esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results and Discussion
### Practical Sample 1: Forecasting the Revenue of Thailand's Medical Tourism Industry
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Actual Values \({}^{\ast}\)**} & \multicolumn{4}{c}{**Relative Percentage Error (\%)**} \\ \cline{3-6}  & & **DGM(2,1)** & **MDGM(2,1)** & **FDGM(2,1)** & **FMDGM(2,1)** \\ \hline
2005 & 754 & 0 & 0 & 0 & 0 \\
2006 & 867 & 7.63 & 9.55 & 19.60 & 0.04 \\
2007 & 978 & 6.48 & 8.74 & 17.38 & 34.72 \\
2008 & 1014 & 5.08 & 19.76 & 16.76 & 0.03 \\
2009 & 938 & 34.93 & 33.33 & 18.12 & 36.20 \\
2010 & 1200 & 27.58 & 31.66 & 14.16 & 0.02 \\
2011 & 3200 & 41.15 & 25.99 & 5.31 & 3.54 \\
2012 & 3700 & 36.48 & 23.37 & 4.59 & 6.12 \\
2013 & 4310 & 31.10 & 19.84 & 3.94 & 2.63 \\ \hline \multicolumn{6}{c}{_RMSE_} & 835.22 & 555.27 & 169.92 & 196.09 \\ \hline \multicolumn{6}{c}{_MAPE_ (\%)} & 23.80 & 21.53 & 12.48 & 10.41 \\ \hline \multicolumn{6}{c}{Precision rate (\%)} & 76.20 & 78.47 & 87.52 & 89.59 \\ \hline \multicolumn{6}{c}{Forecasting grade} & Unqualified & Unqualified & Qualified & Good \\ \hline \multicolumn{6}{c}{\({}^{\ast}\) Data from Ministry of Public Health, Thailand, KasiKon Research Center (USD million).} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Forecasting results for the revenues of Thailand’s medical tourism industry.
# Prediction of Horizontal in Situ Stress in Shale Reservoirs Based on Machine Learning Models
## 2 Experimental Datasets and Methods
### Experimental Methodology
#### 2.2.3 RF-RFE Algorithm  
In feature selection, identifying which features most significantly impact horizontal in situ stress is crucial for developing a prediction model. In well logging curve interpretation, different feature values can be influenced by various potential factors, affecting the final interpretation. To select the key factors influencing horizontal in situ stress, this study combines Recursive Feature Elimination (RFE) with a Random Forest Regression (RF) model [38] to further screen and optimize features. This process removes redundant features and enhances the model's generalization ability. The Random Forest not only offers accurate predictions but also aids in understanding the relative importance of each feature in in situ stress prediction.
# Evaluating V2X-Based Vehicle Control under Unreliable Network Conditions, Focusing on Safety Risk
## 1 Introduction
[11] investigated the impact of On-Board Unit (OBU) placement, relative velocity, and relative altitude on V2X communication. They found OBU placement to have a significant impact because the transmission distance is greatly reduced when messages have to go through different obstacles inside the vehicle. A higher relative velocity results in decreased time when transmission can happen. In the case of altitude, based on their study, it could also provide a constraint to DSRC communication in terms of transmission distance. [PERSON] et al. [12] researched a congestion control scheme that takes into consideration the QoS parameters. In their paper, they focused on the vehicle density effect on their two proposed models. In the first model, they wanted to keep the update rate at the cost of a reduced range, and in the other, they wanted to protect the range at the cost of the update rate. The results showed that V2X applications can be given a degree of freedom in choosing the QoS model. [PERSON] et al. [13] investigated the interferences' effect on DSRC-based communication. They proposed an interference-rejecting scheme, considering vehicle speed, Signal-to-Interference and Noise (SINR), and other channel conditions. This method resulted in a higher SINR performance in both low- and high-velocity cases. In a research conducted by [PERSON] et al. [14], they studied the network-level and application-level reliabilities in connected vehicular networks. They investigated different path-loss models in a moderate transmission distance. To be able to mitigate the signal attenuation effect, they proposed a feedbackless relay method, which selects the farthest vehicle as a relay vehicle and rebroadcasts the message. This method is able to increase the network- and application-level reliabilities. Cooperative driving is a promising way to improve traffic safety, but an unreliable network could lead to severe harm. [PERSON] et al. [15] researched this topic, especially focusing on the age of information. They proposed a solution called safety time function to provide the duration available for a vehicle to react even in dense traffic situations. [PERSON] et al. [16] studied the cooperative adaptive cruise control (CACC) as an advanced function for vehicle platooning that heavily relies on network performance. They proposed a control solution for fatal wireless communication failure. They assumed that, in degraded network conditions, the CACC transitions only to an sensor-based ACC. They proposed a solution during the transition phase to decrease jerk and thereby improve safety.
