esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This work focuses on developing and evaluating the proposed VCMS to store and manage comprehensive information from patient monitors and ventilators and provide integrated monitoring in real time. This study indicates that providing real-time data from multiple patient monitors and ventilators helps clinical staff enhance their quality of care and early-response capabilities for alarms in central monitoring, aids in remote mobile applications, and stores without information loss. The medical software validation was also conducted with 21 pulmonology physicians and staff members with ICU experience. Clinical staff could easily operate with little introduction and training in the VCMS; the average task completion rate was over 80%, and improvement work was carried out through feedback. Furthermore, the satisfaction survey proved the usefulness and applicability of the developed VCMS, with a score of 4.67 out of 5. Nevertheless, challenges remain in improving the proposed VCMS, such as machine learning-based respiratory abnormality prediction using big data and cybersecurity for integrating home ventilators. We expect that the developed VCMS will become a core technology for tele-ICUs, which will help efficiently utilize scarce medical resources during national crises, such as infectious disease outbreaks.  
Conceptualization, K.K., Y.K., Y.S.K., K.B.K. and S.H.L.; formal analysis, K.K., Y.K. and K.B.K.; investigation, K.K., Y.S.K. and S.H.L.; methodology, K.K., Y.K., K.B.K. and S.H.L.; software, K.K.; validation, K.B.K. and S.H.L.; writing--original draft, K.K. and Y.K.; writing--review and editing, Y.S.K., K.B.K. and S.H.L. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the Korea Medical Device Development Fund, awarded by the Korean government (the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health and Welfare, the Ministry of Food and Drug Safety) (project number: RS-2020-KD00032); the National Research Foundation of Korea (NRF) grant, funded by the Korea government (MSIT) (RS-2023-00252863), and the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (RS-2023-00239193, RS-2023-00243656).  
**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Boards of Severance Hospital (1-2022-0036 and 25 July 2022).  
**Informed Consent Statement:** Written informed consent was obtained from all subjects involved in this study.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** Authors [PERSON], [PERSON] and [PERSON] were employed by the company 2 TS Corporation. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Dynamic Workload Management System in the Public Sector: A Comparative Analysis
## 3 Methodology
### Machine Learning Algorithms
#### 3.2.1 Linear Regression  
Linear Regression served as a fundamental baseline for comparison. It provided insights into linear relationships between input factors and employee capability. More specifically, it was deployed to model the relationship between factors considered input (work experience in the public sector, work experience in the private sector, educational history, and age). Linear Regression aims to find the best-fitting linear relationship between the input features and the output, defined as a Time Factor. This allows us to make predictions and understand the impact of individual features on the target variable [44; 45].  
\[Y=b_{0}+b_{1}x_{1}+b_{2}x_{2}+b_{3}x_{3}+b_{4}x_{4}+\epsilon \tag{2}\]  
\(Y\) is the dependent variable (Time Factor).  
\(x_{1},\ x_{2},\ x_{3},\ x_{4}\) are the four independent variables (input features).  
\(b_{0}\) is the intercept.  
\(b_{1},\ b_{2},\ b_{3},\ b_{4}\) are the coefficients associated with each independent variable.  
\(\epsilon\) represents the error term, accounting for unexplained variability.
# Agriculture 5.0 in Colombia: Opportunities Through the Emerging 6G Network
## 2 Background
### Smart Agriculture  
Smart agriculture is based on using accurate data to make decisions that increase productivity, optimize the use of inputs and natural resources, and promote sustainable practices in crops, forests, and livestock. Its main aim is to improve yields, minimize environmental impact, and strengthen the ability of production systems to adapt to the challenges of climate change [10; 21].  
Real-time monitoring of soil parameters such as temperature, moisture, salinity, electrical conductivity (EC), pH, and nutrient levels (nitrogen, potassium, phosphorus).  
* Use intelligent networked sensors to collect and analyze data.
* Integrate with AI-based applications for accurate recommendations on fertilization, irrigation, and disease diagnosis.
* Automate and remotely control farming processes using IoT technologies and cloud computing.
