esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Drawbacks of DIP  
Despite the flexibility of DIP shown in image denoising, its results are in some cases not optimal. First, the generators used for DIP are usually over-parameterized; that is, the number of network parameters is greater than the number of output dimensions, and too many iterations result in an empirically overfitted image. In Figure 3, it can be seen that for each curve, the peak signal-to-noise ratio (PSNR) result continuously improves until it reaches a specific iteration; beyond this iteration, the resultant curve of PSNR begins to decline. Thus, if the iteration process does not stop at the appropriate iteration, the DIP experiences under- or over-fitting problems. Though [PERSON] et al. set a fixed number of iterations (early stopping point) in the deep natural network, it was based on experimental data; thus, it could not guarantee the optimal denoising effect. As shown in Figure 3, regardless of where the iterations end, not all three output images could achieve the optimal denoising effect at the same iteration due to differences in their optimal early stopping points.  
Figure 2: Image space visualization of image denoising using deep image prior.  
Second, according to the data listed in Table 1, the denoising effect of DIP is inferior to that of the mainstream FFDNet method. The main reason is that DIP's loss function is defined as:  
\[Loss_{1}=MSE\Big{(}\hat{x}^{i},x_{0}\Big{)} \tag{5}\]  
where MSE is the mean square error, \(\hat{x}^{i}\) is the output image of the deep neural network, and \(x_{0}\) is the noisy image. The guiding ability of the noisy image \(x_{0}\), which controls the final convergence direction of the output image, is limited. Notably, more noise in the noisy image will weaken its guiding ability, causing slow iteration convergence and poor denoising performance.
## 3 Description of the Environment
### Implementation Details  
In the present study, the core off-policy DRL algorithms (SAC, TD3, and TQC) were implemented using the open-source library Stable-Baselines3 [66]. This framework was selected because it offers reliable, well-documented implementations of modern actor-critic methods that can be adapted to a 6-DOF AUV environment. In particular, the fundamental update rules (e.g., Bellman backups, policy gradient steps) remained consistent with the reference version of Stable-Baselines3, while the following modifications were introduced:  
* **Environment Wrappers:** Custom wrappers were developed to interface the AUV simulator (implemented in Python/C++) with the Stable-Baselines3 API, conforming to the OpenAI Gym specifications (v0.26);
* **Reward Shaping:** Additional terms were incorporated to address the avoidance of collisions and ensure smooth heading changes (Section 3.4);
* **Curriculum Learning:** A gradually tightening goal threshold was implemented (Section 3.5) to prevent overly difficult early training episodes;
* **HER Extension:** The \"future\" HER strategy was adopted by modifying the replay buffer logic, allowing the agent to reinterpret failed episodes for alternative subgoals (a functionality originally present in Stable-Baselines3);
* **Hyperparameter Tuning:** Optuna (v2.10) was employed to systematically search over the learning rates, discount factors, batch sizes, and network depths, culminating in the final architectures shown in Table 2.  
By leveraging Stable-Baselines3, the experiments are grounded in a robust RL framework that minimizes the risk of fundamental implementation errors.
# Cryobiotechnology of Plants: A Hot Topic Not Only for Gene Banks
## 2 Cryopreservation of Plants
### Plant Cryopreservation Methods
#### 2.1.1 Classical Cryopreservation Methods  
Classical cryopreservation methods were developed more than 40 years ago, and two main techniques are traditionally used: (i) slow freezing, and (ii) simple one-step freezing [7]. Slow freezing (also known as the two-step freezing method) includes pretreatment of samples with cryoprotectants such as dimethylsulfoxide (DMSO), glycerol, ethylene glycol, and sucrose. The samples are then slowly cooled at a controlled rate of 0.3-0.5 \({}^{\circ}\)C/min to \(-40\)\({}^{\circ}\)C, and then rapidly immersed in liquid nitrogen. This method requires a programmable freezer [15]. On the other hand, no special equipment is required for simple one-step freezing. Samples pretreated with cryoprotectants are simply frozen at \(-30\)\({}^{\circ}\)C for dehydration, and then immersed directly in liquid nitrogen [16]. Although these classical methods have been successfully applied to a range of plant materials, new and gentler cryogenic methods such as vitrification, encapsulation, and cryo-plates have been developed recently.
