esa-sceva/satcom-chunk-collection huggingface dataset:
The key contributions of this study are as follows:  
* We generated 50,000 abnormal activities by considering all potential anomalies that could occur in a two-resident home, significantly enhancing the reliability of our research findings. * Our research includes a comprehensive guide that examines how varying the training-test splitting ratios and implementing _k_-fold cross-validation impact the performance of these classifiers. * In our study, we also present a detailed analysis of the computational complexity of these classifiers, spanning from the training phase to making predictions. Thisanalysis effectively illustrates the trade-off between performance and computational costs associated with these algorithms. * Our research entails a rigorous comparative analysis of these classifiers using the activity recognition using ambient sensing (ARAS) multi-resident smart home dataset. Additionally, we offer valuable insights and recommendations for future researchers in this field, aiming to guide and inform their work on similar topics or within the same domain. Our research offers valuable insights to fellow researchers by pinpointing the optimal machine learning algorithm within the smart home domain. Additionally, it contributes to the ongoing progress in crafting more efficient and effective anomaly detection methods for two-resident scenarios. These findings carry substantial potential for a wide range of applications across domains such as healthcare, security, and smart home technologies. The rest of the paper is structured as follows: Section 2 covers the literature review. Section 3 explains different machine learning and deep learning classifiers used in this study and their detailed implementation. Section 4 presents the obtained results and comparative analysis among the classifiers, and Section 5 summarizes the proposed study along with future directions.
# Robust Distributed Rendezvous Using Multiple Robots with Variable Range Radars
## 5 Simulation Results
### The Effect of Changing the Radar Sensing Measurement Noise \(D_{r}\)  
Table 2 shows the effect of changing the radar sensing measurement noise \(D_{R}\) in (2). We use \(N^{min}=5\) in this table. Table 2 shows that increasing \(D_{R}\) up to 1 km does not degrade the rendezvous performance significantly. This shows that the proposed rendezvous approach is robust to sensing measurement noise.
# Monitoring Perishable Commodities Using Cellular IoT:
## 2 Materials and Methods
### Preliminary Test  
As a preliminary study of the datalogger's functionality, a two-week static trial was conducted to verify its performance in monitoring the fundamental environmental parameters of temperature, relative humidity and CO\({}_{2}\) concentration, as well as battery level, under stable low-temperature conditions. This test was performed by placing the device inside a refrigerator and a freezer, set to 6 \({}^{\circ}\)C and \(-19\) \({}^{\circ}\)C, respectively, to simulate cold storage conditions typically encountered in refrigerated transport. The device was positioned in an intermediate zone on both occasions, and a 3000 mAh battery was used to evaluate power autonomy.  
As it was a static test, no movement was involved and, therefore, vibrations and luminosity measurements were not evaluated. Also, wireless communication was not a limiting factor in this test, since there were no network signal challenges. The main goal was to confirm reliable measurements of temperature, relative humidity, CO\({}_{2}\) concentration and battery performance in low-temperature environments.
# 3D Positioning of Drones through Images
## 3 Methods
### Improved Attention Structure  
For the multi-altitude positioning proposed in this paper, we aim to enhance the model's adaptability to changes in spatial height during localization. To this end, we designed the aforementioned multi-scale input of UAV images. Using UAV images with the same center point, a \(256\times 256\) UAV image, when reshaped to a uniform size of \(96\times 96\), provides a wider range of ground features. A \(128\times 128\) UAV image, when reshaped to \(96\times 96\), offers richer detail features.  
How can the model compare the two UAV images to identify the UAV's position on the satellite image? We achieve this through an improved attention structure. At each stage of the backbone network, as the model extracts features, information exchange occurs between the UAV images and the satellite image.  
The design concept is to first perform attention interaction between the larger \(256\times 256\) UAV image and the satellite image, creating an area of focus on the satellite image. Then, the smaller \(128\times 128\) UAV image interacts again with the satellite image that now has a focused area, to obtain a more precise matching position.  
Figure 6: Feature extraction backbone. The features gradually decrease in size over the three stages, while the number of feature map channels for these three stages are 64, 128, and 320 respectively.  