# Application of Fly Ash as an Adsorbent for Removal of Air and Water Pollutants
## 4 Application of Fly Ash for Treatment of Pollutants
### Removing Air Pollutants
#### 4.1.2 Removal of Nitrogen Oxides  
A small amount of NOx can be adsorbed by FA because of the unburned carbon in FA and the physical characteristics of FA such as high porosity and specific surface area [120]. In fact, this carbon is a precursor to activated carbon, but it has gone through devolatilization during combustion in the furnace of a power station [121]. Therefore, the internal carbon can be activated by some simple  
Figure 11: SEM images and VOCs adsorption capacity of FA membranes (adapted with permission from [33], Elsevier, 2018).  
Figure 10: Schematic diagram of electrospun fibrous membrane and VOCs adsorption experiment.  
modifications to the FA to increase its adsorption capacity. The general method used is steam activation and physical separation, but, because the ash content of carbon-enriched FA is too high, using chemical demineralization to activate carbon is the best method [122]. [PERSON] et al. [122] studied adsorption of NOx by unburnt carbon from FA and found that the conversion curves of NO on different FA samples reached steady state after 10 h. In addition, the enriched-carbon FA showed high NO abatement capacity due to increased carbon content, surface area, and porosity. [PERSON] et al. [30] synthesized Cu and Fe exchange type Y zeolites from FA for removal of NOx from flue gas. [PERSON] et al. [123] also reported that carbon-rich products from combustion ash could decrease NOx emissions and Hg due to ion-pair interactions between NO\({}^{+}\) and O\({}_{2}\)\({}^{-}\) at the surface of carbon, with subsequent condensation of NO\({}_{2}\) in micropores. In addition, the pore size on the surface of the adsorbent can be changed after adsorption of a certain amount of Hg, producing the optimum pore size (micropores, <2 nm) for adsorbed NOx [124]. [PERSON] et al. [123] also showed that the surface area of adsorbents increased due to adsorption of NOx, as shown in Figure 12. From the above literature, it can be concluded that unburned carbon concentration in FA plays a decisive role for reducing NOx from air, and the high surface area of FA will further improve the NOx adsorption capacity. In addition, the unburned carbon remaining in the FA particles contribute the main surface area to FA, and the carbon can be activated to further improve the adsorption performance of the FA [5].
# A Two-Stage Local Path Planning Algorithm Based on Sampling and Optimization Methods
###### Abstract  
As the application of mobile robots becomes increasingly widespread, many navigation applications now require more than just obstacle avoidance capabilities. There is a growing demand for robots to follow predetermined global paths. Inspired by stamping technology in the metalworking field, this paper proposes a two-stage local path planning algorithm based on sampling and optimization, termed the path stamping forming algorithm. In the exploration stage, the path stamping forming algorithm finds a preliminary path that avoids obstacles and runs parallel to the global path. The subsequent optimization stage determines the optimal local path that meets specific constraints. Finally, we compared the proposed algorithm with existing advanced navigation algorithms through simulations and experiments, demonstrating its superior performance. The results indicate that the proposed algorithm enables mobile robots to avoid obstacles and follow the global path without the need to re-plan the global path. Compared with the traditional local path planning algorithm, the performance of the proposed algorithm in following the global path is improved by up to 52.71%.  
