridges and buildings. These examples demonstrate the critical role of static sensor networks in various domains, justifying this study's focus on static configurations.
###### Abstract  
A differential global positioning system (DGPS) is one of the most widely used augmentation systems for a low-cost L1 (1575.42 MHz) single-frequency GPS receiver. The positioning accuracy of a low-cost GPS receiver decreases because of the spatial decorrelation between the reference station (RS) of the DGPS and the users. Hence, a network real-time kinematic (RTK) solution is used to reduce the decorrelation error in the current DGPS system. Among the various network RTK methods, the Flachen Korrektur parameter (FKP) is used to complement the current DGPS, because its concept and system configuration are simple and the size of additional data required for the network RTK is small. The FKP was originally developed for the carrier-phase measurements of high-cost GPS receivers; thus, it should be modified to be used in the DGPS of low-cost GPS receivers. We propose an FKP-DGPS algorithm as a new augmentation method for the low-cost GPS receivers by integrating the conventional DGPS correction with the modified FKP correction to mitigate the positioning error due to the spatial decorrelation. A real-time FKP-DGPS software was developed and several real-time tests were conducted. The test results show that the positioning accuracy of the DGPS was improved by a maximum of 40%.  
differential global positioning system; Flachen Korrektur parameter; FKP-DGPS; spatial decorrelation +
Footnote †: journal: Geo-Informatics
## 4 Experimental Results  
### Implementation Settings  
We designed experiments with different input sequences for the segmentation task, as listed in Table 3. For the training settings, we set the batch size to 2, optimizer as an  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Input Size** & \(320\times 320\times 64\) & \(320\times 320\times 48\) & \(320\times 320\times 32\) \\ \hline \multirow{4}{*}{**Discriminator**} & \(160\times 160\times 32\) & \(160\times 160\times 24\) & \(160\times 160\times 16\) \\ \cline{2-4}  & \(80\times 80\times 16\) & \(80\times 80\times 12\) & \(80\times 80\times 8\) \\ \cline{2-4}  & \(40\times 40\times 8\) & \(40\times 40\times 6\) & \(40\times 40\times 4\) \\ \cline{2-4}  & \(20\times 20\times 4\) & \(20\times 20\times 3\) & \(20\times 20\times 2\) \\ \hline
**Output Size** & \(10\times 10\times 4\) & \(10\times 10\times 3\) & \(10\times 10\times 2\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Configuration of the patch discriminator.  
Adam optimizer with a learning rate of 0.00001. The epoch was set to 100. We implemented the source code using the Pytorch framework and trained the model on a Linux server with a single Nvidia A100-40 GB GPU. All liver segments were divided into training, validation, and testing sets in a ratio of 7:2:1. In this paper, we loaded the pre-training weights for the model [39] and then continued to fine-tune them on our data. The mask of the segmented vessels was then used as part of the input data for the 3D generation task. On the validation set (i.e., all the vascular segmentation masks in our experiment) the DSC was 0.803.
## 2 Methodologies
### Survey of Archaeological Areas
Hundreds of high-resolution images were taken at a low altitude of the floor covering of each individual room to create an orthogonal photomosaic of the entire floor plan of the villa. The scanning of the villa in the Sirmione area was primarily conducted by drones. For example, the so-called Campo delle Noci area (Figure 4) would have been impossible to capture solely from the ground: its tall structures, its position overlooking the lake, and the presence of vegetation made the use of UAVs indispensable. The zone was divided into sectors that were scanned separately and later merged using known reference points. The points were measured with a total station, starting from geodetic points, the details of which were provided by the Archaeological Park. A chart was provided. For the surveying process, the development of an integrated detection system using different instruments was planned. Before the 3D survey, the areas were divided into multiple quadrants for better data management, error control, and a simpler and more effective survey campaign (implementable). Additionally, we also requested the monographs of the geodetic points positioned in the monument areas in order to topographically frame the monument within the territory. These points were used for georeferencing the 3D model, DTM (Digital Terrain Model), rasters, and contour lines. To obtain an overall point cloud with a reduced density for the Grotte di Catullo area, a drone survey at an altitude of approximately 20 m was planned. From the survey using these instruments, a general point cloud with a point spacing of approximately 5 mm (Sirmione) was obtained. This point cloud was also used for creating a low-polygon model. To increase the point cloud's definition, a morphometric survey of the Grotte di Catullo area was conducted, specifically targeting sub-quadrants, arches, tunnels, and areas inaccessible to drones. This three-dimensional photogrammetric survey using the Structure from Motion (SfM) technique also provided a high-quality texture to overlay onto the previously acquired point cloud. This campaign was carried out using specialised image-based instruments such as calibrated professional mirrorless cameras, ensuring uniformity in exposure, tone, and saturation parameters. During the photogrammetric acquisition campaign, suitable targets (reference points) were placed according to both the drone methodology and the SfM, enabling the referencing of the two surveying systems and obtaining a highly reliable photogrammetric 3D model. For better data management, a low-polygon model was exported. The point cloud was decimated, allowing for a model that preserves the apparent morphological complexity of the Grotte di Catullo area while being compatible with management tools and information systems. Subsequently, the processed data was exported in the required formats for further utilisation. The final processing of these data not only led to the calculation of a mesh with high resolution but was also used for the web application (low-polygon model).
