esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Sensor Precipitation Estimation from Space: Data Sources, Methods and Validation
## 3 Physical Basis of Multi-Sensor Precipitation Estimation
### Regression-Based Method  
The regression-based approach assumes a relationship between PMW estimates and IR channels. The general description of PPMW can be formulated based on the following equation:  
\[P_{\text{PMW}}=f_{6}(Tb_{\text{IR}}) \tag{12}\]where \(Tb_{\text{IR}}\) is derived from an IR channel or multiple channels, and \(f_{6}(Tb_{\text{IR}})\) represents the relationship between \(P_{\text{PMW}}\) and \(Tb_{\text{IR}}\). More specifically, a regression-based technique can be described in which PMW-derived precipitation is used to adjust the basic relationship between IR Tb and surface rain rates.  
[PERSON] and [PERSON] [129] employed a multiple linear regression approach to establish the relationship between SSM/I-based precipitation estimates and Tb10.7 values for all GOES pixels within a given SSM/I pixel. The IR Tb data were divided into distinct intervals, and separate equations were developed for each interval to enhance the technique's capability to handle nonlinearity. [PERSON] et al. [130] proposed the microwave/infrared rain rate algorithm (MIRRA) to calibrate both a minimum T10.7 rain/no-rain threshold and a regression-based relationship with the rain rate by regressing against the minimum T10.7 value. The SCaMPR algorithm proposed by [PERSON] et al. [131] offers a self-calibrating approach for multivariate precipitation retrieval, specifically designed for short-term flash flood forecasting applications. This algorithm enables real-time calibration of an optimal rain-rate predictor (e.g., GMSRA) against PMW precipitation through linear regression, allowing for the identification of raining pixels and the selection of optimal predictors to delineate rain areas separately.  
\[P_{\text{PMW}}=\text{a}\times f_{7}(Tb_{\text{IR}})+\text{b} \tag{13}\]  
where \(f_{7}(Tb_{\text{IR}})\) is a precipitation predictor calculated from the difference between IR channels (\(Tb\), 6.9-12.0 \(\upmu\)m), such as GPI, GMSRA, and the autoestimator [132]. In essence, this approach is similar to the multiple linear regression method. [PERSON] et al. [133; 134; 135] further added some modifications (e.g., an additional PMW source) to SCaMPR. The regression-based method has not been used to obtain global precipitation thus far. At present, this method is often used to obtain regional precipitation estimates.
# Multi-Resolution SPH Simulation of a Laser Powder Bed Fusion Additive Manufacturing Process
###### Abstract  
This paper presents an efficient mesoscale simulation of a Laser Powder Bed Fusion (LPBF) process using the Smoothed Particle Hydrodynamics (SPH) method. The efficiency lies in reducing the computational effort via spatial adaptivity, for which a dynamic particle refinement pattern with an optimized neighbor-search algorithm is used. The melt pool dynamics is modeled by resolving the thermal, mechanical, and material fields in a single laser track application. After validating the solver by two benchmark tests where analytical and experimental data are available, we simulate a single-track LPBF process by adopting SPH in multi resolutions. The LPBF simulation results show that the proposed adaptive refinement with and without an optimized neighbor-search approach saves almost 50% and 35% of the SPH calculation time, respectively. This achievement enables several opportunities for parametric studies and running high-resolution models with less computational effort.  
additive manufacturing; LPBF; numerical simulation; SPH; particle refinement Article
# Medical Image Segmentation Network Based on Dual-Encoder Interactive Fusion
## 4 Experiments and Results
### Datasets  
#### 4.2.1 Synapse Multi-Organ Segmentation  
The Synapse multi-organ dataset [27], originating from the MICCAI 2015 Multi-Atlas Abdomen Labeling Challenge, consists of 30 abdominal CT scans. Each scan is annotated to identify eight distinct organs: the stomach, spleen, pancreas, liver, right kidney, left kidney, gallbladder, and aorta. Importantly, each CT scan comprises between 85 and 198 axial slices, totaling 3779 slices across the dataset. In alignment with the data configuration used by TransUnet [20], the Synapse dataset was divided into 18 training cases (encompassing 2211 axial slices) and 12 test cases.
