esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiment Design and Methodology
### Orthogonal Experimental Design  
Orthogonal experiment is an experimental method designed by Dr. [PERSON] (2015). Orthogonal experimental design is the main method for partial factorial experiments and is mainly used for arranging multifactorial experiments. The points with \"uniform\" and \"neat\" characteristics are selected from the full-scale test for the test arrangement. Therefore, it is highly efficient and widely used.  
Determination of the range of factor levels for the hot compression molding process of high-performance bamboo-based fiber composites:  
1. Density. Density is an essential influence factor on bamboo-based fiber composites, and in this investigation, three density levels of 900 kg/m\({}^{3}\), 1050 kg/m\({}^{3}\), and 1200 kg/m\({}^{3}\) were selected to investigate the effect of different density conditions on the heat transfer properties of hot pressing.
2. Moisture content. Three moisture content levels of 5%, 10% and 15% were selected for analysis, and the moisture content was adjusted using the water spray method and a drying oven.
3. Glue spread of fibrous veneer. The amount of adhesive sizing amount was set at 12%, 14%, and 16% based on actual processing experience.
4. Hot pressing temperature. The adhesive used in this study is phenolic resin, and its curing temperature is 140 \({}^{\circ}\)C. Therefore, the set temperature parameters are 135 \({}^{\circ}\)C, 140 \({}^{\circ}\)C and 145 \({}^{\circ}\)C.
5. Pressure holding time. According to the actual manufacturing experience, the pressure holding time is set to 8 min, 10 min, and 12 min.
6. Ending temperature. According to the practice of production experience, the temperature of the end time is set at 55 \({}^{\circ}\)C, 60 \({}^{\circ}\)C and 65 \({}^{\circ}\)C.  
Based on the above-mentioned parameter selection, the orthogonal experimental table for this experiment is shown in Table 1:  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Test** & **Density** & **MC (\%)** & **GS (\%)** & **SD (rank)** & **PHT (min)** & **ET (\({}^{\circ}\)C)** \\
**Number** & **(kg/m\({}^{3}\))** & & & & & & \\ \hline
1 & 900 & 5 & 12 & 2 & 8 & 55 \\
2 & 900 & 5 & 12 & 2 & 10 & 60 \\
3 & 900 & 5 & 12 & 2 & 12 & 65 \\
4 & 900 & 10 & 14 & 3 & 8 & 55 \\
5 & 900 & 10 & 14 & 3 & 10 & 60 \\
6 & 900 & 10 & 14 & 3 & 12 & 65 \\
7 & 900 & 15 & 16 & 4 & 8 & 55 \\
8 & 900 & 15 & 16 & 4 & 10 & 60 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Orthogonal test table for high performance bamboo-based fiber composites.
# Urban Road Anomaly Monitoring Using Vision-Language Models for Enhanced Safety Management
## 2 Methods
### Performance Evaluation
#### 2.4.2 Real-Time System Architecture  
To test real-time performance, (1) cloud server processing and (2) edge cloud server processing were tested based on the A800 Server and NVIDIA Jetson (NVIDIA Corporation, Santa Clara, CA, USA). First, the time for the full-process inference using the cloud server and the edge was tested. After optimization, (2) edge cloud server processing was proposed, where the edge makes a preliminary inference. Once an event on the road is detected, the cloud server is then used for further inference (Figure 4). This approach enables more efficient collaboration between the edge and the cloud.  
Under our current testing conditions, the downstream speed is 26 Mbps, which fully meets the requirements. According to the specifications of the Vision-Language Model and actual test results, images with a resolution of 448 dpi \(\times\) 448 dpi or higher are sufficient.  
The basic parameters of the cloud and the edge are shown in Table 7.  
Figure 4: The two paths for real-time testing.  
* Response time To further evaluate the model's response time in real-world scenarios, we conducted real-time tests on the roads of a community in Shenzhen over two separate days in February 2025. These tests occurred during both daytime and nighttime, encompassing various weather conditions, including sunny, rainy, and windy. The camera was mounted at the front of the car for monitoring, and various road conditions were selected for video capture. The real-time video stream from the camera was transmitted to both the cloud service and the edge computer via APIs for testing. We assessed the response time of each system.
* Robustness under diverse environmental conditions Regarding the robustness under diverse environmental conditions, we carried out basic qualitative tests. We collected and tested real-time data at night, during winds, and in rainy weather. Then, we made judgments on the collected data to analyze the possible situations.
