esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
A flattening of the velocity profile is observed in the central zone of the channel for larger widths (d = 10 cm and d = 12 cm), indicating a more uniform velocity distribution in this region. This flattening may suggest a more developed convective flow or a transition toward a flow regime less influenced by wall-induced viscous effects in wider channels. The absolute values of velocity remain relatively low (in the order of 10\({}^{-9}\) m/s), which indicates the phenomenon of natural convection with limited intensity, determined by temperature differences and the dimensions of the channel. Analyzing the presented graphs, which illustrate the velocity profiles in the ZY plane, a parabolic velocity distribution is observed within the channel for all five investigated widths. The velocity is minimal (approaching zero) in the immediate vicinity of the channel walls (at Y+ \(\approx\) 0 and Y+ \(\approx\) 1) due to the no-slip condition, and reaches a maximum value at the channel's center (Y+ \(\approx\) 0.5). The magnitude of the maximum velocity attained at the channel center varies significantly with its width. A positive correlation is found between channel width and the fluid's maximum velocity. Wider channels (d = 10 cm and d = 12 cm) enable considerably higher maximum velocities compared to narrower channels (d = 4 cm and d = 6 cm). This observation suggests that the channel dimension directly influences the intensity of the airflow or fluid flow within the passive facade. It is also noted that the slope of the velocity profile near the walls is steeper for narrower channels, indicating a greater velocity gradient in that region. This suggests more pronounced viscous shear forces near the surfaces for channels with a smaller internal distance. The mass flow rate of air through the cavity exhibits an increasing trend with channel width, although not perfectly linear. The lowest mass flow-rate value is recorded for a 4 cm width (6.32 \(\times\) 10\({}^{-12}\) kg/s), while the highest is for a 12 cm width (2.39 \(\times\) 10\({}^{-10}\) kg/s). This increase in mass flow rate is attributed to the reduction in flow resistance within wider channels, thereby promoting more intense natural convection. The transferred heat flux also shows a significant increase with channel width. Expressed in kW, it ranges from 1.1923 \(\times\) 10\({}^{-7}\) kW (0.0001192 W) for 4 cm to 5.2304 \(\times\) 10\({}^{-6}\) kW (0.0052305 W) for 12 cm. This increase indicates that a wider cavity facilitates the transfer of a greater amount of heat, which is a direct consequence of the higher outlet temperature and increased mass flow rate.
## 3 Steel Truss Experiment
### Signal Recognition and Analysis
#### 3.3.3 Analysis of Results and Discussion
Due to experimental constraints, the environmental noise collected in this paper is primarily simulated through a Bluetooth speaker, which differs significantly from the complex and varied noise environments in the real world. In real-world settings, noise is more diverse and often involves mixtures of multiple types of noise, posing greater challenges for accurate sound signal recognition. Therefore, future research should further explore and address these issues to enhance the generalization ability and robustness of the model in practical applications. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Indicator** & **Model** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** \\ \hline \multirow{6}{*}{Precision} & Bayes & 1.00 & 0.96 & 0.91 & 0.84 & 0.96 & 0.92 & 0.92 & 0.77 \\  & KNN & 1.00 & 0.87 & 0.89 & 0.99 & 1.00 & 0.99 & 1.00 & 0.93 \\  & SVM & 1.00 & 0.96 & 0.95 & 0.98 & 0.99 & 0.97 & 1.00 & 0.93 \\  & BP & 1.00 & 0.93 & 0.95 & 0.95 & 1.00 & 0.99 & 1.00 & 0.99 \\  & XGBoost & 1.00 & 0.98 & 0.97 & 0.97 & 0.98 & 0.98 & 1.00 & 0.95 \\  & RF & 0.99 & 0.98 & 0.99 & 0.97 & 1.00 & 0.98 & 1.00 & 0.98 \\  & Bayes & 1.00 & 0.68 & 0.84 & 0.91 & 0.97 & 0.90 & 0.96 & 0.97 \\  & KNN & 1.00 & 0.94 & 0.99 & 0.88 & 1.00 & 0.94 & 1.00 & 0.90 \\  & SVM & 1.00 & 0.97 & 0.98 & 0.95 & 1.00 & 0.96 & 0.99 & 0.93 \\  & BP & 1.00 & 1.00 & 0.95 & 0.95 & 1.00 & 0.97 & 1.00 & 0.94 \\  & XGBoost & 1.00 & 0.98 & 0.97 & 0.97 & 1.00 & 0.96 & 0.98 & 0.97 \\  & RF & 1.00 & 0.99 & 0.97 & 0.99 & 0.99 & 0.97 & 1.00 & 0.99 \\  & Bayes & 1.00 & 0.80 & 0.87 & 0.88 & 0.96 & 0.91 & 0.94 & 0.86 \\  & KNN & 1.00 & 0.90 & 0.94 & 0.93 & 1.00 & 0.97 & 1.00 & 0.92 \\  & SVM & 1.00 & 0.97 & 0.97 & 0.97 & 1.00 & 0.97 & 1.00 & 0.93 \\  & BP & 1.00 & 0.96 & 0.95 & 0.95 & 1.00 & 0.98 & 1.00 & 0.97 \\  & XGBoost & 1.00 & 0.98 & 0.97 & 0.97 & 0.99 & 0.97 & 0.99 & 0.96 \\  & RF & 1.00 & 0.99 & 0.98 & 0.98 & 1.00 & 0.97 & 1.00 & 0.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Recognition results of different training models. Figure 12: Comparison chart of recognition accuracy of different training models.
