esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 5G Technology Enablers for V2X Use Cases
### Positioning Capabilities of 5G and Enablers
#### 3.3.2 Hybrid Localization  
An approach for improving localization within 5G is to use hybrid technologies. The results can be more accurate, the response time can be shorter, and the system is more robust, combining multiple diverse methods. In most cases, each component can independently provide localization information, so even if one part fails, the other can supply the system with position information during reduced operation.  
Hybrid methods extend the ability of localization techniques to handle non-line-of-sight (NLOS) situations when the signal is diffracted or reflected, besides the ideal line-of-sight (LOS) situations. The improvement of regular methods using an intermediate approach with problem decomposition considering an end-to-end perspective is presented in [48].  
The global navigation satellite system (GNSS) forms the basis of today's technology for positioning, so it is an obvious choice to be one of the hybrid solutions' resources. The possibility of combining this with other localization methods, like 5G-based or device-to-device (D2D) positioning techniques, is explored in [49].  
A cellular information-based approach is presented in [44], with a physical-layer abstraction-based simulation methodology.  
In [39], further information on the hybrid method sources is used to improve accuracy: the network density of 5G deployments and visibility mask of satellites.  
Table 4 contains a brief summary of novel hybrid localization techniques using 5G. It is worth noting, however, since the technology of 5G is under development, the results are mostly provided based on a simulation-based evaluation. Nevertheless, it can be seen that there are great possibilities in hybrid techniques with forward-looking results that can be used as inspiration to improve these technologies further.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
## 3 Results
### Comparison of Sampling Techniques and Models
#### 3.3.3. SMOTE Tomek Link Combinatorial Sampling  
In terms of precision, GBDT is better than other models with a value of 0.817, followed by XGBoost with a value of 0.811. In terms of accuracy, recall and F1, XGBoost is better than other models with values of 0.819, 0.816, and 0.816, respectively, followed by GBDT with values of 0.806, 0.801, and 0.806, respectively. Combining the four evaluation metrics, the XGBoost model performs best among the data category imbalance processing methods for SMOTE Tomek link combinatorial sampling, with better generalization ability. The following figure shows the confusion matrix for the XGBoost model. As shown in Figure 5 and Table 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Accuracy** & **Recall** & **Precision** & **F1** \\ \hline SVM & 0.593 & 0.593 & 0.596 & 0.590 \\ Logistic Regression & 0.701 & 0.702 & 0.701 & 0.706 \\ Random Forest & 0.703 & 0.713 & 0.707 & 0.715 \\ GBDT & 0.716 & 0.724 & 0.714 & 0.714 \\ XGBoost & 0.728 & 0.721 & 0.727 & 0.711 \\ \hline \hline \end{tabular}
\end{table}
Table 4: ClusterCentroids downsampling modeling results.  
Figure 4: XGBoost confusion matrix after downsampling.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Accuracy** & **Recall** & **Precision** & **F1** \\ \hline SVM & 0.532 & 0.532 & 0.544 & 0.486 \\ Logistic Regression & 0.691 & 0.691 & 0.693 & 0.691 \\ Random Forest & 0.755 & 0.755 & 0.766 & 0.754 \\ GBDT & 0.722 & 0.722 & 0.722 & 0.722 \\ XGBoost & 0.762 & 0.769 & 0.761 & 0.763 \\ \hline \hline \end{tabular}
\end{table}
Table 3: SMOTE oversampled modeling results.  
In summary, the XGBoost model performed best in all three methods, SMOTE oversampling, ClusterCentroids undersampling, and SMOTE Tomek link combination sampling. Meanwhile, SVM and Logistic Regression outperformed the other two sampling methods under ClusterCentroids, and Random Forest, GBDT, and XGBoost outperformed the other two sampling methods under SMOTE Tomek link combined sampling. Therefore, mortality risk prediction was performed using XGBoost based on SMOTE Tomek link combined sampling, and survival analysis was performed using Cox-PH and DeepSurv based on SMOTE Tomek link combined sampling.