# Empirical Evaluation of TLS-Enhanced MQTT on IoT Devices for V2X Use Cases
###### Abstract  
The rapid growth of Internet of Things (IoT) deployment has led to an unprecedented volume of interconnected, resource-constrained devices. Securing their communication is essential, especially in vehicular environments, where sensitive data exchange requires robust authentication, integrity, and confidentiality guarantees. In this paper, we present an empirical evaluation of TLS (Transport Layer Security)-enhanced MQTT (Message Queuing Telemetry Transport) on low-cost, quad-core Cortex-A72 ARMv8 boards, specifically the Raspberry Pi \(4B\), commonly used as prototyping platforms for On-Board Units (OBUs) and Road-Side Units (RSUs). Three MQTT entities, namely, the broker, the publisher, and the subscriber, are deployed, utilizing Elliptic Curve Cryptography (ECC) for key exchange and authentication and employing the AES_256_GCM and ChaCha20_Poly1305 ciphers for confidentiality via appropriately selected libraries. We quantify resource consumption in terms of CPU utilization, execution time, energy usage, memory footprint, and goodput across TLS phases, cipher suites, message packaging strategies, and both Ethernet and WiFi interfaces. Our results show that (i) TLS 1.3-enhanced MQTT is feasible on Raspberry Pi \(4B\) devices, though it introduces non-negligible resource overheads; (ii) batching messages into fewer, larger packets reduces transmission cost and latency; and (iii) ChaCha20_Poly1305 outperforms AES_256_GCM, particularly in wireless scenarios, making it the preferred choice for resource- and latency-sensitive V2X applications. These findings provide actionable recommendations for deploying secure MQTT communication on an IoT platform.  
Elliptic Curve Cryptography; TLS cipher suites; Internet of Things; MQTT; Vehicle to Everything; efficiency
## 2 Navigation System for Visually Impaired Pedestrians
### Related Works on 3 DMA GNSS
There is research using the Kalman filter (KF) and extended Kalman filter (EKF) to recursively update the recent state through the prediction based on past estimation and error of current measurements [36; 37]. Researchers also use particle filters to effectively distribute and sample the candidates [26; 38]. Moreover, a grid filter was adopted to distribute positioning candidates evenly [34]. The filtering techniques demonstrate excellent results in improving the smoothness of the positioning solution. Meanwhile, a machine learning approach can intelligently predict the change of signal status and estimate the most likely path on the map as the optimized positioning estimation [39]. Another approach is using factor graph optimization (FGO) [40], which optimizes the states of all epochs with many constraints. The solution will be highly robust as FGO optimizes all temporal constraints as a batch approach. Researchers open-sourced the FGO code that integrates GNSS positioning with Doppler measurements to provide a multi-epoch optimized solution [41]. FGO also applies to centimeter-level accuracy positioning via carrier-phase measurements, such as GNSS PPP [42] and RTK [41]. Furthermore, 3 DMA GNSS-based collaborative positioning can benefit from using FGO [43] to optimize the performance of multi-agent collaborative positioning. Different research demonstrates that FGO can provide an excellent positioning performance. This study integrates 3 DMA GNSS with velocity estimated by Doppler measurements as a loosely-coupled solution, and states were optimized via FGO. The integrated solution can provide a more robust trajectory for pedestrian applications, such as this wearable.
# Learning Approach for Angle Estimation Based on Characteristics of Phase Drift
## 4 Performance Evaluation
### Analysis of Experiment Results
#### 4.3.1 Accuracy of Angle
The smooth convergence of both losses suggests that the sliding window approach used for time series data does not introduce significant inconsistencies in capturing temporal dependencies. Overall, the model demonstrates stable learning dynamics, with validation loss aligning closely with training loss, indicating improved generalization and effective learning of underlying patterns. Figure 12 illustrates the error distribution by angle for the GRU layer model. The x-axis represents the angle (degrees), while the y-axis represents the error values. The blue line in the figure represents the variance, which reflects the overall spread of prediction errors across different angles. The blue dots indicate the standard deviation at each angle, showing the typical amount of deviation from the mean error. The red vertical bars illustrate the error range, extending above and below the mean error to visualize how much the prediction errors can vary. This representation provides a comprehensive view of both the average deviation and the overall error distribution across angles. The results indicate that the error values are consistently low and stable across most angles, with minimal fluctuations. The variance remains close to zero, and the standard deviation is also low, suggesting that the GRU model provides reliable and consistent predictions across different angles without significant deviations or spikes.