## 2 Materials and Methods
### Sample Instrumental Analysis  
The samples were thawed at 4 \({}^{\circ}\)C on ice for 1 h and subsequently placed in the cooled sample tray. The sample tray was maintained at 4 \({}^{\circ}\)C during the sample instrumental analysis. The extraction was carried out using the fully automated HS-SPME mode on the MPS system (Gerstel\({}^{\circledR}\), Linthicum Heights, MD, USA). Initially, the sample was incubated for 10 min at the extraction temperature with an agitation speed of 250 rpm. Then, the headspace extraction was performed at the determined temperature and time using a PDMS/CAR/DVB fiber. Before sample extraction each day, the fiber was conditioned at 270 \({}^{\circ}\)C for 30 min, and then thermally desorbed into the GC\(\times\)GC to ensure it is cleanliness.  
After sample extraction, the fiber was injected into the Pegasus 4D (LECO Corporation\({}^{\circledR}\), St. Joseph, MI, USA) GC\(\times\)GC-ToFMS with an Agilent 6890 GC (Agilent Technologies, Santa Clara, CA, USA) through the Septumless Head (SLH) injector and desorbed at 270 \({}^{\circ}\)C for 5 min. The primary column was a standard non-polar Rxi-5 MS (Restek Corporation, Bellefonte, PA, USA) (30m \(\times\) 250 \(\mu\)m; 0.25 \(\mu\)m \(d_{\circledR}\)). The second column was a polar Stabilwax (Crossbond\({}^{\circledR}\) polyethylene glycol) (Restek Corporation, Bellefonte, PA, USA) (1.5 m \(\times\) 250 \(\mu\)m; 0.25 \(\mu\)m \(d_{\circledR}\)). The primary GC oven temperature was initially set at 40 \({}^{\circ}\)C, held for 2 min, and increased at 5 \({}^{\circ}\)C/min to 230 \({}^{\circ}\)C. The temperature offset for the secondary oven was 5 \({}^{\circ}\)C relative to the primary oven. A modulation period of 2 s was used. A quad-jet dual-stage thermal modulator was used for modulation. The temperature offset for the modulator was 15 \({}^{\circ}\)C relative to the secondary oven. The mass spectrometry was performed at the electron ionization (EI) mode at 70 eV. The mass spectrum was collected within the range of m/z 30-400 at a sampling rate of 200 Hz.  
Instrument reproducibility was evaluated by running three Grob programmed test mix injections (Supelco, Bellefonte, PA, USA) before running the actual samples. The percent relative standard deviation (%RSD) of the area counts of each peak in the standard mix was proved to be less than 15%. This test proved the system stability and ensured the instrument is in good condition to analyze samples. One fiber blank was analyzed each day. The peak number should be less than 200, and the maximum peak area should be less than 1 \(\times\) 106. One alkane standard mix injection (Supelco, Bellefonte, PA, USA) and two Grob programmed test mix injections were included in each batch of sample analysis. Instrument tune and leak tests were performed daily. Carry-over was assessed by desorbing the blank fiber after the Grob programmed test injection. No carry-over was observed after analyzing the actual samples.