two-stage local path planning; path stamping forming; sampling and optimization; obstacle avoidance +
Footnote †: journal: Applied Sciences
# Traceability and Spectral Analysis of Transmission Error in Rotary Vector Planetary Gear Reducers
## 5 Results and Discussion
### Test Results of 65 BX-121-RVE Reducers
For prototype 8, the main reason for its subpar transmission accuracy lies in the cycloidal gear's composite errors and positional accuracy issues with the fitting holes, indicating that these areas require quality enhancement. Given the significant impact of the cycloidal gear on the RV's TE as shown in the results, improvements in geometric design, process design, and manufacturing are expected to enhance and ensure the stability of the TE of the RV reducer. Due to the real-time changes in the meshing points and meshing force distribution of the gear teeth as the cycloidal wheel rotates, different contact deformations are generated. Therefore, optimizing the geometry of the cycloidal wheel by considering contact deformation is one of the important directions to improve and ensure TE. In the manufacturing stage, it is also necessary to plan the process path reasonably to ensure the consistency of processing. Through reasonable fixture design, the installation reference and the overall concentricity and coaxiality of the gear tooth surface should be ensured. While the exclusion method can provide a preliminary identification of the primary components influencing TE based on the results from multiple components, it is primarily suitable for approximate use in engineering applications. This is because the fast Fourier transform (FFT) essentially treats the time-domain signal of TE as a superposition of a series of harmonic waves. The superposition of harmonic waves at the same frequency results in a new harmonic of the same frequency, making it impossible to further separate errors of the same frequency originating from multiple sources. In the future, novel artificial intelligence techniques, such as machine learning, could be explored to more effectively track and analyze these multi-source TEs.
## 4 Progress in the Understanding of Illuminated B-O LID Mitigation Processes
### Rapid Laser-Based Processes for LID Elimination  
The improved fundamental understanding of defect formation and the mechanisms involved in the permanent deactivation of B-O defects have enabled the development of rapid illuminated processes for eliminating LID. The requirement of high-intensity illumination to accelerate defect formation and for hydrogen charge state manipulations lean towards the use of laser-based hydrogenation processes [136]. Localised laser-based hydrogenation processes were demonstrated by [PERSON] et al. with time-constants for the regeneration reaction on the order of 0.1 s, provided that defects had been pre-formed [105]. A subsequent study using full-area laser-illumination demonstrated the ability to simultaneously form and passivate B-O defects to completely eliminate B-O related LID within ten seconds on standard aluminium back surface field solar cells, in a time-frame suitable for mass production [137, 138]. These rapid processes were performed at a temperature of approximately 300 \({}^{\circ}\)C, substantially higher than that used for effectively mitigating B-O related LID in previous studies.
## 2 Materials and Methods
### HPLC Analysis  
For the HPLC analysis, an LC-40 HPLC system from Shimadzu (Milan, Italy) was employed. The instrument was equipped with an HPG binary pump and a Shimadzu SPD40 PDA spectrophotometric detector. To find the best analytical set-up, we compared three different instrumental methods. The protocols have in common the use of the following RP C18 column: Shimadzu GIST-HP C18 (3 \(\times\) 150 mm, 3 \(\upmu\)m) and the detector wavelength settings.  
Method 1: The HPLC system was operated in binary gradient mode; solvent A was water:MeOH 5 mM AA and solvent B was 1--PrOH:MeOH 5 mM AA. The chromatographic gradient was set as follows: flow, 0.2 mL/min; initial conditions, t = 0 min B % = 30, t = 2 min B % = 50, t = 35 min B % = 85, t = 37 min B % = 100, t = 43 min B % = 100. The column reconditioning was performed for the next 6 min as follows: t = 43.1 min B % = 30, t = 49 min B % = 30. Total analysis time was 49 min. Column was maintained at RT.  
Method 2: HPLC was operated in binary gradient mode, solvent A was 90:9.5: 0.5 water:MeOH: OA, while solvent B was 55 40: 4.5: 0.5 = MeOH:ACN:water:OA. Flow was set at 0.3 mL/min and the solvent ramp was as follows: t = 0 min B % = 70, t = 35 min B % = 100, t = 40 min B % = 100. The reconditioning was as follows: t = 40.1 min B % = 70, t = 45 min B % = 70. Total analysis time was 45 min. Column was maintained at RT.  
Method 3: HPLC was operated in binary gradient, A: 0.1% formic acid in water, B: 0.1% formic acid in ACN. The design was as follows: t = 0 min B % = 70, t = 17 min B % = 100, t = 23 min B % = 100. The reconditioning was as follows: t = 23.1 min B % = 70, t = 31 min B % = 70. Total analysis time was 31 min. Flow was set at 0.2 mL/min. Column temperature was set at 60 \({}^{\circ}\)C.  