# A Systematic Parameter Analysis of Cloud Simulation Tools in Cloud Computing Environments
## 2 Highly Prominent and Extensible Categories of Cloud Simulators
### Prominent Cloud Simulators
#### 2.1.4 iFogSim  
[PERSON] and [PERSON] [21] suggest that iFogSim is a popular fog computing simulator. iFogSim has been established, and all CloudSim elements and features have been incorporated into iFogSim. The research community has overwhelmingly supported iFogSim, which can identify various performance parameters such as energy consumption, latency, response time, cost, and so on. It also allows for the replication of research in a stable environment. It employs a stream processing model in which sensors continuously generate data that fog devices evaluate, allowing users to quickly compare the results of various placement algorithms. [PERSON] [22] suggested that iFogSim is one of the most popular CloudSim simulators. iFogSim can model IoT and fog conditions to calculate latency, network congestion, and device energy consumption. However, a simplified approach to modeling latency workloads is used. iFogSim will simulate a three-layer IoT fog system in various configurations. Although the simulator is used to test a variety of workloads, it fails in three-layer IoT fog systems because it fails to accurately model the computation schedule when processing items. It is well known that multi-core CPU efficiency interactions between device components do not expand sequentially. [PERSON] and [PERSON] [17] indicated that there are also more advanced iFogSim extensions. MyiFogSim (available on GitHub) helps mobile users handle VM migration. A key feature is the ability to replicate user mobility and its relationship to the VM migration policy. The iFogSimWithDataPlacement tool (available on GitHub) is another extension that proposes a method for data management studies into how data is stored in a fog system. For data location, it also analyzes latency, network utilization, and energy expenditure. Unfortunately, many simulators (such as Fog-Torch, OPNET, or SpanEdge) are designed to represent a single aspect of computing, making them less productive for general simulations.
## 3 Case Study: Evaluation of a Passive Back-Support Exoskeleton
### Stage II: Detailed Experimental Planning
#### 3.2.2 Tools and Equipment  
The present protocol allows for the analysis of any passive exoskeleton for lumbar support. The task designed for the experimental study simulates a palletizing activity, involving the handling of twelve cardboard boxes (44 \(\times\) 31.5 \(\times\) 28.5 cm) each weighing 10 kg. The instruments chosen for data collection include a set of sensorized baropodometric insoles, a pulse oximeter, a sensorized wristband, and a motion capture garment. Specifically, the FlexInFit by Sensor Medica [38] insoles are adopted to monitor the pressure of the feet on the ground. These 0.3 mm thick insoles are worn inside the shoes and contain 214 resistive sensors, with a data acquisition frequency of up to 100 Hz and data transmission via Bluetooth. The insoles communicate with the freeStep biomechanical and  
Figure 4: Anthropometric measures collected according to the ISO 7250-1:2017 [37] for each user participating in the experimental study: height (L), shoulder width (A), wrist circumference (B), forearm circumference (C), forearm length (D), thigh circumference (E), arm circumference (F), chest circumference (G), hip width (H), and waist circumference (I).  
postural analysis software by Sensor Medica [39]. A standard pulse oximeter is adopted to monitor the blood oxygen saturation throughout the test. The physiological parameters, such as the pulse rate, the HRV, the EDA, and skin surface temperature, are monitored with the EmbracePlus wristband by Empatica [40].  
The motion capture garment for posture assessment is the second generation of the Pivot Yoga suit by Turingsense EU Lab [41]. This sensorized garment, consisting of a long-sleeved shirt and pants, provides full-body, real-time 3D joint kinematics. It detects any variations in postural position during the task execution. The garment communicates via Wi-Fi with the Gemma software 1.0., which manages communication and calibration processes, serves as a graphical user interface for visualizing the reconstructed manikin, and allows for the export of the complete set of recorded joint angles in a.csv file.  
Figure 5 shows the technological setup of the study with the motion capture garment, sensorized baropodometric insoles, the sensorized wristband, and the passive back-support exoskeleton IX BACK AIR [42] worn by the user.
## 2 Literature Review
### Modern Applications and Enterprise Management in the Era of Industry 4.0
#### 2.4.2 Natural Language Processing in an Industry 4.0  
Figure 4 below explains how natural language processing is applied to the connected device that enables communication in business to help advance business decision-making. As days go by communication is much more promoted to expand beyond human-human. With the advancements in technology, NLP can link the business management system with a modern system. The linguistic nature of things enabling understanding of customer behavior with the help of internet services and NLP is very important to modern business. Today, emotional intelligence is capable to uses linguistic attributes to provide insight into how customers react and think about a company's products.  
NLP organizes unstructured data for businesses by analyzing its relevance, correlation, and semantic meaning. NLP helps connect devices that allow and reorganize proper  
Figure 4: Natural language processing for industrial use.  
decision-making with the help of text, emails, SMS, audio, and video conferences using ERP network systems tailored to businesses.  