# Moment-Based Stochastic Analysis of a Bistable Energy Harvester with Matching Network
## 4 Dimensionless SDEs System  
For practical manipulation, it is often more convenient to work with dimensionless equations. For this purpose, consider an SDEs system written in the form  
\[d\mathbf{Z}_{t}=\big{(}\hat{\mathbf{A}}\,\mathbf{Z}_{t}+\hat{\mathbf{n}}( \mathbf{Z}_{t})\big{)}dt+\varepsilon\hat{\mathbf{B}}\,dW_{t} \tag{12}\]  
where \(\hat{\mathbf{A}}\in\mathbb{R}^{n,n}\), is a matrix representing the coefficients of the linear part of the drift vector, while \(\hat{\mathbf{n}}:\mathbb{R}^{n}\mapsto\mathbb{R}^{n}\) collects the nonlinear part, and \(\hat{\mathbf{B}}\in\mathbb{R}^{n}\) is a constant diffusion vector. Consider the linear variable transformation \(\mathbf{y}=\mathbf{P}\mathbf{z}\), where \(\mathbf{P}\in\mathbb{R}^{n,n}\) is a constant regular matrix. For dimensionless variables, matrix \(\mathbf{P}\) is diagonal with entries represented by normalizing parameters. Using [PERSON]'s lemma [30; 31], the following SDEs system for the stochastic processes \(\mathbf{Y}_{t}\) is obtained:  
\[d\mathbf{Y}_{t}=\Big{(}\mathbf{P}\hat{\mathbf{A}}\mathbf{P}^{-1}\mathbf{Y}_{t }+\mathbf{P}\hat{\mathbf{n}}(\mathbf{P}^{-1}\mathbf{Y}_{t})\Big{)}dt+ \varepsilon\,\mathbf{P}\,\hat{\mathbf{B}}\,dW_{t} \tag{13}\]  
As a dimensionless time, we introduce the new time variable \(\tau(t)=\omega\,t\), where \(\omega>0\) is a frequency. If \(\mathbf{Y}_{t}\) solves (13), then \(\mathbf{Y}_{\tau}\) solves the SDEs system  
\[d\mathbf{Y}_{\tau}=\frac{1}{\omega}\Big{(}\mathbf{P}\hat{\mathbf{A}}\mathbf{P }^{-1}\mathbf{Y}_{\tau}+\mathbf{P}\hat{\mathbf{n}}(\mathbf{P}^{-1}\mathbf{Y}_ {\tau})\Big{)}d\tau+\varepsilon\,\mathbf{P}\,\hat{\mathbf{B}}\,dW_{t} \tag{14}\]The change of time theorem for Ito integrals ([31], p. 156) implies that  
\[W_{\tau(t)}\sim\sqrt{\tau^{\prime}(t)}\,W_{t}=\sqrt{\omega}\,W_{t} \tag{15}\]  
where symbol \(\sim\) means, again, \"distributed as\". Denoting as \(\mathbf{X}_{\tau}\) the solution to the SDEs system  
\[d\mathbf{X}_{\tau}=\frac{1}{\omega}(\mathbf{A}\mathbf{X}_{\tau}+\mathbf{n}( \mathbf{X}_{\tau}))d\tau+\frac{\varepsilon}{\sqrt{\omega}}\mathbf{B}\,dW_{\tau} \tag{16}\]  
where \(\mathbf{A}=\mathbf{P}\hat{\mathbf{A}}\mathbf{P}^{-1}\), \(\mathbf{n}(\mathbf{x})=\mathbf{P}\hat{\mathbf{n}}(\mathbf{P}^{-1}\mathbf{x})\), and \(\mathbf{B}=\mathbf{P}\hat{\mathbf{B}}\), it follows that \(\mathbf{X}_{\tau}\sim\mathbf{Y}_{\tau}\), because they are solutions for the same SDEs system, for two different realizations of the Wiener process.  
In most practical applications the distribution of the stochastic process is the most relevant information because knowledge of the expected quantities is more important than knowledge of the particular solution associated to a specific realization of the noise process, e.g., a strong solution.
# Pedestrian Trajectory Prediction Based on Dual Social Graph Attention Network
## 4 Experiment and Results  
### Datasets  
This study conducted experimental evaluations on the ETH [38] and UCY [39] benchmark datasets, which encompass five distinct scenarios. The ETH dataset comprises \"eth\" (university campus environment) and \"hotel\" (hotel surroundings) scenarios, while the UCY dataset contains \"Univ\" (large academic campus), \"zara1\", and \"zara2\" (both representing commercial district environments).  