# Position Accuracy Improvement by Implementing the DGNSS-CP Algorithm in Smartphones
###### Abstract  
The position accuracy of Global Navigation Satellite System (GNSS) modules is one of the most significant factors in determining the feasibility of new location-based services for smartphones. Considering the structure of current smartphones, it is impossible to apply the ordinary range-domain Differential GNSS (DGNSS) method. Therefore, this paper describes and applies a DGNSS-correction projection method to a commercial smartphone. First, the local line-of-sight unit vector is calculated using the elevation and azimuth angle provided in the position-related output of Android's LocationManager, and this is transformed to Earth-centered, Earth-fixed coordinates for use. To achieve position-domain correction for satellite systems other than GPS, such as GLONASS and BeiDou, the relevant line-of-sight unit vectors are used to construct an observation matrix suitable for multiple constellations. The results of static and dynamic tests show that the standalone GNSS accuracy is improved by about 30%-60%, thereby reducing the existing error of 3-4 m to just 1 m. The proposed algorithm enables the position error to be directly corrected via software, without the need to alter the hardware and infrastructure of the smartphone. This method of implementation and the subsequent improvement in performance are expected to be highly effective to portability and cost saving.  
s 2016169103390/s16060910
# Multi-Scale Aggregation Residual Channel Attention Fusion Network for Single Image Deraining
## 5 Conclusions  
The proposed MARCAFNet architecture uses kernels of different dilation rates for the recovery of image features and the elimination of rain streaks. The proposed model applies the RCA block to the CS block in accordance with the encoders-decoders of U-Net. The RCA block is meant to enhance the representation of image content in order to obtain detailed information related to features in the rain-streaked image, thereby enabling the CS block to obtain global features as well as attention features. The proposed CSFF block connects encoder-decoders of the same scale to pass channel features to the kernel with larger receptive fields to enhance the image detail information. The consecutive connection of three CS blocks enables the capturing of textural features, contour features, and rain streak features, which are then aggregated into a new feature map by the SB block. Finally, the image is recovered through adaptive summation. The proposed MARCAFNet outperformed the state-of-the-art methods, and the present findings revealed that deraining a single image requires the integration of global and local loss to preserve the structure of the image as well as the local features.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline Dilation Rate & 1,1,1 & 2,2,2 & 3,3,3 & 1,2,3 \\ \hline PSNR & 33.99 & 34.46 & 32.70 & 35.04 \\ \hline SSIM & 0.9403 & 0.9329 & 0.9259 & 0.9406 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Average PSNR and SSIM of proposed MARCAFNet when using CS blocks with various dilation rates in two synthetic datasets (Rain100H and Rain 100L).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **M1** & **M2** & **M3** & **M4** \\ \hline CSFF & ✗ & ✗ & ✓ & ✓ \\ SB & ✗ & ✓ & ✗ & ✓ \\ \hline PSNR & 27.87 & 33.65 & 27.94 & 35.04 \\ \hline SSIM & 0.8118 & 0.9403 & 0.8131 & 0.9406 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average ablation results for various combinations of the proposed sub-models when applied to various synthetic datasets.
## 2 Materials and Methods
### Analysis of Phenolic Composition
#### 2.6.3 HPLC-DAD  
The phenolic compounds were quantified using a UHPLC Vanquish (Thermo Fisher Scientific, Waltham, MA, USA) equipped with an auto-sampler, pump, and Vanquish diode array detector (DAD). The chromatographic separation of compounds was carried on a Luna C18 reversed phase (Luna 5 um C18 (2) 100 A, 250 \(\times\) 4 mm; Phenomenex) and a Manu-cart RP-18 pre-column in a thermostatic oven at 35 \({}^{\circ}\)C. DAD was programmed for a scanning between 192 and 798 nm at a speed of 1 Hz with a bandwidth of 5 nm. The detection was monitored using three individual channels (280, 320, and 360 nm) at a speed of 10 Hz with a bandwidth of 11 nm. The auto sampler's temperature was set at 12 \({}^{\circ}\)C and the injection volume applied was 20 uL. The mobile phase consisted of water:formic acid (99.5%:0.5%) as eluent A and acetonitrile:formic acid (99.5%:0.5%) as eluent B at a flow rate of 0.30 mL/min. All solvents were filtered through a 0.22 um PVDF membrane (Millipore, Billerica, MA, USA) prior to analysis. The system was run with the following gradient program: 0-10 min from 99 to 95% A; 10-30 min from 95 to 82% A; 30-44 min from 82 to 64% A; 44-64 min at 64% A; 64-90 min from 64 to 10% A; 90-100 min at 10% A; 100-101 min from 10 to 95% A; 101-120 min at 95% A; and finally returning to the initial conditions. The quantification of the phenolic compounds was performed by a calibration curve (0.78-100 ppm) obtained with gallic acid, chlorogenic acid, and quercetin-3-hexoside. The data acquisition system was the Chromeleon version 7.0 (Waltham, MA, USA) for DAD.