In the three different methods, the PDA or the UV-Vis detector were set to acquire at 250 or 205 nm, respectively, for the keto boswellic acids (AKBA, KBA) and the boswellic acids (A-BA, B-BA, A-ABA, A-BBA). Slit width was set at 1.2 nm. Acquisition bandwidth was set at \(\pm\)4 nm.
# Explainable Ensemble Learning Model for Residual Strength Forecasting of Defective Pipelines
## 3 Case Study
### Overfitting Analysis of the BO-XGBoost Model  
Similarly to the robustness analysis of the model, after developing and initially evaluating the remaining strength prediction model of defective pipelines using datasets, it is necessary to conduct an overfitting indicator evaluation. Overfitting is a common issue in developing and applying machine learning prediction models. Many machine learning models tend to overfit the training data they are exposed to when dealing with complex datasets containing multiple nonlinear relationships, noise, or unpredictable fluctuations. This often results in low prediction errors for the training set but poor generalization when applied to test data. Therefore, it is necessary to conduct an unbiased evaluation of the constructed model to measure the degree of overfitting during the training process [47]. The specific formula is shown in Equation (2), a practical method that entails calculating an overfitting index (OFI) by utilizing a model's Performance Index (PI) with both training (PItr) and testing (PIts) datasets, along with the number of data instances in the training (Ktr) and testing (Kts) datasets. The Performance Index (PI) is used to evaluate the model's predictive power and generalization performance. A lower OFI value signifies a decreased likelihood of overfitting.  
\[OFI=\bigg{(}\frac{K_{\text{tr}}-K_{\text{ts}}}{K}\bigg{)}PI_{\text{tr}}+2\bigg{(} \frac{K_{\text{ts}}}{K}\bigg{)}PI_{\text{ts}} \tag{2}\]  
Table 4 presents the performance of different models on the dataset, specifically their overfitting index (_OFI_) values when applied to the remaining strength dataset of defective pipelines. The results indicate that the BO-XGBoost model has the lowest _OFI_, while the SVM model has the highest. Based on the OFI values, the models can be ranked as follows: BO-XGBoost (least overfitting) < GBT < RF < LightGBM < MLP < SVM (most overfitting). This ranking indicates that BO-XGBoost is the least prone to overfitting compared to the other five models.  
Figure 13: The R\({}^{2}\) values predicted by different models as the level of noise changes.
## 4 Security Challenges and Solutions in 5G Network and LPWAN-IoT
### Security Challenges and Solutions in LPWAN-IoT and 5G
#### 4.2.2 Security Challenges and Solutions in 5G
This is illustrated as follows: (1) network access security: security of user data should be guaranteed and this includes confidentiality and integrity of signaling in both the access network and the core network, and the UE and network in CP/UP; (2) network domain security: the security of exchange in both signaling and user data between different network entities including RAN and service network public or external nodes, home environment and service network external nodes, service network external nodes and network slices, and home environment and network slices; (3) initial authentication and key management: various mechanisms for authentication and key management should be included that exemplify the unified authentication framework, including operator-security-credentials based security credential authentication between UE and 3 GPP networks, including the key management of user data protection after successful authentication. Detailed illustrations involving domains (4) to (8) are found in [26]. Table 6 shows the cryptographic mechanisms and their encryption algorithms in 5G-LPWAN-IoT. From Table 6, 5G and most LPWAN use a symmetric encryption algorithm that uses s single key for encryption and decryption of cipher data/message. This is not very secure like the asymmetric encryption that uses public and private keys for encryption/decryption. Most LPWAN use symmetric encryption because of their less computational complexity. The asymmetric encryption such as Rivest-Shamir-Adleman (RSA) and Elliptic Curve Cryptography (ECC) are applicable mainly in 5G and most 3 GPP (cellular) LPWANs. The computational complexity in most non-cellular LPWANs cannot withstand the RSA/ECC algorithms. Hence, this is a possible area of research for most LPWANs.