The right side of Figure 7 shows the attention mechanism part. In transforming input patches into K and V, we continue to use the SRA module [19] method to reduce computational load. First, the drone and satellite patches are transformed into Qu, Ku, Vu for the UAV and Qs, Ks, Vs for the satellite image through linear transformation and SRA. The UAV image first undergoes self-attention, followed by cross-attention between the UAV and satellite images. The vector obtained after cross-attention is concatenated with the drone1 vector, and the process of linear transformation, self-attention, and cross-attention is repeated. Finally, the vectors obtained from the two self-attention operations and the last cross-attention operation are concatenated to form the output of the attention operation.  
MH-PCPVT adopts asymmetric cross-attention during the operation of the attention mechanism. Self-attention operations are allowed for each sequence, and the combination  
Figure 7: Transformer stage and attention operation.  
of feature extraction and relationship modeling is realized through sequence concatenation and cross-attention operations. In the process of attention calculation, the UAV features perform self-attention. The advantages are: first, a full crossover operation will require more computation; second, the self-attention process of the UAV image enhances the information of the UAV feature branch. Through the method of joint feature extraction and information exchange, the satellite image and two UAV images establish information exchange, so that the positioning performance is improved.
# Intelligent Total Transportation Management System for Future Smart Cities
## 3 Results--Examples of Sub-Model Developments
### A New Car-Following Model
The reaction time was taken as 0.7 s. The GHR and the Markov models are quite similar. The changes in velocities are nearly the same for each vehicle in the case of using the GHR model. The S-curve model initiates an earlier reaction to the changes in the condition of the first vehicle. Simultaneously, the dynamics of the motion of the following cars lead to slower stabilizing the conditions. Only the Markov model really takes into account the changes in the relative distance between the vehicles. Therefore, the following car's velocity increases or decreases more than the leader car's velocity, which means that the changes in relative distances between the vehicles are much (nearly four times) smaller than in cases of the GHR or S-curve models. Figure 6 shows some exciting results of changes in model parameters. Figure 6a,b illustrates the sensitivity of GHR models to changes in their parameters. The GHR model is not sensitive to the relative distances as visible in Figure 6c. In case of starting the simulation with a car velocity being equal to 30 m/s, beside the eighth vehicle with 25 m/s, only; the velocity of the eighth vehicle quickly increases to 30 m/s, but the relative distance between the seventh and eighth as well as between the eighth and ninth cars also rise quickly. The Markov model also highly depends on the changes in parameters (Figure 6d-g). Figure 6e demonstrates that the changes in the coefficient take into account the relative distances between the vehicles, which may cause a collision. In the given simulation, at least three cars out of 20 will have accidents. On the other hand, the Markov model has no such significant sensitivity to the changes in initial conditions (Figure 6i). Figure 5: Simulation results obtained with the Gazis–Herman–Rothery (GHR), S-curve, and Markov car-following models (the horizontal axis is time; the vertical axis is value).
# Graphene-Based Materials for the Separator Functionalization of Lithium-Ion/Metal/Sulfur Batteries
###### Abstract  
With the escalating demand for electrochemical energy storage, commercial lithium-ion and metal battery systems have been increasingly developed. As an indispensable component of batteries, the separator plays a crucial role in determining their electrochemical performance. Conventional polymer separators have been extensively investigated over the past few decades. Nevertheless, their inadequate mechanical strength, deficient thermal stability, and constrained porosity constitute serious impediments to the development of electric vehicle power batteries and the progress of energy storage devices. Advanced graphene-based materials have emerged as an adaptable solution to these challenges, owing to their exceptional electrical conductivity, large specific surface area, and outstanding mechanical properties. Incorporating advanced graphene-based materials into the separator of lithium-ion and metal batteries has been identified as an effective strategy to overcome the aforementioned issues and enhance the specific capacity, cycle stability, and safety of batteries. This review paper provides an overview of the preparation of advanced graphene-based materials and their applications in lithium-ion, lithium-metal, and lithium-sulfur batteries. It systematically elaborates on the advantages of advanced graphene-based materials as novel separator materials and outlines future research directions in this field.  