###### Abstract  
(1) Background: Throwing accuracy is one of the main factors that affect performance in sports that require this motor skill. There are multiple training methods that are based on the basic principles of motor learning, but the emergence of immersive virtual reality has brought about a profound change in training methodology. The objective of the present study was to analyze the effect of a coordination training program that takes place in immersive virtual reality environments on manual throwing accuracy in a school population. (2) Methods: A total of 58 schoolchildren (16.21 \(\pm\) 0.41 years) volunteered and completed the study. The explosive strength of the upper body, level of physical activity, speed and accuracy were evaluated. (3) Results: the findings show the existence of significant differences between both groups (experimental-control), showing that the developed program has positive effects on strength parameters (3.86 \(\pm\) 0.68 vs. 4.06 \(\pm\) 0.67 m); (3.91 \(\pm\) 1.07 vs. 3.71 \(\pm\) 1.22 m), speed (2.26 \(\pm\) 0.28 vs. 2.44 \(\pm\) 0.27 s); (2.26 \(\pm\) 0.22 vs. 2.03 \(\pm\) 0.19 s) and accuracy (820.00 \(\pm\) 794.12 vs. 1630.00 \(\pm\) 854.66 pts); (996.43 \(\pm\) 936.69 vs. 986.29 \(\pm\) 737.03 pts) compared to the control group. (4) Conclusions: the coordinative physical exercise program developed in immersive virtual reality environments has had a positive impact on the accuracy of manual throwing in schoolchildren.  
motor competence; exergames; adolescent; physical activity; health; pitch accuracy Article
# Tunneling Current Variations in Small-Sized Devices Based on a Compact Threshold Voltage Model
## 2 Theoretical Model Derivation
### Long-Channel Threshold Voltage Model  
Strictly speaking, modeling a MOSFET is a three-dimensional problem. However, from a practical standpoint (unless the channel width W and channel length L are tiny), it can be approximated as a two-dimensional system, which means considering only the y-direction along the channel and the x-direction perpendicular to the channel. Additionally, if the variation in the electric field \(\mathrm{E_{y}}\) along the y-direction is much smaller than that in the corresponding electric field \(\mathrm{E_{x}}\) along the x-direction, the device can be treated using a one-dimensional approximation method.
# Modelling of Evenness of Runways as an Element of Sustainable Airport Maintenance
## 2 Methods for Measuring the Evenness of Airport Runways
### Methods for Modelling the Evenness of Airport Runways  
The evenness of airport runways is a typical example of the spatial and/or temporal distribution of physical phenomena, which can be approximated by a function depending on the location of the phenomena in 3D space. The aforementioned phenomena are characterized by measured point data irregularly distributed in time and space--the visualization, analysis, and modelling within a GIS is usually based on a raster representation.  
Many interpolation and approximation methods have been developed to predict values of spatial phenomena in unsampled location. In GIS, these methods were developed to support transformation between different discrete and continuous representations of spatial fields, typically to transform irregular point data into a raster representation or to convert between different raster resolutions [54]. The following methods were considered:  
* approximation with regression plane,
* inverse distance weighted interpolation (IWD) and
* interpolation based on triangulated irregular network (TIN).
## 3 Leveraging Machine Learning in a TDTSP Heuristic
### ETA Estimation  
In order to estimate the ETA of a customer \(j\) in an optimal solution, an artificial neural network (ANN) is used in conjunction with an exact algorithm for the TDTSP [21]. The ANN we chose is a _Multilayer Perceptron Regressor_ (MPR) [38]. An MPR consists of at least three layers of nodes: an input layer, one or more hidden layer and an output layer. Except for the input nodes, each node uses a nonlinear activation function.  