Subject extraction as a tool of NLP helps provide insights for effective content creation of products and services of a company to customers. The option of subject extraction helps provide exact content to potential customers and in return boost sales. NLP provides sentiment analysis determinant tools for businesses. The sentiment analysis determinant helps businesses to understand if one communication method receives different feedback than another. The sentiment analysis determinant in business is part of the KPI that helps to evaluate how well a product is doing [60]. Audience identification for targeted messaging is an NLP tool used as part of ERP in nowadays business [61, 62]. Companies use glean themes and keyword categories phrases to learn more about what clients are talking about their products, where the gaps in their products are, and how they can address and fix them. NLP replaces the manual processes of financial institutions by employing unstructured data in a more usable format [63]. For instance, NLP is automation that helps capture earnings calls, management presentations, and acquisition announcements.  
**Four ways NLP applications advance business.**  
The following points are some of the steps used by companies to enhance their activities.  
* **Lead capturing**. The use of chatbots to capture customers' insight with the help of auto-responder.
* **Sentiment Analysis**. NLP applications of listening skills and records help businesses to track what the audience perceives about business content.
* **SEO ranking**. AI text generators designed for optimization of keywords best support search engine crawls and indexes.
* **Audience reach**. The use of voice search help users targets the right content faster. The use of voice search gives access to everyone no matter their situation. Thanks to voice search people with disability can now perform search queries with no problem.
# Dynamic Optimization Using Local Collocation Methods and Improved Multiresolution Technique
## 5 Conclusions  
An improved multiresolution technique has been developed in this paper for solving dynamic optimization problems. The technique is based on local direct collocation methods and an improved multiresolution-based mesh refinement method. The technique is demonstrated on three chemical process optimization problems of different complexity. When compared with existing multiresolution techniques, it is found that the method of this paper generates a smaller mesh size because it removes most of the redundant mesh points in the constant control regions. Furthermore, the method of this paper is more convenient to use because it overcomes the dyadic limitations and ensures that the optimization starts with the coarsest mesh. Comparison with the _hp_-adaptive pseudospectral method shows that the method of this paper generates a much smaller mesh size and requires fewer mesh iterations, while the solutions are of comparable accuracy. The present study also demonstrates that chemical process optimization problems can be solved accurately and efficiently by the local collocation methods and the improved multiresolution technique.  
The present method can only remove the redundant mesh points in the constant control regions. Future work may focus on removing the redundant mesh points in the non-constant control regions. The data compression algorithms [45] have great potential on this topic.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Objective 1** & \
\begin{tabular}{c} **Mesh** \\ **Iterations** \\ \end{tabular} & **Mesh Points** &
\begin{tabular}{c} **Max** \\ **Resolution** \\ \end{tabular} & **CPU Time (s)** \\ \hline The proposed method & 4768.313612 & 8 & 101 & 2561 & 10.7 \\ The proposed method & 4768.313475 & 8 & 162 & 2561 & 21.5 \\ (ignoring MRA-III) & 4768.313425 & 13 & 309 & — & 21.1 \\ \hline \hline \end{tabular}  
* The objective calculated using an indirect multiple shooting method is \(-\)4768.314 [5].  
\end{table}
Table 1: Mesh refinement results for the semi-batch reactor problem using different methods.  
Figure 12: Control solutions for the Williams-Otto semi-batch reactor problem using the _hp_-adaptive pseudospectral method [35]: **(a)** Feed flow rate \(F_{B,n}(t)\); **(b)** cooling water temperature \(T_{W}(t)\).  
**Author Contributions:** Conceptualization, J.Z.; Methodology, J.Z. and T.S.; Writing--Review & Editing, J.Z. and T.S.; Supervision, Project Administration and Funding Acquisition, J.Z.  