# Scheduling Sparse LEO Satellite Transmissions for Remote Water Level Monitoring
### Relation to Previous Work  
To the best our knowledge, one previous paper [11] has examined this problem and proposed an online learning algorithm, which can learn sample-by-sample in the field, as opposed to offline in batched datasets. That work on indirect-to-satellite communications is not applicable here, as it assumes perfect knowledge of uplink availability, unpredictable multiple streams of data that can easily overflow the buffers, and is thus posed as a queue scheduling problem [11]. They then propose an online learning algorithm based on Lyupanov optimization, which is a common approach for similar queue optimization problems [11; 14].  
In contrast, our paper deals with direct-to-satellite communications with a known data production rate. The algorithm presented in this paper is derived from reinforcement learning, specifically Monte Carlo learning and the \(k\)-armed bandit problem. Because of the relative youth of the LEO satellite service (provided commercially by Swarm Technologies), an integrative approach is taken to the design from requirements, to communications technology selection, to hardware, and finally to software and algorithm design. In doing this, this paper aims to highlight key design considerations for creating a low-cost, low-power communications scheme for an IoRT device. The key contribution of this paper is the online learning-based direct-to-satellite scheduling, and associated energy model.
# Edge-Guided Dual-Stream U-Net for Secure Image Steganography
## 3 The Proposed Method
### Encoder Architecture
#### 3.1.2 Dense Block Structure  
After the U-Net architecture, we introduce a dedicated Dense Block module [35]. Its core function is to deeply fuse the secret message M with image features. Unlike traditional convolutional blocks, Dense Block uses dense connectivity, enabling the network to better utilize feature information.  
A Dense Block consists of three sequentially connected convolutional layers. Each layer receives not only the output of the previous layer but also the outputs of all preceding layers and the original input features. This dense connectivity offers two advantages: it enhances the model's parameter efficiency through feature reuse. Moreover, each processing stage reintroduces the original features and the secret message M, effectively preventing the decay or loss of critical information during multi-layer transmission and providing a rich information foundation for subsequent processing.
## 6 Conclusions  
Based on the landslide data provided by the Zhaotong City Natural Resources and Planning Bureau, this study identified 161 landslides and 3646 potential geological disasters based on visual interpretation and the Google Earth platform. Six common influencing factors were selected for spatial distribution analysis of the geohazards of the Zhaotong area. The following conclusions were obtained:  
The distribution of potential geological disasters corresponds closely to that of landslides, with the most concentrated landslide occurrences observed at the junction of Yiliang County, Zhaotong City, and Daguan County. The greatest population distribution in the Zhaotong area is opposite to the potential geological disaster density, except for Yiliang County and Daguan County, where the population is relatively dense and landslides are concentrated, which should be paid more attention in terms of regional geological disaster prevention and reduction.  
Based on Principal Component Analysis, all six selected influencing factors affect landslide occurrences. Elevation, rainfall, and geological unit have the highest contribution rates, followed by distance to rivers, slope angle, and slope aspect. Landslides primarily occur in the Mesozoic and Lower Paleozoic, with a northeast slope orientation and rainfall ranging from 900 to 1046 mm. Further analysis of the relationship between elevation, slope angle, and distance to rivers indicates that landslides are more likely to occur in the areas with lower elevation and closer proximity to rivers, emphasizing the need for effective protective measures in these areas.  
Writing--original draft, W.W.; Writing--review & editing, S.M.; Supervision, W.Y.; Project administration, R.Y. All authors have read and agreed to the published version of the manuscript. This study was supported by the National Natural Science Foundation of China (42307268). We thank Google Earth platform for the free access satellite images used in this study. The landslides and potential geological disasters inventory of the Zhaotong area are available from the corresponding author upon request. Contact address: [EMAIL_ADDRESS]; [EMAIL_ADDRESS]. The authors declare no conflict of interest.
# Capability Analysis of Earth Observation Data for Integrated Emergency Management
## 2 Conceptual Foundations
### Integrated Emergency Management Framework in the UK  
The UK Civil contingencies Act 2004 describes an emergency as an \"event or situation which threatens severe damage to the UK's human welfare, environment, or security\" [25]. Moreover, it outlines the UK's framework for responding to and managing emergencies, ensuring public safety and the continuity of services. Figure 1 shows the IntegratedEmergency Management Framework (IEMF) model, which consists of a six-stage cycle that supports the UK Government Cabinet Office's resilience management and is used by the UK civil protection sector.  