# Visual Tracking Using Wang-Landau Reinforcement Sampler
## 1 Introduction
### Our Contributions  
* We propose a new Q-learning algorithm, augmented by Wang-Landau sampling, in which the exploitation and exploration abilities of reinforcement learning are balanced in searching target states. Conventional Q-learning methods typically select an action that maximizes a current action-value for exploitation, whereas the methods choose an action at random with a probability \(\epsilon\) for exploration. However, it is nontrivial to determine the optimal \(\epsilon\), which can balance exploitation and exploration abilities. In contrast, the proposed method can balance between the exploitation and exploration processes based on the Wang-Landau algorithm. The method adapts to control the randomness of policy, using statistics on the number of visits in a particular state. Thus, our method considerably enhances conventional Q-learning algorithm performance, which also enhances visual tracking performance.
* We present a novel visual tracking system based on the Wang-Landau reinforcement sampler. We exhaustively evaluate the proposed visual tracker and numerically demonstrate the effectiveness of the Wang-Landau reinforcement sampler.
* Our visual tracker shows state-of-the-art performance in terms of frames per seconds (FPS) and runs in realtime because our method contains no complicated deep neural network architectures.  
The remainder of this paper is organized as follows. In Section 2, we introduce relevant visual tracking algorithms. We explain reinforcement learning-based visual tracking in Section 3.2 and enhance the proposed visual tracking using Wang-Landau sampling in Section 3.3. In Section 4, we evaluate visual tracking algorithms quantitatively and qualitatively. In Section 5, we conclude the paper.
## 5 Field Testing  
### Lower-Level Controller Verification  
#### 5.1.1 Steering Controller Verification  
The articulated angular speed tracking experiments are performed under no-load and full-load conditions to verify the proposed steering controller. The reference articulated angular speed is a sinusoidal curve with a period of \(T=8\) s and an amplitude of 8 degrees. The result indicates that the steering controller enables the vehicle to accurately track the desired articulated angular speed, as shown in Figure 13.
## 4 Approach
### HiL Interface
#### 4.4.2 OMNeT++ VICINITY Adapter  
To allow communication between VICINITY and simulated objects, independent of the network used inside the simulation, a simulated access-point is used. This access-point acts as a router between VICINITY and the simulation, i.e., relaying and protocol-conversion of requests and responses. The access point module VicinityAccesspoint communicates with VicinityAdapter, a subclass of SocketServer, which runs the socket server thread to handle requests from VICINITY. As for the concept of synchronization, as shown in Figure 3, VicinityAdapter has the role of the socket server, while VicinityAccesspoint is the handler module. In the following the functionality of the two modules is shown:  
* VicinityAdapter: The VicinityAdapter is initialized with a port to listen on. When started by calling startAdapter(), a future-object is returned. As soon as a request is received, an object of type RequestStruct is made available, which holds all relevant data of the request. Additionally, promise-object is passed, which is used to set a ResponseStruct. In this struct, all data relevant for the response is transmitted, together with a new promise-object for the next incoming request.  
Figure 3: Simulation- and server-thread synchronization.  
VicinityAccesspoint: The VicinityAccesspoint module is responsible for routing adapter-requests into the simulation. Additionally, VicinityAccesspoint has to provide object-discovery data when requested by the adapter. To collect all necessary data for object-discovery, the access-point broadcasts a message of type ObjectDiscoveryPkt (see Figure 4) with isRequest=true to all connected modules. Every module which shall be accessible via VICINITY then responds with an ObjectDiscoveryPkt message with isRequest=false and its own object-data JSON in object]son. The access-point then composes a JSON object with all collected ObjectDiscoveryPkt responses and serves it when requested by the adapter. When a request to set/read a property or action is incoming, the access-point forwards it to the corresponding module with a VnetPkt message. The receiving module then processes the request and responds back to the access-point with another VnetPkt message. After the message is processed, the access-point generates a response for the adapter and the request is completed. If an addressed module does not respond in a given time, a timeout occurs and the access-point responds to the adapter with VININITY_RESPONSE_NOT_FOUND, which finishes the request with an error-code.