**Funding:** This research was funded by the Fundamental Research Funds for the Central Universities of China under grant number NS2016087.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Preamble-Based Noncoherent Synchronization in Molecular Communication: A Machine Learning Approach
## 2 Concentration-Encoded Molecular Communication
### Channel and Receiver  
Channel modeling is necessary for data transmission in the communication process. Especially in environments with limited resources like MC, it is important to consider the influence of the channel during transmission and reception. The MC channel has been described by the channel quantum response, and can be expressed as follows [33]:  
\[C(t)=\frac{1}{(4\pi Dt)^{\frac{3}{2}}}e^{-\frac{t^{2}}{4 Dt}}, \tag{2}\]  
where \(D\) represents the diffusion coefficient, which quantifies the rate at which molecules spread through the medium, and \(r\) signifies the distance between the transmitter and the receiver. The signal received at the receiver is a noisy version of the transmitted signal. Denoting the received signal as \(U(r,t)\), it can be expressed as [27]  
\[U(r,t)=\int_{0}^{T_{s}}\frac{Q(\tau)}{4\pi D(t-\tau)^{\frac{3}{2}}}\cdot e^{- \frac{t^{2}}{4D(t-\tau)}}\,d\tau, \tag{3}\]  
where \(\tau\) represents the propagation delay, and \(T_{s}\) is the observation time during which the signal is monitored.  
When molecules reach the receiver, they collide with receptors at the receiver and trigger a very small chemical pulse. This process allows for the detection of the concentration of received molecules. This process is known as the LRBP [26; 27]. Some of the molecules that do not collide with receptors remain as residual molecules, generating noise and ISI. To calculate the average number of chemical reactions between the molecules arriving at the receiver and the receptors, a propensity function is needed. Assuming that \(a(U(r,t))dt\) represents the probability of a reaction occurring at the receptor, \(a(U(r,t))\) represents the propensity function, and can be expressed as follows [28]:  
\[a(U(r,t))=\delta C_{R}U(r,t), \tag{4}\]  
where \(\delta dt\) represents the probability of random reaction between receptors and molecules during the time interval \([t+dt]\), where \(C_{R}\) denotes the concentration of receptors on the surface of the receiver. The propensity function can be approximated as a normal distribution with equal mean and variance when dt is very small and within the given [\(t\), \(dt\)] interval, molecules are assumed to be independent of each other, and sufficient chemical reactions have occurred.
## 6 Actor-Critic Methods
### Deep Deterministic Policy Gradient
#### 6.1.5. DDPG for Path Planning in Cluttered Indoor Spaces  
In the study by [PERSON] et al. [31], incremental training, parameter transfer, and a hybrid PRM+TD3 planner are introduced to improve development efficiency, convergence, and generalization in indoor mobile robot path planning. The training process employs an incremental strategy starting in a lightweight 2D simulation environment and gradually transitioning to a complex 3D environment. The training in the 2D environment uses simpler simulations to debug parameters and optimize the reward function before transferring the network to the 3D environment. The transfer of parameters follows:  
\[\theta_{\text{initial}}\leftarrow\theta_{2D}, \tag{78}\]  
where \(\theta_{\text{initial}}\) denotes the initial weights transferred from the 2D environment.  
The hybrid planner PRM+TD3 combines the Probabilistic Roadmap (PRM) algorithm for global path planning and TD3 for local control. PRM generates intermediate waypoints:  
\[W=\{w_{1},w_{2},\dots,w_{n}\}, \tag{79}\]which decompose the global path into local sub-paths for TD3 to navigate.  
TD3 enhances DDPG by addressing overestimation bias using two Q-networks and delayed updates. The Q-value update is as follows:  
\[y(r,s^{\prime},d)=r+\gamma(1-d)\min_{i=1,2}Q_{\phi_{\mathrm{target}}}(s^{\prime},\pi r^{\prime}(s^{\prime};\theta^{\prime})), \tag{80}\]  
where \(d\) is the termination flag, \(r\) is the reward, and \(\pi^{\prime}(s^{\prime};\theta^{\prime})\) is the target policy.  
The critic networks are updated by minimizing the loss:  
\[L(\phi_{i})=\frac{1}{|B|}\sum_{(s,a,r,s^{\prime},d)\in B}\big{(}Q_{\phi_{i}}(s,a)-y(r,s^{\prime},d)\big{)}^{2},\quad i=1,2. \tag{81}\]  
The actor network is updated using the policy gradient:  
\[\
abla_{\theta^{\pi}}J=\mathbb{E}_{\mathrm{s}\sim B}\Big{[}\
abla_{a}Q_{\phi_ {1}}(s,a)\big{|}_{a=\pi(s;\theta^{\pi})}\
abla_{\theta^{\pi}}\pi(s;\theta^{ \pi})\Big{]}. \tag{82}\]  
Target networks for both actor and critic are updated softly:  
\[\theta^{\prime}\leftarrow\tau\theta+(1-\tau)\theta^{\prime},\quad\phi^{\prime }\leftarrow\tau\phi+(1-\tau)\phi^{\prime}, \tag{83}\]  
where \(\tau\) controls the rate of update.  