The ETH dataset was collected and released by the Computer Vision Laboratory at ETH Zurich, while the UCY dataset was developed by the University of Cyprus. Both datasets have been anonymized, and the pedestrian data included do not contain personal information. Figure 5 illustrates representative frames from these five scenarios (labeled a-e corresponding to eth, hotel, Univ, zara1, and zara2, respectively).  
The details of the originally collected data are shown in Table 1. Each dataset contains approximately 12,380 frames, recorded at a rate of about 25 frames per second. The number of pedestrians varies depending on the specific scene.  
In practical applications, a sampling interval of every 0.4 s is used to record the pixel coordinates of pedestrians in the scene, thereby collecting the trajectory data of pedestrians. To facilitate a comparison with other methods, we adopt a similar observation and prediction time-step division strategy as in the existing research: after observing the pedestrian trajectories for 3.2 s (i.e., 8 time steps), we predict the pedestrian trajectories for the next 4.8 s (i.e., 12 time steps). Similar to the method in the literature [31], during the division of the training and prediction sets, we allocate a portion of the data in the corresponding scenario dataset for testing, and the remaining part is used for model training together with the data from the other four scenarios.
## 4 Experiment and Analysis
### Experiment Analysis  
Based on the results of the two experiments, an experimental analysis was conducted. Figure 24 below demonstrates the AO-PSO-PF algorithm's superior performance in minimizing errors and enhancing positioning accuracy across various metrics compared to the WiFi, FPF, and PDR algorithms in the first experiment. Figure 25 below highlights the AO-PSO-PF algorithm as the most effective in minimizing errors and enhancing positioning accuracy across all metrics in the second experiment. It consistently outperforms PF, APF, and PDR, demonstrating its superiority in achieving precise and reliable position estimation. It is evident that the AO-PSO-PF algorithm outperforms other algorithms (WiFi, PDR, PF, APF, and FPF) as a positioning algorithm.
## 4 Quantitative descriptions of scale threshold and scale domain  
After discussing on the main causes of scale issues and the effects of scale on the measurements, models and products, the analysis of scale threshold and scale domain becomes an another critical problem waiting to be resolved. It is the basis of understanding scale issues. At present, one great advantage of remote sensing is the capacity to provide data at various resolutions. It may become easier to identify the scale thresholds below by identifying which biophysical or geophysical variables are spatially dependent and whether they become less dependent or independent. Here, we may take the scale domain as the appropriate scale for a given geographical environment. The relevant knowledge of scale threshold and scale domain may benefit the understanding of the validation scope of the retrieval model, dynamics of landscapes. In the following, several representative methods will be presented. Although these approaches may not necessarily apply to all cases, they in fact provide effective ways to cope with the problems.
# Proposal for a System Model for Offline Seismic Event Detection in Colombia
## 3 Earthquake Detection Methodologies  
Seismic detection algorithms are used by public and/or private services dedicated to monitor and study seismic activity. Several agencies dedicate efforts to maintain an updated database of information that can help scientists and engineers analyze any activity that could represent a hazard to the infrastructure and population, including volcanic and seismic activities [19]. Data collected include ground motion records (accelerations, velocities, and/or displacements), which are used by detection algorithms as input data.  
Several approaches have been conceived to perform seismic events detection. In the seismic signal, amplitude, shape, power, or several other time-domain characteristics can be used to formulate a detection procedure [19; 20], depending on the desired purpose of the outcome. In practical terms, seismic signals are identified by monitoring isolated ground vibrations, which under changes in amplitude, frequency content, or motion direction indicate the arrival of seismic waves [20]. Current developments on earthquake signals monitoring aim to provide faster and more reliable detection algorithms for warning systems [21].
# Automated DNA Extraction Monitoring System Based on MTConnect Technology
## 2 Materials and Methods
### MTConnect Standard  
MTConnect is a lightweight scalable standard protocol that defines the interface between devices and machine tools and information systems or applications to collect and manage manufacturing data [18]. MTConnect consists of devices, adapters, agents, networks, and client applications. Unlike Open Platform Communications (OPC), MTConnect provides easily scalable protocols and data expression and exchange methods between production equipment and various applications, but it only specifies a one-way communication protocol that flows from a device to an application.  