graphene; separator; lithium-ion battery; lithium-metal battery; lithium-sulfur battery +
Footnote †: journal: materials
# A Review of Regional and Global Gridded Forest Biomass Datasets
## 3 Current Gridded Forest Biomass Maps
### AGB Maps Derived from a Combination of Field Biomass, LiDAR Data and Optical and/or Radar Data
#### 3.4.9 French Guiana AGB Map  
[PERSON] et al. [94] produced a French Guiana AGB map at 1 km resolution using a calibrated regression model and a combination of field data, spaceborne LiDAR, optical and radar data, as well as environmental data. AGB precision estimates of 50.2 Mg/ha and a R\({}^{2}\) of 0.66 was achieved at the 1000 m resolution, while RMSEs of the biomass map with a spatial resolution of 500 m and 2 km were 72.8 Mg/ha and 43.2 Mg/ha, respectively.
# Execution Model to Reduce the Interference of Shared Memory in ARINC 653 Compliant Multicore RTOS
## 3 Execution Models for Multicore RTOS
### Implementation of Execution Models
#### 3.2.3 Proposed Model  
The memory bus of our target board has two coherences in one cycle meaning that two requests for the main memory can be processed at once. We propose a multi-TDMA model that can improve the TDMA and AER models using hardware characteristics. The goal of the proposed multi-TDMA is to increase utilization over TDMA and AER while allowing slight interference due to memory sharing. However, to satisfy the deterministic characteristics of the avionics system, the balance of utilization and interference should be considered.  
Multi-TDMA runs two cores in parallel, as shown in Figure 10. This implementation of the multi-TDMA model reduces the number of pseudo-partitions by two compared to the TDMA model. With the same major time frame as the AER model, an additional safety margin in partition execution can be set. Furthermore, multi-TDMA can diminish memory usage by reducing the pseudo-partitions. Because with TDMA two cores access the memory simultaneously, more interference is expected to affect the execution time.
## 5 Data Analysis and Results
### Reliability and Validity Tests  
#### 5.2.1 Reliability Tests  
The internal consistency reliability Cronbach's Alpha of each variable was calculated using SPSS 26.0 software to ensure the reliability of the sample data. The results indicated that the Cronbach's Alpha and CR values were above 0.7, indicating that the research sample data had good reliability. The analysis's findings are displayed in Table 2.
# The Influence of Ionizing Radiation on Paclitaxel-Loaded Nanoparticles Based on PLGA
## 3 Results and Discussion
### In Vitro Drug Release
Only negligible differences (in the frame of the scatter of experimental data) in the kinetic profiles of PTX release from PLGA drug carriers were observed after irradiation. This suggests the good resistance of PLGA nanoparticles to irradiation. Despite some degradation observed within the polymer matrix itself, drug release profile from the irradiated samples did not vary significantly compared to the non-irradiated samples. This may be explained to some extent by the radio-protective effect of PTX resulting from its molecular structure. Aromatic groups present in PTX structure act as energy sinks via the energy transfer mechanism and stabilize the molecule [38; 39].
# Comparison of DFN Modelled Microfracture Systems with Petrophysical Data in Excavation Damaged Zone
## 2 Materials and Methods  
This study considered 16 specimens from two of the vertical drill holes (Set 1a, purple colour) shown in Figure 2. Specimens were selected based on four key criteria [6]:  
1. Specimens should be from as close to the tunnel floor as possible to capture the excavation damage effect.
2. Specimens should represent the dominant rock types as well as possible.
3. The distribution of rock types should represent the geology of the study area.
4. Specimens from each hole should create a sequence as uniform as possible (ideally with no large fractures or core loss in between).  
Rock types present in the specimens are veined gneiss (39 pcs, 49%), diatexitic gneiss (13 pcs, 16%), and pegmatoid granite (28 pcs, 35%). Gneiss specimens were cored such that the foliation plane was either approximately perpendicular or parallel to the specimen axis. Pegmatoid was assumed to have an isotropic material structure. Best sequences of veined gneiss (10 pcs, VGN) and pegmatoid granite (10 pcs, PGR) were selected for specimen Set 1a, bearing in mind the geological representativeness. The top depths of the gneissic specimens span a range from 0.07 to 0.77 m, and pegmatoids from 0.06 to 0.76 m from the excavated surface.