# Current Progress Regarding _Cordyceps militaris_, Its Metabolite Function, and Its Production
## 2 Biological Compounds of _C. militaris_
### D-Mannitol  
One of the most important products of _C. militaris_ metabolism is D-mannitol, commonly known as cordycepic acid, which has a chemical formula of C\({}_{6}\)H\({}_{14}\)O\({}_{6}\), with a molecular weight of 182.172 g/mol, and is classified as a polyhydric alcohol (polyol) [16]. Mannitol has various medical applications due to its osmotic properties, and it is used in the food and pharmaceutical industries. It also stimulates the release of renal prostaglandandins, resulting in renal vasodilation and increased urine flow through the tubules, which is thought to prevent renal injury by reducing tubular obstruction [90]. The biological activities of mannitol can be broadly classified into several categories, including antioxidant [27], anti-inflammatory, antitumor, hypolipidemic, and hepatotoxicity properties.  
As for the antioxidant ability of mannitol, it was shown to possess free radical-scavenging properties _in vitro_, protecting active substances such as hyaluronic acid from degradation by oxygen-derived free radicals. Moreover, it inhibits the release of ROS caused by the thermal- or photodecomposition of tetracyclines [91].  
Mannitol was shown to have anti-inflammatory effects by inhibiting lipid peroxidation and formation of the NF-\(\kappa\)B complex [92].  
Tumor-bearing animals treated with a high dose of cisplatin (CDDP; 9.5 mg/kg body weight (BW) i.v.) and mannitol survived longer than those given CDDP (7.0 or 9.5 mg/kg BW i.v.) alone, which suggests that mannitol can effectively enhance antitumor ability [29].  
Mannitol (10 mg/kg BW) demonstrated hypolipidemic effects in hypercholesterolemic mice that were comparable to those of ezetimibe (10 mg/kg BW), without inducing liver damage or impacting high-density lipoprotein cholesterol (HDL-C) levels [30].  
Mannitol administration has the potential to mitigate or alleviate liver injury caused by experimental obstructive jaundice, as elevated levels of platelet-activating factor (PAF) in both plasma and liver tissue were observed, which are thought to contribute to the progression of the injury [31].
# Analysis of Power Allocation for NOMA-Based D2D Communications Using GADIA
## 4 Principle of NOMA System
### SIC Technology  
The basic principle of SIC technology is to adopt a step-by-step interference elimination strategy. In the received signal, users are judged one by one according to the order of power. After the amplitude is restored, the signal's multiple access interference are subtracted from the received signal. Then, judge the remaining users again, and repeat the operation until all the multiple access interference is eliminated. Therefore, SIC technology has more significant advantages for users with weak signals.  
Figure 2 illustrates the typical diagram of SIC strategy for three users. Assume the three users superimposed on the same sub-channel are \(x_{1}\), \(x_{2}\), \(x_{3}\) where the corresponding channel gains are, respectively, \(h_{1}\), \(h_{2}\), and \(h_{3}\) under \(h_{1}>h_{2}>h_{3}\) condition; i.e., the users are ranked based on SNR. When the SIC receives the signal from the wireless channel, it first sorts all user signals according to their power from strong to weak and then performs detection according to the power. Since \(x_{3}\) has the strongest signal power and the corresponding SINR, SIC detects \(x_{3}\) first, and then detects \(x_{2}\) after obtaining the signal of \(x_{3}\), and finally \(x_{1}\).  
There are several potential problems with SIC receivers that may adversely affect the performance of NOMA. The first disadvantage is that it is more sensitive to the power level, i.e., if two or more UEs have similar power, the performance will be significantly reduced. If two or more UEs have similar channel gains, the NOMA scheme will assign them the same power allocation coefficient, lowering the SIC's performance. Another drawback of SIC is its high dependency on the correct decoding of the first UE. If the first UE fails to decode successfully, this decoding error propagates through the lower-order of UEs and results in system performance degradation. Another limitation of the SIC receiver comes from the decoding delay for low-order UEs due to the nature of the SIC serial decoding process. To reduce the decoding delay, NOMA must group a small number of UEs with significantly different channel gains, limiting the system capacity. Finally, because the NOMA protocol serves users based on the SIC sequence, the rate achieved between users is different.  
Figure 2: SIC receiver principle.
## 2 Study Area and Datasets
### Meteorological Data  
In this study, we used the China Meteorological Forcing Dataset (CMFD) containing air temperature (Ta), precipitation (P), and downward shortwave radiation (Rs) developed by the Institute of Tibetan Plateau Research, Chinese Academy of Sciences [57]. These products cover the period of 1982-2018, with a spatial and temporal resolution of 10 km and 3 h. We resampled the dataset of the study area to a 1 km (5 km) spatial resolution using the nearest neighbor method. We also extracted the CMFD Ta, P, and Rs values corresponding to the ground-based sampling sites, which can be used as independent variables for training the models.  
The CMFD Ta dataset was constructed by merging observations from 740 meteorological stations and the corresponding Princeton meteorological forcing data [58]. The CMFD P dataset was developed by combining three precipitation datasets including those of the same 740 meteorological stations, the Tropical Rainfall Measuring Mission (TRMM) 3B42 precipitation products [59], and the Asian Precipitation-Highly Resolved Observational Data Integration Towards Evaluation of the Water Resources project (APHROIDITE) [60]. The CMFD Rs dataset was constructed by Global Energy and Global Energy and Water cycle Experiment-Surface Radiation Budget (GEWEX-SRB) radiation data and meteorological station measurements [61]. The 3-hourly Ta, P, and Rs were subsequently aggregated into daily means. Monthly P values were obtained by multiplying the average daily mean by 30 or 31 (days) [62].