The UK Civil Contingencies Act 2004 constitutes roles and responsibilities involved in emergency preparation and response. It classifies emergency responders as Category 1 (fire and ambulance services, police, local authorities, Environment Agency and National Health Service (NHS) bodies) and Category 2 (cooperating bodies responsible for their sector, e.g., utilities, telecommunications and transport companies) [25]. Key emergency responders and specific supporting agencies for a given region together form an LRF, a multi-agency partnership made up of Category 1 and 2 representatives from local public services, including the emergency services, local authorities, the NHS, Environment Agency, and others [25]. The LRF's geographical coverage of jurisdiction is based on police areas. In the North East, these cover Northumbria, County Durham and Darlington, and Cleveland. Initial discussions with three of the North East LRFs indicated that responders are often made aware of potential applications of satellite data but might not have the knowledge, access, or capacity to make the best use of the data before (building resilience), during (responding to) and after (recovering from) emergency incidents.
###### Abstract  
This study introduces an enhanced ORB-SLAM3 algorithm to address the limitations of traditional visual SLAM systems in feature extraction and localization accuracy within the challenging terrains of open-pit mining environments. It also tackles the issue of sparse point cloud maps for mobile robot navigation. By combining point-line features with a Micro-Electro-Mechanical System (MEMS) Inertial Measurement Unit (IMU), the algorithm improves the feature matching's reliability, particularly in low-texture areas. The method integrates dense point cloud mapping and an octree structure, optimizing both navigation and path planning while reducing storage demands and improving query efficiency. The experimental results using the TUM dataset and conducting tests in a simulated open-pit mining environment show that the proposed algorithm reduces the absolute trajectory error by 44.33% and the relative trajectory error by 14.34% compared to the ORB-SLAM3. The algorithm generates high-precision dense point cloud maps and uses an octree structure for efficient 3D spatial representation. In simulated open-pit mining scenarios, the dense mapping outperforms at reconstructing complex terrains, especially in low-texture gravel and uneven surfaces. These results highlight the robustness and practical applicability of the algorithm in dynamic and challenging environments, such as open-pit mining.  
point-line features; open-pit mining; dense mapping; octree map; visual SLAM +
Footnote †: journal: Applied sciences  
Article
## 4 Numerical Results and Discussion
This outcome proves that the selection of the best \(s\) values is more conducive to obtaining the fading parameters of the approximate distribution accurately in the approximation process based on the PDF and MGF equations, wherein the system performance can be analyzed more accurately. Figure 6: Comparison of analytical and simulated results of the system bit error rate (BER) and the per hop average SNR. Figure 7: Comparison of analytical and simulated outage probabilities for various values of \(R_{\text{th}}\) thresholds. The two graphs in Figure 9 compare the relationship of the system BER with the fading parameters \(m_{R}\) and \(\sigma_{D}\) under different channel parameter settings. We assume here that the fading parameters are 2.2 and 2.8 and that the average SNR \(\Delta\) values are 12 and 15 dB, respectively. To highlight the contrast, \(p\) and \(T\) have two additional values of 0.001 and 100 respectively in addition to the initial default values. For the horizontal and vertical analyses of Figure 9, the following conclusions can be drawn: (1) Figure 9a shows that the system BER decreases as the \(m_{R}\) increases (the wireless link fading is reduced), while the other parameters remain unchanged. When \(m_{R}\) is larger than 2.5, the BER curve gradually flattens and the influence on the BER performance decreases. Similarly, Figure 9b shows that the BER performance of the system increases with the increase of \(\sigma_{D}\) (serious power line link fading) under the condition that other parameters are constant, and the change trend of the BER curve is more significant than that of Figure 9a. Therefore, we conclude that the BER performance of the system is more sensitive to the selection of \(\sigma_{D}\) than that of \(m_{R}\). Therefore, for the dual-hop hybrid fading system, the LogN link occupies the dominant link, and the system performance can be optimized by significantly adjusting the environment variables of the link. (2) Figure 9a,b shows that the change of the channel noise parameters has a small impact on the BER. For example, when other parameters remain unchanged, the system performance improves slightly after \(p\) changes from 0.1 to 0.001, and  
Figure 8: Influence of the selection of the \(s\) value in the two approximation algorithms on the system BER. the BER changes slightly after \(T\) changes from 10 to 100. (3) When the fading parameters \(p\) and \(T\) are the same, the BER performance of the system will significantly improve by increasing \(\Delta\), as reflected in both graphs. However, the improvement of the system will be different by increasing the same \(\Delta\) under different fading parameters.