## 1 Introduction
### Stress Detection and Monitoring
[12] presented a physiochemically sensitive e-skin designed to capture a wide range of stress-related biomarkers, including sweat biochemical changes, providing an innovative multimodal platform for monitoring stress responses with high sensitivity. Such technologies are part of a growing trend toward integrating biosensor platforms with machine learning for stress detection. [PERSON] et al. [13] highlighted that biosensor-based stress monitoring is not only continuous and real-time, but can also be applied discretely, offering new opportunities for long-term health monitoring.
## 2 Materials and Methods
### Plasma Diagnostic Methods
#### 2.2.2 Optical Emission Spectroscopy  
The gas temperature (T\({}_{\mathrm{gas}}\)) is an important plasma characteristic that defines the reaction rate of the occurring chemical processes. In nonequilibrium plasmas, it is a widely accepted approximation that rotational and translational (gas) temperatures are assumed to be in equilibrium due to the fast rotational-translational energy transfer, taking place within less than 1 ns at atmospheric pressure [32]. Since N\({}_{2}\) is the dominant component in the used gas, the gas temperature was measured by recording the emission signal originating from the second positive system of N\({}_{2}\) (N\({}_{2}\) (C\({}^{3}\)I\({}_{\mathrm{u}}\) - B\({}^{3}\)I\({}_{\mathrm{g}}\))). Accordingly, optical emission spectroscopy (OES) was performed employing high-resolution spectrometers (Horiba, iHR550, Horiba Ltd., Kyoto, Japan with 1200 gr/mm grating and FC-UVIR600-2, Avantes, Apeldoorn, The Netherlands) with UV-VIS optical fiber and a collimating lens, which were placed in front of the fused silica window located in the outer wall of the plasma reactor, as shown in Figure 1c. Nitrogen transitions in the wavelength range 300-385 nm were recorded, and the gas temperature was obtained, matching the simulated and experimental spectra via Massive OES software [33; 34]. An example of a fitted spectrum is shown in the SI, Figure S3. In this work, time and spatially averaged measurements were carried out, meaning that the temperatures determined via this approach correspond to the plasma core region. Additionally, OES in the optical range of 200-900 nm were also recorded using a broad band, low resolution spectrometer (Ocean Optics S2000, 1.4 nm spectral resolution, Duiven, the Netherlands) to monitor the differences in plasma composition between Me-Me and Me-L configurations based on the emission signal from electronically excited states. All measured spectra were calibrated using a standardized tungsten halogen lamp (Oriel: model 63355, 250-2400 nm optical range, MKS Instruments Inc., Irvine, CA, USA).
## 3 Metal-Organic Frameworks  
Metal-organic frameworks (MOFs), also known as porous coordination polymers [88], are multi-dimensional lattices [89] self-assembled from metal ions serving as central ions, inorganic metal ions, or metal ion clusters, and organic ligands connected through coordination bonds [90]. The principle of MOF precipitation primarily involves the formation of coordination compounds between metal ions and organic ligands through coordination bonds. These coordination compounds begin to aggregate when they reach a certain concentration, forming small aggregates known as nuclei. After the formation of MOF nuclei, monomers of MOF in the surrounding solution diffuse to the nuclei and undergo coordination reactions, causing the nuclei to gradually grow and ultimately form granular solid-phase products. The metal ions or clusters [91; 92; 93; 94] within MOFs are predominantly derived from transition elements or lanthanides found in the periodic table [22]. The organic ligands in MOFs mainly consist of polyacidic ligands, poly-pyridine-type ligands, and functional group hybrid ligands. Owing to the multifunctionality of metal nodes and ligands, along with their rich geometric shapes and diverse connectivity, MOFs possess structural flexibility, tunable nanospace, and controllable synthesis advantages.  
