esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 YOLO Algorithm and Its Improvements  
### YOLOv5 and Enhanced Model Architecture  
The YOLOv5 architecture includes five distinct network scales: YOLOv5s, YOLOv5m, YOLOv5l, YOLOv5x, and YOLOv5n. These variants are all built on the same framework but differ in the depth and width of CSPNet, leading to variations in model size and parameter count. This architecture is divided into three primary components: the backbone, the neck, and the head. The backbone is tasked with extracting image features through a series of convolutional and pooling layers. The neck network employs a feature pyramid structure [28] for top-down feature extraction, enhancing the detection capabilities for targets of various scales. The head network consists of three detection layers that handle specific tasks such as classification, detection, or segmentation, and utilizes a grid-basedanchoring strategy for object detection, particularly improving the detection of small objects. Through the collaborative functioning of these three parts, YOLOv5 achieves efficient and accurate detection of multi-scale targets while maintaining speed and flexibility, making it suitable for a wide range of application scenarios. The network structure of YOLOv5 is illustrated in Figure 1.  
This paper proposes a novel YOLOv5 model architecture. Firstly, the AFAM is introduced into the C3 modules of the backbone network, creating new C3 AFAM modules. Subsequently, the convolution modules in the backbone network are replaced with DownC downsampling modules. In the neck network structure, an additional detection layer is added specifically for small object detection, and EMA modules are incorporated from the second to fifth C3 modules. Finally, features downsampled by factors of 4\(\times\), 8\(\times\), and 16\(\times\) are combined and input into the detection head for prediction. The proposed YOLOv5 model architecture is illustrated in Figure 2.  
Figure 1: YOLOv5 Model Architecture. The YOLOv5 framework comprises three core components: the backbone, the neck, and the head. The backbone is composed of multi-scale convolutional layers, batch normalization, and Mish activation functions for feature extraction. The C3 modules enhance feature processing through convolutional and bottleneck layers. The neck employs a feature pyramid structure and upsampling, using Concat operations to integrate features across scales, thereby improving multi-scale target detection efficiency. The head network includes three detection layers designed for classification, detection, or segmentation.
## 4 Results and Evaluation
### Preparation  
#### 4.1.1 Hardware Configuration  
All experimental results were produced on an Nvidia RTX 3090 Ti GPU. To avoid errors due to hardware differences, we did not directly use the data from the comparison methods' papers. Instead, we downloaded the open-source implementations of these methods and ran them in our local environment to obtain experimental data.
## 4 Results
### Forecasts of Traffic Demand at Pula Airport  
Table 3 shows historical traffic demand data (passengers and aircraft operations) for Pula Airport in the period 2009-2019.  
Figure 10a shows graphically the forecasted values (with its confidence intervals--upper/lower limit marked with dashed lines) of the number of passengers at Pula Airport for the period 2020-2026, using the historical data from 2009 to 2019. In the figure, this is called the pre-COVID-19 scenario, which was chosen as more reliable. The evaluation criteria RMSE is 97,448, and SMAPE is 0.14 (or 14%) which indicates good fitness of the model.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Year** & **Lower Limit** & **Forecasted Number of Passengers** & **Upper Limit** & **Lower Limit** & **Forecasted Number of Aircraft Operations** & **Upper Limit** \\ \hline
2020 & 721,898 & 863,116 & 1,004,334 & 9331 & 11,247 & 13,164 \\
2021 & 673,500 & 863,584 & 1,053,668 & 8920 & 11,499  
For the purpose of presenting the robustness of this approach, Figure 10b shows an alternative forecast for the period 2023-2026 if the historical data from 2009 to 2022 were used, i.e., if post-COVID-19 data were used. This is called the post-COVID-19 scenario. The evaluation criteria RMSE is 296,977, and SMAPE is 0.60 (or 60%), which indicates poor fitness of the model.  
Figure 10: Cont.  
Figure 9(c) shows the comparison of these two scenarios. The yellow dashed line shows the trend of historical data of the pre-COVID-19 period, which lines up with forecasted values of the future period, 2020-2026, while the green dashed line represents the trend of historical data of the post-COVID-19 period (starting at the point where the pattern begins to form again--after the COVID-19 lockdown). This green trendline is increasing much faster than before the COVID-19 lockdown, as can be observed; it lines up with the upper limit forecast values of the future period, 2023-2026.  