## 4 Conclusions  
This study presents a sustainable approach to waste management and resource recovery in the banana chip industry by converting banana peel waste into porous adsorbents for purifying used palm oil. Through chemical and thermal activation, the research demonstrates the effectiveness of 5% \(w/v\) acetic acid as an environmentally friendly alternative to conventional strong activators, such as sulfuric acid. This mild activation process is particularly advantageous for small- and medium-sized enterprises (SMEs) aiming to implement sustainable practices. The adsorption experiments utilizing used palm oil demonstrated that A-BP effectively reduced the free fatty acid (FFA) content from 3.108% to 1.69%, corresponding to a reduction of approximately 54.38%. This significant decrease underscores the potential of A-BP for industrial applications in FFA removal. Based on the adsorption test results, the adsorption mechanism primarily follows a physisorption process, in which the adsorption sites are occupied by multiple layers of adsorbate.  
These findings emphasize the potential of utilizing agricultural waste in valorization processes to advance a circular economy, thereby contributing to environmental sustainability and driving innovation in waste-to-resource conversion. Beyond their application  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type of Heavy Metal** & **Amount (mg/kg)** & **Method of Analysis** \\ \hline Lead (Pb) & 0.34 & \\ \hline Cadmium (Cd) & Not detected & EPA 3052 \\ \hline Mercury (Hg) & Not detected & by ICP-OES technique \\ \hline Arsenic (As) & Not detected & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Heavy metal contents detected in the adsorbent prepared from raw banana peels.  
Figure 5: (**a**) Pseudo-first-order kinetic plot and (**b**) pseudo-second-order kinetic plot for the degradation of used oil at a concentration of 100% \(\
icefrac{{v}}{{\gamma}}\).  
in used oil purification, banana-peel-derived adsorbents have demonstrated remarkable efficacy in wastewater treatment, facilitating the removal of dyes, heavy metals, and organic pollutants from industrial effluents through bio-based adsorption processes.  
Conceptualization, P.J. and D.C.; methodology, P.J. and P.T.; validation, P.J.; formal analysis, P.J., P.T. and D.C.; investigation, P.J., P.T. and D.C.; resources, W.K. and A.N.; writing--original draft preparation, D.C.; writing--review and editing, D.C.; supervision, W.K. and A.N.; funding acquisition, W.K. and A.N. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Naresuan University (NU) and the National Science, Research and Innovation Fund (NSRF). Grant No. R2568B031.  
Not applicable.  
The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# From DevOps to MLOps: Overview and Application to Electricity Market Forecasting
## 3 An Overview of DevOps and MLOps
### Deployment
#### 3.2.2 MLOps Pipeline  
In MLOps, the following standard pipeline components are implemented [69, 131, 62]:  
* Continuous Integration (CI);
* Continuous Deployment (CD);
* Continuous Training (CT).  
Continuous Integration  
As in DevOps, the CI pipeline is about the testing and validation of code components. For ML applications, data and model validations are added along with classical unit and integration tests [65]. Unit tests are written to cover the changes in feature engineering, and different methods used to implement the models. Moreover, tests should be written to check the convergence of the model training. During training, a machine learning model reaches a convergence state when the model loss value settles within an error range, after which any additional training might not improve the model's accuracy [156].  
There are considerable changes in the Continuous Deployment pipeline compared to DevOps. As the ML models evolve continuously, verifying the models' compatibility with the target deployment environments with respect to computing power and any changes in the deployment environments is essential. The process changes depending on the use case and whether the ML prediction is online or batch processing [157].  
This new pipeline component is unique to MLOps., The Continuous Training (CT) pipeline automatically retrains the model [158]. Different ML components explained in Section 3.2.1 are automated to work in a sequence to achieve this. Retraining the model is essential, as the data keep changing or updating in any ML application. To cope with the new incoming data, the model needs retraining. This retraining includes automating several model retraining, data validation, and model validation processes. To initiate such processes, triggers are included in the pipeline.  
Some additional components, such as feature storage and metadata management, are used along with these pipelines in MLOps. They help in managing data and reproducibility aspects. These are discussed below.