The reward function is designed to incentivize collision-free navigation and goal-reaching:  
\[r(s_{t},a_{t})=\begin{cases}r_{\mathrm{goal}},&\text{if }d_{t}<d_{\mathrm{ threshold}},\\ r_{\mathrm{collision}},&\text{if }d_{\mathrm{min}}<d_{t},\\ \alpha(d_{\mathrm{goal}}-d_{t}),&\text{otherwise},\end{cases} \tag{84}\]  
where \(d_{t}\) is the distance to the target, \(d_{\mathrm{threshold}}\) is the goal proximity threshold, and \(\alpha\) is a scaling factor.  
This approach enhances convergence, reduces training time, and improves generalization to complex environments. See Algorithm 5 for a step-by-step pseudocode of the proposed method.
## 2 Materials and Methods
### Determining Convection Coefficients and Simulation Boundary Conditions
In the Solidworks software used in the work carried out, the \(h_{r}\) coefficient is determined from the value of \(\varepsilon\). If the value of the \(h_{r}\) coefficient is required, Equation (6) [41] can be used:  
\[h_{r}=\varepsilon\cdot\sigma_{c}\cdot\left(T_{S}+T_{a}\right)\left(T_{S}^{2}+ T_{a}^{2}\right) \tag{6}\]  
where \(\sigma_{c}\) is the Stefan-Boltzmann constant equal to 5.67 \(\times\) 10\({}^{-8}\) (W\(\cdot\)m\({}^{-2}\cdot\)K\({}^{-4}\)), \(T_{S}\) is the surface temperature (K), and \(T_{a}\) is the air temperature (K). In addition to the value of the \(h_{r}\) coefficient, the value of the coefficient \(h_{c}\) must be determined. This coefficient can be determined from Equation (7) [42]:  
\[h_{c}=\frac{Nu\cdot k}{L} \tag{7}\]  
where \(h_{c}\) is the convection coefficient of flat surfaces, \(Nu\) is the Nusselt number (-), and \(L\) is the characteristic length in meters (for a vertical wall, it is the height). The Nusselt number for forced convection is described by Equation (8) [43]:  
\[Nu=\mathrm{C}\cdot\mathrm{Re}^{a}\cdot\mathrm{Pr}^{b} \tag{8}\]  
where Nu (-) is the Nusselt number, Pr (-) is the Prandtl number, Re (-) is the Reynolds number, and \(C,a,b\) are coefficients that depend on surface orientation (horizontal/vertical). The values of \(C,a,\) and \(b\) coefficients are also dependent on the type of airflow. For flat horizontal surfaces, for laminar flow, Equation (8) takes the form of Equation (9) [44]:  
\[Nu=0.664\cdot\mathrm{Re}^{0.5}\cdot\mathrm{Pr}^{1/3} \tag{9}\]  
Instead, for turbulent flow, Equation (8) takes the form of Equation (10) [44]:  
\[Nu=0.0369\cdot\mathrm{Re}^{0.8}\cdot\mathrm{Pr}^{1/3} \tag{10}\]  
The Prandtl number can be obtained using Equation (11) [45]:  
\[P_{r}=\frac{c\cdot\eta}{k} \tag{11}\]  
where \(c\) is the specific heat of air equal to 1005 (J\(\cdot\)kg\({}^{-1}\cdot\)K\({}^{-1}\)) in 293.15 (K), and \(\eta\) is the dynamic air viscosity equal to 1.75 \(\times\) 10\({}^{-5}\) (kg\(\cdot\)m\({}^{-1}\cdot\)s\({}^{-1}\)) in 273.15 (K). The Reynolds number can be obtained from Equation (12) [46]:  
\[Re=\frac{V\cdot\rho\cdot L}{\eta} \tag{12}\]  
where \(V\) (m/s) is the average linear velocity of the fluid flow, and \(\rho\) is the air density equal to 1.21 (kg\(\cdot\)m\({}^{-3}\)) in 273.15 (K). The roller surface value of the convection coefficient \(h_{cr}\) can be obtained from Equation (13) [47]:  
\[h_{cr} =5.9\cdot d^{0.05}\cdot(\frac{0.5\cdot V^{2}}{d}+\frac{9.81}{\frac{T_{a} }{T_{S}-T_{a}}+0.5})^{0.35} \tag{13}\]  
where \(d\) (m) is the dimension of the roller.