For each instance of the training set, the exact algorithm is used to obtain the arrival times at the customers. In order to overcome the drawbacks described in the following subsection, the service territory is partitioned in \(K\) zones and, for each training instance, an average zone ETA, dubbed \(ZETA_{k}\), is computed for each zone \(k=1,\ldots,K\).  
The neural network has \(k\) inputs and \(k\) outputs: the inputs are constituted by the customer distribution in the network (the number \(n_{k}\) of customers in each of the \(k\) zones); the outputs are the \(k\)\(ZETA_{k}\) estimates (\(k=1,\ldots,K\)).
# 6G Cognitive Information Theory: A Mailbox Perspective
## 4 Distributed Intelligent Network
### Communication, Computing and Caching Integration
In contrast, if an AI-based automatic-analysis method is adopted, then the network-optimization process becomes more intelligent, and this method is also conducive to the establishment of effective models with low complexity in various unmeasurable environments;
* _Mathematical representation and establishment of the model_. Through the analysis of network resources and conditions, mathematical formulas can be derived to express the index data that needs to be optimized in the future, i.e., an optimization model containing objective functions and several constraints can be developed. The optimization objectives of the model can be, for instance, throughput, spectrum utilization, user-perceived delay, energy consumption and gain, facility deployment costs, or other parameters;
* _Collaborative optimization and algorithm execution_. Collaborative optimization can be achieved using multiple performance-index parameters. Currently, the most commonly used methods for solving optimization problems are mathematical derivation-based methods, heuristic algorithms, and self-learning algorithms based on machine learning. The former adopts a mathematical derivation process, such as the Lagrange-multiplier or gradient-descent methods. These methods are ideal for solving problems involving explicit and convex objective functions. Heuristic algorithms adopt the heuristic neighborhood search process to find an optimal solution. Heuristic algorithms include the genetic algorithm, simulated annealing, particle swarm optimization, and the firefly algorithm. These algorithms do not require a derivative of an objective function. However, if the optimization complexity is sufficiently high, then they can usually provide high quality solutions to complicated optimization problems. In the case of self-learning algorithms that are based on machine learning, the game-theory technologies, such as the noncooperative game, cooperative game, and Bayesian game, have been successfully used to solve optimization problems using interactive learning with functional nodes and automatic configuration strategies;
* _Performance evaluation and decision making_. Depending on the optimization results, settings and operations can be reconfigured by the system to adjust the performance. Possible configuration actions and decisions can include computation offloading, content caching, network sharing, task scheduling, routing planning, resource allocation, etc. After the configuration is completed, data perception and analysis are conducted based on decision execution results by the system to evaluate the algorithm's performance. Then, the optimization process is repeated to keep the system in an appropriate working state. In the following, we present a brief overview of existing research covering: fusion and joint optimization of caching at different nodes, communication, and computing (3C fusion) at the forefront of network communication and transmission fields from the perspectives of traditional algorithms and AI technologies.
# Bluetooth-Based Healthcare Information and Medical Resource Management System
## 3 Materials and Methods
### System Server
#### 3.1.2 Data Processing Module  
A data processing module is set up at the back-end of the system. This module interfaces with the MQTT Broker and is responsible for the following tasks:  
1. Data packet format parsing: The data processing module will register all the Topics on this platform, so this module will receive all the messages transmitted on the Topics,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Medical Information Categories** & **Topic Names** \\ \hline Patient physiological monitoring information & Measurement/ \(\dots\) \\ \hline Medical staff and equipment positioning information & Positioning/ \(\dots\) \\ \hline Medical staff work coordination information & Message/ \(\dots\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Topic Name Categories.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Department** & **Topic Name** \\ \hline Internal medicine & Message/Medicine \\ \hline Surgery & Message/Surgery \\ \hline Family medicine & Message/Medicine/Family Medicine \\ \hline Neurosurgery & Message/Surgery/Neurosurgery \\ \hline \hline \end{tabular}
\end{table}
Table 2: Theme names of each department.  