###### Abstract  
(1) Background: In the last two decades, anticancer treatment has been extensively developed based on various physical methods, including electroporation (EP). On the basis of many in vitro and in vivo studies, electroporation and further electrochemotherapy (ECT) have been established as a treatment method for cutaneous and subcutaneous lesions. In this procedure, after placing electrodes in the tumor mass and the generation of electrical pulses, a reversible or irreversible rearrangement of the cell membrane occurs. Calcium electroporation has already been applied to treat skin tumors and subcutaneous tissue tumors. Here, we demonstrate the first application of irreversible electroporation (IRE) in combination with calcium ions and chemotherapy for patients with cancer. (2) Methods: This study aimed to present and compare the findings and outcomes of patients with locally advanced pancreatic adenocarcinoma who were qualified for the procedure. Two patients were treated with IRE with calcium ions after chemotherapy (paclitaxel + Gemcitabine (GCB) or FOLOX) or only FOLFOX (folinic acid calcium foliate, fluorouracil, and oxaliplatin). The clinicopathological data, overall survival, and the safety of the procedure were analyzed. (3) Results: Two patients were treated with calcium electroporation. One of the patients developed pancreatitis and the second developed pancreatic fistula, but both of them continued standard systemic treatment. Overall survival was 9 months in the first case and 21 months in the second case (and the patient is still alive). Calcium electroporation had a good impact on QOL (Quality of Life). (4) Conclusions: IRE accompanied chemotherapy, and intratumoral calcium ions administration might represent an additional therapy to surgery and chemotherapy in patients with locally advanced pancreatic cancer (LAPC), particularly in unresectable cases. However, further studies of randomized trials should be undertaken to elucidate the role of chemotherapy in IRE protocols.  
Keywords: pancreatic cancer; calcium electroporation; irreversible electroporation; electrochemotherapy +
Footnote †: journal: Applied Soft Matter
## 4 Modeling of C and Water Dynamics in Terrestrial Ecosystems
### Modeling Dynamics of Stable C Isotopic Exchange between Ecosystem and the Atmosphere  
It is recognized that the atmospheric measurements are still too sparse, relative to its spatial variability, to be used for inferring the surface flux at high spatial resolution [168]. The use of the isotope ratio as an additional constraint to identify various C sources and sinks can contribute to a significant reduction in the uncertainty. Though available isotopic datasets are being accumulated quickly [117,169-171], isotope measurements are still lacking considering land surface diversity and heterogeneity. This shortage of long-term measurements and of sampling frequency still limits C isotopic studies.  
Mechanistic ecosystem models that couple micrometeorological and eco-physiological theories have the potential to shed light on how to extend efforts and applications of stable isotopes of CO\({}_{2}\) to global C budgeting, because biophysical models have the capacities of simulating isotope discrimination in response to environmental perturbations and can produce information on its diurnal, seasonal and interannual dynamics. Few biophysical models, however, have been developed to assess stable C discrimination between a plant canopy and the atmosphere [94,87,135,]. Most existing biophysical models are based on individual leaf level discrimination equations given by [PERSON] _et al._[173,174] and only focus on the land surface layer (ignoring vertical and horizontal advection effects beyond 50-100 m above the ground [172]. However, in nature, the convective boundary layer (CBL) integrates the effects of photosynthesis, respiration, and turbulent transport of CO\({}_{2}\) over the landscape [107,175]. The influence of the CBL cannot be ignored when using isotope composition of CO\({}_{2}\) to investigate biological processes [176], because the effect of atmospheric stability on turbulent mixing/diffusion has an important impact on scalar fluxes and concentration fields within and above canopies [177,178]. Few such models considering the CBL effects on isotope fractionation have been developed to date [107,178-182].
# Effect of Temperatures on Polyphenols during Extraction
## 7 Challenges in the Analysis of the Extractant  
To develop an understanding of the impact of temperature on the extraction of polyphenols, the extractants must be critically analysed. The total phenolic content (TPC) and antioxidant activity (AOA) are the most commonly used measures. The TPC is usually calculated using the Folin-Ciocalteu assay or by HPLC analysis. The AOA can be calculated using a variety of assays: ABTS (2,2-azinobis (3-ethyl-benzothazoline-6-sulfonic acid)), DPPH (2,2-diphenyl-1-picrylhydrazyl), FRAP (ferric-reducing antioxidant power or ORAC (oxygen radical absorption capacity) assays [86]. Most studies have performed more than one antioxidant assay to analyse the extractant.