Figure 11 shows graphically the forecasted values (with their confidence intervals--upper/lower limit marked with dashed lines) of the number of aircraft operations at Pula Airport for the period 2020-2026. The evaluation criteria RMSE is 897, and SMAPE is 0.08 (or 8%), which indicates very good fitness of the model.  
Figure 10: Forecasted number of passengers at Pula Airport **(a)** in the period 2020–2026 (pre-COVID-19 version); **(b)** in the period 2023–2026 (post-COVID-19 version); **(c)** comparison of pre-COVID-19 and post-COVID-19 trends.
# Evaluation of Water and Carbon Estimation Models in the Caatinga Biome Based on Remote Sensing
## 2 Material and Methods
### Remote Sensing Data  
The remote sensing data were obtained through MODIS products for the year 2013. The following MODIS products were used: MOD11A2 (surface temperature), MOD09A1 (surface reflectance), MOD07_L2 (air temperature and dew point), MOD05 (precipitable water), MOD03 (geolocation), MCD12Q1 (ground cover), MOD13Q1 (vegetation index), MOD16A2 (ET), and MOD17A2H (GPP) ([[https://modis.gsfc.nasa.gov/data](https://modis.gsfc.nasa.gov/data)]([https://modis.gsfc.nasa.gov/data](https://modis.gsfc.nasa.gov/data)) (accessed on 23 October 2022)).  
The spatial subset for the remote sensing estimates was 1 km around each tower, which corresponds to a 3 \(\times\) 3 window, totaling 9 pixels. We performed analyses in relation to the footprint between the towers and the remote sensing data and observed that the subsetting at 1 km showed more similar results. Although the footprint does not cover a completely homogeneous region, they are representative samples as we are monitoring areas that correspond to the same elements, with a greater proportion of this portion (greater than 90%) being the focus of our study (DC and SC).
# Optimizing the Shape of the Spinning Electrode for Needleless Coaxial Electrospinning
## 2 Design and Simulations  
### Mathematical Model of the Original Design  
The mathematical model of the spinner shown in Figure 4a consists of four essential parts: the electrode, the collector, the close vicinity of the electrode and the collector formed by air and the other space bounded in reality by the frame of the device, also formed by air. The air space was, thus, divided into two parts due to the different sizes of the network elements. The elements were too small for the inner part to obtain results with higher accuracy than for the outer part. The mesh for the entire model was made up of tetrahedrons. The electrode itself was simplified to contain essential parts only. Structural elements such as screws, inlet channels, cavities and collection containers were removed, and the model consisted only of the electrode body and the wire. A relative permittivity of 1 was set for air, with 2.3 for the HDPE electrode body. For the metal parts of the model (collector, wire, metal), the relative permittivity was set to the value \(10^{9}\). To obtain more accurate results, the network of the wire model was thickened in the monitored locations to achieve a relative accuracy of 2 percent, as shown in Figure 4b,c. The boundary conditions were chosen as follows: A voltage of +25,000 V was applied to the surface of the electrode wire, and a voltage of \(-\)25,000 V was applied to the surface of the collector. A voltage of 0 V was applied to the outer surfaces of the model since the machine frame was grounded. The electrical stiffness value was entered for all boundary conditions in the same way, namely \(10^{9}\) A/V.  
The metal wire mesh on the top of the electrode was densified, and, thus, the subtraction of electric field values was carried out in the three most important areas. Important points where the value of the electric field was monitored were located in the center of the spinning electrode, and this value was marked as the middle value (middle value of the electrical field magnitude--E\({}_{\rm mid}\)), and 40 mm from the center of the electrode to each side, marked as the edge value (edge value of the electrical field magnitude--E\({}_{\rm edge}\)). This  
Figure 4: (**a**) The concept of a mathematical model; (**b**) density increasing of mesh; (**c**) accuracy in the densified spot.  
Figure 3: The formation of corona discharges at the edges of the electrode.  
distance resulted from the fact that spinning took place over 80 mm, as mentioned in the previous chapter.  
Figure 5 shows the simulation result for the electrode of the original concept. The result shows that the value in the middle of the wire, or the middle of the area where spinning appears (Emid), is lower than in the extreme positions (Eedge), and the distribution of the electric field along the entire length of the spinning area is not homogeneous, which is undesirable and very problematic for the spinning process.