# Application Layer Protocol Identification Method Based on ResNet
## 1 Introduction
This approach comprehensively represents the data features and provides more distinct features for training tasks. 3. Utilized the composite feature images for training and testing on the ResNet pre-processing model: Results demonstrate that after adopting the proposed method, the accuracy, precision, and recall of application layer protocol recognition all exceed 98%, affirming the effectiveness of this method. The reason for choosing ResNet in this paper is its greater flexibility and adaptability, especially when dealing with unknown or complex patterns. While rule-based approaches perform well in known scenarios, they often struggle to cover all possible variations when handling a large number of changing network behaviors. Heuristic methods, through self-learning and data-driven approaches, can perform better across a broader range of scenarios, and are particularly more robust when dealing with novel attacks and network anomalies. The structure of this paper is as follows: Section 1 provides an introduction to protocol recognition and outlines the main contributions of this paper. Section 2 reviews related research in the field. Section 3 describes the specific implementation steps of the proposed method. Section 4 introduces the software and hardware environment used for experimentation, along with the experimental results and analysis. Finally, Section 5 concludes the paper and suggests avenues for future research.
# Quantum-Key-Distribution (QKD) Networks Enabled by Software-Defined Networks (SDN)
## 5 Three-Use Cases in QKD Networks
### Construction of QKP in the Networks  
In QKD networks, QKP is a key device used for the storage of secret keys. When security demands arrive, the number of secret keys can be used for the encryption and decryption. However, with the expansion of network scale, user numbers and security requirements, it is necessary to achieve efficient scheduling of wavelength resources and secret-key resources.  
To realize efficient generation and consumption of secret-key resources, a construction of QKP in QKD network is proposed in this section. QKP here represents a device abstracted by each pair of nodes for storing the secret keys generated between them. As shown in Figure 7, Dch and Qch represent channels used for the transmission of optical signals and quantum signals based on QKD process, respectively. OTDM technology can be used here to divide transmission channels into various time slices. During each time slot, a certain number of secret keys can be generated and stored in QKP by designing a routing path and allocating time slices. Thus, a routing, wavelength, and key assignment (RWKA) algorithm is designed for allocating available time slots to generate secret keys to fill QKPs. The RWKA algorithm is divided into three steps, i.e., calculating routes and assigning wavelengths for secret-key provisioning services through Dchs and Qchs. First, the k-short path algorithm is used to select short route, and then, first fit (FF) algorithm is used for wavelength allocation to occupy available wavelengths for key distribution. Besides, please note that secret keys in QKPs are stored in bits and cannot be reused for another encryption.  
We evaluated simulation performances of RWKA algorithm in QKD networks with NSFnet topology (i.e., 14 nodes and 21 links). Compared with uniform (case 1) and non-uniform (case 2) time-slot allocation, the blocking probability and time-slice utilization of secret-key provisioning service are shown in Figure 8a,b. It is clear to see that blocking probability and resource utilization of secret-key provisioning services gradually increases with bigger traffic load. This is because the wavelength resources in each single fiber are limited and the wavelength channels need to carry a large number of services. The simulation evaluated the security probability of the control channel and the  
Figure 6: (**a**) The blocking probability, (**b**) the resource utilization.  
Figure 7: The QKD network architecture with QKP.  
security probability of the data channel respectively, and the results were shown to verify the efficiency of the solution. In addition, the size of QKP can be further studied to match the secret-key rate of QKD system and security demands in a real situation.
## 3 System Model and Problem Formulation
### Offloading Cost
#### 3.3.3 Offloaded with Privacy Protection  
In this subsection, we formulate the cost associated with privacy protection. Privacy cost \(P_{n}\) of the \(n\)-th ST is introduced by the latency and energy consumption of dummy tasks with a random size. Similar to real tasks, the cost of dummy tasks also needs to consider the different offloading location \(x_{n}\) separately, including offloading to the SatEC server (\(x_{n}=1\)) and offloading to TC ( \(x_{n}=0\)). Let the size of dummy tasks that are randomly generated by the \(n\)-th ST be \(L_{d}\) and the required computational density be \(k_{d}\), then we have the following.  