## 2 System Description and Mathematical Modeling
### System Mathematical Modeling
\begin{split}\dot{x}_{1}=x_{2}\\ \dot{x}_{2}=\frac{u_{1}}{m}\text{cos}(\psi_{1})\text{sin}(\theta_{1})\text{ cos}(\varphi_{1})+\text{sin}(\psi_{1})\text{sin}(\varphi_{1})-\frac{C_{dr}}{m}x_{2}+w_{k} \end{split}  \tag{2}\]  
\[ \begin{split}\dot{y}_{1}=y_{2}\\ \dot{y}_{2}=\frac{u_{1}}{m}\text{cos}(\varphi_{1})\text{sin}( \varphi_{1})\text{sin}(\theta_{1})-\text{cos}(\varphi_{1})\text{sin}(\varphi_ {1})-\frac{C_{dr}}{m}y_{2}+w_{k}\end{split}  \tag{3}\]  
\[ \begin{split}\dot{z}_{1}=z_{2}\\ \dot{z}_{2}=\frac{u_{1}}{m}\text{cos}(\theta_{1})\text{cos}( \varphi_{1})-\frac{C_{dr}}{m}z_{2}-g+w_{k}\end{split}  \tag{4}\]  
\[ \begin{split}\dot{\varphi}_{1}=\varphi_{2}\\ \dot{\varphi}_{2}=\frac{u_{2}}{T_{x}}-\frac{C_{dr}}{l_{x}}\ \varphi_{2} ^{2}-\frac{l_{x}\Omega_{x}}{l_{x}}\theta_{2}-\frac{\left(l_{x}-l_{y}\right)}{ l_{x}}\theta_{2}\psi_{2}+w_{k}\end{split}  \tag{5}\]  
\[ \begin{split}\dot{\theta}_{1}=\theta_{2}\\ \dot{\theta}_{2}=\frac{u_{2}}{l_{y}}-\frac{C_{dr}}{l_{y}}\ \theta_{2}^{2}+\frac{l_{x}\Omega_{x}}{l_{y}}\theta_{2}-\frac{\left(l_{x}-l_{z }\right)}{l_{y}}\theta_{2}\psi_{2}+w_{k}\end{split}  \tag{6}\]  
\[ \begin{split}\dot{\psi}_{1}=\psi_{2}\\ \dot{\psi}_{2}=\frac{u_{4}}{l_{z}}\ -\ \frac{C_{dr}}{l_{z}}\ \psi_{2} ^{2}\ -\ \frac{\left(l_{y}-l_{x}\right)}{l_{z}}\varphi_{2}\theta_{2}+w_{k}\end{split}  \tag{7}\]  
where:  
* \(w_{k}\) and \(v_{k}\) are, respectively, the model uncertainties and sensor measurement noise. They correspond to unmeasurable white noise that affects the model dynamics;
* \(u_{1}\) represents the total thrust force, while \(u_{2},u_{3}\) and \(u_{4}\) are the total torque for roll, pitch, and yaw, respectively. * \((x_{1},\,y_{1},\,z_{1})^{T}\) and \((\varphi_{1},\,\theta_{1},\,\psi_{1})^{T}\) are, respectively, linear and angular positions. * \((x_{2},\,y_{2},\,z_{2})^{T}\) and \((\varphi_{2},\,\theta_{2},\,\psi_{2})^{T}\) are, respectively, linear and angular velocities. * To simplify the notation, we define \(\Omega_{r}=\omega_{1}^{2}\ -\ \omega_{2}^{2}\ +\ \omega_{3}^{2}\ -\ \omega_{4}^{2}\). The quadrotor trajectory can be created by combining the speeds of the four rotors. Then, the control inputs can be defined as:  
\[ \begin{split}\begin{pmatrix}u_{1}\\ u_{2}\\ u_{3}\\ u_{4}\end{pmatrix}=\begin{pmatrix}K_{b}&K_{b}&K_{b}\\ K_{b}&0&-K_{b}&0\\ 0&K_{b}&0&-K_{b}\\ -K_{d}&K_{d}&-K_{d}&K_{d}\end{pmatrix}\begin{pmatrix}\omega_{1}^{2}\\ \omega_{2}^{2}\\ \omega_{3}^{2}\\ \omega_{4}^{2}\end{pmatrix}\end{split}  \tag{8}\]
# Reviewing 6D Pose Estimation: Model Strengths, Limitations, and Application Fields
## 4 Models
### Real-Time Models
The model EPro-PnP [52], published in 2025, is a probabilistic PnP layer that transforms traditional non-differentiable pose estimation into an end-to-end learnable framework by modeling pose as a probability distribution on the SE(3) manifold, enhancing 2D-3D correspondence learning with KL divergence minimization and derivative regularization, demonstrating strong applicability in robotics and autonomous driving. The model \"Focal Segmentation\" proposed in [53], published in 2024, presents an improved 6D pose estimation method for augmented reality, enhancing PVNet with a focal segmentation mechanism to improve object pixel segmentation under severe occlusion, enabling robust keypoint localization and pose estimation via a PnP algorithm while maintaining real-time performance, with future work exploring backbone network modifications and hyperparameter tuning. The model proposed by [PERSON] et al. [54], introduces a lightweight 6 DoF pose estimation method that enhances PVNet with depth-wise separable convolutions, coordinate attention, and an improved ASPP module for better multi-scale feature fusion, achieving robustness to scale variations while reducing computational complexity, making it suitable for deployment on