# Comparison of Various Reinforcement Learning Environments in the Context of Continuum Robot Control
## 2 Materials and Methods
### The Learning Algorithm  
The continuous action and state of the continuum robot's environment is a crucial factor in determining the ideal RL algorithm. We employ the DDPG algorithm, which permits continuous action and state spaces, to address this issue. This algorithm utilizes neural network approximations and is based on the actor-critic method. The actor is ultimately responsible for determining the best course of action in a given situation, while the critic evaluates the efficacy of their choice. A replay buffer stores past experiences and a target network helps to stabilize the learning process in order to improve the control policy over time.  
The DDPG algorithm collects information from its environment to determine the optimal course of action for controlling future actions in order to maximize rewards. The target network is used to stabilize the learning process, while the replay buffer is utilized to continuously update the actor and critic networks. The actor network computes the optimal response for a given state, forming the basis for the ultimate control strategy. The deterministic policy is trained in an off-policy way by DDPG. However, because the policy is deterministic, it is possible that the agent will not initially explore a wide enough variety of on-policy actions to collect useful learning signals. To address this problem, authors have improved DDPG policies' capacity for exploration by introducing noise to their actions during training [2]. For this study, we utilized the Ornstein-Uhlenbeck process to produce noise. The Ornstein-Uhlenbeck process produces noise that is correlated to the preceding noise, in order to avoid the noise cancelling out the overall dynamics. You can find the parameters of the Ornstein-Uhlenbeck process, which generates temporally correlated exploration, in Table 4.  
The success of the DDPG algorithm is strongly influenced by the state space, action space, and reward function of the environment. Consequently, it is essential to meticulously  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Reward** & **Attribute** \\ \hline \(r_{1}\) & squared norm of the distance \\ \(r_{2}\) & differential of distance \\ \(r_{3}\) & scaling distance \\ \(r_{4}\) & variable reward based on distance to goal state \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of the reward functions.  
design and evaluate multiple environment configurations in order to determine the most suitable control strategy for the continuum robot.
# Resource Allocation in Wireless Powered IoT System: A Mean Field Stackelberg Game-Based Approach
## 3 System Model and Problem Formulation
### Problem Formulation  
In this sub-section, we will give the optimal power control problem for the HAP and the SNs. We want to find the optimal power level for both the HAP and the SNs based on the proposed model. For the HAP, the optimal power strategy for energy transfer could be obtained by minimize the following utility function, which is:  
\[J_{0}^{N}\Big{(}p_{0},p^{N}\Big{)}=\int_{0}^{T}\biggl{\{}\mu_{0}\Big{(}x_{0}(t )-H_{0}x^{N}(t)\Big{)}^{2}+v_{0}(p_{0}(t))^{2}\biggr{\}}dt \tag{3}\]  
where \(\mu_{0}\geq 0\) and \(v_{0}>0\), are positive weighting factors representing relative importance of the objective components. The objective of the HAP is a linear combination of two components. The first component is the utility function denoted by \(\mu_{0}\big{(}x_{0}(t)-H_{0}x^{N}(t)\big{)}^{2}\), which means the available energy for transfer, compared to the mass behavior of the energy of SNs. In the first component, \(x^{N}(t)=(1/N)\sum_{i=1}^{N}x_{i}(t)\) denotes the mean field term that captures the mass behavior of the SNs. The second part is the payment earned from the SNs for energy transfer, and is denoted by \(v_{0}(p_{0}(t))^{2}\). Therefore, minimize the utility function of the HAP gives us the following objective function:  
\[p_{0}^{*}(t)=\underset{p_{0}(t)}{\text{argmin}}\ J_{0}^{N}\Big{(}p_{0},p^{N} \Big{)} \tag{4}\]  
For the SNs, we want to find the optimal power strategies for information transmission considering a large population. Then, for any specific SN, its cost function is given as follow:  
\[J_{i}^{N}(p_{i},p_{-i},p_{0})=\int_{0}^{T}\biggl{\{}\mu_{i}\Big{(}x_{i}(t)-H_{ i}x^{N}(t)\Big{)}^{2}+\
u_{i}(p_{i}(t))^{2}+\eta_{i}p_{0}(t)p_{i}(t) \biggr{\}}dt \tag{5}\]  
where \(\mu_{i}\geq 0\), \(\
u_{i}>0\) and \(\eta_{i}>0\) are positive weighting factors. The objective for any specific SN is composed by three parts. The first part is \(\mu_{i}\big{(}x_{i}(t)-H_{i}x^{N}(t)\big{)}^{2}\), which means the available energy for information transmission, compared to the mass behavior of the SNs. The second part of the objective is \(\
u_{i}(p_{i}(t))^{2}\), denotes the power cost component for information transmission. The third part of the objective is \(\eta_{i}p_{0}(t)p_{i}(t)\), which is the payment for the energy harvesting, depends on both the harvested energy and the power for information transmission. Therefore, minimize the objective for any specific SN gives us the following objective function:  
\[p_{i}^{*}(t)=\underset{p_{i}(t)}{\mathrm{argmin}}\;\int_{i}^{N}(p_{i},p_{-i},p_ {0}) \tag{6}\]  
For both the HAP and the SNs, the objective functions are formulated with the mean field game framework through the mean field term \(x^{N}(t)\). Based on the mean field term, we can analyze the IoT system with a large population. Both the HAP and the SNs can obtain their distributed equilibriums by the estimation of the mass response.