When the computational task is offloaded to the SatEC server, the transmission latency of the 1 st hop can be represented as \(T_{1,d}=L_{d}/C_{1,n}\). Then the energy consumed by dummy tasks for the transmission is given by \(E_{1,d}=p_{n}T_{1,d}\). The computational latency at the SatEC server can be represented as \(T_{1,d}^{c}=k_{d}L_{d}/f_{1}\); thus, the energy consumed by the SatEC sever for dummy tasks is given by \(E_{1,d}^{c}=p_{c}T_{1,d}^{c}\). Therefore, in this case, the total latency and energy consumed by privacy protection for the \(n\)-th ST are, respectively, given by  
\[T_{d}^{SAT}=T_{1,d}+T_{1,d}^{c} \tag{5}\]  
\[E_{d}^{SAT}=E_{1,d}+E_{1,d}^{c} \tag{6}\]  
When the computational task is offloaded to the urban TC, the total transmission latency additionally includes the transmission delay caused by the 2 nd hop (i.e., from the satellite forwarding to the TC). Like the real task, the transmission latency and energy consumption of the 2 nd hop caused by dummy tasks can be expressed as \(T_{2,d}=L/C_{2,n}\) and \(E_{2,d}=p_{SAT}T_{2,d}\), respectively. Since the computing energy consumption of TC can be ignored, the total latency and energy consumed by privacy protection for the \(n\)-th ST are, respectively, given by  
\[T_{d}^{TC}=T_{1,d}+T_{2,d}+T_{2,d}^{c} \tag{7}\]  
\[E_{d}^{TC}=E_{1,d}+E_{2,d} \tag{8}\]  
In summary, the overheads arising from privacy protection \(P_{n}\) for tasks of the n-th ST can be expressed as  
\[P_{n}=x_{n}(T_{d}^{SAT}+E_{d}^{SAT})+(1-x_{n})(T_{d}^{TC}+E_{d}^{TC}) \tag{9}\]
## 2 Materials and Methods  
This study focuses on enhancing the accuracy of structural FEM simulations by incorporating the material property variations introduced during the stamping process. The methodology integrates multiple steps, as shown in Figure 1, starting with the simulation of the stamping process, which forms the foundation for accurately extracting non-uniform properties, such as thickness and strain distributions. These properties are critical for understanding the material behavior of the component. The stamping simulation was performed using specialized software to model the deformation of sheet-metal under realistic manufacturing conditions. The material's behavior was assumed to be elastic-plastic, and we recorded changes in thickness and strain accurately. The simulation outputs provided key insights into localized thinning and stress variations, which were crucial for the upcoming analysis. To ensure reliability, the simulation results were validated using simplified calculations and comparisons with published studies.  
The next step was property mapping, which was used as a connection between the stamping simulation and the structural FEM analysis, where we implemented the non-uniform properties obtained from the stamping simulation directly in the FEM model. Then, a custom script was developed to process the simulation outputs and convert them into a format compatible with FEM software Altair Hyperworks 2021.1. Afterward, the mapped properties were assessed for accuracy using visualization methods, ensuring that critical regions of high deformation were accurately represented in the structural model.  
Considering mapped material properties, the structural FEM model was developed to simulate the performance of the stamped component under static loading conditions defined by relevant standards. The model included precise geometry and boundary conditions to replicate experimental setups, where two types of material behavior were considered: linear elastic and nonlinear with plastic deformation and strain hardening. The  
Figure 1: Workflow for integrating stamping simulation with FEM analysis and experimental validation.  
nonlinear model captured the residual plasticity and stress redistributions, providing more accurate predictions of structural performance.  
Finally, experimental validation was conducted to compare the FEM predictions with physical tests. The stamped component was subjected to static loads, and displacement measurements were taken at key points using precise instrumentation. The experimental data were then compared with FEM results to evaluate the accuracy of the simulations.
# Impact of the Elevation Angle on CYGNSS GNSS-R
## 4 Data and Methods
Then, the corrections for surface roughness [47; 48], effective soil temperature, and vegetation water content (VWC) were applied.