# Spectral Data-Driven Prediction of Soil Properties Using LSTM-CNN-Attention Model
## 2 Materials and Methods
### Model and Methodology
#### 2.5.2 One-Dimensional Convolutional Neural Network (1D-CNN)  
CNNs are widely used in deep learning due to their ability to capture spatial patterns in data, particularly in high-dimensional datasets such as hyperspectral imagery. CNN architectures typically consist of an input layer and multiple convolutional and pooling layers, followed by fully connected layers, and concluding with an output layer [42]. In this study, we utilized a one-dimensional convolutional neural network (1D-CNN) specifically designed to process one-dimensional spectral data related to soil properties. The 1D-CNN architecture begins with an input layer that receives the spectral data, followed by multiple hidden layers comprising convolutional and pooling operations. Convolutional layers apply filters to extract relevant features from the input data, with each filter scanning across  
Figure 3: Diagram of the LSTM model structure featuring the forget gate, input gate, and output gate.  
the spectral data to detect local patterns. Pooling layers subsequently reduce dimensionality, condensing the extracted features to maintain computational efficiency while preserving essential information.  
In this study, the architecture consists of five convolutional layers, with the first two layers followed by an average pooling layer and the final layer followed by an adaptive average pooling layer. The convolutional layers employ 1D filters to detect features within the spectral data, processing information across multiple wavelengths. To introduce nonlinearity and enhance the model's ability to capture complex relationships, a Rectified Linear Unit (ReLU) activation function is applied after each layer [43]. Additionally, batch normalization is applied after each convolutional layer to stabilize the training process and accelerate convergence by normalizing activations within each mini-batch [44]. The final output is generated by a fully connected (FC) layer that integrates the features extracted by the convolutional layers. To prevent overfitting during training, a dropout layer with a rate of 0.02 was added to the FC layer. Dropout randomly disables certain neurons during training, encouraging the model to learn more robust features [45]. This architecture allows the model to efficiently capture local characteristics of the spectral data and gradually aggregate them into global information, leading to accurate predictions of soil properties. The ability of CNNs to learn hierarchical representations makes them particularly well suited for the complex, high-dimensional nature of soil spectral data, thereby enhancing predictive performance.
###### Abstract
Relative to the previous GSR studies, the GSPOX concept developed in this study aims to evaluate:  
* An alternative oxygen carrier for H\({}_{2}\) production plants with comparable efficiency levels, mitigating the safety concerns related to Nickel. Furthermore, the influence of operating pressure on the performance of GSR and GSPOX designs is assessed. * A process design that eliminates the potentially disadvantageous feature of electricity consumption in H\({}_{2}\) production plants with CCS, by efficiently integrating a power cycle with the cluster, making effective use of the low-grade heat sources throughout the plant for additional electricity production, instead of LP steam export. The GSPOX concepts present a significant electricity output as an additional revenue source. * A simplification of the plant section dedicated to H\({}_{2}\) production, by eliminating the water gas shift conversion and associated heat recovery units, substantially reducing the Steam to Carbon (S/C) plant requirements for natural gas conversion, and therefore reducing capital costs. * From a modeling perspective, this study provides an integrated plant model between the dynamic clusters (GSR and GSPOX) and the stationary flowsheet simulation, avoiding cumbersome information exchange interfaces between platforms, making the process synthesis and evaluation more agile. Figure 1: Illustration of the different reactor steps in the two GS reactor concepts. This new process topology will be benchmarked against conventional unabated SMR plant, SMR with MDEA CO\({}_{2}\) capture and the previously studied GSR-H\({}_{2}\) plant, under consistent modeling assumptions. The following section provides a detailed description of the dynamic model development and its integration in the process flowsheet. The narrative for the GS-based H\({}_{2}\) and electricity production plants as well as the different benchmark processes is provided. Subsequently, the key performance indicators of the plants are presented based on pre-defined reference technologies, and a brief description of the modeling approach for the essential technological components is given. Results are shown attending to the efficiency definitions, energy breakdown across the plant and CO\({}_{2}\) emissions performance. Finally, the key results are summarized, and the main conclusions of this study are discussed.