_Article_  
**Comparison of Iodide, Iodate, and Iodine-Chitosan Complexes for the Biofortification of Lettuce**  
**[PERSON] 1**\({}^{\copyright}\)**, [PERSON] 2, [PERSON] 3**\({}^{\copyright}\),  
**[PERSON] 4**\({}^{\copyright}\), [PERSON] 5**\({}^{\copyright}\),  
**[PERSON] 1**\({}^{\copyright}\), [PERSON] 1 and  
**[PERSON] 1,*\({}^{\copyright}\)**  
\({}^{1}\) Department of Horticulture, Universidad Autonoma Agraria [PERSON], Saltillo, Coahuila 25315, Mexico; [EMAIL_ADDRESS] (I.E.D.R.); [EMAIL_ADDRESS] (B.C.G.);  
[EMAIL_ADDRESS] (M.C.D.I.F)  
\({}^{2}\) Soil Science-Plant Nutrition, Colegio de Postgraduados Campus Montecillo, Carretera Mexico-Texocco km 36.5, Montecillo, Texocco, Estado de Mexico 562330, Mexico; [EMAIL_ADDRESS]  
\({}^{3}\) Department of Advanced Materials, Centro de Investigacion en Quimica Aplicada, Saltillo, Coahuila 25294, Mexico; [EMAIL_ADDRESS]  
\({}^{4}\) Department of Botany, Universidad Autonoma Agraria [PERSON], Saltillo 25315, Mexico; [EMAIL_ADDRESS]  
\({}^{5}\) CONACYT-UAAAN, Universidad Autonoma Agraria [PERSON], Saltillo 25315, Mexico;  
[EMAIL_ADDRESS]  
\({}^{*}\) Correspondence: [EMAIL_ADDRESS]; Tel.: +52-844-411-0303  
Received: 24 February 2020; Accepted: 27 March 2020; Published: 31 March 2020  
**Featured Application: Use of Cs-I complexes as material to biofortify soilless crops with iodine. under conditions of protected agriculture.**  
**Abstract:** Iodine is an essential trace nutrient for humans; its deficit can affect motor and cognitive development. Biofortifying crops with iodine is a way of promoting the adequate intake of this element. The uses of chitosan-iodine complexes for crop biofortification have not been previously studied. The present work evaluated the effects of KIO\({}_{3}\) and KI salts, chitosan-KIO\({}_{3}\) complex (Cs-KIO\({}_{3}\)), and chitosan-KI complex (Cs-KI) application on lettuce, with a chitosan-only treatment as a control and water as the absolute control. Each treatment involved the application of 0, 5, and 25 mg I kg\({}^{-1}\) soil applied before transplanting or 25 mg I kg\({}^{-1}\) soil applied as split doses of 12.5 mg kg\({}^{-1}\), once immediately before transplanting and the second application 15 days later. Single application of Cs-KIO\({}_{3}\) at 5 and 25 mg I kg\({}^{-1}\) increased lettuce biomass while the split-dose application (SDA) of Cs-KI (25 mg I kg\({}^{-1}\)) led to a decrease in biomass. Maximum accumulation of iodine in lettuce was observed after the application of KIO\({}_{3}\) (25 mg I kg\({}^{-1}\)) in two parts. This study shows that the use of chitosan complexes, especially Cs-KIO\({}_{3}\), may be a viable alternative for crop biofortification with iodine without affecting crop yields.  