In recent years, MOFs as enzyme immobilization carriers have been widely investigated. Currently, methods for in situ enzyme immobilization using MOFs mainly include the bottom-up synthesis approach [95; 96]. This method primarily involves using enzyme molecules as cores to induce the formation of the MOF framework, allowing the in situ growth of MOF crystals around them, ultimately forming a highly ordered and stable MOF shell layer around the enzyme molecules [97]. This achieves the in situ immobilization of enzyme molecules within the MOF crystals [22]. In this method, the required structural properties of MOFs, such as surface area, pore size, shape, and pore volume, can be easily controlled [98], and the presence of the MOF shell shields the enzyme from the impact of extreme environments, improving the enzyme's storage stability and tolerance to temperature, pH, and organic solvents [99; 100; 101] (refer to Table 2). The immobilization of enzymes with MOFs enhances stability, possibly for the following reasons [102]: (1) enzymes are fixed in the pores through various interactions, tending to maintain their active conformation, aiding in increased stability and reduced enzyme leakage during repeated use; (2) the appropriate pore structure in MOFs can provide size selectivity for specific substrates, offering an additional protective layer for the enzyme, and substrates must diffuse through the pore channels to approach the enzyme, positively affecting the catalytic process; (3) MOFs prevent enzyme aggregation through physical isolation. The bottom-up synthesis approach, also known as in situ encapsulation, can be further categorized into co-precipitation and biomimetic mineralization methods based on whether additional co-precipitants are needed to form immobilized enzymes [100].
# A Comparative Analysis of International Standards on Curved Surface Isolators for Buildings
## 3 Design Procedures
Moreover, international seismic standards such as EC8 [57] and ASCE/SEI 7-22 [58] standardize their acceleration response spectra for a default damping value of 5% for building structures. Based on the above key parameters, the allowable analytical procedures for evaluating base-isolated structures differ between European and U.S. standards, as outlined below:  
1. **European Standard (EN 1998-1--Eurocode 8)**[57]
* Equivalent Linear Analysis; 1. Simplified Linear Analysis; 2. Modal Simplified Linear Analysis;
* Nonlinear Time-History Analysis. 2. **United States Standard (ASCE/SEI 7-22)**[58]
* Equivalent Lateral Force Procedure (ELF);
* Dynamic Analysis Procedure; 1. Response Spectrum Procedure; 2. Response History Procedure (Time-History Analysis). For the scope of this study, we focus on the Equivalent Linear Analysis (ELA) approach. Specifically, we apply:  
1. The Simplified Linear Analysis method as per Eurocode 8 (EC8) [57]. 2. The Equivalent Lateral Force (ELF) Procedure as prescribed in ASCE/SEI 7-22 [58].
# Adaptive Navigation in Collaborative Robots: A Reinforcement Learning and Sensor Fusion Approach
## 3 Methodology  
The proposed methodology system combines a model-free RL algorithm with real-time sensor data to give the Cobot flexibility in navigation and the ability to avoid obstacles effectively. The system allows the Cobot to make on-the-fly decisions about its path by utilizing the Ublox Neo 6M GPS, which is combined with a tri-axis magnetometer for navigation and ultrasonic sensors to fine-tune its path as needed. Thus, this approach helps to navigate effectively, ensuring that the Cobot can safely move to its destination in a complex or unknown environment.  
This study presents the development and implementation of a cost-effective prototype robotic system, providing a practical solution for various autonomous navigation tasks. Apart from the use of Cobot, this method can also have applications in deploying vehicle robots into remote or potentially dangerous environments where human presence is not practical. An overview of the RL Cobot system is shown in Figure 2.  
Cobot is fed with the destination GPS coordinates alone to the test vehicle, which then uses the readings from the GPS and tri-axis magnetometer sensors to adjust its heading angle and move forward. Additionally, three ultrasonic sensors are used to detect obstacles and ensure the vehicle follows a drivable path. When an obstacle is detected, the vehicle system employs a model-free reinforcement learning algorithm to navigate around it.