As a software component of MTConnect, there is an agent at the center of communication, and an adapter is implemented for each of the various existing devices or sensors to relay communication between the device and the agent. The agent transmits the status of the device to the monitoring/analysis application program using a standard web protocol and XML. The data in XML delivered by MTConnect for data exchange are defined by three types of standard schemas: a device schema that describes the type and configuration of the  
Figure 3: DNA extraction system block diagram. A hardware abstraction layer (HAL) directly drives the motor driver and auxiliary (AUX.) devices such as Liner actuator and magnet. Flask is a micro web application framework (WAF) written in Python. It communicates HTTP with the web application control (Ctrl.) UI client.  
equipment, a stream schema that describes the state of the general data during operation of the equipment, and an error schema that provides information about errors or warnings. A system for monitoring multiple devices follows these schemas and MTConnect communication protocols. When a new device is added, the data for the new device are described only within the context of the exchanged XML document data, without overall modification of the system, enabling flexible expansion [19].  
In MTConnect, the client requesting data and the server (or agent) providing data communicate through an HTTP protocol, and the data exchanged over HTTP are in XML format. The main elements of XML defined in MTConnect can be extended by users. The MTConnect agent stores data from manufacturing facilities in memory or storage by first in, first out (FIFO) means, and when a request from a client is received, the agent provides an XML document containing predefined elements as a response to the request. The elements and content contained in the XML document depend on the type of request. The types of requests that the MTConnect client can send to the MTConnect agent are probe, current, and sample requests.
# Improved Cell Allocation Strategies Using K-Means Clustering in Congested 6 TiSCH Environments
## 7 Performance Evaluation  
This section explains the simulators and parameters used in the experiments and our methods for comparing the routing protocols. It also explains how each RPL affects packet data transmission and how the node coordinate clustering results affect the tested performance metrics, including the packet delivery ratio, throughput, latency, and joining time of every node.
## 3 Methodology and Case Description
### Case Description  
We illustrate three 2023 landfalling typhoons: Doksuri, Khanun, and Haikui.  
Figure 2: (**a**) The center of Typhoon Mawar (black cross symbol) determined from HY-2B scatterometer observations of sea surface wind vectors (black arrow) and wind speed (color shading); (**b**) A schematic illustration of the TC center positioning method applied to data within the 3\({}^{\circ}\)\(\times\) 3\({}^{\circ}\) box centered at the maximum wind speed (black dashed box in (**a**) at 2045 UTC on 25 May 2023. Indicated in (**b**) are the maximum wind speed position (triangle), all minimum wind speed positions (black circle), eight values around each black circle, and the center of Typhoon Mawar (black cross).  
* **Doksuri** (5 th typhoon in 2023 NW Pacific): Formed on 21 July east of the Philippines. Rapidly intensified (RI) before crossing Luzon and reached super typhoon status around 27 July (max wind \(\sim\)62 m s\({}^{-1}\)). Made landfall around 28 July near Jinjiang, Fujian (\(\sim\)50 m s\({}^{-1}\), 945 hPa), then moved north, with heavy rains. Weakened to depression by 29 July.
* **Khanun** (6 th): Formed on 28 July and reached super typhoon strength around 31 July (935 hPa, 52 m s\({}^{-1}\)). Initially moved westward, turned northeast around 4 August in the East China Sea, then north. Landfall near Gyeongsangnam, South Korea on 10 August (28 m s\({}^{-1}\), 975 hPa), and again near Liaoning, China on 11 August.
* **Haikui** (11 th): Formed on 28 August, reached a severe tropical storm around 29 August, and became a typhoon by around 1 September. Landed in southeastern Taiwan on 3 September (\(\sim\)30 m s\({}^{-1}\)), and a second landfall on 5 September in Dongshan County, Fujian (\(\sim\)975 hPa).
# A Review of Irrigation Information Retrievals from Space and Their Utility for Users
## 3. Irrigation Remote Sensing
### Microwave-Based Methods  
Microwaves have the advantage that they are not hindered by weather conditions, and are independent of illumination. Microwaves are sensitive to the water content in the soil surface and vegetation and therefore have the potential to monitor changes in soil moisture as a result of irrigation events.