**Keywords:** trace element; biopolymer; iodine; complex; biofortification
# The Influence of Different Cleaning Protocols on the Surface Roughness of Orthodontic Retainers
## 2 Materials and Methods
### Sample Size  
Considering the study design and normality assumption, comparison of the surface roughness of 2 brands of TORs before and after 5 different cleaning protocols indicated F test groups for sample size analysis: ANOVA for repeated measures, within-between interactions, effect size f (0.25), \(\alpha\) err prob. (0.05), and power (0.8) = 80. Every TOR brand should have 40 samples, 8 in each cleaning protocol group for achieving the appropriate power. A priori statistical analysis was performed to ensure adequate statistical power in the study. This analysis was conducted using the program Statistica (TIBCO(r) Statistica(tm) Version 14.0.0.15, Palo Alto, CA, USA). The main objective was to determine the difference in surface roughness changes between the two investigated PET-G brands.
# LIF-M: A Manifold-Based Approach for 3D Robot Localization in Unstructured Environments
## 3 System and Sensor Models
### Definition of Point Cloud Model  
The point cloud data structure \(O_{t}=\{a_{0},\ldots,a_{N}\}\) from a lidar sensor (with \(t\) representing data for each frame of the lidar) typically consists of beams \(N_{t}\) and point cloud data \(P_{t}\) for each beam:  
\[O_{t}=(N_{t},P_{t}). \tag{11}\]  
To provide precise observation solutions for the overall algorithm in sparse or feature-scarce environments, in this study we opt for CUDA-based D2D-NDT (a distribution-to-distribution matching algorithm) to process the point cloud data. Unlike the single-point processing of ICP, D2D-NDT discretizes the point cloud map and real-time lidar point cloud into Gaussian distributions. It then utilizes the Newton optimization method to find the shortest distance in terms of probability density, resulting in the corresponding transformation matrix \(T\).  
For two sets of point clouds \(S_{pt}\), \(H_{pt}\in O_{t}\), the D2D-NDT formulation can be simplified to the form of Equation (12), where \(p_{1}\), \(p_{2}\) is the parameter factor and \(m_{ij}\), \(C_{i}\) respectively represent the difference in means and the covariance between the distinct point clouds.  
\[\Theta=\sum_{i=1,j=1}^{S,H}-p_{1}\exp(-\frac{p_{2}}{2}m_{ij}^{T}(R^{T}C_{i}R+C _{i})^{-1}m_{ij}) \tag{12}\]  
Due to the potential issue of local minima, \(\Sigma\) is incorporated to aggregate the probability densities of multiple grids. This enhances the robustness of the point cloud registration process, enabling greater resilience in the face of challenges posed by local minima.
## 4 Methodology
### PIN Requirements  
PIN imposes two requirements on the information that is sent through the network:  
* Information is divided into packets that could either be processed independently from the others (_i.e._, processed on a PbP basis) or be processed as a group (_i.e._, processed on a BbB basis) that can be considered independent from the others;
* Information processing can be divided into phases.  
In addition, there are two more requirements for the nodes in the network:  
* Network nodes can perform some processing;
* The nodes must have sufficient storage capacity to process at least one packet (in the case of a PbP basis) or at least a full block (in the case of a BbB basis).
## 1 Introduction
This is the premise for the development of a new, innovative system that reduces fuel consumption and quantitative exhaust gas emissions by adapting the drive rotational speed to current operating conditions. The concept of such a system in machinery intended for forestry is a maintenance-free and adaptive control system of a wood chipper drive with low-power spark-ignition engines [22]. Such a system has two idle working states. In the first state (1), high rotational speed allows for reaching either maximum power or maximum torque (like in the classic control method). The second (2) idle working state enables automatic reduction of engine rotational speed given that the internal combustion number of cycles is reduced. As a result, this leads to both decreased fuel consumption and quantitative exhaust gas emissions. The respective working states are activated maintenance-free via optical sensors located in the feed channel. The sensor used in the discussed solution is triggered by the detection of objects in the feed channel. When this happens, the rotational speed of the idling engine increases (the first state (1) is activated). After the chipping process is complete, the rotational speed is reduced to low (the second (2) state is activated). This concept does not affect the operating parameters of the drive unit during the chipping process (the third state (3)). This paper presents the results of fuel consumption tests of a cylindrical chipper driven by a low-power spark-ignition internal combustion engine with three different configurations. The first configuration (A) is an engine with a carburetor fuel feed system utilizing the classic control concept. The second (B) is an engine with an injection system that uses the classic control concept. The third (C) is an engine with an injection system fitted with an adaptive control system. Throughout the research, various active and idling operation times were considered. The purpose of this paper is to measure and demonstrate the differences in fuel consumption depending on the system's design and the adopted idea of chipper drive control.