###### Abstract  
Magnetic resonance imaging (MRI) is a non-invasive technique used in cardiac diagnosis. Using it, specialists can measure the masses and volumes of the right ventricle (RV), left ventricular cavity (LVC), and myocardium (MYO). Segmenting these structures is an important step before this measurement. However, this process can be laborious and error-prone when done manually. This paper proposes a multi-stage method for cardiac segmentation in short-axis MRI based on fully convolutional networks (FCNs). This automatic method comprises three main stages: (1) the extraction of a region of interest (ROI); (2) MYO and LVC segmentation using a proposed FCN called EAIS-Net; and (3) the RV segmentation using another proposed FCN called IRAX-Net. The proposed method was tested with the ACDC and M&Ms datasets. The main evaluation metrics are end-diastolic (ED) and end-systolic (ES) Dice. For the ACDC dataset, the Dice results (ED and ES, respectively) are 0.960 and 0.904 for the LVC, 0.880 and 0.892 for the MYO, and 0.910 and 0.860 for the RV. For the M&Ms dataset, the ED and ES Dices are 0.861 and 0.805 for the LVC, 0.733 and 0.759 for the MYO, and 0.721 and 0.694 for the RV. These results confirm the feasibility of the proposed method.  
cardiac segmentation; magnetic resonance imaging; fully convolutional networks; ACDC dataset; M&Ms dataset; deep learning Article
# A Gradient-Based Cuckoo Search Algorithm for a Reservoir-Generation Scheduling Problem
## 5 Case Study  
### Study Area  
The Jinsha River is the upper reach of the Yangtze River in south-west China. It flows through Qinghai, Sichuan and Yunnan provinces. The Jinsha River is rich in water resources, and is being heavily developed for hydroelectric power. In this case study, we discuss four hydropower stations with total installed capacity of 42,960 MW on the river, including Wudongde, Baihetan, Xiluodu and Xiangjiaba. The locations of these hydropower stations are signed in Figure 4, and the major parameters of these hydropower stations are listed in Table 1.  
The aim of long-term hydropower generation scheduling is to determine the water release process of all hydropower stations at each period. The total power generation of different schemes will be  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & **Wudongde** & **Baihetan** & **Xiluodu** & **Xiangjiaba** \\ \hline Dead water level (m) & 945 & 765 & 540 & 370 \\ Normal water level (m) & 977 & 825 & 600 & 380 \\ Flood limit water level (m) & 952 & 785 & 560 & 370 \\ Installed capacity (\(10^{4}\) kw) & 1020 & 1600 & 1260 & 600 \\ Total capacity (\(10^{8}\) m\({}^{3}\)) & 74.08 & 206.27 & 126.7 & 51.63 \\ Minimum outflow (m\({}^{3}\)/s) & 906 & 905 & 1500 & 1500 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main parameters of the four cascade hydropower stations in the Jinsha River.  
Figure 4: Hydrological stations in the Jinsha River.  
different, thus we need a suitable method to solve this problem. In this case, we do the scheduling simulation of the cascade hydropower stations using the proposed GCS algorithm.  
The topology of the four reservoirs in the Jinsha River is shown in Figure 5. The historical observed streamflow of Pingshan hydrological station is used as the input of the LHGS model.
## 2 Background and Literature Review
### SDN Model for NDN
#### 2.2.2 B. Control Layer  
The management of network traffic falls within the purview of the control layer. It is made up of SDN controllers that allow for logical network control and give an overall picture of the underlying devices of the network. It is the SDN middle or control layer, through which the applications which execute on the controller control the behavior of the infrastructure layer [21].
# Fault Prediction Modeling for High-Impact Recorders Based on IPSO-SVM
## 3 Experiments and Results
### Data Collection
#### 3.1.1 Determining the Experimental Range  
Before the experiments, simulations of the PCB's response under high-impact conditions were conducted using Ansys and Is-Dyna. The purpose of these simulations was to analyze the stress distribution and dynamic response of the recorder across different impact acceleration ranges to determine a reasonable range for the experimental accelerations.  
Ansys: The finite element method was used to construct the structural model of the recorder, simulating the stress distribution and strain under various impact acceleration conditions. The simulation identified potential weak points and vulnerable areas in the recorder, providing a design basis for the experiments.  
Is-Dyna: The dynamic response of the recorder under high-impact loading was simulated, predicting its failure behavior within the acceleration range of 10,000 g to 100,000 g. The Is-Dyna simulation further calibrated the Ansys analysis results, ultimately determining that controlling the experimental range between 20,000 g and 90,000 g was more reasonable.