## 4 Discussion
### Scalability and Hardware Constraints  
In addition to technical considerations, practical cost implications are a key concern for hospitals deploying large language models (LLMs) and vision-language models (VLMs). Although JAVIS itself remains agnostic regarding specific GPUs or model architectures, the computational requirements of underlying LLMs can be substantial. For instance, running a 7-8B model often requires approximately 6-10 GB of GPU memory under 4-bit quantization, an optimization strategy that helps reduce resource usage. As the number of active users grows, so does the demand for additional GPU replicas to maintain acceptable latency, thus influencing both the hardware outlay and operational expenditures (e.g., power and cooling).  
Scalability tests demonstrated that JAVIS performs reliably even on older hardware configurations, such as a four-A6000 GPU cluster. In peak-performance trials, it supported up to 50 simultaneous users for 8B models, 100 users for 7B VLMs, and 150 users for 1B LLMs with acceptable latency. This highlights the system's robustness yet also underscores a broader limitation: larger user bases require proportionally more GPU resources. Notably, in practical usage scenarios, real concurrency levels tend to be lower. For instance, in our pilot environment for a 40-person lab, we utilized only half of a single A6000 card (effectively 24 GB of GPU memory) and still maintained responsive performance, as not all users simultaneously query the system at peak load.  
Despite these demands, JAVIS's modular design and orchestration via Ray allow hospitals to scale resources incrementally. This approach mitigates the capital expenditure required for immediate large-scale deployment, enabling a more gradual expansion as adoption increases. Additionally, cost-saving strategies can be employed by leveraging smaller or specialized models (e.g., 3B parameters) for routine inquiries, substantially reducing memory and power consumption. We observed that a single 7-8B model instancecan handle multiple concurrent users; in real-world conditions, user queries are typically staggered, further lowering the need for constant peak performance.  
Looking ahead, ongoing innovations in model distillation and quantization will continue to reduce the computational overhead associated with LLMs and VLMs. By maintaining a container-based, software-centric architecture, JAVIS can seamlessly integrate these more efficient models as they emerge. Consequently, hospitals can adopt a cost-effective, future-proof strategy that aligns with both existing resource constraints and evolving AI technologies.
## 2 Materials and Methods
### Soil Sampling and Gravimetric Measurements  
From the two selected soil textures, 300 soil samples were collected between June and October 2023 using a hand auger (Figure 2B). Sampling was conducted at five depths (5, 20, 40, 60, and 80 cm) from three sampling points near the sensor installations, following a random sampling grid approach. Soil samples were collected at 10 different dates spaced two weeks apart, resulting in a total of 150 samples from each soil texture.  
Soil moisture content was calculated using the gravimetric method based on dry weight [31]. The samples from each depth and soil texture were placed in pre-weighed aluminum containers and dried in an oven at 105 \({}^{\circ}\)C until a consistent weight was achieved. The containers were then weighed again to calculate dry weight of the soil. The soil moisture content (\(\theta\)) was calculated using the following Equation (1):  
\[\theta=\frac{Mw}{Md}\times 100 \tag{1}\]  
where  
\(\theta=\) Soil Moisture Content (\(\%\));  
\(Mw=\) Mass of water (g) = (Wet weight \(-\) Dry weight);  
\(Md=\) Mass of dry soil (g).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Soil Texture** & **pH** & \begin{tabular}{c} **Sand \%** \\ **Range** \\ \end{tabular} & \begin{tabular}{c} **Silt \%** \\ **Range** \\ \end{tabular} & \begin{tabular}{c} **Clay \%** \\ **Range** \\ \end{tabular} & \begin{tabular}{c} **Mean** \\ **SMC \%** \\ \end{tabular} & \begin{tabular}{c} **Std of** \\ **SMC \%** \\ \end{tabular} & \begin{tabular}{c} **Bulk** \\ **Density** \\ \end{tabular} \\ \hline \
\begin{tabular}{c} Loam \\ Slit loam \\ \end{tabular} & 7.614 & 40.99–49.01 & 40.91–45.97 & 10.10–14.50 & 13.79 & 3.94 & 1.538 \\
\begin{tabular}{c} Slit loam \\ \end{tabular} & 7.45 & 12.36–15.31 & 63.70–70.19 & 17.40–21 & 16.95 & 5.07 & 1.52 \\ \hline \end{tabular}
\end{table}
Table 1: Soil properties averages and ranges of soil content of sand, silt, and clay in the five depths in the two studied soil textures with mean SMC.  
Figure 2: (**A**) IoT-based meteorological sensor station within the field. (**B**) Collecting soil samples using a hand auger from sampling points around the sensor stations.