low-performance devices such as mobile platforms. The model Lite-HRPE [55], published in 2024, is a lightweight 6 DoF pose estimation network designed for intelligent robotics, incorporating a multi-branch parallel structure, Ghost modules, and an optimized feature extraction network to reduce computational complexity while maintaining accuracy, making it well-suited for real-time deployment in resource-constrained environments. The model YOLOX-6D-Pose [56], published in 2024, is an end-to-end multi-object 6D pose estimation framework that enhances the YOLOX detector to directly predict object poses from a single RGB image, eliminating the need for correspondences, CAD models, or post-processing, making it a highly efficient and accurate solution for real-time applications. The model IFFNeRF [57], published in 2024, is a real-time 6 DoF camera pose estimation method that utilizes NeRF-based Metropolis-Hastings sampling and an attention-driven ray matching mechanism to estimate poses without an initial guess, demonstrating improved robustness and efficiency across synthetic and real-world datasets
## 6 Conclusions and Recommendations  
### Conclusions  
The comprehensive analysis of Dar es Salaam Port using Markov processes, cellular automata, and max-flow models provides significant insights into its operational dynamics and highlights critical areas for improvement. The Markov process analysis reveals that cargo spends approximately 15% of the time waiting at the port, 50% in transit, 10% delayed, and 25% successfully delivered. This indicates that while the system manages to deliver a substantial portion of cargo efficiently, there are notable inefficiencies, particularly in cargo handling and delays. Addressing these inefficiencies could involve enhancing port infrastructure and optimizing the processing workflow to reduce the time cargo spends waiting and being delayed.  
The cellular automata model simulates traffic flow over a 20 km stretch from the port, highlighting severe congestion, especially for heavy trucks. With an average travel time of 45 min for trucks due to poor road conditions and frequent construction zones, compared to 30 min for smaller vehicles, the simulation underscores the impact of infrastructure limitations. Traffic density peaks at 200 vehicles per kilometer near the port entrance, with significant bottlenecks exacerbated by construction zones and poor road maintenance. Seasonal rainfall increases travel times by an additional 10 min and reduces road capacity by 20% during the rainy season. These findings suggest an urgent need for road maintenance, expansion, and improved traffic management to alleviate congestion and enhance flow efficiency.  
The max-flow model further elucidates the capacity and demand dynamics of the transportation network. With a cargo demand of 6000 to 8000 tons per day from Dar es Salaam Port, the capacity of various routes is critical. For example, the road from Dar es Salaam to Lusaka has a capacity of 1800 tons per hour, but with a demand of 1800 tons per day, only 300 tons per day can be handled by road due to the limited rail capacity of 1500 tons per day. Additionally, the road to Lubbamshi, with a capacity of 2000 tons per hour, falls short of the demand of 2500 tons per day, revealing a need for capacity enhancements. Efficient allocation of flows across routes, balancing road and rail usage, and addressing bottlenecks are essential for meeting demand and optimizing cargo distribution.  
Addressing the identified inefficiencies through targeted investments in port infrastructure, road maintenance, and traffic management is crucial for improving operational efficiency. Enhancing the coordination between road and rail transport, along with adapting to environmental challenges, will support the port's role in regional economic development and ensure a more reliable and efficient cargo movement system.