Figure 12: Schematic diagram of hierarchical structure of naming.  
parse the JSON format of the messages after receiving them, and save the data to the database after obtaining the packet contents.
2. Generating a \"system symmetric key\": To protect the security of message transmission on this platform, the data processing module generates a \"system symmetric key\" to encrypt and decrypt messages transmitted on the platform. The data processing module will also send this symmetric key to all clients.
3. Equipment positioning information calculation: In the location tracking [2] of medical personnel and equipment, the located equipment will put the IDs of the three closest Bluetooth Beacon devices, i.e., RSSI signal strength values, into the medical information (Position Information) packet and send it back. After parsing the packet, the data processing module will obtain the coordinates of the Beacon devices from the Beacon Table based on the returned Beacon device IDs and perform the indoor positioning algorithm with the RSSI signal strength values to calculate the current coordinates of the positioned devices. Finally, the coordinates are updated to the Equipment Table in the database.
## 2 Materials and Methods
### Evaluation of Antioxidant Capacity Using the Trolox Equivalent Antioxidant Capacity (TEAC) Assay  
To analyze the antioxidant activity of a B-type trimer, B-type tetramer, B-type pentamer and B-type cinchonain trimer, the antioxidant capacity values were determined in Trolox equivalents [22]. An aqueous solution of 2.45 mM potassium peroxodisulfate and 7 mM ABTS was stored for minimum 12 h under exclusion of light at room temperature to obtain the ABTS radical cation solution. The dark blue solution was diluted with ethanol (1 + 50, \(v/v\)) to an absorbance value E of E = 0.70 \(\pm\) 0.20 at 30 \({}^{\circ}\)C and \(\lambda\) = 734 nm [23]. The four test-substances as well as the crude extract, quercetin and Trolox were dissolved and diluted in ethanol. In each well of the 96-well plates (Sarstedt AG & Co. KG, Numbrecht, Germany), 2 \(\mu\)L of the diluted sample, Trolox and blank (solvent ethanol) were submitted, and 198 \(\mu\)L of the diluted ABTS radical cation solution was added. Final concentration of Trolox was between 0 and 30 \(\mu\)M and of the tested compounds between 0 and 12 \(\mu\)M, respectively, between 0 and 100 \(\mu\)g/mL for the crude extract. Each compound was measured in triplicates, and each experiment was conducted three times. The 96-well plate was sealed off and directly analyzed in a Tecan infinite 200 PRO microplate reader (Tecan Trading AG, Mannedorf, Switzerland). After a six-minute incubation period at 30 \({}^{\circ}\)C in the plate reader, for each well the absorbance was measured at \(\lambda\) = 734 nm. The relative inhibition of absorbance to the blank is plotted against the concentrations of the test compounds and Trolox. To determine the TEAC value, the slope of the linear regression of the test compound is divided by the slope of the linear regression of Trolox.
## 2 Materials and Methods
### Statistical Analysis  
Means \(\pm\) standard deviations (SD) were calculated for each tested variable. Data were examined for normality using Shapiro-Wilk test, with no notable violations. Pearson product moment correlation (\(r\)) was used to examine linear associations between muscle architecture, CMJ and ILP force-time parameters. Pearson correlation coefficients [39] were considered as: trivial \(r\) < 0.10, small \(r\) = 0.10-0.29, moderate \(r\) = 0.30-0.49, \(r\) = 0.50-0.69 large, \(r\) = 0.70-0.89 very large and \(r\geq\) 0.90 almost perfect. Stepwise regression analysis (backward elimination) was used to investigate the effects of force-time and muscle architecture characteristics on CMJ power. Multicollinearity was examined using variance inflation factor. Intraclass correlation coefficients (ICCs) (two-way mixed effects) were used to assess test-retest reliability. Additionally, the standard error of measurement (SEM) was calculated. Statistical significance was set at \(p\) < 0.05. All statistical analyses were performed with SPSS (IBM SPSS Statistics Version 25).
## 4 Implementation
### Negotiation Algorithms
#### 4.3.4 New Path Assignment  
This approach finds a new route to reach the assigned target cell in the task allocation process putting the collision cell as an obstacle. Since finding a new route cannot be assured, this approach not always provides a solution for a collision event (see Figure 17).